Face-name association learning in early Alzheimer's disease: a comparison of learning methods and their underlying mechanisms.

PubMed

Bier, Nathalie; Van Der Linden, Martial; Gagnon, Lise; Desrosiers, Johanne; Adam, Stephane; Louveaux, Stephanie; Saint-Mleux, Julie

2008-06-01

This study compared the efficacy of five learning methods in the acquisition of face-name associations in early dementia of Alzheimer type (AD). The contribution of error production and implicit memory to the efficacy of each method was also examined. Fifteen participants with early AD and 15 matched controls were exposed to five learning methods: spaced retrieval, vanishing cues, errorless, and two trial-and-error methods, one with explicit and one with implicit memory task instructions. Under each method, participants had to learn a list of five face-name associations, followed by free recall, cued recall and recognition. Delayed recall was also assessed. For AD, results showed that all methods were efficient but there were no significant differences between them. The number of errors produced during the learning phases varied between the five methods but did not influence learning. There were no significant differences between implicit and explicit memory task instructions on test performances. For the control group, there were no differences between the five methods. Finally, no significant correlations were found between the performance of the AD participants in free recall and their cognitive profile, but generally, the best performers had better remaining episodic memory. Also, case study analyses showed that spaced retrieval was the method for which the greatest number of participants (four) obtained results as good as the controls. This study suggests that the five methods are effective for new learning of face-name associations in AD. It appears that early AD patients can learn, even in the context of error production and explicit memory conditions.

Closed-Loop and Robust Control of Quantum Systems

PubMed Central

Wang, Lin-Cheng

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H ∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention. PMID:23997680

Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

Web-Based Learning in a Geometry Course

ERIC Educational Resources Information Center

Chan, Hsungrow; Tsai, Pengheng; Huang, Tien-Yu

2006-01-01

This study concerns applying Web-based learning with learner controlled instructional materials in a geometry course. The experimental group learned in a Web-based learning environment, and the control group learned in a classroom. We observed that the learning method accounted for a total variation in learning effect of 19.1% in the 3rd grade and…

Efficient model learning methods for actor-critic control.

PubMed

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Adaptive Importance Sampling for Control and Inference

NASA Astrophysics Data System (ADS)

Kappen, H. J.; Ruiz, H. C.

2016-03-01

Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feynman-Kac PI and can be estimated using Monte Carlo sampling. In this contribution we review PI control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. We review the most commonly used methods in robotics and control. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers are state feedback controllers and the use of these requires an efficient representation. Learning and representing effective state-feedback controllers for non-linear stochastic control problems is a very challenging, and largely unsolved, problem. We show how to learn and represent such controllers using ideas from the cross entropy method. We derive a gradient descent method that allows to learn feed-back controllers using an arbitrary parametrisation. We refer to this method as the path integral cross entropy method or PICE. We illustrate this method for some simple examples. The PI control methods can be used to estimate the posterior distribution in latent state models. In neuroscience these problems arise when estimating connectivity from neural recording data using EM. We demonstrate the PI control method as an accurate alternative to particle filtering.

Reinforcement learning for a biped robot based on a CPG-actor-critic method.

PubMed

Nakamura, Yutaka; Mori, Takeshi; Sato, Masa-aki; Ishii, Shin

2007-08-01

Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the "CPG-actor-critic" method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.

Model-based reinforcement learning with dimension reduction.

PubMed

Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi

2016-12-01

The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fuzzy and neural control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods

PubMed Central

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Background: Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. Materials and Methods: A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Results: Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Conclusions: Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills. PMID:29861761

Impedance learning for robotic contact tasks using natural actor-critic algorithm.

PubMed

Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul

2010-04-01

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.

Using a collaborative Mobile Augmented Reality learning application (CoMARLA) to improve Improve Student Learning

NASA Astrophysics Data System (ADS)

Hanafi, Hafizul Fahri bin; Soh Said, Che; Hanee Ariffin, Asma; Azlan Zainuddin, Nur; Samsuddin, Khairulanuar

2016-11-01

This study was carried out to improve student learning in ICT course using a collaborative mobile augmented reality learning application (CoMARLA). This learning application was developed based on the constructivist framework that would engender collaborative learning environment, in which students could learn collaboratively using their mobile phones. The research design was based on the pretest posttest control group design. The dependent variable was students’ learning performance after learning, and the independent variables were learning method and gender. Students’ learning performance before learning was treated as the covariate. The sample of the study comprised 120 non-IT (non-technical) undergraduates, with the mean age of 19.5. They were randomized into two groups, namely the experimental and control group. The experimental group used CoMARLA to learn one of the topics of the ICT Literacy course, namely Computer System; whereas the control group learned using the conventional approach. The research instrument used was a set of multiple-choice questions pertaining to the above topic. Pretesting was carried out before the learning sessions, and posttesting was performed after 6 hours of learning. Using the SPSS, Analysis of Covariance (ANCOVA) was performed on the data. The analysis showed that there were main effects attributed to the learning method and gender. The experimental group outperformed the control group by almost 9%, and male students outstripped their opposite counterparts by as much as 3%. Furthermore, an interaction effect was also observed showing differential performances of male students based on the learning methods, which did not occur among female students. Hence, the tool can be used to help undergraduates learn with greater efficacy when contextualized in an appropriate setting.

Learning Sequential Composition Control.

PubMed

Najafi, Esmaeil; Babuska, Robert; Lopes, Gabriel A D

2016-11-01

Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller. As these controllers are designed offline, sequential composition cannot address unmodeled situations that might occur during runtime. This paper proposes a learning approach to augment the standard sequential composition framework by using online learning to handle unforeseen situations. New controllers are acquired via learning and added to the existing supervisory control structure. In the proposed setting, learning experiments are restricted to take place within the domain of attraction (DOA) of the existing controllers. This guarantees that the learning process is safe (i.e., the closed loop system is always stable). In addition, the DOA of the new learned controller is approximated after each learning trial. This keeps the learning process short as learning is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two nonlinear systems: 1) a nonlinear mass-damper system and 2) an inverted pendulum. The results show that in both cases a new controller can be rapidly learned and added to the supervisory control structure.

Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

PubMed

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

Scheduled power tracking control of the wind-storage hybrid system based on the reinforcement learning theory

NASA Astrophysics Data System (ADS)

Li, Ze

2017-09-01

In allusion to the intermittency and uncertainty of the wind electricity, energy storage and wind generator are combined into a hybrid system to improve the controllability of the output power. A scheduled power tracking control method is proposed based on the reinforcement learning theory and Q-learning algorithm. In this method, the state space of the environment is formed with two key factors, i.e. the state of charge of the energy storage and the difference value between the actual wind power and scheduled power, the feasible action is the output power of the energy storage, and the corresponding immediate rewarding function is designed to reflect the rationality of the control action. By interacting with the environment and learning from the immediate reward, the optimal control strategy is gradually formed. After that, it could be applied to the scheduled power tracking control of the hybrid system. Finally, the rationality and validity of the method are verified through simulation examples.

Comparison of Conditioning Impairments in Children with Down Syndrome, Autistic Spectrum Disorders and Mental Age-Matched Controls

ERIC Educational Resources Information Center

Reed, P.; Staytom, L.; Stott, S.; Truzoli, R.

2011-01-01

Background: This study investigated the relative ease of learning across four tasks suggested by an adaptation of Thomas's hierarchy of learning in children with Down syndrome, autism spectrum disorders and mental age-matched controls. Methods: Learning trials were carried out to investigate observational learning, instrumental learning, reversal…

Improving Students’ Motivation in Learning ICT Course With the Use of A Mobile Augmented Reality Learning Environment

NASA Astrophysics Data System (ADS)

Fahri Hanafi, Hafizul; Soh Said, Che; Helmy Wahab, Mohd; Samsuddin, Khairulanuar

2017-08-01

Studies have shown that many Malaysian non-technical students have low motivation in learning ICT course due to a number of reasons, such as a lack of learning practice and effective learning applications. In view of such a problem, the researchers carried out a quasi-experimental study to examine the impact of a novel mobile augmented reality learning application (MARLA) on students’ motivation in learning a topic of a university ICT course. The research was based on the pretest-posttest control group design, and the study sample consisted of 120 non-technical undergraduates majoring in social science, with a mean age of 19.5 years. They were divided into an experimental group and a control group. The dependent variable was students’ motivation in learning, and the independent variables were learning method and gender. The experimental group used MARLA on their mobile devices to learn one of the topics of the ICT Competency course, namely Computer System; whereas the control group used a similar application on their desktop computers. The Intrinsic Motivation Inventory (IMI) was the research instrument used to measure students’ motivation before and after learning sessions, which spanned 6 hours. Utilizing the SPSS (version 21), an analysis of covariance was performed, showing there was a main effect attributed to gender only, with male and female students attaining mean scores of 4.24 and 3.90 respectively for the motivation construct. This finding showed male students were more motivated than their opposite counterparts. In contrast, no such main effect attributed to learning method was observed, as evidenced from the mean scores of 4.08 and 4.07 of the experimental group and control group respectively for the measured construct, suggesting both methods were both equally effective. Additionally, there was an interaction effect between gender and learning method, with male students attaining different levels of motivation based on learning method. Arguably, such a mobile learning tool can be used to help non-technical undergraduates learn with greater motivation, but its success will rely on proper planning and implementation by considering students’ demographic background.

Rule-based mechanisms of learning for intelligent adaptive flight control

NASA Technical Reports Server (NTRS)

Handelman, David A.; Stengel, Robert F.

1990-01-01

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

PubMed

Lewis, F L; Vamvoudakis, Kyriakos G

2011-02-01

Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

On the integration of reinforcement learning and approximate reasoning for control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

The author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.

Learning from adaptive neural dynamic surface control of strict-feedback systems.

PubMed

Wang, Min; Wang, Cong

2015-06-01

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Optimal critic learning for robot control in time-varying environments.

PubMed

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Investigations of Students' Motivation Towards Learning Secondary School Physics through Mastery Learning Approach

ERIC Educational Resources Information Center

Changeiywo, Johnson M.; Wambugu, P. W.; Wachanga, S. W.

2011-01-01

Teaching method is a major factor that affects students' motivation to learn physics. This study investigated the effects of using mastery learning approach (MLA) on secondary school students' motivation to learn physics. Solomon four non-equivalent control group design under the quasi-experimental research method was used in which a random sample…

Framework for robot skill learning using reinforcement learning

NASA Astrophysics Data System (ADS)

Wei, Yingzi; Zhao, Mingyang

2003-09-01

Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

The impact of rigorous mathematical thinking as learning method toward geometry understanding

NASA Astrophysics Data System (ADS)

Nugraheni, Z.; Budiyono, B.; Slamet, I.

2018-05-01

To reach higher order thinking skill, needed to be mastered the conceptual understanding. RMT is a unique realization of the cognitive conceptual construction approach based on Mediated Learning Experience (MLE) theory by Feurstein and Vygotsky’s sociocultural theory. This was quasi experimental research which was comparing the experimental class that was given Rigorous Mathematical Thinking (RMT) as learning method and control class that was given Direct Learning (DL) as the conventional learning activity. This study examined whether there was different effect of two learning method toward conceptual understanding of Junior High School students. The data was analyzed by using Independent t-test and obtained a significant difference of mean value between experimental and control class on geometry conceptual understanding. Further, by semi-structure interview known that students taught by RMT had deeper conceptual understanding than students who were taught by conventional way. By these result known that Rigorous Mathematical Thinking (RMT) as learning method have positive impact toward Geometry conceptual understanding.

Fuzzy self-learning control for magnetic servo system

NASA Technical Reports Server (NTRS)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Learning-based controller for biotechnology processing, and method of using

DOEpatents

Johnson, John A.; Stoner, Daphne L.; Larsen, Eric D.; Miller, Karen S.; Tolle, Charles R.

2004-09-14

The present invention relates to process control where some of the controllable parameters are difficult or impossible to characterize. The present invention relates to process control in biotechnology of such systems, but not limited to. Additionally, the present invention relates to process control in biotechnology minerals processing. In the inventive method, an application of the present invention manipulates a minerals bioprocess to find local exterma (maxima or minima) for selected output variables/process goals by using a learning-based controller for bioprocess oxidation of minerals during hydrometallurgical processing. The learning-based controller operates with or without human supervision and works to find processor optima without previously defined optima due to the non-characterized nature of the process being manipulated.

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

The Effectiveness of the Game-Based Learning System for the Improvement of American Sign Language Using Kinect

ERIC Educational Resources Information Center

Kamnardsiri, Teerawat; Hongsit, Ler-on; Khuwuthyakorn, Pattaraporn; Wongta, Noppon

2017-01-01

This paper investigated students' achievement for learning American Sign Language (ASL), using two different methods. There were two groups of samples. The first experimental group (Group A) was the game-based learning for ASL, using Kinect. The second control learning group (Group B) was the traditional face-to-face learning method, generally…

Developing Learning Tool of Control System Engineering Using Matrix Laboratory Software Oriented on Industrial Needs

NASA Astrophysics Data System (ADS)

Isnur Haryudo, Subuh; Imam Agung, Achmad; Firmansyah, Rifqi

2018-04-01

The purpose of this research is to develop learning media of control technique using Matrix Laboratory software with industry requirement approach. Learning media serves as a tool for creating a better and effective teaching and learning situation because it can accelerate the learning process in order to enhance the quality of learning. Control Techniques using Matrix Laboratory software can enlarge the interest and attention of students, with real experience and can grow independent attitude. This research design refers to the use of research and development (R & D) methods that have been modified by multi-disciplinary team-based researchers. This research used Computer based learning method consisting of computer and Matrix Laboratory software which was integrated with props. Matrix Laboratory has the ability to visualize the theory and analysis of the Control System which is an integration of computing, visualization and programming which is easy to use. The result of this instructional media development is to use mathematical equations using Matrix Laboratory software on control system application with DC motor plant and PID (Proportional-Integral-Derivative). Considering that manufacturing in the field of Distributed Control systems (DCSs), Programmable Controllers (PLCs), and Microcontrollers (MCUs) use PID systems in production processes are widely used in industry.

Structured Kernel Subspace Learning for Autonomous Robot Navigation.

PubMed

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

Strategic Management: An Evaluation of the Use of Three Learning Methods.

ERIC Educational Resources Information Center

Jennings, David

2002-01-01

A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…

[Cancer nursing care education programs: the effectiveness of different teaching methods].

PubMed

Cheng, Yun-Ju; Kao, Yu-Hsiu

2012-10-01

In-service education affects the quality of cancer care directly. Using classroom teaching to deliver in-service education is often ineffective due to participants' large workload and shift requirements. This study evaluated the learning effectiveness of different teaching methods in the dimensions of knowledge, attitude, and learning satisfaction. This study used a quasi-experimental study design. Participants were cancer ward nurses working at one medical center in northern Taiwan. Participants were divided into an experimental group and control group. The experimental group took an e-learning course and the control group took a standard classroom course using the same basic course material. Researchers evaluated the learning efficacy of each group using a questionnaire based on the quality of cancer nursing care learning effectiveness scale. All participants answered the questionnaire once before and once after completing the course. (1) Post-test "knowledge" scores for both groups were significantly higher than pre-test scores for both groups. Post-test "attitude" scores were significantly higher for the control group, while the experimental group reported no significant change. (2) after a covariance analysis of the pre-test scores for both groups, the post-test score for the experimental group was significantly lower than the control group in the knowledge dimension. Post-test scores did not differ significantly from pre-test scores for either group in the attitude dimension. (3) Post-test satisfaction scores between the two groups did not differ significantly with regard to teaching methods. The e-learning method, however, was demonstrated as more flexible than the classroom teaching method. Study results demonstrate the importance of employing a variety of teaching methods to instruct clinical nursing staff. We suggest that both classroom teaching and e-learning instruction methods be used to enhance the quality of cancer nursing care education programs. We also encourage that interactivity between student and instructor be incorporated into e-learning course designs to enhance effectiveness.

A study of the impact of collaborative learning on student learning of major concepts in a microbiology laboratory exercise

NASA Astrophysics Data System (ADS)

Baumgarten, Kristyne A.

This study investigated the possible relationship between collaborative learning strategies and the learning of core concepts. This study examined the differences between two groups of nursing students enrolled in an introductory microbiology laboratory course. The control group consisted of students enrolled in sections taught in the traditional method. The experimental group consisted of those students enrolled in the sections using collaborative learning strategies. The groups were assessed on their degrees of learning core concepts using a pre-test/post-test method. Scores from the groups' laboratory reports were also analyzed. There was no difference in the two group's pre-test scores. The post-test scores of the experimental group averaged 11 points higher than the scores of the control group. The lab report scores of the experimental group averaged 15 points higher than those scores of the control group. The data generated from this study demonstrated that collaborative learning strategies can be used to increase students learning of core concepts in microbiology labs.

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

PubMed

Edwards, Ann L; Dawson, Michael R; Hebert, Jacqueline S; Sherstan, Craig; Sutton, Richard S; Chan, K Ming; Pilarski, Patrick M

2016-10-01

Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Case series study. We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses. © The International Society for Prosthetics and Orthotics 2015.

Students perception on the usage of PowerPoint in learning calculus

NASA Astrophysics Data System (ADS)

Othman, Zarith Sofiah; Tarmuji, Nor Habibah; Hilmi, Zulkifli Ab Ghani

2017-04-01

Mathematics is a core subject in most of the science and technology courses and in some social sciences programs. However, the low achievement of students in the subject especially in topics such as Differentiation and Integration is always an issue. Many factors contribute to the low performance such as motivation, environment, method of learning, academic background and others. The purpose of this paper is to determine the perception of learning mathematics using PowerPoint on Integration concepts at the undergraduate level with respect to mathematics anxiety, learning enjoyment, mobility and learning satisfaction. The main content of the PowerPoint presentation focused on the integration method with historical elements as an added value. The study was conducted on 48 students randomly selected from students in computer and applied sciences program as experimental group. Questionnaires were distributed to students to explore their learning experiences. Another 51 students who were taught using the traditional chalkboard method were used as the control group. Both groups were given a test on Integration. The statistical methods used were descriptive statistics and independent sample t-test between the experimental and the control group. The finding showed that most students perceived positively to the PowerPoint presentations with respect to mobility and learning satisfaction. The experimental group performed better than the control group.

Artificial neural networks and approximate reasoning for intelligent control in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

A statistical learning strategy for closed-loop control of fluid flows

NASA Astrophysics Data System (ADS)

Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

2016-12-01

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

Measuring strategic control in implicit learning: how and why?

PubMed

Norman, Elisabeth

2015-01-01

Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge are consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodological and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures.

Measuring strategic control in implicit learning: how and why?

PubMed Central

Norman, Elisabeth

2015-01-01

Several methods have been developed for measuring the extent to which implicitly learned knowledge can be applied in a strategic, flexible manner. Examples include generation exclusion tasks in Serial Reaction Time (SRT) learning (Goschke, 1998; Destrebecqz and Cleeremans, 2001) and 2-grammar classification tasks in Artificial Grammar Learning (AGL; Dienes et al., 1995; Norman et al., 2011). Strategic control has traditionally been used as a criterion for determining whether acquired knowledge is conscious or unconscious, or which properties of knowledge are consciously available. In this paper I first summarize existing methods that have been developed for measuring strategic control in the SRT and AGL tasks. I then address some methodological and theoretical questions. Methodological questions concern choice of task, whether the measurement reflects inhibitory control or task switching, and whether or not strategic control should be measured on a trial-by-trial basis. Theoretical questions concern the rationale for including measurement of strategic control, what form of knowledge is strategically controlled, and how strategic control can be combined with subjective awareness measures. PMID:26441809

Comparing nurses' knowledge retention following electronic continuous education and educational booklet: a controlled trial study

PubMed Central

Badiei, Mahmud; Gharib, Mitra; Zolfaghari, Mitra; Mojtahedzadeh, Rita

2016-01-01

Background: Training methods that enhance nurses’ learning and retention will increase the quality of patient care. This study aimed to compare the effectiveness of electronic learning and educational booklet on the nurses’ retention of diabetes updates. Methods: In this controlled trial study, convenience sampling was used to select 123 nurses from the endocrinology and internal medicine wards of three hospitals affiliated to Tehran University of Medical Sciences (Tehran, Iran). The participants were allocated to three groups of manual, electronic learning, and control. The booklet and electronic learning groups were trained using educational booklet and electronic continuous medical education (CME) website, respectively. The control group did not receive any intervention. In all the three groups, the nurses' knowledge was measured before the intervention, and one and four weeks after the intervention. Data were collected by a questionnaire. Results: Significant differences were observed between the mean scores of the three groups one and four weeks after the intervention (F=26.17, p=0.001 and F=4.07, p=0.020, respectively), and post hoc test showed that this difference was due to the higher score in e-learning group. Both e-learning and booklet methods could effectively improve nurses' knowledge (χ²=23.03, p=0.001 and χ²=51.71, p=0.001, respectively). Conclusion: According to the results of this study, electronic learning was more effective than booklet in enhancing the learning and retention of knowledge. Electronic learning is suggested as a more suitable method as it provides appropriate interactions and attractive virtual environments to motivate the learners and promote retention. PMID:27493908

Lack of Interaction between Sensing-Intuitive Learning Styles and Problem-First versus Information-First Instruction: A Randomized Crossover Trial

ERIC Educational Resources Information Center

Cook, David A.; Thompson, Warren G.; Thomas, Kris G.; Thomas, Matthew R.

2009-01-01

Background: Adaptation to learning styles has been proposed to enhance learning. Objective: We hypothesized that learners with sensing learning style would perform better using a problem-first instructional method while intuitive learners would do better using an information-first method. Design: Randomized, controlled, crossover trial. Setting:…

Beyond adaptive-critic creative learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.

Meaningful Engagement in Facebook Learning Environments: Merging Social and Academic Lives

ERIC Educational Resources Information Center

Wang, Jenny; Lin, Chun-Fu C.; Yu, Wei-Chieh W.; Wu, Emily

2013-01-01

This study compared the effectiveness of different learning environments between interactive Facebook instructional method and non-Facebook instructional method for undergraduate students. Two outcome dimensions were measured: student grades and learning engagement. A pre-test-posttest control group experimental design was used. The experimental…

Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems.

PubMed

Wang, Cong; Wang, Min; Liu, Tengfei; Hill, David J

2012-10-01

This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.

A mathematical theory of learning control for linear discrete multivariable systems

NASA Technical Reports Server (NTRS)

Phan, Minh; Longman, Richard W.

1988-01-01

When tracking control systems are used in repetitive operations such as robots in various manufacturing processes, the controller will make the same errors repeatedly. Here consideration is given to learning controllers that look at the tracking errors in each repetition of the process and adjust the control to decrease these errors in the next repetition. A general formalism is developed for learning control of discrete-time (time-varying or time-invariant) linear multivariable systems. Methods of specifying a desired trajectory (such that the trajectory can actually be performed by the discrete system) are discussed, and learning controllers are developed. Stability criteria are obtained which are relatively easy to use to insure convergence of the learning process, and proper gain settings are discussed in light of measurement noise and system uncertainties.

Effectiveness of Mutual Learning Approach in the Academic Achievement of B.Ed Students in Learning Optional II English

ERIC Educational Resources Information Center

Arulselvi, Evangelin

2013-01-01

The present study aims at finding out the effectiveness of Mutual learning approach over the conventional method in learning English optional II among B.Ed students. The randomized pre-test, post test, control group and experimental group design was employed. The B.Ed students of the same college formed the control and experimental groups. Each…

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

PubMed Central

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Version Control in Project-Based Learning

ERIC Educational Resources Information Center

Milentijevic, Ivan; Ciric, Vladimir; Vojinovic, Oliver

2008-01-01

This paper deals with the development of a generalized model for version control systems application as a support in a range of project-based learning methods. The model is given as UML sequence diagram and described in detail. The proposed model encompasses a wide range of different project-based learning approaches by assigning a supervisory…

The Effects of Variations in Lesson Control and Practice on Learning from Interactive Video.

ERIC Educational Resources Information Center

Hannafin, Michael J.; Colamaio, MaryAnne E.

1987-01-01

Discussion of the effects of variations in lesson control and practice on the learning of facts, procedures, and problem-solving skills during interactive video instruction focuses on a study of graduates and advanced level undergraduates learning cardiopulmonary resuscitation (CPR). Embedded questioning methods and posttests used are described.…

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

PubMed

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

2015-02-01

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Quality Control Guidelines for SAM Chemical Methods

EPA Pesticide Factsheets

Learn more about quality control guidelines and recommendations for the analysis of samples using the chemistry methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).

Quality Control Guidelines for SAM Pathogen Methods

EPA Pesticide Factsheets

Learn more about quality control guidelines and recommendations for the analysis of samples using the biotoxin methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).

Quality Control Guidelines for SAM Radiochemical Methods

EPA Pesticide Factsheets

Learn more about quality control guidelines and recommendations for the analysis of samples using the radiochemistry methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).

General Quality Control (QC) Guidelines for SAM Methods

EPA Pesticide Factsheets

Learn more about quality control guidelines and recommendations for the analysis of samples using the methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).

Quality Control Guidelines for SAM Biotoxin Methods

EPA Pesticide Factsheets

Learn more about quality control guidelines and recommendations for the analysis of samples using the pathogen methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).

Methods of learning in statistical education: Design and analysis of a randomized trial

NASA Astrophysics Data System (ADS)

Boyd, Felicity Turner

Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus nonparticipants or controls, adjusting for other performance predictors. Students who preferred learning by reflective observation and active experimentation experienced improved performance through internet learning (5.9 points, 95% CI: 1.2, 10.6) and cooperative learning (2.9 points, 95% CI: 0.6, 5.2), respectively. Learning style did not influence study participation. Conclusions. No performance differences by group were observed by intent-to-treat analysis. Participation in active learning appears to improve student performance in an introductory biostatistics course and provides opportunities for enhancing understanding beyond that attained in traditional didactic classrooms.

Analysis of Learning Achievement and Teacher-Student Interactions in Flipped and Conventional Classrooms

ERIC Educational Resources Information Center

Sun, Jerry Chih-Yuan; Wu, Yu-Ting

2016-01-01

This study aimed to investigate the effectiveness of two different teaching methods on learning effectiveness. OpenCourseWare was integrated into the flipped classroom model (experimental group) and distance learning (control group). Learning effectiveness encompassed learning achievement, teacher-student interactions, and learning satisfaction.…

Applying Learning Analytics to Investigate Timed Release in Online Learning

ERIC Educational Resources Information Center

Martin, Florence; Whitmer, John C.

2016-01-01

Adaptive learning gives learners control of context, pace, and scope of their learning experience. This strategy can be implemented in online learning by using the "Adaptive Release" feature in learning management systems. The purpose of this study was to use learning analytics research methods to explore the extent to which the adaptive…

Prediction of final error level in learning and repetitive control

NASA Astrophysics Data System (ADS)

Levoci, Peter A.

Repetitive control (RC) is a field that creates controllers to eliminate the effects of periodic disturbances on a feedback control system. The methods have applications in spacecraft problems, to isolate fine pointing equipment from periodic vibration disturbances such as slight imbalances in momentum wheels or cryogenic pumps. A closely related field of control design is iterative learning control (ILC) which aims to eliminate tracking error in a task that repeats, each time starting from the same initial condition. Experiments done on a robot at NASA Langley Research Center showed that the final error levels produced by different candidate repetitive and learning controllers can be very different, even when each controller is analytically proven to converge to zero error in the deterministic case. Real world plant and measurement noise and quantization noise (from analog to digital and digital to analog converters) in these control methods are acted on as if they were error sources that will repeat and should be cancelled, which implies that the algorithms amplify such errors. Methods are developed that predict the final error levels of general first order ILC, of higher order ILC including current cycle learning, and of general RC, in the presence of noise, using frequency response methods. The method involves much less computation than the corresponding time domain approach that involves large matrices. The time domain approach was previously developed for ILC and handles a certain class of ILC methods. Here methods are created to include zero-phase filtering that is very important in creating practical designs. Also, time domain methods are developed for higher order ILC and for repetitive control. Since RC and ILC must be implemented digitally, all of these methods predict final error levels at the sample times. It is shown here that RC can easily converge to small error levels between sample times, but that ILC in most applications will have large and diverging intersample error if in fact zero error is reached at the sample times. This is independent of the ILC law used, and is purely a property of the physical system. Methods are developed to address this issue.

Non-linguistic learning in aphasia: Effects of training method and stimulus characteristics

PubMed Central

Vallila-Rohter, Sofia; Kiran, Swathi

2013-01-01

Purpose The purpose of the current study was to explore non-linguistic learning ability in patients with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method Eighteen patients with aphasia and eight healthy controls participated in this study. All participants completed four computerized, non-linguistic category-learning tasks. We probed learning ability under two methods of instruction: feedback-based (FB) and paired-associate (PA). We also examined the impact of task complexity on learning ability, comparing two stimulus conditions: typical (Typ) and atypical (Atyp). Performance was compared between groups and across conditions. Results Results demonstrated that healthy controls were able to successfully learn categories under all conditions. For our patients with aphasia, two patterns of performance arose. One subgroup of patients was able to maintain learning across task manipulations and conditions. The other subgroup of patients demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusions Results support the hypothesis that impairments of general learning are present in aphasia. Some patients demonstrated the ability to extract category information under complex training conditions, while others learned only under conditions that were simplified and emphasized salient category features. Overall, the typical training condition facilitated learning for all participants. Findings have implications for therapy, which are discussed. PMID:23695914

Nonlinguistic Learning in Individuals with Aphasia: Effects of Training Method and Stimulus Characteristics

ERIC Educational Resources Information Center

Vallila-Rohter, Sofia; Kiran, Swathi

2013-01-01

Purpose: The purpose of the current study was to explore nonlinguistic learning ability in individuals with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method: Eighteen individuals with aphasia and 8 nonaphasic controls participated in this study. All participants completed 4 computerized,…

Learning-based position control of a closed-kinematic chain robot end-effector

NASA Technical Reports Server (NTRS)

Nguyen, Charles C.; Zhou, Zhen-Lei

1990-01-01

A trajectory control scheme whose design is based on learning theory, for a six-degree-of-freedom (DOF) robot end-effector built to study robotic assembly of NASA hardwares in space is presented. The control scheme consists of two control systems: the feedback control system and the learning control system. The feedback control system is designed using the concept of linearization about a selected operating point, and the method of pole placement so that the closed-loop linearized system is stabilized. The learning control scheme consisting of PD-type learning controllers, provides additional inputs to improve the end-effector performance after each trial. Experimental studies performed on a 2 DOF end-effector built at CUA, for three tracking cases show that actual trajectories approach desired trajectories as the number of trials increases. The tracking errors are substantially reduced after only five trials.

Tuning fuzzy PD and PI controllers using reinforcement learning.

PubMed

Boubertakh, Hamid; Tadjine, Mohamed; Glorennec, Pierre-Yves; Labiod, Salim

2010-10-01

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Kinespell: Kinesthetic Learning Activity and Assessment in a Digital Game-Based Learning Environment

NASA Astrophysics Data System (ADS)

Cariaga, Ada Angeli; Salvador, Jay Andrae; Solamo, Ma. Rowena; Feria, Rommel

Various approaches in learning are commonly classified into visual, auditory and kinesthetic (VAK) learning styles. One way of addressing the VAK learning styles is through game-based learning which motivates learners pursue knowledge holistically. The paper presents Kinespell, an unconventional method of learning through digital game-based learning. Kinespell is geared towards enhancing not only the learner’s spelling abilities but also the motor skills through utilizing wireless controllers. It monitors player’s performance through integrated assessment scheme. Results show that Kinespell may accommodate the VAK learning styles and is a promising alternative to established methods in learning and assessing students’ performance in Spelling.

Optimal control in microgrid using multi-agent reinforcement learning.

PubMed

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

An iterative learning control method with application for CNC machine tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

A Hierarchical Learning Control Framework for an Aerial Manipulation System

NASA Astrophysics Data System (ADS)

Ma, Le; Chi, yanxun; Li, Jiapeng; Li, Zhongsheng; Ding, Yalei; Liu, Lixing

2017-07-01

A hierarchical learning control framework for an aerial manipulation system is proposed. Firstly, the mechanical design of aerial manipulation system is introduced and analyzed, and the kinematics and the dynamics based on Newton-Euler equation are modeled. Secondly, the framework of hierarchical learning for this system is presented, in which flight platform and manipulator are controlled by different controller respectively. The RBF (Radial Basis Function) neural networks are employed to estimate parameters and control. The Simulation and experiment demonstrate that the methods proposed effective and advanced.

Effectiveness of Jigsaw learning compared to lecture-based learning in dental education.

PubMed

Sagsoz, O; Karatas, O; Turel, V; Yildiz, M; Kaya, E

2017-02-01

The objective of this study was to evaluate the success levels of students using the Jigsaw learning method in dental education. Fifty students with similar grade point average (GPA) scores were selected and randomly assigned into one of two groups (n = 25). A pretest concerning 'adhesion and bonding agents in dentistry' was administered to all students before classes. The Jigsaw learning method was applied to the experimental group for 3 weeks. At the same time, the control group was taking classes using the lecture-based learning method. At the end of the 3 weeks, all students were retested (post-test) on the subject. A retention test was administered 3 weeks after the post-test. Mean scores were calculated for each test for the experimental and control groups, and the data obtained were analysed using the independent samples t-test. No significant difference was determined between the Jigsaw and lecture-based methods at pretest or post-test. The highest mean test score was observed in the post-test with the Jigsaw method. In the retention test, success with the Jigsaw method was significantly higher than that with the lecture-based method. The Jigsaw method is as effective as the lecture-based method. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Effects of e-learning, lectures, and role playing on nursing students’ knowledge acquisition, retention and satisfaction

PubMed Central

Pourghaznein, Tayebeh; Sabeghi, Hakimeh; Shariatinejad, Keyvan

2015-01-01

Background: Nursing education can maintain its dynamic quality when it moves toward innovation and modern methods of teaching and learning. Therefore, teachers are required to employ up to date methods in their teaching plans. This study evaluated the effects of e-learning, lectures, and role playing on nursing students’ learning, retention, and satisfaction. Methods: Sixty nursing students were selected as an experiment and control groups during two consecutive semesters. The educational content was presented as e-learning and role playing during one semester (experiment group) and as lectures in the next semester (control group). A questionnaire containing three parts was used to assess demographics, learning and satisfaction statuses. The questionnaire also included a final openended question to evaluate the students’ ideas about the whole course. Results: The mean scores of posttest were 16.13 ± 1.37 using role playing, 15.50 ± 1.44 using e-learning and 16.45 ± 1.23 using lectures. The differences between the mean scores of posttest and pretest were 12.84 ± 1.43, 12.56 ± 1.57, and 13.73 ± 1.53 in the mentioned methods, respectively. Lectures resulted in significantly better learning compared to role playing and e-learning. In contrast, retention rates were significantly lower using lectures than using role playing and e-learning. Students’ satisfaction from e-learning was significantly lower than lecturing and role playing. Conclusion: Due to the lower rates of retention following lectures, the teachers are recommended to use student- centered approaches in their lectures. Since students’ satisfaction with e-learning was lower than the other methods, further studies are suggested to explore the problems of e-learning in Iran. PMID:26000257

Flipped Learning With Simulation in Undergraduate Nursing Education.

PubMed

Kim, HeaRan; Jang, YounKyoung

2017-06-01

Flipped learning has proliferated in various educational environments. This study aimed to verify the effects of flipped learning on the academic achievement, teamwork skills, and satisfaction levels of undergraduate nursing students. For the flipped learning group, simulation-based education via the flipped learning method was provided, whereas traditional, simulation-based education was provided for the control group. After completion of the program, academic achievement, teamwork skills, and satisfaction levels were assessed and analyzed. The flipped learning group received higher scores on academic achievement, teamwork skills, and satisfaction levels than the control group, including the areas of content knowledge and clinical nursing practice competency. In addition, this difference gradually increased between the two groups throughout the trial. The results of this study demonstrated the positive, statistically significant effects of the flipped learning method on simulation-based nursing education. [J Nurs Educ. 2017;56(6):329-336.]. Copyright 2017, SLACK Incorporated.

Orthopaedic resident preparedness for closed reduction and pinning of pediatric supracondylar fractures is improved by e-learning: a multisite randomized controlled study.

PubMed

Hearty, Thomas; Maizels, Max; Pring, Maya; Mazur, John; Liu, Raymond; Sarwark, John; Janicki, Joseph

2013-09-04

There is a need to provide more efficient surgical training methods for orthopaedic residents. E-learning could possibly increase resident surgical preparedness, confidence, and comfort for surgery. Using closed reduction and pinning of pediatric supracondylar humeral fractures as the index case, we hypothesized that e-learning could increase resident knowledge acquisition for case preparation in the operating room. An e-learning surgical training module was created on the Computer Enhanced Visual Learning platform. The module provides a detailed and focused road map of the procedure utilizing a multimedia format. A multisite prospective randomized controlled study design compared residents who used a textbook for case preparation (control group) with residents who used the same textbook plus completed the e-learning module (test group). All subjects completed a sixty-question test on the theory and methods of the case. After completion of the test, the control group then completed the module as well. All subjects were surveyed on their opinion regarding the effectiveness of the module after performing an actual surgical case. Twenty-eight subjects with no previous experience in this surgery were enrolled at four academic centers. Subjects were randomized into two equal groups. The test group scored significantly better (p < 0.001) and demonstrated competence on the test compared with the control group; the mean correct test score (and standard deviation) was 90.9% ± 6.8% for the test group and 73.5% ± 6.4% for the control group. All residents surveyed (n = 27) agreed that the module is a useful supplement to traditional methods for case preparation and twenty-two of twenty-seven residents agreed that it reduced their anxiety during the case and improved their attention to surgical detail. E-learning using the Computer Enhanced Visual Learning platform significantly improved preparedness, confidence, and comfort with percutaneous closed reduction and pinning of a pediatric supracondylar humeral fracture. We believe that adapting such methods into residency training programs will improve efficiency in surgical training.

A study: Effect of Students Peer Assisted Learning on Magnetic Field Achievement

NASA Astrophysics Data System (ADS)

Mueanploy, Wannapa

2016-04-01

This study is the case study of Physic II Course for students of Pathumwan Institute of Technology. The purpose of this study is: 1) to develop cooperative learning method of peer assisted learning (PAL), 2) to compare the learning achievement before and after studied magnetic field lesson by cooperative learning method of peer assisted learning. The population was engineering students of Pathumwan Institute of Technology (PIT’s students) who registered Physic II Course during year 2014. The sample used in this study was selected from the 72 students who passed in Physic I Course. The control groups learning magnetic fields by Traditional Method (TM) and experimental groups learning magnetic field by method of peers assisted learning. The students do pretest before the lesson and do post-test after the lesson by 20 items achievement tests of magnetic field. The post-test higher than pretest achievement significantly at 0.01 level.

Active Learning Methods

ERIC Educational Resources Information Center

Zayapragassarazan, Z.; Kumar, Santosh

2012-01-01

Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

The effects of traditional learning and a learning cycle inquiry learning strategy on students' science achievement and attitudes toward elementary science

NASA Astrophysics Data System (ADS)

Ebrahim, Ali

The purpose of this study is to examine the impact of two instructional methods on students' academic achievement and attitudes toward elementary science in the State of Kuwait: traditional teaching method and the 4-E learning cycle inquiry teaching method. The subjects were 111 students from four intact 4th grade classes. The experiment group (n = 56) received the learning cycle instruction while the control group (n = 55) received a more traditional approach over a four week period. The same female teacher taught the experimental and control groups for boys and a different female teacher taught experimental and control groups for girls. The dependent variables were measured through the use of: (1) a science achievement test to assess student achievement; and (2) an attitude survey to measure students' attitudes toward science. Quantitative data were collected on students' pre- and post-treatment achievement and attitudes measures. The two way MANOVA reveals that: the 4-E learning cycle instructional method produces significantly greater achievement and attitudes among fourth grade science students than the traditional teaching approach F (2, 93) = 19.765, (P = .000), corresponding to Wilks' Lambda = .702 with an effect size of .298 and a power of 1. In light of these findings, it is therefore suggested that students can achieve greater and have higher science attitudes when the 4-E learning cycle is used. In addition, these findings support the notion that effective instruction in teaching science, such as the 4-E learning cycle instruction, should be proposed and implemented in elementary schools.

Effects of Cooperative Learning Method on the Development of Listening Comprehension and Listening Skills

ERIC Educational Resources Information Center

Kirbas, Abdulkadir

2017-01-01

In this study, the effect of the learning together technique, which is one of the cooperative learning methods, on the development of the listening comprehension and listening skills of the secondary school eighth grade students was investigated. Regarding the purpose of the research, experimental and control groups consisting of 75 students from,…

Reform-Based-Instructional Method and Learning Styles on Students' Achievement and Retention in Mathematics: Administrative Implications

ERIC Educational Resources Information Center

Modebelu, M. N.; Ogbonna, C. C.

2014-01-01

This study aimed at determining the effect of reform-based-instructional method learning styles on students' achievement and retention in mathematics. A sample size of 119 students was randomly selected. The quasiexperimental design comprising pre-test, post-test, and randomized control group were employed. The Collin Rose learning styles…

Drosophila learn efficient paths to a food source.

PubMed

Navawongse, Rapeechai; Choudhury, Deepak; Raczkowska, Marlena; Stewart, James Charles; Lim, Terrence; Rahman, Mashiur; Toh, Alicia Guek Geok; Wang, Zhiping; Claridge-Chang, Adam

2016-05-01

Elucidating the genetic, and neuronal bases for learned behavior is a central problem in neuroscience. A leading system for neurogenetic discovery is the vinegar fly Drosophila melanogaster; fly memory research has identified genes and circuits that mediate aversive and appetitive learning. However, methods to study adaptive food-seeking behavior in this animal have lagged decades behind rodent feeding analysis, largely due to the challenges presented by their small scale. There is currently no method to dynamically control flies' access to food. In rodents, protocols that use dynamic food delivery are a central element of experimental paradigms that date back to the influential work of Skinner. This method is still commonly used in the analysis of learning, memory, addiction, feeding, and many other subjects in experimental psychology. The difficulty of microscale food delivery means this is not a technique used in fly behavior. In the present manuscript we describe a microfluidic chip integrated with machine vision and automation to dynamically control defined liquid food presentations and sensory stimuli. Strikingly, repeated presentations of food at a fixed location produced improvements in path efficiency during food approach. This shows that improved path choice is a learned behavior. Active control of food availability using this microfluidic system is a valuable addition to the methods currently available for the analysis of learned feeding behavior in flies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A description of the verbal behavior of students during two reading instruction methods

PubMed Central

Daly, Patricia M.

1987-01-01

The responses of students during two reading methods, the language experience approach and two Mastery Learning programs, were analyzed using verbal operants. A description of student responding was generated for these methods. The purpose of the study was to answer the questions: What are the major controlling variables determining student reading behavior during the language experience approach and two Mastery Learning programs, and how do these controlling variables change across story reading sessions and across stories in the first method? Student responses by verbal operant were compared for both reading methods. Findings indicated higher frequencies of textual operants occurred in responses during the Mastery Learning programs. A greater reliance on intraverbal control was evident in responses during the language experience approach. It is suggested that students who can generate strong intraverbal responses and who may have visual discrimination problems during early reading instruction may benefit from use of the language experience approach at this stage. ImagesFigure 2Figure 3 PMID:22477535

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design.

PubMed

Juang, C F; Lin, J Y; Lin, C T

2000-01-01

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

PubMed

Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

2018-06-01

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

A delivery mode study: The effect of self-paced video learning on first-year college students' achievement in calculus

NASA Astrophysics Data System (ADS)

Oktaviyanthi, Rina; Herman, Tatang

2016-10-01

In this paper, the effect of two different modes of deliver are proposed. The use of self-paced video learning and conventional learning methods in mathematics are compared. The research design classified as a quasi-experiment. The participants were 80 students in the first-year college and divided into two groups. One group as an experiment class received self-paced video learning method and the other group as a control group taught by conventional learning method. Pre and posttest were employed to measure the students' achievement, while questionnaire and interviews were applied to support the pre and posttest data. Statistical analysis included the independent samples t-test showed differences (p < 0.05) in posttest between the experimental and control groups, it means that the use of self-paced video contributed on students' achievement and students' attitudes. In addition, related to corresponding to the students' answer, there are five positive gains in using self-paced video in learning Calculus, such as appropriate learning for both audio and visual of students' characteristics, useful to learn Calculus, assisting students to be more engaging and paying attention in learning, helping students in making the concepts of Calculus are visible, interesting media and motivating students to learn independently.

Articulatory Control in Childhood Apraxia of Speech in a Novel Word-Learning Task

ERIC Educational Resources Information Center

Case, Julie; Grigos, Maria I.

2016-01-01

Purpose: Articulatory control and speech production accuracy were examined in children with childhood apraxia of speech (CAS) and typically developing (TD) controls within a novel word-learning task to better understand the influence of planning and programming deficits in the production of unfamiliar words. Method: Participants included 16…

Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

NASA Astrophysics Data System (ADS)

Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

2018-05-01

In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.

PubMed

Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong

2015-11-01

The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.

Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

PubMed

Addeh, Abdoljalil; Khormali, Aminollah; Golilarz, Noorbakhsh Amiri

2018-05-04

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Blended Learning: A Mixed-Methods Study on Successful Schools and Effective Practices

ERIC Educational Resources Information Center

Mathews, Anne

2017-01-01

Blended learning is a teaching technique that utilizes face-to-face teaching and online or technology-based practice in which the learner has the ability to exert some level of control over the pace, place, path, or time of learning. Schools that employ this method of teaching often demonstrate larger gains than traditional face-to-face programs…

Inquiry-Based Integrated Science Education: Implementation of Local Content “Soil Washing” Project To Improve Junior High School Students’ Environmental Literacy

NASA Astrophysics Data System (ADS)

Syifahayu

2017-02-01

The study was conducted based on teaching and learning problems led by conventional method that had been done in the process of learning science. It gave students lack opportunities to develop their competence and thinking skills. Consequently, the process of learning science was neglected. Students did not have opportunity to improve their critical attitude and creative thinking skills. To cope this problem, the study was conducted using Project-Based Learning model through inquiry-based science education about environment. The study also used an approach called Sains Lingkungan and Teknologi masyarakat - “Saling Temas” (Environmental science and Technology in Society) which promoted the local content in Lampung as a theme in integrated science teaching and learning. The study was a quasi-experimental with pretest-posttest control group design. Initially, the subjects were given a pre-test. The experimental group was given inquiry learning method while the control group was given conventional learning. After the learning process, the subjects of both groups were given post-test. Quantitative analysis was performed using the Mann-Whitney U-test and also a qualitative descriptive. Based on the result, environmental literacy skills of students who get inquiry learning strategy, with project-based learning model on the theme soil washing, showed significant differences. The experimental group is better than the control group. Data analysis showed the p-value or sig. (2-tailed) is 0.000 <α = 0.05 with the average N-gain of experimental group is 34.72 and control group is 16.40. Besides, the learning process becomes more meaningful.

The 21st century skills with model eliciting activities on linear program

NASA Astrophysics Data System (ADS)

Handajani, Septriana; Pratiwi, Hasih; Mardiyana

2018-04-01

Human resources in the 21st century are required to master various forms of skills, including critical thinking skills and problem solving. The teaching of the 21st century is a teaching that integrates literacy skills, knowledge, skills, attitudes, and mastery of ICT. This study aims to determine whether there are differences in the effect of applying Model Elliciting Activities (MEAs) that integrates 21st century skills, namely 4C and conventional learning to learning outcomes. This research was conducted at Vocational High School in the odd semester of 2017 and uses the experimental method. The experimental class is treated MEAs that integrates 4C skills and the control class is given conventional learning. Methods of data collection in this study using the method of documentation and test methods. The data analysis uses Z-test. Data obtained from experiment class and control class. The result of this study showed there are differences in the effect of applying MEAs that integrates 4C skills and conventional learning to learning outcomes. Classes with MEAs that integrates 4C skills give better learning outcomes than the ones in conventional learning classes. This happens because MEAs that integrates 4C skills can improved creativity skills, communication skills, collaboration skills, and problem-solving skills.

Effectiveness of Case-Based Learning Instruction on Epistemological Beliefs and Attitudes Toward Chemistry

NASA Astrophysics Data System (ADS)

Çam, Aylin; Geban, Ömer

2011-02-01

The purpose of the study was to investigate the effectiveness of case-based learning instruction over traditionally designed chemistry instruction on eleventh grade students' epistemological beliefs and their attitudes toward chemistry as a school subject. The subjects of this study consisted of 63 eleventh grade students from two intact classes of an urban high school instructed with same teacher. Each teaching method was randomly assigned to one class. The experimental group received case-based learning and the control group received traditional instruction. At the experimental group, life cases were presented with small group format; at the control group, lecturing and discussion was carried out. The results showed that there was a significant difference between the experimental and control group with respect to their epistemological beliefs and attitudes toward chemistry as a school subject in favor of case-based learning method group. Thus, case base learning is helpful for development of students' epistemological beliefs and attitudes toward chemistry.

Integration Method of Emphatic Motions and Adverbial Expressions with Scalar Parameters for Robotic Motion Coaching System

NASA Astrophysics Data System (ADS)

Okuno, Keisuke; Inamura, Tetsunari

A robotic coaching system can improve humans' learning performance of motions by intelligent usage of emphatic motions and adverbial expressions according to user reactions. In robotics, however, method to control both the motions and the expressions and how to bind them had not been adequately discussed from an engineering point of view. In this paper, we propose a method for controlling and binding emphatic motions and adverbial expressions by using two scalar parameters in a phase space. In the phase space, variety of motion patterns and verbal expressions are connected and can be expressed as static points. We show the feasibility of the proposing method through experiments of actual sport coaching tasks for beginners. From the results of participants' improvements in motion learning, we confirmed the feasibility of the methods to control and bind emphatic motions and adverbial expressions, as well as confirmed contribution of the emphatic motions and positive correlation of adverbial expressions for participants' improvements in motion learning. Based on the results, we introduce a hypothesis that individually optimized method for binding adverbial expression is required.

Refining fuzzy logic controllers with machine learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1994-01-01

In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

Problem-Based Learning Method: Secondary Education 10th Grade Chemistry Course Mixtures Topic

ERIC Educational Resources Information Center

Üce, Musa; Ates, Ismail

2016-01-01

In this research; aim was determining student achievement by comparing problem-based learning method with teacher-centered traditional method of teaching 10th grade chemistry lesson mixtures topic. Pretest-posttest control group research design is implemented. Research sample includes; two classes of (total of 48 students) an Anatolian High School…

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

The differential effects of teaching addition through strategy instruction versus drill and practice to students with and without learning disabilities.

PubMed

Tournaki, Nelly

2003-01-01

Forty-two second-grade general education students and 42 students with learning disabilities (LD) were taught basic, one-digit addition facts (e.g., 5 + 3 = _). Students received instruction via (a) a minimum addend strategy, (b) drill and practice, or (c) control. The effectiveness of the two methods was measured through students' accuracy and latency scores on a posttest and a transfer task (e.g., 5 + 3 + 7 =_). Students with LD improved significantly only in the strategy condition, as compared to drill-and-practice and control conditions, whereas general education students improved significantly both in the strategy and the drill-and-practice conditions as compared to the control condition. However, in the transfer task, students from all groups became significantly more accurate only in the strategy condition, while all students were significantly faster than their control group peers regardless of teaching method. The implications for teachers' differential choices of methods of instruction for students with different learning characteristics are discussed.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of e-learning, lectures, and role playing on nursing students' knowledge acquisition, retention and satisfaction.

PubMed

Pourghaznein, Tayebeh; Sabeghi, Hakimeh; Shariatinejad, Keyvan

2015-01-01

Nursing education can maintain its dynamic quality when it moves toward innovation and modern methods of teaching and learning. Therefore, teachers are required to employ up to date methods in their teaching plans. This study evaluated the effects of e-learning, lectures, and role playing on nursing students' learning, retention, and satisfaction. Sixty nursing students were selected as an experiment and control groups during two consecutive semesters. The educational content was presented as e-learning and role playing during one semester (experiment group) and as lectures in the next semester (control group). A questionnaire containing three parts was used to assess demographics, learning and satisfaction statuses. The questionnaire also included a final openended question to evaluate the students' ideas about the whole course. The mean scores of posttest were 16.13 ± 1.37 using role playing, 15.50 ± 1.44 using e-learning and 16.45 ± 1.23 using lectures. The differences between the mean scores of posttest and pretest were 12.84 ± 1.43, 12.56 ± 1.57, and 13.73 ± 1.53 in the mentioned methods, respectively. Lectures resulted in significantly better learning compared to role playing and e-learning. In contrast, retention rates were significantly lower using lectures than using role playing and e-learning. Students' satisfaction from e-learning was significantly lower than lecturing and role playing. Due to the lower rates of retention following lectures, the teachers are recommended to use student- centered approaches in their lectures. Since students' satisfaction with e-learning was lower than the other methods, further studies are suggested to explore the problems of e-learning in Iran.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions

PubMed Central

Box, Simon

2014-01-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable. PMID:26064570

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

PubMed

Box, Simon

2014-12-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

Parameter learning for performance adaptation

NASA Technical Reports Server (NTRS)

Peek, Mark D.; Antsaklis, Panos J.

1990-01-01

A parameter learning method is introduced and used to broaden the region of operability of the adaptive control system of a flexible space antenna. The learning system guides the selection of control parameters in a process leading to optimal system performance. A grid search procedure is used to estimate an initial set of parameter values. The optimization search procedure uses a variation of the Hooke and Jeeves multidimensional search algorithm. The method is applicable to any system where performance depends on a number of adjustable parameters. A mathematical model is not necessary, as the learning system can be used whenever the performance can be measured via simulation or experiment. The results of two experiments, the transient regulation and the command following experiment, are presented.

Machine Learning: A Crucial Tool for Sensor Design

PubMed Central

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E.

2009-01-01

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. PMID:20191110

Reinforcement Learning with Orthonormal Basis Adaptation Based on Activity-Oriented Index Allocation

NASA Astrophysics Data System (ADS)

Satoh, Hideki

An orthonormal basis adaptation method for function approximation was developed and applied to reinforcement learning with multi-dimensional continuous state space. First, a basis used for linear function approximation of a control function is set to an orthonormal basis. Next, basis elements with small activities are replaced with other candidate elements as learning progresses. As this replacement is repeated, the number of basis elements with large activities increases. Example chaos control problems for multiple logistic maps were solved, demonstrating that the method for adapting an orthonormal basis can modify a basis while holding the orthonormality in accordance with changes in the environment to improve the performance of reinforcement learning and to eliminate the adverse effects of redundant noisy states.

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

PubMed

Taylor, Jonathan Christopher; Fenner, John Wesley

2017-11-29

Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson's disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.

The impact of blended teaching on knowledge, satisfaction, and self-directed learning in nursing undergraduates: a randomized, controlled trial.

PubMed

Gagnon, Marie-Pierre; Gagnon, Johanne; Desmartis, Marie; Njoya, Merlin

2013-01-01

This study aimed to assess the effectiveness of a blended-teaching intervention using Internet-based tutorials coupled with traditional lectures in an introduction to research undergraduate nursing course. Effects of the intervention were compared with conventional, face-to-face classroom teaching on three outcomes: knowledge, satisfaction, and self-learning readiness. A two-group, randomized, controlled design was used, involving 112 participants. Descriptive statistics and analysis of covariance (ANCOVA) were performed. The teaching method was found to have no direct impact on knowledge acquisition, satisfaction, and self-learning readiness. However, motivation and teaching method had an interaction effect on knowledge acquisition by students. Among less motivated students, those in the intervention group performed better than those who received traditional training. These findings suggest that this blended-teaching method could better suit some students, depending on their degree of motivation and level of self-directed learning readiness.

The Impact of Team-Based Learning on Nervous System Examination Knowledge of Nursing Students.

PubMed

Hemmati Maslakpak, Masomeh; Parizad, Naser; Zareie, Farzad

2015-12-01

Team-based learning is one of the active learning approaches in which independent learning is combined with small group discussion in the class. This study aimed to determine the impact of team-based learning in nervous system examination knowledge of nursing students. This quasi-experimental study was conducted on 3(rd) grade nursing students, including 5th semester (intervention group) and 6(th) semester (control group). The traditional lecture method and the team-based learning method were used for educating the examination of the nervous system for intervention and control groups, respectively. The data were collected by a test covering 40-questions (multiple choice, matching, gap-filling and descriptive questions) before and after intervention in both groups. Individual Readiness Assurance Test (RAT) and Group Readiness Assurance Test (GRAT) used to collect data in the intervention group. In the end, the collected data were analyzed by SPSS ver. 13 using descriptive and inferential statistical tests. In team-based learning group, mean and standard deviation was 13.39 (4.52) before the intervention, which had been increased to 31.07 (3.20) after the intervention and this increase was statistically significant. Also, there was a statistically significant difference between the scores of RAT and GRAT in team-based learning group. Using team-based learning approach resulted in much better improvement and stability in the nervous system examination knowledge of nursing students compared to traditional lecture method; therefore, this method could be efficiently used as an effective educational approach in nursing education.

Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

NASA Astrophysics Data System (ADS)

Hsu, Roy CHaoming; Jian, Jhih-Wei; Lin, Chih-Chuan; Lai, Chien-Hung; Liu, Cheng-Ting

2013-01-01

The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.

Facts and fiction of learning systems. [decision making intelligent control

NASA Technical Reports Server (NTRS)

Saridis, G. N.

1975-01-01

The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

Concept and benefits of the Inverted Classroom method for a competency-based biochemistry course in the pre-clinical stage of a human medicine course of studies

PubMed Central

Kühl, Susanne J.; Toberer, Matthias; Keis, Oliver; Tolks, Daniel; Fischer, Martin R.; Kühl, Michael

2017-01-01

Background: Medical students often have a problem recognising the relevance of basic science subjects for their later professional work in the pre-clinical stage of their studies. This can lead to a lower motivation to learn biochemical content and dissatisfaction in the courses amongst the students. Alternative teaching methods such as the Inverted Classroom (IC) method can address this deficiency. The goal of this study was: to analyse the motivation and satisfaction of the students in a biochemistry seminar through the use of the e-learning-based IC method, to investigate the acceptance against the IC teaching method in biochemistry, and to compare the learning success achieved using the IC approach with that of a traditional course. We also investigated how a biochemistry course in the pre-clinical stage of a human medicine course of studies can be successfully organised according to the IC method. Furthermore, we examined the benefits of the IC method over conventional teaching formats. Method: The IC method was implemented in accordance with the guidelines of the GMA committee “New Media” [30] in a biochemistry seminar for two student IC intervention groups with 42 students. A part of the factual knowledge from the on-site phase in the form of teaching videos together with self-learning control tasks were provided online before the seminar for both IC intervention groups. Exporting content to the self-learning phase creates new free time in the on-site phase, during which the content can be critically considered and processed and additional competency-based learning objectives can be taught. Identical biochemistry teaching content was taught in parallel control groups (14 student groups with n=299 students), but no material was handed out beforehand for a self-learning phase. These students only received the materials after the on-site phase. Motivation and satisfaction as well as the acceptance for the teaching methods were recorded by questionnaires, the acquisition of knowledge by MC exams. Results: On a Likert scale from 1 (strongly disagree) to 6 (strongly agree), the students in the IC intervention groups could be seen to be much more motivated (5.53) than students in the control group (4.01). Students in the IC intervention groups also recognised the relevance of the learning content much more clearly (5.44) than students in the control group (4.01). Furthermore, the IC group also observed that additional competencies were trained in addition to the biochemistry content. In addition, the IC intervention group award the event a school grade of 1.53, the traditional control group a grade of 2.96. The teaching videos were rated very positively by both groups with an average school grade of 1.3 in each case. A qualitative analysis showed that the motivation and a positive attitude of the lecturers played a decisive role in the successful implementation of the IC method. Discussion and conclusion: Pre-clinical students display a high acceptance of the e-learning-based IC method. Teaching communication competencies in a biochemistry seminar was also rated very positively by the students. The quality of the teaching video and the motivation of the lecturers were shown to be a critical parameter for the successful performance of the IC method. What’s more, the IC method can contribute to implementing a competence orientation in medical studies. PMID:28890922

Concept and benefits of the Inverted Classroom method for a competency-based biochemistry course in the pre-clinical stage of a human medicine course of studies.

PubMed

Kühl, Susanne J; Toberer, Matthias; Keis, Oliver; Tolks, Daniel; Fischer, Martin R; Kühl, Michael

2017-01-01

Background: Medical students often have a problem recognising the relevance of basic science subjects for their later professional work in the pre-clinical stage of their studies. This can lead to a lower motivation to learn biochemical content and dissatisfaction in the courses amongst the students. Alternative teaching methods such as the Inverted Classroom (IC) method can address this deficiency. The goal of this study was: to analyse the motivation and satisfaction of the students in a biochemistry seminar through the use of the e-learning-based IC method, to investigate the acceptance against the IC teaching method in biochemistry, and to compare the learning success achieved using the IC approach with that of a traditional course. We also investigated how a biochemistry course in the pre-clinical stage of a human medicine course of studies can be successfully organised according to the IC method. Furthermore, we examined the benefits of the IC method over conventional teaching formats. Method: The IC method was implemented in accordance with the guidelines of the GMA committee "New Media" [30] in a biochemistry seminar for two student IC intervention groups with 42 students. A part of the factual knowledge from the on-site phase in the form of teaching videos together with self-learning control tasks were provided online before the seminar for both IC intervention groups. Exporting content to the self-learning phase creates new free time in the on-site phase, during which the content can be critically considered and processed and additional competency-based learning objectives can be taught. Identical biochemistry teaching content was taught in parallel control groups (14 student groups with n=299 students), but no material was handed out beforehand for a self-learning phase. These students only received the materials after the on-site phase. Motivation and satisfaction as well as the acceptance for the teaching methods were recorded by questionnaires, the acquisition of knowledge by MC exams. Results: On a Likert scale from 1 (strongly disagree) to 6 (strongly agree), the students in the IC intervention groups could be seen to be much more motivated (5.53) than students in the control group (4.01). Students in the IC intervention groups also recognised the relevance of the learning content much more clearly (5.44) than students in the control group (4.01). Furthermore, the IC group also observed that additional competencies were trained in addition to the biochemistry content. In addition, the IC intervention group award the event a school grade of 1.53, the traditional control group a grade of 2.96. The teaching videos were rated very positively by both groups with an average school grade of 1.3 in each case. A qualitative analysis showed that the motivation and a positive attitude of the lecturers played a decisive role in the successful implementation of the IC method. Discussion and conclusion: Pre-clinical students display a high acceptance of the e-learning-based IC method. Teaching communication competencies in a biochemistry seminar was also rated very positively by the students. The quality of the teaching video and the motivation of the lecturers were shown to be a critical parameter for the successful performance of the IC method. What's more, the IC method can contribute to implementing a competence orientation in medical studies.

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Consensus-based distributed cooperative learning from closed-loop neural control systems.

PubMed

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

Brain-Emulating Cognition and Control Architecture (BECCA) v. 0.2 beta

DOE Office of Scientific and Technical Information (OSTI.GOV)

ROHRER, BRANDON; & MORROW, JAMES

2009-06-16

BECCA is a learning and control method based on the function of the human brain. The goal behind its creation is to learn to control robots in unfamiliar environments in a way that is very robust, similar to the way that an infant learns to interact with her environment by trial and error. As of this release, this software contains an application for controlling robot hardware through a socket. The code was created so as to make it extensible to new applications. It is modular, object-oriented code in which the portions of the code that are specific to one robotmore » are easily separable from those portions that are the constant between implementations. BECCA makes very few assumptions about the robot and environment it is learning, and so is applicable to a wide range of learning and control problems.« less

The entropy reduction engine: Integrating planning, scheduling, and control

NASA Technical Reports Server (NTRS)

Drummond, Mark; Bresina, John L.; Kedar, Smadar T.

1991-01-01

The Entropy Reduction Engine, an architecture for the integration of planning, scheduling, and control, is described. The architecture is motivated, presented, and analyzed in terms of its different components; namely, problem reduction, temporal projection, and situated control rule execution. Experience with this architecture has motivated the recent integration of learning. The learning methods are described along with their impact on architecture performance.

Process-Oriented Guided-Inquiry Learning Improves Long-Term Retention of Information

ERIC Educational Resources Information Center

Vanags, Thea; Pammer, Kristen; Brinker, Jay

2013-01-01

Many chemistry educators have adopted the process-oriented guided instructional learning (POGIL) pedagogy. However, it is not clear which aspects of POGIL are the most important in terms of actual learning. We compared 354 first-year undergraduate psychology students' learning in physiological psychology using four teaching methods: control,…

Implementation of Project Based Learning in Mechatronic Lab Course at Bandung State Polytechnic

ERIC Educational Resources Information Center

Basjaruddin, Noor Cholis; Rakhman, Edi

2016-01-01

Mechatronics is a multidisciplinary that includes a combination of mechanics, electronics, control systems, and computer science. The main objective of mechatronics learning is to establish a comprehensive mindset in the development of mechatronic systems. Project Based Learning (PBL) is an appropriate method for use in the learning process of…

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

PubMed

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

PubMed

Jia, Zi-Jun; Song, Yong-Duan

2017-06-01

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

Use of the 5E learning cycle model combined with problem-based learning for a fundamentals of nursing course.

PubMed

Jun, Won Hee; Lee, Eun Ju; Park, Han Jong; Chang, Ae Kyung; Kim, Mi Ja

2013-12-01

The 5E learning cycle model has shown a positive effect on student learning in science education, particularly in courses with theory and practice components. Combining problem-based learning (PBL) with the 5E learning cycle was suggested as a better option for students' learning of theory and practice. The purpose of this study was to compare the effects of the traditional learning method with the 5E learning cycle model with PBL. The control group (n = 78) was subjected to a learning method that consisted of lecture and practice. The experimental group (n = 83) learned by using the 5E learning cycle model with PBL. The results showed that the experimental group had significantly improved self-efficacy, critical thinking, learning attitude, and learning satisfaction. Such an approach could be used in other countries to enhance students' learning of fundamental nursing. Copyright 2013, SLACK Incorporated.

Direct heuristic dynamic programming for damping oscillations in a large power system.

PubMed

Lu, Chao; Si, Jennie; Xie, Xiaorong

2008-08-01

This paper applies a neural-network-based approximate dynamic programming method, namely, the direct heuristic dynamic programming (direct HDP), to a large power system stability control problem. The direct HDP is a learning- and approximation-based approach to addressing nonlinear coordinated control under uncertainty. One of the major design parameters, the controller learning objective function, is formulated to directly account for network-wide low-frequency oscillation with the presence of nonlinearity, uncertainty, and coupling effect among system components. Results include a novel learning control structure based on the direct HDP with applications to two power system problems. The first case involves static var compensator supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a difficult complex system challenge by providing a new solution to a large interconnected power network oscillation damping control problem that frequently occurs in the China Southern Power Grid.

Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes

NASA Astrophysics Data System (ADS)

Wang, Limin; Shen, Yiteng; Yu, Jingxian; Li, Ping; Zhang, Ridong; Gao, Furong

2018-01-01

In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended information, the multi-phase batch process is converted into a two-dimensional Fornasini-Marchesini (2D-FM) switched system with different dimensions. Second, a switching signal is designed using the average dwell-time method integrated with the related switching conditions to give sufficient conditions ensuring stable running for the system. Finally, the minimum running time of the subsystems and the control law gains are calculated by solving the linear matrix inequalities. Meanwhile, a compound 2D controller with robust performance is obtained, which includes a robust extended feedback control for ensuring the steady-state tracking error to converge rapidly. The application on an injection molding process displays the effectiveness and superiority of the proposed strategy.

The use of CORE model by metacognitive skill approach in developing characters junior high school students

NASA Astrophysics Data System (ADS)

Fisher, Dahlia; Yaniawati, Poppy; Kusumah, Yaya Sukjaya

2017-08-01

This study aims to analyze the character of students who obtain CORE learning model using metacognitive approach. The method in this study is qualitative research and quantitative research design (Mixed Method Design) with concurrent embedded strategy. The research was conducted on two groups: an experimental group and the control group. An experimental group consists of students who had CORE model learning using metacognitive approach while the control group consists of students taught by conventional learning. The study was conducted the object this research is the seventh grader students in one the public junior high schools in Bandung. Based on this research, it is known that the characters of the students in the CORE model learning through metacognitive approach is: honest, hard work, curious, conscientious, creative and communicative. Overall it can be concluded that CORE model learning is good for developing characters of a junior high school student.

Terminal iterative learning control based station stop control of a train

NASA Astrophysics Data System (ADS)

Hou, Zhongsheng; Wang, Yi; Yin, Chenkun; Tang, Tao

2011-07-01

The terminal iterative learning control (TILC) method is introduced for the first time into the field of train station stop control and three TILC-based algorithms are proposed in this study. The TILC-based train station stop control approach utilises the terminal stop position error in previous braking process to update the current control profile. The initial braking position, or the braking force, or their combination is chosen as the control input, and corresponding learning law is developed. The terminal stop position error of each algorithm is guaranteed to converge to a small region related with the initial offset of braking position with rigorous analysis. The validity of the proposed algorithms is verified by illustrative numerical examples.

Online learning control using adaptive critic designs with sparse kernel machines.

PubMed

Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

2013-05-01

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

Self-Controlled Practice Enhances Motor Learning in Introverts and Extroverts

ERIC Educational Resources Information Center

Kaefer, Angélica; Chiviacowsky, Suzete; Meira, Cassio de Miranda, Jr.; Tani, Go

2014-01-01

Purpose: The purpose of the present study was to investigate the effects of self-controlled feedback on the learning of a sequential-timing motor task in introverts and extroverts. Method: Fifty-six university students were selected by the Eysenck Personality Questionnaire. They practiced a motor task consisting of pressing computer keyboard keys…

Learning in robotic manipulation: The role of dimensionality reduction in policy search methods. Comment on "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands" by Marco Santello et al.

NASA Astrophysics Data System (ADS)

Ficuciello, Fanny; Siciliano, Bruno

2016-07-01

A question that often arises, among researchers working on artificial hands and robotic manipulation, concerns the real meaning of synergies. Namely, are they a realistic representation of the central nervous system control of manipulation activities at different levels and of the sensory-motor manipulation apparatus of the human being, or do they constitute just a theoretical framework exploiting analytical methods to simplify the representation of grasping and manipulation activities? Apparently, this is not a simple question to answer and, in this regard, many minds from the field of neuroscience and robotics are addressing the issue [1]. The interest of robotics is definitely oriented towards the adoption of synergies to tackle the control problem of devices with high number of degrees of freedom (DoFs) which are required to achieve motor and learning skills comparable to those of humans. The synergy concept is useful for innovative underactuated design of anthropomorphic hands [2], while the resulting dimensionality reduction simplifies the control of biomedical devices such as myoelectric hand prostheses [3]. Synergies might also be useful in conjunction with the learning process [4]. This aspect is less explored since few works on synergy-based learning have been realized in robotics. In learning new tasks through trial-and-error, physical interaction is important. On the other hand, advanced mechanical designs such as tendon-driven actuation, underactuated compliant mechanisms and hyper-redundant/continuum robots might exhibit enhanced capabilities of adapting to changing environments and learning from exploration. In particular, high DoFs and compliance increase the complexity of modelling and control of these devices. An analytical approach to manipulation planning requires a precise model of the object, an accurate description of the task, and an evaluation of the object affordance, which all make the process rather time consuming. The integration of learning into control naturally leads to relaxing the above requirements through the adoption of coordinated motion patterns and sensory-motor synergies as useful tools leading to a problem of reduced dimension. To this purpose, model-based control strategies relying on synergistic models of manipulation activities learned from human experience can be integrated with real-time learning from actions strategies [5]. In [6] a classification of learning strategies for robotics is provided, while the difference between imitation learning and reinforcement learning (RL) is highlighted in [7]. From recent research in the field [8,9], it seems that RL represents the future toward autonomous and intelligent robots since it provides learning capabilities as those of humans, i.e. based on exploration and trial-and-error policies. In this context, suitable policy search methods to be implemented in a synergy-based framework are to be sought in order to reduce the search space dimension while guaranteeing the convergence and efficiency of the learning algorithm.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Collaborating Fuzzy Reinforcement Learning Agents

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

Earlier, we introduced GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Relearning and at the local level, each agent learns and operates based on ANTARCTIC, a technique for fuzzy reinforcement learning. In this paper, we show that it is possible for these agents to compete in order to affect the selected control policy but at the same time, they can collaborate while investigating the state space. In this model, the evaluator or the critic learns by observing all the agents behaviors but the control policy changes only based on the behavior of the winning agent also known as the super agent.

Discrete time learning control in nonlinear systems

NASA Technical Reports Server (NTRS)

Longman, Richard W.; Chang, Chi-Kuang; Phan, Minh

1992-01-01

In this paper digital learning control methods are developed primarily for use in single-input, single-output nonlinear dynamic systems. Conditions for convergence of the basic form of learning control based on integral control concepts are given, and shown to be satisfied by a large class of nonlinear problems. It is shown that it is not the gross nonlinearities of the differential equations that matter in the convergence, but rather the much smaller nonlinearities that can manifest themselves during the short time interval of one sample time. New algorithms are developed that eliminate restrictions on the size of the learning gain, and on knowledge of the appropriate sign of the learning gain, for convergence to zero error in tracking a feasible desired output trajectory. It is shown that one of the new algorithms can give guaranteed convergence in the presence of actuator saturation constraints, and indicate when the requested trajectory is beyond the actuator capabilities.

Experimental Methodology in English Teaching and Learning: Method Features, Validity Issues, and Embedded Experimental Design

ERIC Educational Resources Information Center

Lee, Jang Ho

2012-01-01

Experimental methods have played a significant role in the growth of English teaching and learning studies. The paper presented here outlines basic features of experimental design, including the manipulation of independent variables, the role and practicality of randomised controlled trials (RCTs) in educational research, and alternative methods…

Gr-GDHP: A New Architecture for Globalized Dual Heuristic Dynamic Programming.

PubMed

Zhong, Xiangnan; Ni, Zhen; He, Haibo

2017-10-01

Goal representation globalized dual heuristic dynamic programming (Gr-GDHP) method is proposed in this paper. A goal neural network is integrated into the traditional GDHP method providing an internal reinforcement signal and its derivatives to help the control and learning process. From the proposed architecture, it is shown that the obtained internal reinforcement signal and its derivatives can be able to adjust themselves online over time rather than a fixed or predefined function in literature. Furthermore, the obtained derivatives can directly contribute to the objective function of the critic network, whose learning process is thus simplified. Numerical simulation studies are applied to show the performance of the proposed Gr-GDHP method and compare the results with other existing adaptive dynamic programming designs. We also investigate this method on a ball-and-beam balancing system. The statistical simulation results are presented for both the Gr-GDHP and the GDHP methods to demonstrate the improved learning and controlling performance.

Neurofeedback Control of the Human GABAergic System Using Non-invasive Brain Stimulation.

PubMed

Koganemaru, Satoko; Mikami, Yusuke; Maezawa, Hitoshi; Ikeda, Satoshi; Ikoma, Katsunori; Mima, Tatsuya

2018-06-01

Neurofeedback has been a powerful method for self-regulating brain activities to elicit potential ability of human mind. GABA is a major inhibitory neurotransmitter in the central nervous system. Transcranial magnetic stimulation (TMS) is a tool that can evaluate the GABAergic system within the primary motor cortex (M1) using paired-pulse stimuli, short intracortical inhibition (SICI). Herein we investigated whether neurofeedback learning using SICI enabled us to control the GABAergic system within the M1 area. Forty-five healthy subjects were randomly divided into two groups: those receiving SICI neurofeedback learning or those receiving no neurofeedback (control) learning. During both learning periods, subjects made attempts to change the size of a circle, which was altered according to the degree of SICI in the SICI neurofeedback learning group, and which was altered independent of the degree of SICI in the control learning group. Results demonstrated that the SICI neurofeedback learning group showed a significant enhancement in SICI. Moreover, this group showed a significant reduction in choice reaction time compared to the control group. Our findings indicate that humans can intrinsically control the intracortical GABAergic system within M1 and can thus improve motor behaviors by SICI neurofeedback learning. SICI neurofeedback learning is a novel and promising approach to control our neural system and potentially represents a new therapy for patients with abnormal motor symptoms caused by CNS disorders. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

An Examination of Strategy Implementation during Abstract Nonlinguistic Category Learning in Aphasia

ERIC Educational Resources Information Center

Vallila-Rohter, Sofia; Kiran, Swathi

2015-01-01

Purpose: Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method: Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases…

Friend or Foe? Flipped Classroom for Undergraduate Electrocardiogram Learning: a Randomized Controlled Study.

PubMed

Rui, Zeng; Lian-Rui, Xiang; Rong-Zheng, Yue; Jing, Zeng; Xue-Hong, Wan; Chuan, Zuo

2017-03-07

Interpreting an electrocardiogram (ECG) is not only one of the most important parts of clinical diagnostics but also one of the most difficult topics to teach and learn. In order to enable medical students to master ECG interpretation skills in a limited teaching period, the flipped teaching method has been recommended by previous research to improve teaching effect on undergraduate ECG learning. A randomized controlled trial for ECG learning was conducted, involving 181 junior-year medical undergraduates using a flipped classroom as an experimental intervention, compared with Lecture-Based Learning (LBL) as a control group. All participants took an examination one week after the intervention by analysing 20 ECGs from actual clinical cases and submitting their ECG reports. A self-administered questionnaire was also used to evaluate the students' attitudes, total learning time, and conditions under each teaching method. The students in the experimental group scored significantly higher than the control group (8.72 ± 1.01 vs 8.03 ± 1.01, t = 4.549, P = 0.000) on ECG interpretation. The vast majority of the students in the flipped classroom group held positive attitudes toward the flipped classroom method and also supported LBL. There was no significant difference (4.07 ± 0.96 vs 4.16 ± 0.89, Z = - 0.948, P = 0.343) between the groups. Prior to class, the students in the flipped class group devoted significantly more time than those in the control group (42.33 ± 22.19 vs 30.55 ± 10.15, t = 4.586, P = 0.000), whereas after class, the time spent by the two groups were not significantly different (56.50 ± 46.80 vs 54.62 ± 31.77, t = 0.317, P = 0.752). Flipped classroom teaching can improve medical students' interest in learning and their self-learning abilities. It is an effective teaching model that needs to be further studied and promoted.

Distance learning in academic health education.

PubMed

Mattheos, N; Schittek, M; Attström, R; Lyon, H C

2001-05-01

Distance learning is an apparent alternative to traditional methods in education of health care professionals. Non-interactive distance learning, interactive courses and virtual learning environments exist as three different generations in distance learning, each with unique methodologies, strengths and potential. Different methodologies have been recommended for distance learning, varying from a didactic approach to a problem-based learning procedure. Accreditation, teamwork and personal contact between the tutors and the students during a course provided by distance learning are recommended as motivating factors in order to enhance the effectiveness of the learning. Numerous assessment methods for distance learning courses have been proposed. However, few studies report adequate tests for the effectiveness of the distance-learning environment. Available information indicates that distance learning may significantly decrease the cost of academic health education at all levels. Furthermore, such courses can provide education to students and professionals not accessible by traditional methods. Distance learning applications still lack the support of a solid theoretical framework and are only evaluated to a limited extent. Cases reported so far tend to present enthusiastic results, while more carefully-controlled studies suggest a cautious attitude towards distance learning. There is a vital need for research evidence to identify the factors of importance and variables involved in distance learning. The effectiveness of distance learning courses, especially in relation to traditional teaching methods, must therefore be further investigated.

Probabilistic dual heuristic programming-based adaptive critic

NASA Astrophysics Data System (ADS)

Herzallah, Randa

2010-02-01

Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.

Integration of Traditional and E-Learning Methods to Improve Learning Outcomes for Dental Students in Histopathology.

PubMed

Ariana, Armin; Amin, Moein; Pakneshan, Sahar; Dolan-Evans, Elliot; Lam, Alfred K

2016-09-01

Dental students require a basic ability to explain and apply general principles of pathology to systemic, dental, and oral pathology. Although there have been recent advances in electronic and online resources, the academic effectiveness of using self-directed e-learning tools in pathology courses for dental students is unclear. The aim of this study was to determine if blended learning combining e-learning with traditional learning methods of lectures and tutorials would improve students' scores and satisfaction over those who experienced traditional learning alone. Two consecutive cohorts of Bachelor of Dentistry and Oral Health students taking the general pathology course at Griffith University in Australia were compared. The control cohort experienced traditional methods only, while members of the study cohort were also offered self-directed learning materials including online resources and online microscopy classes. Final assessments for the course were used to compare the differences in effectiveness of the intervention, and students' satisfaction with the teaching format was evaluated using questionnaires. On the final course assessments, students in the study cohort had significantly higher scores than students in the control cohort (p<0.01). Analysis of questionnaire results showed improved student satisfaction with the course in the study cohort. These findings suggest that the use of e-learning tools such as virtual microscopy and interactive online resources for delivering pathology instruction can be an effective supplement for developing dental students' competence, confidence, and satisfaction.

E-Learning Optimization: The Relative and Combined Effects of Mental Practice and Modeling on Enhanced Podcast-Based Learning--A Randomized Controlled Trial

ERIC Educational Resources Information Center

Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P.; LeBlanc, Vicki R.

2016-01-01

Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced…

Emotionalized learning experiences: Tapping into the affective domain.

PubMed

Green, Zane Asher; Batool, Sadia

2017-06-01

The experimental study was undertaken to examine the effect of emotionalized learning experiences on the academic achievement of students at Preston University. The major objectives of the study were to identify the effect of teaching methods on students' academic achievement and to evaluate the relationship between affective learning conditions and students' academic achievement. Based on four intact semesters, the population of the study comprised 140 students from the Bachelors of Business Administration Program. The whole population was considered as the sample. The control group (28 students) was taught through the interactive lecture method, whereas, the experimental group 1 (35 students), experimental group 2 (46 students) and experimental group 3 (31 students) were taught through the activity method, reflective learning method and cooperative learning method respectively. Results indicated a significant difference between the pretest and posttest scores obtained in the achievement test as a result of the effect of teaching methods used for offering the emotionalized learning experiences. There was also a significant relationship between affective leaning conditions and students' academic achievement. Furthermore, it was found that students' academic achievement in the affective domain was highest with regard to workshops 1, 2 and 3. It was concluded that the emotionalized learning experiences offered to the students via the four teaching methods helped students in enhancing their knowledge, changing their attitudes and developing their skills with regard to living a happy, healthy and meaningful life. However, the reflective learning method proved to be the most suitable followed by the interactive lecture method, the cooperative learning method and the activity method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Synchronization of Chaotic Systems without Direct Connections Using Reinforcement Learning

NASA Astrophysics Data System (ADS)

Sato, Norihisa; Adachi, Masaharu

In this paper, we propose a control method for the synchronization of chaotic systems that does not require the systems to be connected, unlike existing methods such as that proposed by Pecora and Carroll in 1990. The method is based on the reinforcement learning algorithm. We apply our method to two discrete-time chaotic systems with mismatched parameters and achieve M step delay synchronization. Moreover, we extend the proposed method to the synchronization of continuous-time chaotic systems.

Automated edge finishing using an active XY table

DOEpatents

Loucks, Clifford S.; Starr, Gregory P.

1993-01-01

The disclosure is directed to an apparatus and method for automated edge finishing using hybrid position/force control of an XY table. The disclosure is particularly directed to learning the trajectory of the edge of a workpiece by "guarded moves". Machining is done by controllably moving the XY table, with the workpiece mounted thereon, along the learned trajectory with feedback from a force sensor. Other similar workpieces can be mounted, without a fixture on the XY table, located and the learned trajectory adjusted

Feedback error learning control of magnetic satellites using type-2 fuzzy neural networks with elliptic membership functions.

PubMed

Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Reyhanoglu, Mahmut; Kaynak, Okyay

2015-04-01

A novel type-2 fuzzy membership function (MF) in the form of an ellipse has recently been proposed in literature, the parameters of which that represent uncertainties are de-coupled from its parameters that determine the center and the support. This property has enabled the proposers to make an analytical comparison of the noise rejection capabilities of type-1 fuzzy logic systems with its type-2 counterparts. In this paper, a sliding mode control theory-based learning algorithm is proposed for an interval type-2 fuzzy logic system which benefits from elliptic type-2 fuzzy MFs. The learning is based on the feedback error learning method and not only the stability of the learning is proved but also the stability of the overall system is shown by adding an additional component to the control scheme to ensure robustness. In order to test the efficiency and efficacy of the proposed learning and the control algorithm, the trajectory tracking problem of a magnetic rigid spacecraft is studied. The simulations results show that the proposed control algorithm gives better performance results in terms of a smaller steady state error and a faster transient response as compared to conventional control algorithms.

Practice and effectiveness of web-based problem-based learning approach in a large class-size system: A comparative study.

PubMed

Ding, Yongxia; Zhang, Peili

2018-06-12

Problem-based learning (PBL) is an effective and highly efficient teaching approach that is extensively applied in education systems across a variety of countries. This study aimed to investigate the effectiveness of web-based PBL teaching pedagogies in large classes. The cluster sampling method was used to separate two college-level nursing student classes (graduating class of 2013) into two groups. The experimental group (n = 162) was taught using a web-based PBL teaching approach, while the control group (n = 166) was taught using conventional teaching methods. We subsequently assessed the satisfaction of the experimental group in relation to the web-based PBL teaching mode. This assessment was performed following comparison of teaching activity outcomes pertaining to exams and self-learning capacity between the two groups. When compared with the control group, the examination scores and self-learning capabilities were significantly higher in the experimental group (P < 0.01) compared with the control group. In addition, 92.6% of students in the experimental group expressed satisfaction with the new web-based PBL teaching approach. In a large class-size teaching environment, the web-based PBL teaching approach appears to be more optimal than traditional teaching methods. These results demonstrate the effectiveness of web-based teaching technologies in problem-based learning. Copyright © 2018. Published by Elsevier Ltd.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

Mathematical Critical Thinking and Curiosity Attitude in Problem Based Learning and Cognitive Conflict Strategy: A Study in Number Theory Course

ERIC Educational Resources Information Center

Zetriuslita; Wahyudin; Jarnawi

2017-01-01

This research aims to describe and analyze result of applying Problem-Based Learning and Cognitive Conflict Strategy (PBLCCS) in increasing students' Mathematical Critical Thinking (MCT) ability and Mathematical Curiosity Attitude (MCA). Adopting a quasi-experimental method with pretest-posttest control group design and using mixed method with…

Methods of Adapting Digital Content for the Learning Process via Mobile Devices

ERIC Educational Resources Information Center

Lopez, J. L. Gimenez; Royo, T. Magal; Laborda, Jesus Garcia; Calvo, F. Garde

2009-01-01

This article analyses different methods of adapting digital content for its delivery via mobile devices taking into account two aspects which are a fundamental part of the learning process; on the one hand, functionality of the contents, and on the other, the actual controlled navigation requirements that the learner needs in order to acquire high…

Web-Based Distance Learning: Substitute or Alternative to the Traditional Classroom--Making the Delivery Method Decision

ERIC Educational Resources Information Center

Hunt, David Marshall

2005-01-01

When a distance learning program administrator makes the critical choice of delivery methods, she/he needs to consider factors such as program developer centrism, international experience, cultural similarity, and desired level of control which will all be elaborated on in this article. The aim of this manuscript is to assist international…

The Effect of Process Oriented Guided Inquiry Learning (POGIL) on 11th Graders' Conceptual Understanding of Electrochemistry

ERIC Educational Resources Information Center

Sen, Senol; Yilmaz, Ayhan; Geban, Ömer

2016-01-01

The purpose of this study was to investigate the effect of Process Oriented Guided Inquiry Learning (POGIL) method compared to traditional teaching method on 11th grade students' conceptual understanding of electrochemistry concepts. Participants were 115 students from a public school in Turkey. Nonequivalent control group design was used. Two…

The Effect of Virtual Language Learning Method on Writing Ability of Iranian Intermediate EFL Learners

ERIC Educational Resources Information Center

Khoshsima, Hooshang; Sayadi, Fatemeh

2016-01-01

This study aimed at investigating the effect of virtual language learning method on Iranian intermediate EFL learners writing ability. The study was conducted with 20 English Translation students at Chabahar Maritime University who were assigned into two groups, control and experimental, after ensuring of their homogeneity by administering a TOEFL…

Enhancement of Self Efficacy of Vocational School Students in Buffer Solution Topics through Guided Inquiry Learning

NASA Astrophysics Data System (ADS)

M, Ardiany; W, Wahyu; A, Supriatna

2017-09-01

The more students who feel less confident in learning, so doing things that are less responsible, such as brawl, drunkenness and others. So researchers need to do research related to student self efficacy in learning, in order to reduce unwanted things. This study aims to determine the effect of guided inquiry learning on improving self-efficacy of learners in the buffer solution topics. The method used is the mixed method which is the two group pretest postest design. The subjects of the study are 60 students of class XI AK in one of the SMKN in Bandung, consisting of 30 experimental class students and 30 control class students. The instruments used in this study mix method consist of self-efficacy questionnaire of pretest and posttest learners, interview guides, and observation sheet. Data analysis using t test with significant α = 0,05. Based on the result of inquiry of guided inquiry study, there is a significant improvement in self efficacy aspect of students in the topic of buffer solution. Data of pretest and posttest interview, observation, questionnaire showed significant result, that is improvement of experimental class with conventionally guided inquiry learning. The mean of self-efficacy of student learning there is significant difference of experiment class than control class equal to 0,047. There is a significant relationship between guided inquiry learning with self efficacy and guided inquiry learning. Each correlation value is 0.737. The learning process with guided inquiry is fun and challenging so that students can expose their ideas and opinions without being forced. From the results of questionnaires students showed an attitude of interest, sincerity and a good response of learning. While the results of questionnaires teachers showed that guided inquiry learning can make students learn actively, increased self-efficacy.

Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

PubMed

Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

2015-05-01

This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

Impact on learning of an e-learning module on leukaemia: a randomised controlled trial

PubMed Central

2012-01-01

Background e-learning resources may be beneficial for complex or conceptually difficult topics. Leukaemia is one such topic, yet there are no reports on the efficacy of e-learning for leukaemia. This study compared the learning impact on senior medical students of a purpose-built e-learning module on leukaemia, compared with existing online resources. Methods A randomised controlled trial was performed utilising volunteer senior medical students. Participants were randomly allocated to Study and Control groups. Following a pre-test on leukaemia administered to both groups, the Study group was provided with access to the new e-learning module, while the Control group was directed to existing online resources. A post-test and an evaluation questionnaire were administered to both groups at the end of the trial period. Results Study and Control groups were equivalent in gender distribution, mean academic ability, pre-test performance and time studying leukaemia during the trial. The Study group performed significantly better than the Control group in the post-test, in which the group to which the students had been allocated was the only significant predictor of performance. The Study group’s evaluation of the module was overwhelmingly positive. Conclusions A targeted e-learning module on leukaemia had a significant effect on learning in this cohort, compared with existing online resources. We believe that the interactivity, dialogic feedback and integration with the curriculum offered by the e-learning module contributed to its impact. This has implications for e-learning design in medicine and other disciplines. PMID:22640463

Childhood fever management program for Korean pediatric nurses: A comparison between blended and face-to-face learning method.

PubMed

Jeong, Yong Sun; Kim, Jin Sun

2014-01-01

A blended learning can be a useful learning strategy to improve the quality of fever and fever management education for pediatric nurses. This study compared the effects of a blended and face-to-face learning program on pediatric nurses' childhood fever management, using theory of planned behavior. A nonequivalent control group pretest-posttest design was used. A fever management education program using blended learning (combining face-to-face and online learning components) was offered to 30 pediatric nurses, and 29 pediatric nurses received face-to-face education. Learning outcomes did not significantly differ between the two groups. However, learners' satisfaction was higher for the blended learning program than the face-to-face learning program. A blended learning pediatric fever management program was as effective as a traditional face-to-face learning program. Therefore, a blended learning pediatric fever management-learning program could be a useful and flexible learning method for pediatric nurses.

The impact of the learning contract on self-directed learning and satisfaction in nursing students in a clinical setting.

PubMed

Sajadi, Mahboobeh; Fayazi, Neda; Fournier, Andrew; Abedi, Ahmad Reza

2017-01-01

Background: The most important responsibilities of an education system are to create self-directed learning opportunities and develop the required skills for taking the responsibility for change. The present study aimed at determining the impact of a learning contract on self-directed learning and satisfaction of nursing students. Methods: A total of 59 nursing students participated in this experimental study. They were divided into six 10-member groups. To control the communications among the groups, the first 3 groups were trained using conventional learning methods and the second 3 groups using learning contract method. In the first session, a pretest was performed based on educational objectives. At the end of the training, the students in each group completed the questionnaires of self-directed learning and satisfaction. The results of descriptive and inferential statistical methods (dependent and independent t tests) were presented using SPSS. Results: There were no significant differences between the 2 groups in gender, grade point average of previous years, and interest toward nursing. However, the results revealed a significant difference between the 2 groups in the total score of self-directed learning (p= 0.019). Although the mean satisfaction score was higher in the intervention group, the difference was not statistically significant. Conclusion: This study suggested that the use of learning contract method in clinical settings enhances self-directed learning among nursing students. Because this model focuses on individual differences, the researcher highly recommends the application of this new method to educators.

Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

PubMed

Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

2012-08-01

An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. Copyright © 2012 Elsevier Ltd. All rights reserved.

Game-Based E-Learning Is More Effective than a Conventional Instructional Method: A Randomized Controlled Trial with Third-Year Medical Students

PubMed Central

Boeker, Martin; Andel, Peter; Vach, Werner; Frankenschmidt, Alexander

2013-01-01

Background When compared with more traditional instructional methods, Game-based e-learning (GbEl) promises a higher motivation of learners by presenting contents in an interactive, rule-based and competitive way. Most recent systematic reviews and meta-analysis of studies on Game-based learning and GbEl in the medical professions have shown limited effects of these instructional methods. Objectives To compare the effectiveness on the learning outcome of a Game-based e-learning (GbEl) instruction with a conventional script-based instruction in the teaching of phase contrast microscopy urinalysis under routine training conditions of undergraduate medical students. Methods A randomized controlled trial was conducted with 145 medical students in their third year of training in the Department of Urology at the University Medical Center Freiburg, Germany. 82 subjects where allocated for training with an educational adventure-game (GbEl group) and 69 subjects for conventional training with a written script-based approach (script group). Learning outcome was measured with a 34 item single choice test. Students' attitudes were collected by a questionnaire regarding fun with the training, motivation to continue the training and self-assessment of acquired knowledge. Results The students in the GbEl group achieved significantly better results in the cognitive knowledge test than the students in the script group: the mean score was 28.6 for the GbEl group and 26.0 for the script group of a total of 34.0 points with a Cohen's d effect size of 0.71 (ITT analysis). Attitudes towards the recent learning experience were significantly more positive with GbEl. Students reported to have more fun while learning with the game when compared to the script-based approach. Conclusions Game-based e-learning is more effective than a script-based approach for the training of urinalysis in regard to cognitive learning outcome and has a high positive motivational impact on learning. Game-based e-learning can be used as an effective teaching method for self-instruction. PMID:24349257

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

A comparison of problem-based learning and conventional teaching in nursing ethics education.

PubMed

Lin, Chiou-Fen; Lu, Meei-Shiow; Chung, Chun-Chih; Yang, Che-Ming

2010-05-01

The aim of this study was to compare the learning effectiveness of peer tutored problem-based learning and conventional teaching of nursing ethics in Taiwan. The study adopted an experimental design. The peer tutored problem-based learning method was applied to an experimental group and the conventional teaching method to a control group. The study sample consisted of 142 senior nursing students who were randomly assigned to the two groups. All the students were tested for their nursing ethical discrimination ability both before and after the educational intervention. A learning satisfaction survey was also administered to both groups at the end of each course. After the intervention, both groups showed a significant increase in ethical discrimination ability. There was a statistically significant difference between the ethical discrimination scores of the two groups (P < 0.05), with the experimental group on average scoring higher than the control group. There were significant differences in satisfaction with self-motivated learning and critical thinking between the groups. Peer tutored problem-based learning and lecture-type conventional teaching were both effective for nursing ethics education, but problem-based learning was shown to be more effective. Peer tutored problem-based learning has the potential to enhance the efficacy of teaching nursing ethics in situations in which there are personnel and resource constraints.

Enhancing Self-Efficacy in Elementary Science Teaching with Professional Learning Communities

ERIC Educational Resources Information Center

Mintzes, Joel J.; Marcum, Bev; Messerschmidt-Yates, Christl; Mark, Andrew

2013-01-01

Emerging from Bandura's Social Learning Theory, this study of in-service elementary school teachers examined the effects of sustained Professional Learning Communities (PLCs) on self-efficacy in science teaching. Based on mixed research methods, and a non-equivalent control group experimental design, the investigation explored changes in…

Efficacy of Group Based Learning in Learning Moral Value

ERIC Educational Resources Information Center

Singaravelu, G.

2008-01-01

The present study highlights the efficacy of Group Based Learning on cultivating moral value of the students at Standard VIII. Parallel group Experimental method was adopted in the study. Eighty students (control group = 40 students + experimental = 40 students) were selected as sample for the study. Researcher self-made achievement tool was…

Systemic Synthesis Questions [SSynQs] as Tools to Help Students to Build Their Cognitive Structures in a Systemic Manner

NASA Astrophysics Data System (ADS)

Hrin, Tamara N.; Fahmy, Ameen F. M.; Segedinac, Mirjana D.; Milenković, Dušica D.

2016-08-01

Many studies dedicated to the teaching and learning of organic chemistry courses have emphasized that high school students have shown significant difficulties in mastering the concepts of this discipline. Therefore, the aim of our study was to help students to overcome these difficulties by applying systemic synthesis questions, [SSynQs], as the instructional method in our intervention. This work shows that students from the group exposed to the new teaching method achieved higher scores on final testing than students from the control group, who were taught by the traditional method, when students' achievements in conventional, linear questions [LQs] and in [SSynQs] were studied. These results were followed by observation of lower levels of mental effort by students from the intervention group, and higher levels of mental effort in the control group, invested during solving both types of questions. This correlation between achievement and mental effort resulted in high instructional efficiency for the applied method in the intervention group, [SSynQs], and low instructional efficiency for the traditional teaching and learning method applied in the control group. A systemic triangular relation between achievement, mental effort, and instructional efficiency, established by each group and gender, emphasized that the application of [SSynQs] was more suited to female students than for male students because of [SSynQs] characteristics as teaching and learning tools and because of learning style and ability differences between genders.

Relearning of Activities of Daily Living: A Comparison of the Effectiveness of Three Learning Methods in Patients with Dementia of the Alzheimer Type.

PubMed

Bourgeois, J; Laye, M; Lemaire, J; Leone, E; Deudon, A; Darmon, N; Giaume, C; Lafont, V; Brinck-Jensen, S; Dechamps, A; König, A; Robert, P

2016-01-01

This study examined the effectiveness of three different learning methods: trial and error learning (TE), errorless learning (EL) and learning by modeling with spaced retrieval (MR) on the relearning process of IADL in mild-to-moderately severe Alzheimer's Dementia (AD) patients (n=52), using a 6-weeks randomized controlled trial design. The participants had to relearn three IADLs. Repeated-measure analyses during pre-intervention, post-intervention and 1-month delayed sessions were performed. All three learning methods were found to have similar efficiency. However, the intervention produced greater improvements in the actual performance of the IADL tasks than on their explicit knowledge. This study confirms that the relearning of IADL is possible with AD patients through individualized interventions, and that the improvements can be maintained even after the intervention.

Student-Generated Visualization as a Study Strategy for Science Concept Learning

ERIC Educational Resources Information Center

Hsieh, Yi-Chuan Jane; Cifuentes, Lauren

2006-01-01

Mixed methods were adopted to explore the effects of student-generated visualization on paper and on computers as a study strategy for middle school science concept learning. In a post-test-only-control-group design, scores were compared among a control-group (n=28), a group that was trained to visualize on paper (n=30), and a group that was…

Mental Effort and Performance as Determinants for the Dynamic Selection of Learning Tasks in Air Traffic Control Training

ERIC Educational Resources Information Center

Salden, Ron J.C.M.; Paas, Fred; Broers, Nick J.; van Merrienboer, Jeroen J. G.

2004-01-01

The differential effects of four task selection methods on training efficiency and transfer in computer-based training for Air Traffic Control were investigated. A non-dynamic condition, in which the learning tasks were presented to the participants in a fixed, predetermined sequence, was compared to three dynamic conditions, in which learning…

"Learn Young, Learn Fair", a Stress Management Program for Fifth and Sixth Graders: Longitudinal Results from an Experimental Study

ERIC Educational Resources Information Center

Kraag, Gerda; Van Breukelen, Gerard J. P.; Kok, Gerjo; Hosman, Clemens

2009-01-01

Background: This study examined the effects of a universal stress management program (Learn Young, Learn Fair) on stress, coping, anxiety and depression in fifth and sixth grade children. Methods: Fifty-two schools (1467 children) participated in a clustered randomized controlled trial. Data was collected in the fall of 2002, the spring of 2003,…

A reductionist approach to the analysis of learning in brain-computer interfaces.

PubMed

Danziger, Zachary

2014-04-01

The complexity and scale of brain-computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.

The role of strategies in motor learning

PubMed Central

Taylor, Jordan A.; Ivry, Richard B.

2015-01-01

There has been renewed interest in the role of strategies in sensorimotor learning. The combination of new behavioral methods and computational methods has begun to unravel the interaction between processes related to strategic control and processes related to motor adaptation. These processes may operate on very different error signals. Strategy learning is sensitive to goal-based performance error. In contrast, adaptation is sensitive to prediction errors between the desired and actual consequences of a planned movement. The former guides what the desired movement should be, whereas the latter guides how to implement the desired movement. Whereas traditional approaches have favored serial models in which an initial strategy-based phase gives way to more automatized forms of control, it now seems that strategic and adaptive processes operate with considerable independence throughout learning, although the relative weight given the two processes will shift with changes in performance. As such, skill acquisition involves the synergistic engagement of strategic and adaptive processes. PMID:22329960

Fuzzy Sarsa with Focussed Replacing Eligibility Traces for Robust and Accurate Control

NASA Astrophysics Data System (ADS)

Kamdem, Sylvain; Ohki, Hidehiro; Sueda, Naomichi

Several methods of reinforcement learning in continuous state and action spaces that utilize fuzzy logic have been proposed in recent years. This paper introduces Fuzzy Sarsa(λ), an on-policy algorithm for fuzzy learning that relies on a novel way of computing replacing eligibility traces to accelerate the policy evaluation. It is tested against several temporal difference learning algorithms: Sarsa(λ), Fuzzy Q(λ), an earlier fuzzy version of Sarsa and an actor-critic algorithm. We perform detailed evaluations on two benchmark problems : a maze domain and the cart pole. Results of various tests highlight the strengths and weaknesses of these algorithms and show that Fuzzy Sarsa(λ) outperforms all other algorithms tested for a larger granularity of design and under noisy conditions. It is a highly competitive method of learning in realistic noisy domains where a denser fuzzy design over the state space is needed for a more precise control.

Comparison of computer-assisted instruction (CAI) versus traditional textbook methods for training in abdominal examination (Japanese experience).

PubMed

Qayumi, A K; Kurihara, Y; Imai, M; Pachev, G; Seo, H; Hoshino, Y; Cheifetz, R; Matsuura, K; Momoi, M; Saleem, M; Lara-Guerra, H; Miki, Y; Kariya, Y

2004-10-01

This study aimed to compare the effects of computer-assisted, text-based and computer-and-text learning conditions on the performances of 3 groups of medical students in the pre-clinical years of their programme, taking into account their academic achievement to date. A fourth group of students served as a control (no-study) group. Participants were recruited from the pre-clinical years of the training programmes in 2 medical schools in Japan, Jichi Medical School near Tokyo and Kochi Medical School near Osaka. Participants were randomly assigned to 4 learning conditions and tested before and after the study on their knowledge of and skill in performing an abdominal examination, in a multiple-choice test and an objective structured clinical examination (OSCE), respectively. Information about performance in the programme was collected from school records and students were classified as average, good or excellent. Student and faculty evaluations of their experience in the study were explored by means of a short evaluation survey. Compared to the control group, all 3 study groups exhibited significant gains in performance on knowledge and performance measures. For the knowledge measure, the gains of the computer-assisted and computer-assisted plus text-based learning groups were significantly greater than the gains of the text-based learning group. The performances of the 3 groups did not differ on the OSCE measure. Analyses of gains by performance level revealed that high achieving students' learning was independent of study method. Lower achieving students performed better after using computer-based learning methods. The results suggest that computer-assisted learning methods will be of greater help to students who do not find the traditional methods effective. Explorations of the factors behind this are a matter for future research.

Original science-based music and student learning

NASA Astrophysics Data System (ADS)

Smolinski, Keith

American middle school student science scores have been stagnating for several years, demonstrating a need for better learning strategies to aid teachers in instruction and students in content learning. It has also been suggested by researchers that music can be used to aid students in their learning and memory. Employing the theoretical framework of brain-based learning, the purpose of this study was to examine the impact of original, science-based music on student content learning and student perceptions of the music and its impact on learning. Students in the treatment group at a public middle school learned songs with lyrics related to the content of a 4-week cells unit in science; whereas an equally sized control group was taught the same material using existing methods. The content retention and learning experiences of the students in this study were examined using a concurrent triangulation, mixed-methods study. Independent sample t test and ANOVA analyses were employed to determine that the science posttest scores of students in the treatment group (N = 93) were significantly higher than the posttest scores of students in the control group (N = 93), and that the relative gains of the boys in the treatment group exceeded those of the girls. The qualitative analysis of 10 individual interviews and 3 focus group interviews followed Patton's method of a priori coding, cross checking, and thematic analysis to examine the perceptions of the treatment group. These results confirmed that the majority of the students thought the music served as an effective learning tool and enhanced recall. This study promoted social change because students and teachers gained insight into how music can be used in science classrooms to aid in the learning of science content. Researchers could also utilize the findings for continued investigation of the interdisciplinary use of music in educational settings.

Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai

2017-03-01

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

A Novel Clustering Method Curbing the Number of States in Reinforcement Learning

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Nunobiki, Masayuki; Taniguchi, Kenji

We propose an efficient state-space construction method for a reinforcement learning. Our method controls the number of categories with improving the clustering method of Fuzzy ART which is an autonomous state-space construction method. The proposed method represents weight vector as the mean value of input vectors in order to curb the number of new categories and eliminates categories whose state values are low to curb the total number of categories. As the state value is updated, the size of category becomes small to learn policy strictly. We verified the effectiveness of the proposed method with simulations of a reaching problem for a two-link robot arm. We confirmed that the number of categories was reduced and the agent achieved the complex task quickly.

Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

The effect of active learning on student characteristics in a human physiology course for nonmajors.

PubMed

Wilke, R Russell

2003-12-01

This study investigated the effect of active-learning strategies on college students' achievement, motivation, and self-efficacy in a human physiology course for nonmajors. Variables were studied via a quasi-experimental, Solomon four-group design on 141 students at a small west-Texas university. Treatment groups were taught using a continuum-based, active-learning model implemented over the course of a semester. Control groups were taught using traditional didactic lecture methods. To assess the effects of the continuum-based active learning strategies, students were administered a comprehensive physiology content exam, the Motivated Strategies for Learning Questionnaire, and attitude surveys. Factorial analyses indicated that the treatment groups acquired significantly more content knowledge and were significantly more self-efficacious than students in the control groups. There were no significant differences in motivation. Attitude surveys indicated that students in both the treatment and control groups demonstrated a positive attitude toward active learning, believed it helped (or would help) them to learn the material, and would choose an active learning course in the future.

Adolescent Learning in the Zoo: Embedding a Non-Formal Learning Environment to Teach Formal Aspects of Vertebrate Biology

NASA Astrophysics Data System (ADS)

Randler, Christoph; Kummer, Barbara; Wilhelm, Christian

2012-06-01

The aim of this study was to assess the outcome of a zoo visit in terms of learning and retention of knowledge concerning the adaptations and behavior of vertebrate species. Basis of the work was the concept of implementing zoo visits as an out-of-school setting for formal, curriculum based learning. Our theoretical framework centers on the self-determination theory, therefore, we used a group-based, hands-on learning environment. To address this questions, we used a treatment—control design (BACI) with different treatments and a control group. Pre-, post- and retention tests were applied. All treatments led to a substantial increase of learning and retention knowledge compared to the control group. Immediately after the zoo visit, the zoo-guide tour provided the highest scores, while after a delay of 6 weeks, the learner-centered environment combined with a teacher-guided summarizing scored best. We suggest incorporating the zoo as an out-of-school environment into formal school learning, and we propose different methods to improve learning in zoo settings.

A new computational account of cognitive control over reinforcement-based decision-making: Modeling of a probabilistic learning task.

PubMed

Zendehrouh, Sareh

2015-11-01

Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement learning (RL) domain, the habitual behaviors are connected with model-free methods, in which appropriate actions are learned through trial-and-error experiences. However, goal-directed behaviors are associated with model-based methods of RL, in which actions are selected using a model of the environment. Studies on cognitive control also suggest that during processes like decision-making, some cortical and subcortical structures work in concert to monitor the consequences of decisions and to adjust control according to current task demands. Here a computational model is presented based on dual system theory and cognitive control perspective of decision-making. The proposed model is used to simulate human performance on a variant of probabilistic learning task. The basic proposal is that the brain implements a dual controller, while an accompanying monitoring system detects some kinds of conflict including a hypothetical cost-conflict one. The simulation results address existing theories about two event-related potentials, namely error related negativity (ERN) and feedback related negativity (FRN), and explore the best account of them. Based on the results, some testable predictions are also presented. Copyright © 2015 Elsevier Ltd. All rights reserved.

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation.

PubMed

Oyama, Shintaro; Shimoda, Shingo; Alnajjar, Fady S K; Iwatsuki, Katsuyuki; Hoshiyama, Minoru; Tanaka, Hirotaka; Hirata, Hitoshi

2016-01-01

Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

PubMed

Terunuma, Toshiyuki; Tokui, Aoi; Sakae, Takeji

2018-03-01

Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.

Policy improvement by a model-free Dyna architecture.

PubMed

Hwang, Kao-Shing; Lo, Chia-Yue

2013-05-01

The objective of this paper is to accelerate the process of policy improvement in reinforcement learning. The proposed Dyna-style system combines two learning schemes, one of which utilizes a temporal difference method for direct learning; the other uses relative values for indirect learning in planning between two successive direct learning cycles. Instead of establishing a complicated world model, the approach introduces a simple predictor of average rewards to actor-critic architecture in the simulation (planning) mode. The relative value of a state, defined as the accumulated differences between immediate reward and average reward, is used to steer the improvement process in the right direction. The proposed learning scheme is applied to control a pendulum system for tracking a desired trajectory to demonstrate its adaptability and robustness. Through reinforcement signals from the environment, the system takes the appropriate action to drive an unknown dynamic to track desired outputs in few learning cycles. Comparisons are made between the proposed model-free method, a connectionist adaptive heuristic critic, and an advanced method of Dyna-Q learning in the experiments of labyrinth exploration. The proposed method outperforms its counterparts in terms of elapsed time and convergence rate.

A comparison of the cooperative learning and traditional learning methods in theory classes on nursing students' communication skill with patients at clinical settings.

PubMed

Baghcheghi, Nayereh; Koohestani, Hamid Reza; Rezaei, Koresh

2011-11-01

The purpose of this study was to compare the effect of traditional learning and cooperative learning methods on nursing students' communication skill with patients. This was an experimental study in which 34 nursing students in their 2nd semester of program participated. They were divided randomly into two groups, a control group who were taught their medical/surgical nursing course by traditional learning method and an experimental group, who were taught the same material using cooperative learning method. Before and after the teaching intervention, the students' communication skills with patients at clinical settings were examined. The results showed that no significant difference between the two groups in students' communication skills scores before the teaching intervention, but did show a significant difference between the two groups in the interaction skills and problem follow up sub-scales scores after the teaching intervention. This study provides evidence that cooperative learning is an effective method for improving and increasing communication skills of nursing students especially in interactive skills and follow up the problems sub-scale, thereby it is recommended to increase nursing students' participation in arguments by applying active teaching methods which can provide the opportunity for increased communication skills. Copyright © 2011 Elsevier Ltd. All rights reserved.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluating Listening and Speaking Skills in a Mobile Game-Based Learning Environment with Situational Contexts

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Shih, Timothy K.; Ma, Zhao-Heng; Shadiev, Rustam; Chen, Shu-Yu

2016-01-01

Game-based learning activities that facilitate students' listening and speaking skills were designed in this study. To participate in learning activities, students in the control group used traditional methods, while students in the experimental group used a mobile system. In our study, we looked into the feasibility of mobile game-based learning…

Teaching Parents about Responsive Feeding through a Vicarious Learning Video: A Pilot Randomized Controlled Trial

ERIC Educational Resources Information Center

Ledoux, Tracey; Robinson, Jessica; Baranowski, Tom; O'Connor, Daniel P.

2018-01-01

The American Academy of Pediatrics and World Health Organization recommend responsive feeding (RF) to promote healthy eating behaviors in early childhood. This project developed and tested a vicarious learning video to teach parents RF practices. A RF vicarious learning video was developed using community-based participatory research methods.…

A Computer-Assisted Learning Model Based on the Digital Game Exponential Reward System

ERIC Educational Resources Information Center

Moon, Man-Ki; Jahng, Surng-Gahb; Kim, Tae-Yong

2011-01-01

The aim of this research was to construct a motivational model which would stimulate voluntary and proactive learning using digital game methods offering players more freedom and control. The theoretical framework of this research lays the foundation for a pedagogical learning model based on digital games. We analyzed the game reward system, which…

An Evaluation of the Implementation of Hand Held Health Records with Adults with Learning Disabilities: A Cluster Randomized Controlled Trial

ERIC Educational Resources Information Center

Turk, Vicky; Burchell, Sarah; Burrha, Sukhjinder; Corney, Roslyn; Elliott, Sandra; Kerry, Sally; Molloy, Catherine; Painter, Kerry

2010-01-01

Background: Personal health records were implemented with adults with learning disabilities (AWLD) to try to improve their health-care. Materials and Method: Forty GP practices were randomized to the Personal Health Profile (PHP) implementation or control group. Two hundred and one AWLD were interviewed at baseline and 163 followed up after 12…

Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network.

PubMed

Jeng, J T; Lee, T T

2000-01-01

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Fuzzy controller training using particle swarm optimization for nonlinear system control.

PubMed

Karakuzu, Cihan

2008-04-01

This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.

Can we (control) Engineer the degree learning process?

NASA Astrophysics Data System (ADS)

White, A. S.; Censlive, M.; Neilsen, D.

2014-07-01

This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

PubMed

Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

2011-01-01

As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. © 2011 IEEE

Effects of peer review on communication skills and learning motivation among nursing students.

PubMed

Yoo, Moon Sook; Chae, Sun-Mi

2011-04-01

The purpose of this study was to investigate the effects of video-based peer review on communication skills and learning motivation among nursing students. A non-equivalent control with pretest-posttest design was used. The participants were 47 sophomore nursing students taking a fundamentals of nursing course at a nursing college in Korea. Communication with a standardized patient was videotaped for evaluation. The intervention group used peer reviews to evaluate the videotaped performance; a small group of four students watched the videotape of each student and then provided feedback. The control group assessed themselves alone after watching their own videos. Communication skills and learning motivation were measured. The intervention group showed significantly higher communication skills and learning motivation after the intervention than did the control group. The findings suggest that peer review is an effective learning method for nursing students to improve their communication skills and increase their motivation to learn. Copyright 2011, SLACK Incorporated.

Attention control learning in the decision space using state estimation

NASA Astrophysics Data System (ADS)

Gharaee, Zahra; Fatehi, Alireza; Mirian, Maryam S.; Nili Ahmadabadi, Majid

2016-05-01

The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.

Is there evidence of learned helplessness in horses?

PubMed

Hall, Carol; Goodwin, Deborah; Heleski, Camie; Randle, Hayley; Waran, Natalie

2008-01-01

Learned helplessness is a psychological condition whereby individuals learn that they have no control over unpleasant or harmful conditions, that their actions are futile, and that they are helpless. In a series of experiments in which dogs were exposed to inescapable shocks, this lack of control subsequently interfered with the ability to learn an avoidance task. There is evidence that both neural adaptations and behavioral despair occur in response to uncontrollable aversive experiences in rodents, although this has yet to be demonstrated in other species such as horses. However, certain traditional methods of horse training and some behavioral modification techniques--it has been suggested--may involve aversive conditions over which the horse has little or no control. When training and management procedures are repeatedly unpleasant for the horse and there is no clear association between behavior and outcome, this is likely to interfere with learning and performance-in addition to compromising welfare. This article reviews published literature and anecdotal evidence to explore the possibility that the phenomenon, learned helplessness, occurs in the horse.

Rapidly Measuring the Speed of Unconscious Learning: Amnesics Learn Quickly and Happy People Slowly

PubMed Central

Dienes, Zoltan; Baddeley, Roland J.; Jansari, Ashok

2012-01-01

Background We introduce a method for quickly determining the rate of implicit learning. Methodology/Principal Findings The task involves making a binary prediction for a probabilistic sequence over 10 minutes; from this it is possible to determine the influence of events of a different number of trials in the past on the current decision. This profile directly reflects the learning rate parameter of a large class of learning algorithms including the delta and Rescorla-Wagner rules. To illustrate the use of the method, we compare a person with amnesia with normal controls and we compare people with induced happy and sad moods. Conclusions/Significance Learning on the task is likely both associative and implicit. We argue theoretically and demonstrate empirically that both amnesia and also transient negative moods can be associated with an especially large learning rate: People with amnesia can learn quickly and happy people slowly. PMID:22457759

Nature vs Nurture: Effects of Learning on Evolution

NASA Astrophysics Data System (ADS)

Nagrani, Nagina

In the field of Evolutionary Robotics, the design, development and application of artificial neural networks as controllers have derived their inspiration from biology. Biologists and artificial intelligence researchers are trying to understand the effects of neural network learning during the lifetime of the individuals on evolution of these individuals by qualitative and quantitative analyses. The conclusion of these analyses can help develop optimized artificial neural networks to perform any given task. The purpose of this thesis is to study the effects of learning on evolution. This has been done by applying Temporal Difference Reinforcement Learning methods to the evolution of Artificial Neural Tissue controller. The controller has been assigned the task to collect resources in a designated area in a simulated environment. The performance of the individuals is measured by the amount of resources collected. A comparison has been made between the results obtained by incorporating learning in evolution and evolution alone. The effects of learning parameters: learning rate, training period, discount rate, and policy on evolution have also been studied. It was observed that learning delays the performance of the evolving individuals over the generations. However, the non zero learning rate throughout the evolution process signifies natural selection preferring individuals possessing plasticity.

Systematic construction and control of stereo nerve vision network in intelligent manufacturing

NASA Astrophysics Data System (ADS)

Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

2017-10-01

A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

PubMed

Zeng, Ling-Li; Wang, Huaning; Hu, Panpan; Yang, Bo; Pu, Weidan; Shen, Hui; Chen, Xingui; Liu, Zhening; Yin, Hong; Tan, Qingrong; Wang, Kai; Hu, Dewen

2018-04-01

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.

Quasi-experimental study on the effectiveness of a flipped classroom for teaching adult health nursing.

PubMed

Park, Esther O; Park, Ji Hyun

2018-04-01

The effectiveness of flipped learning as one of the teaching methods of active learning has been left unexamined in nursing majors, compared to the frequent attempts to uncover the effectiveness of it in other disciplines. The purpose of this study was to reveal the effectiveness of flipped learning pedagogy in an adult health nursing course, controlling for other variables. The study applied a quasi-experimental approach, comparing pre- and post-test results in learning outcomes. Included in this analysis were the records of 81 junior nursing major students. The convenience sampling method was used to select the participants. Those in the experimental group were exposed to a flipped classroom experience that was given after the completion of their traditional class. The students' learning outcomes and the level of critical thinking skills were evaluated before and after the intervention of the flipped classroom. After the flipped classroom experience, the scores of the students' achievement in subject topics and critical thinking skills, specifically intellectual integrity and creativity, showed a greater level of increase than those of their controlled counterparts. This remained true even after controlling for previous academic performance and the level of creativity. This study confirmed the effectiveness of the flipped classroom as a measure of active learning by applying a quantitative approach. But, regarding the significance of the initial contribution of flipped learning in the discipline of nursing science, carrying out a more authentic experimental study could justify the impact of flipped learning pedagogy. © 2017 Japan Academy of Nursing Science.

Examining the impact of the Guided Constructivist teaching method on students' misconceptions about concepts of Newtonian physics

NASA Astrophysics Data System (ADS)

Ibrahim, Hyatt Abdelhaleem

The effect of Guided Constructivism (Interactivity-Based Learning Environment) and Traditional Expository instructional methods on students' misconceptions about concepts of Newtonian Physics was investigated. Four groups of 79 of University of Central Florida students enrolled in Physics 2048 participated in the study. A quasi-experimental design of nonrandomized, nonequivalent control and experimental groups was employed. The experimental group was exposed to the Guided Constructivist teaching method, while the control group was taught using the Traditional Expository teaching approach. The data collection instruments included the Force Concept Inventory Test (FCI), the Mechanics Baseline Test (MBT), and the Maryland Physics Expectation Survey (MPEX). The Guided Constructivist group had significantly higher means than the Traditional Expository group on the criterion variables of: (1) conceptions of Newtonian Physics, (2) achievement in Newtonian Physics, and (3) beliefs about the content of Physics knowledge, beliefs about the role of Mathematics in learning Physics, and overall beliefs about learning/teaching/appropriate roles of learners and teachers/nature of Physics. Further, significant relationships were found between (1) achievement, conceptual structures, beliefs about the content of Physics knowledge, and beliefs about the role of Mathematics in learning Physics; (2) changes in misconceptions about the physical phenomena, and changes in beliefs about the content of Physics knowledge. No statistically significant difference was found between the two teaching methods on achievement of males and females. These findings suggest that differences in conceptual learning due to the nature of the teaching method used exist. Furthermore, greater conceptual learning is fostered when teachers use interactivity-based teaching strategies to train students to link everyday experience in the real physical world to formal school concepts. The moderate effect size and power of the study suggest that the effect may not be subtle, but reliable. Physics teachers can use these results to inform their decisions about structuring learning environment when conceptual learning is important.

Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control.

PubMed

Wang, Youqing; Dassau, Eyal; Doyle, Francis J

2010-02-01

A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

Adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction.

PubMed

Wei, Jianming; Zhang, Youan; Sun, Meimei; Geng, Baoliang

2017-09-01

This paper presents an adaptive iterative learning control scheme for a class of nonlinear systems with unknown time-varying delays and control direction preceded by unknown nonlinear backlash-like hysteresis. Boundary layer function is introduced to construct an auxiliary error variable, which relaxes the identical initial condition assumption of iterative learning control. For the controller design, integral Lyapunov function candidate is used, which avoids the possible singularity problem by introducing hyperbolic tangent funciton. After compensating for uncertainties with time-varying delays by combining appropriate Lyapunov-Krasovskii function with Young's inequality, an adaptive iterative learning control scheme is designed through neural approximation technique and Nussbaum function method. On the basis of the hyperbolic tangent function's characteristics, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Verbal learning in the context of background music: no influence of vocals and instrumentals on verbal learning

PubMed Central

2014-01-01

Background Whether listening to background music enhances verbal learning performance is still a matter of dispute. In this study we investigated the influence of vocal and instrumental background music on verbal learning. Methods 226 subjects were randomly assigned to one of five groups (one control group and 4 experimental groups). All participants were exposed to a verbal learning task. One group served as control group while the 4 further groups served as experimental groups. The control group learned without background music while the 4 experimental groups were exposed to vocal or instrumental musical pieces during learning with different subjective intensity and valence. Thus, we employed 4 music listening conditions (vocal music with high intensity: VOC_HIGH, vocal music with low intensity: VOC_LOW, instrumental music with high intensity: INST_HIGH, instrumental music with low intensity: INST_LOW) and one control condition (CONT) during which the subjects learned the word lists. Since it turned out that the high and low intensity groups did not differ in terms of the rated intensity during the main experiment these groups were lumped together. Thus, we worked with 3 groups: one control group and two groups, which were exposed to background music (vocal and instrumental) during verbal learning. As dependent variable, the number of learned words was used. Here we measured immediate recall during five learning sessions (recall 1 – recall 5) and delayed recall for 15 minutes (recall 6) and 14 days (recall 7) after the last learning session. Results Verbal learning improved during the first 5 recall sessions without any strong difference between the control and experimental groups. Also the delayed recalls were similar for the three groups. There was only a trend for attenuated verbal learning for the group passively listened to vocals. This learning attenuation diminished during the following learning sessions. Conclusions The exposure to vocal or instrumental background music during encoding did not influence verbal learning. We suggest that the participants are easily able to cope with this background stimulation by ignoring this information channel in order to focus on the verbal learning task. PMID:24670048

Methods of integrating Islamic values in teaching biology for shaping attitude and character

NASA Astrophysics Data System (ADS)

Listyono; Supardi, K. I.; Hindarto, N.; Ridlo, S.

2018-03-01

Learning is expected to develop the potential of learners to have the spiritual attitude: moral strength, self-control, personality, intelligence, noble character, as well as the skills needed by themselves, society, and nation. Implementation of role and morale in learning is an alternative way which is expected to answer the challenge. The solution offered is to inject student with religious material Islamic in learning biology. The content value of materials teaching biology includes terms of practical value, religious values, daily life value, socio-political value, and the value of art. In Islamic religious values (Qur'an and Hadith) various methods can touch human feelings, souls, and generate motivation. Integrating learning with Islamic value can be done by the deductive or inductive approach. The appropriate method of integration is the amtsal (analog) method, hiwar (dialog) method, targhib & tarhib (encouragement & warning) method, and example method (giving a noble role model / good example). The right strategy in integrating Islamic values is outlined in the design of lesson plan. The integration of Islamic values in lesson plan will facilitate teachers to build students' character because Islamic values can be implemented in every learning steps so students will be accustomed to receiving the character value in this integrated learning.

Peer Teaching to Foster Learning in Physiology

PubMed Central

Srivastava, Tripti K; Waghmare, Lalitbhushan S.; Mishra, Ved Prakash; Rawekar, Alka T; Quazi, Nazli; Jagzape, Arunita T

2015-01-01

Introduction Peer teaching is an effective tool to promote learning and retention of knowledge. By preparing to teach, students are encouraged to construct their own learning program, so that they can explain effectively to fellow learners. Peer teaching is introduced in present study to foster learning and pedagogical skills amongst first year medical under-graduates in physiology with a Hypothesis that teaching is linked to learning on part of the teacher. Materials and Methods Non-randomized, Interventional study, with mixed methods design. Cases experienced peer teaching whereas controls underwent tutorials for four consecutive classes. Quantitative Evaluation was done through pre/post test score analysis for Class average normalized gain and tests of significance, difference in average score in surprise class test after one month and percentage of responses in closed ended items of feedback questionnaire. Qualitative Evaluation was done through categorization of open ended items and coding of reflective statements. Results The average pre and post test score was statistically significant within cases (p = 0.01) and controls (p = 0.023). The average post test scores was more for cases though not statistically significant. The class average normalized gain (g) for Tutorials was 49% and for peer teaching 53%. Surprise test had average scoring of 36 marks (out of 50) for controls and 41 marks for cases. Analysed section wise, the average score was better for Long answer question (LAQ) in cases. Section wise analysis suggested that through peer teaching, retention was better for descriptive answers as LAQ has better average score in cases. Feedback responses were predominantly positive for efficacy of peer teaching as a learning method. The reflective statements were sorted into reflection in action, reflection on action, claiming evidence, describing experience, and recognizing discrepancies. Conclusion Teaching can stimulate further learning as it involves interplay of three processes: metacognitive awareness; deliberate practice, and self-explanation. Coupled with immediate feedback and reflective exercises, learning can be measurably enhanced along with improved teaching skills. PMID:26435969

Motor learning benefits of self-controlled practice in persons with Parkinson's disease.

PubMed

Chiviacowsky, Suzete; Wulf, Gabriele; Lewthwaite, Rebecca; Campos, Tiago

2012-04-01

The present study examined the effectiveness of a training method to enhance balance in people with PD, which could potentially reduce their risk for falls. Specifically, we investigated whether the benefits of the self-controlled use of a physical assistance device for the learning of a balance task, found previously in healthy adults, would generalize to adults with PD. Twenty-eight individuals with PD were randomly assigned to one of two groups, a self-control and a yoked (control) group. The task required participants to stand on a balance platform (stabilometer), trying to keep the platform as close to horizontal as possible during each 30-s trial. In the self-control group, participants had a choice, on each of 10 practice trials, to use or not to use a balance pole. Participants in the yoked group received the same balance pole on the schedule used by their counterparts in the self-control group, but did not have a choice. Learning was assessed one day later by a retention test. The self-control group demonstrated more effective learning of the task than the yoked group. Questionnaire results indicated that self-control participants were more motivated to learn the task, were less nervous, and less concerned about their body movements relative to yoked participants. Possible reasons for the learning benefits of self-controlled practice, including a basic psychological need for autonomy, are discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.

PubMed

Ribeiro, C H; Hemerly, E M

2000-02-01

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.

Applying machine learning to identify autistic adults using imitation: An exploratory study.

PubMed

Li, Baihua; Sharma, Arjun; Meng, James; Purushwalkam, Senthil; Gowen, Emma

2017-01-01

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

A Development of Game-Based Learning Environment to Activate Interaction among Learners

NASA Astrophysics Data System (ADS)

Takaoka, Ryo; Shimokawa, Masayuki; Okamoto, Toshio

Many studies and systems that incorporate elements such as “pleasure” and “fun” in the game to improve a learner's motivation have been developed in the field of learning environments. However, few are the studies of situations where many learners gather at a single computer and participate in a game-based learning environment (GBLE), and where the GBLE designs the learning process by controlling the interactions between learners such as competition, collaboration, and learning by teaching. Therefore, the purpose of this study is to propose a framework of educational control that induces and activates interaction between learners intentionally to create a learning opportunity that is based on the knowledge understanding model of each learner. In this paper, we explain the design philosophy and the framework of our GBLE called “Who becomes the king in the country of mathematics?” from a game viewpoint and describe the method of learning support control in the learning environment. In addition, we report the results of the learning experiment with our GBLE, which we carried out in a junior high school, and include some comments by a principal and a teacher. From the results of the experiment and some comments, we noticed that a game may play a significant role in weakening the learning relationship among students and creating new relationships in the world of the game. Furthermore, we discovered that learning support control of the GBLE has led to activation of the interaction between learners to some extent.

Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

NASA Astrophysics Data System (ADS)

Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric

2017-01-01

Recent methods of Reinforcement Learning have enabled to solve difficult, high dimensional, robotic tasks under unknown dynamics using iterative Linear Quadratic Gaussian control theory. These algorithms are based on building a local time-varying linear model of the dynamics from data gathered through interaction with the environment. In such tasks, the cost function is often expressed directly in terms of the state and control variables so that it can be locally quadratized to run the algorithm. If the cost is expressed in terms of other variables, a model is required to compute the cost function from the variables manipulated. We propose a method to learn the cost function directly from the data, in the same way as for the dynamics. This way, the cost function can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out. With our method, any sensor information can be used to design the cost function. We demonstrate the efficiency of this method through simulating, with the V-REP software, the learning of a Cartesian positioning task on several industrial robots with different characteristics. The robots are controlled in joint space and no model is provided a priori. Our results are compared with another model free technique, consisting in writing the cost function as a state variable.

Auditory Learning Using a Portable Real-Time Vocoder: Preliminary Findings

PubMed Central

Pisoni, David B.

2015-01-01

Purpose Although traditional study of auditory training has been in controlled laboratory settings, interest has been increasing in more interactive options. The authors examine whether such interactive training can result in short-term perceptual learning, and the range of perceptual skills it impacts. Method Experiments 1 (N = 37) and 2 (N = 21) used pre- and posttest measures of speech and nonspeech recognition to find evidence of learning (within subject) and to compare the effects of 3 kinds of training (between subject) on the perceptual abilities of adults with normal hearing listening to simulations of cochlear implant processing. Subjects were given interactive, standard lab-based, or control training experience for 1 hr between the pre- and posttest tasks (unique sets across Experiments 1 & 2). Results Subjects receiving interactive training showed significant learning on sentence recognition in quiet task (Experiment 1), outperforming controls but not lab-trained subjects following training. Training groups did not differ significantly on any other task, even those directly involved in the interactive training experience. Conclusions Interactive training has the potential to produce learning in 1 domain (sentence recognition in quiet), but the particulars of the present training method (short duration, high complexity) may have limited benefits to this single criterion task. PMID:25674884

Integrating simulated teaching/learning strategies in undergraduate nursing education.

PubMed

Sinclair, Barbara; Ferguson, Karen

2009-01-01

In this article, the results of a mixed-methods study integrating the use of simulations in a nursing theory course in order to assess students' perceptions of self-efficacy for nursing practice are presented. Nursing students in an intervention group were exposed to a combination of lecture and simulation, and then asked to rate their perceptions of self-efficacy, satisfaction and effectiveness of this combined teaching and learning strategy. Based on Bandura's (1977, 1986) theory of self-efficacy, this study provides data to suggest that students' self-confidence for nursing practice may be increased through the use of simulation as a method of teaching and learning. Students also reported higher levels of satisfaction, effectiveness and consistency with their learning style when exposed to the combination of lecture and simulation than the control group, who were exposed to lecture as the only method of teaching and learning.

Communal Learning versus Individual Learning: An Exploratory Convergent Parallel Mixed-Method Study to Describe How Young African American Novice Programmers Learn Computational Thinking Skills in an Informal Learning Environment

ERIC Educational Resources Information Center

Hatley, Leshell April Denise

2016-01-01

Today, most young people in the United States (U.S.) live technology-saturated lives. Their educational, entertainment, and career options originate from and demand incredible technological innovations. However, this extensive ownership of and access to technology does not indicate that today's youth know how technology works or how to control and…

Applying team-based learning of diagnostics for undergraduate students: assessing teaching effectiveness by a randomized controlled trial study

PubMed Central

Zeng, Rui; Xiang, Lian-rui; Zeng, Jing; Zuo, Chuan

2017-01-01

Background We aimed to introduce team-based learning (TBL) as one of the teaching methods for diagnostics and to compare its teaching effectiveness with that of the traditional teaching methods. Methods We conducted a randomized controlled trial on diagnostics teaching involving 111 third-year medical undergraduates, using TBL as the experimental intervention, compared with lecture-based learning as the control, for teaching the two topics of symptomatology. Individual Readiness Assurance Test (IRAT)-baseline and Group Readiness Assurance Test (GRAT) were performed in members of each TBL subgroup. The scores in Individual Terminal Test 1 (ITT1) immediately after class and Individual Terminal Test 2 (ITT2) 1 week later were compared between the two groups. The questionnaire and interview were also implemented to survey the attitude of students and teachers toward TBL. Results There was no significant difference between the two groups in ITT1 (19.85±4.20 vs 19.70±4.61), while the score of the TBL group was significantly higher than that of the control group in ITT2 (19.15±3.93 vs 17.46±4.65). In the TBL group, the scores of the two terminal tests after the teaching intervention were significantly higher than the baseline test score of individuals. IRAT-baseline, ITT1, and ITT2 scores of students at different academic levels in the TBL teaching exhibited significant differences, but the ITT1-IRAT-baseline and ITT2-IRAT-baseline indicated no significant differences among the three subgroups. Conclusion Our TBL in symptomatology approach was highly accepted by students in the improvement of interest and self-directed learning and resulted in an increase in knowledge acquirements, which significantly improved short-term test scores compared with lecture-based learning. TBL is regarded as an effective teaching method worthy of promoting. PMID:28331383

Game-based e-learning is more effective than a conventional instructional method: a randomized controlled trial with third-year medical students.

PubMed

Boeker, Martin; Andel, Peter; Vach, Werner; Frankenschmidt, Alexander

2013-01-01

When compared with more traditional instructional methods, Game-based e-learning (GbEl) promises a higher motivation of learners by presenting contents in an interactive, rule-based and competitive way. Most recent systematic reviews and meta-analysis of studies on Game-based learning and GbEl in the medical professions have shown limited effects of these instructional methods. To compare the effectiveness on the learning outcome of a Game-based e-learning (GbEl) instruction with a conventional script-based instruction in the teaching of phase contrast microscopy urinalysis under routine training conditions of undergraduate medical students. A randomized controlled trial was conducted with 145 medical students in their third year of training in the Department of Urology at the University Medical Center Freiburg, Germany. 82 subjects where allocated for training with an educational adventure-game (GbEl group) and 69 subjects for conventional training with a written script-based approach (script group). Learning outcome was measured with a 34 item single choice test. Students' attitudes were collected by a questionnaire regarding fun with the training, motivation to continue the training and self-assessment of acquired knowledge. The students in the GbEl group achieved significantly better results in the cognitive knowledge test than the students in the script group: the mean score was 28.6 for the GbEl group and 26.0 for the script group of a total of 34.0 points with a Cohen's d effect size of 0.71 (ITT analysis). Attitudes towards the recent learning experience were significantly more positive with GbEl. Students reported to have more fun while learning with the game when compared to the script-based approach. Game-based e-learning is more effective than a script-based approach for the training of urinalysis in regard to cognitive learning outcome and has a high positive motivational impact on learning. Game-based e-learning can be used as an effective teaching method for self-instruction.

Neural dynamic programming and its application to control systems

NASA Astrophysics Data System (ADS)

Seong, Chang-Yun

There are few general practical feedback control methods for nonlinear MIMO (multi-input-multi-output) systems, although such methods exist for their linear counterparts. Neural Dynamic Programming (NDP) is proposed as a practical design method of optimal feedback controllers for nonlinear MIMO systems. NDP is an offspring of both neural networks and optimal control theory. In optimal control theory, the optimal solution to any nonlinear MIMO control problem may be obtained from the Hamilton-Jacobi-Bellman equation (HJB) or the Euler-Lagrange equations (EL). The two sets of equations provide the same solution in different forms: EL leads to a sequence of optimal control vectors, called Feedforward Optimal Control (FOC); HJB yields a nonlinear optimal feedback controller, called Dynamic Programming (DP). DP produces an optimal solution that can reject disturbances and uncertainties as a result of feedback. Unfortunately, computation and storage requirements associated with DP solutions can be problematic, especially for high-order nonlinear systems. This dissertation presents an approximate technique for solving the DP problem based on neural network techniques that provides many of the performance benefits (e.g., optimality and feedback) of DP and benefits from the numerical properties of neural networks. We formulate neural networks to approximate optimal feedback solutions whose existence DP justifies. We show the conditions under which NDP closely approximates the optimal solution. Finally, we introduce the learning operator characterizing the learning process of the neural network in searching the optimal solution. The analysis of the learning operator provides not only a fundamental understanding of the learning process in neural networks but also useful guidelines for selecting the number of weights of the neural network. As a result, NDP finds---with a reasonable amount of computation and storage---the optimal feedback solutions to nonlinear MIMO control problems that would be very difficult to solve with DP. NDP was demonstrated on several applications such as the lateral autopilot logic for a Boeing 747, the minimum fuel control of a double-integrator plant with bounded control, the backward steering of a two-trailer truck, and the set-point control of a two-link robot arm.

Hunting for Hydrothermal Vents at the Local-Scale Using AUV's and Machine-Learning Classification in the Earth's Oceans

NASA Astrophysics Data System (ADS)

White, S. M.

2018-05-01

New AUV-based mapping technology coupled with machine-learning methods for detecting individual vents and vent fields at the local-scale raise the possibility of understanding the geologic controls on hydrothermal venting.

Can Dialectical Behavior Therapy Be Learned in Highly Structured Learning Environments? Results from a Randomized Controlled Dissemination Trial

ERIC Educational Resources Information Center

Dimeff, Linda A.; Woodcock, Eric A.; Harned, Melanie S.; Beadnell, Blair

2011-01-01

This study evaluated the efficacy of methods of training community mental health providers (N=132) in dialectical behavior therapy (DBT) distress tolerance skills, including (a) Linehan's (1993a) Skills Training Manual for Borderline Personality Disorder (Manual), (b) a multimedia e-Learning course covering the same content (e-DBT), and (c) a…

The Effects of Project-Based Learning Activities on Academic Achievement and Motivation in Mathematics in Eighth-Grade Students

ERIC Educational Resources Information Center

Mudrich, Rachel Marie

2017-01-01

The purpose of this research study was to determine if project-based learning activities (PBLA) incorporated into an eighth-grade mathematics classroom have an effect on students' academic achievement and motivation toward learning. The control group used the traditional instruction method to cover mathematic objective skills that are Common Core…

The Investigation of the Level of Self-Directed Learning Readiness According to the Locus of Control and Personality Traits of Preschool Teacher Candidates

ERIC Educational Resources Information Center

Balaban Dagal, Asude; Bayindir, Dilan

2016-01-01

The aim of this study is to investigate the relationship between the level of self-directed learning readiness, locus of control and the personality traits of preschool teacher candidates. The survey method was used for this study. The study group consisted of 151 teacher candidates who volunteered to participate in the study from Preschool…

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

PubMed

Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

PubMed Central

Zhang, Hongjun; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score. PMID:28894463

Randomized comparative evaluation of endoscopic submucosal dissection self-learning software in France and Japan.

PubMed

Pioche, Mathieu; Rivory, Jérôme; Nishizawa, Toshihiro; Uraoka, Toshio; Touzet, Sandrine; O'Brien, Marc; Saurin, Jean-Christophe; Ponchon, Thierry; Denis, Angélique; Yahagi, Naohisa

2016-12-01

Background and study aim: Endoscopic submucosal dissection (ESD) is currently the reference method to achieve an en bloc resection for large lesions; however, the technique is difficult and risky, with a long learning curve. In order to reduce the morbidity, training courses that use animal models are recommended. Recently, self-learning software has been developed to assist students in their training. The aim of this study was to evaluate the impact of this tool on the ESD learning curve. Methods: A prospective, randomized, comparative study enrolled 39 students who were experienced in interventional endoscopy. Each student was randomized to one of two groups and performed 30 ESDs of 30 mm standardized lesions in a bovine colon model. The software group used the self-learning software whereas the control group only observed an ESD procedure video. The primary outcome was the rate of successful ESD procedures, defined as complete en bloc resection without any perforation and performed in less than 75 minutes. Results: A total of 39 students performed 1170 ESDs. Success was achieved in 404 (70.9 %) in the software group and 367 (61.2 %) in the control group ( P  = 0.03). Among the successful procedures, there were no significant differences between the software and control groups in terms of perforation rate (22 [4.0 %] vs. 29 [5.1 %], respectively; P  = 0.27) and mean (SD) procedure duration (34.1 [13.4] vs. 32.3 [14.0] minutes, respectively; P  = 0.52). For the 30th procedure, the rate of complete resection was superior in the software group (84.2 %) compared with the control group (50.0 %; P  = 0.01). Conclusion: ESD self-learning software was effective in improving the quality of resection compared with a standard teaching method using procedure videos. This result suggests the benefit of incorporating such software into teaching programs. © Georg Thieme Verlag KG Stuttgart · New York.

An on-line modified least-mean-square algorithm for training neurofuzzy controllers.

PubMed

Tan, Woei Wan

2007-04-01

The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared.

Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments.

PubMed

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2015-09-01

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots.

Comparison of peer-tutoring learning model through problem-solving approach and traditional learning model on the cognitive ability of grade 10 students at SMKN 13 Bandung on the topic of Stoichiometry

NASA Astrophysics Data System (ADS)

Hayat, A. Z.; Wahyu, W.; Kurnia

2018-05-01

This study aims to find out the improvement of cognitive ability of students on the implementation of cooperative learning model of peer-tutoring by using problem-solving approach. The research method used is mix method of Sequential Explanatory strategy and pretest post-test non-equivalent control group design. The participants involved in this study were 68 grade 10 students of Vocational High School in Bandung that consisted of 34 samples of experimental class and 34 samples of control class. The instruments used include written test and questionnaires. The improvement of cognitive ability of students was calculated using the N- gain formula. Differences of two average scores were calculated using t-test at significant level of α = 0.05. The result of study shows that the improvement of cognitive ability in experimental class was significantly different compared to the improvement in the control class at significant level of α = 0.05. The improvement of cognitive ability in experimental class is higher than in control class.

Online learning versus blended learning of clinical supervisee skills with pre-registration nursing students: A randomised controlled trial.

PubMed

McCutcheon, Karen; O'Halloran, Peter; Lohan, Maria

2018-06-01

The World Health Organisation amongst others recognises the need for the introduction of clinical supervision education in health professional education as a central strategy for improving patient safety and patient care. Online and blended learning methods are growing exponentially in use in higher education and the systematic evaluation of these methods will aid understanding of how best to teach clinical supervision. The purpose of this study was to test whether undergraduate nursing students who received clinical supervisee skills training via a blended learning approach would score higher in terms of motivation and attitudes towards clinical supervision, knowledge of clinical supervision and satisfaction of learning method, when compared to those students who received an online only teaching approach. A post-test-only randomised controlled trial. Participants were a total of 122 pre-registration nurses enrolled at one United Kingdom university, randomly assigned to the online learning control group (n = 60) or the blended learning intervention group (n = 62). The blended learning intervention group participated in a face-to-face tutorial and the online clinical supervisee skills training app. The online learning control group participated in an online discussion forum and the same online clinical supervisee skills training app. The outcome measures were motivation and attitudes using the modified Manchester Clinical Supervision Scale, knowledge using a 10 point Multiple Choice Questionnaire and satisfaction using a university training evaluation tool. Statistical analysis was performed using independent t-tests to compare the differences between the means of the control group and the intervention group. Thematic analysis was used to analyse responses to open-ended questions. All three of our study hypotheses were confirmed. Participants who received clinical supervisee skills training via a blended learning approach scored higher in terms of motivation and attitudes - mean (m) = 85.5, standard deviation (sd) = 9.78, number of participants (n) = 62 - compared to the online group (m = 79.5, sd = 9.69, n = 60) (p = .001). The blended learning group also scored higher in terms of knowledge (m = 4.2, sd = 1.43, n = 56) compared to the online group (m = 3.51, sd = 1.51, n = 57) (p = .015); and in terms of satisfaction (m = 30.89, sd = 6.54, n = 57) compared to the online group (m = 26.49, sd = 6.93, n = 55) (p = .001). Qualitative data supported results. Blended learning provides added pedagogical value when compared to online learning in terms of teaching undergraduate nurses clinical supervision skills. The evidence is timely given worldwide calls for expanding clinical skills supervision in undergraduate health professional education to improve quality of care and patient safety. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

NASA Astrophysics Data System (ADS)

Yan, Fei; Tian, Fuli; Shi, Zhongke

2016-10-01

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

The effect of computer-assisted learning versus conventional teaching methods on the acquisition and retention of handwashing theory and skills in pre-qualification nursing students: a randomised controlled trial.

PubMed

Bloomfield, Jacqueline; Roberts, Julia; While, Alison

2010-03-01

High quality health care demands a nursing workforce with sound clinical skills. However, the clinical competency of newly qualified nurses continues to stimulate debate about the adequacy of current methods of clinical skills education and emphasises the need for innovative teaching strategies. Despite the increasing use of e-learning within nurse education, evidence to support its use for clinical skills teaching is limited and inconclusive. This study tested whether nursing students could learn and retain the theory and skill of handwashing more effectively when taught using computer-assisted learning compared with conventional face-to-face methods. The study employed a two group randomised controlled design. The intervention group used an interactive, multimedia, self-directed computer-assisted learning module. The control group was taught by an experienced lecturer in a clinical skills room. Data were collected over a 5-month period between October 2004 and February 2005. Knowledge was tested at four time points and handwashing skills were assessed twice. Two-hundred and forty-two first year nursing students of mixed gender; age; educational background and first language studying at one British university were recruited to the study. Participant attrition increased during the study. Knowledge scores increased significantly from baseline in both groups and no significant differences were detected between the scores of the two groups. Skill performance scores were similar in both groups at the 2-week follow-up with significant differences emerging at the 8-week follow-up in favour of the intervention group, however, this finding must be interpreted with caution in light of sample size and attrition rates. The computer-assisted learning module was an effective strategy for teaching both the theory and practice of handwashing to nursing students and in this study was found to be at least as effective as conventional face-to-face teaching methods. Copyright 2009 Elsevier Ltd. All rights reserved.

Learning-Based Cell Injection Control for Precise Drop-on-Demand Cell Printing.

PubMed

Shi, Jia; Wu, Bin; Song, Bin; Song, Jinchun; Li, Shihao; Trau, Dieter; Lu, Wen F

2018-06-05

Drop-on-demand (DOD) printing is widely used in bioprinting for tissue engineering because of little damage to cell viability and cost-effectiveness. However, satellite droplets may be generated during printing, deviating cells from the desired position and affecting printing position accuracy. Current control on cell injection in DOD printing is primarily based on trial-and-error process, which is time-consuming and inflexible. In this paper, a novel machine learning technology based on Learning-based Cell Injection Control (LCIC) approach is demonstrated for effective DOD printing control while eliminating satellite droplets automatically. The LCIC approach includes a specific computational fluid dynamics (CFD) simulation model of piezoelectric DOD print-head considering inverse piezoelectric effect, which is used instead of repetitive experiments to collect data, and a multilayer perceptron (MLP) network trained by simulation data based on artificial neural network algorithm, using the well-known classification performance of MLP to optimize DOD printing parameters automatically. The test accuracy of the LCIC method was 90%. With the validation of LCIC method by experiments, satellite droplets from piezoelectric DOD printing are reduced significantly, improving the printing efficiency drastically to satisfy requirements of manufacturing precision for printing complex artificial tissues. The LCIC method can be further used to optimize the structure of DOD print-head and cell behaviors.

Fuzzy Adaptive Control for Intelligent Autonomous Space Exploration Problems

NASA Technical Reports Server (NTRS)

Esogbue, Augustine O.

1998-01-01

The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of these are in progress in our laboratory while others await additional support. All of these enhancements will improve the attractiveness of the controller as an effective tool for the on line control of an array of complex process environments.

Effectiveness of a blended learning course and flipped classroom in first year anaesthesia training.

PubMed

Marchalot, Antoine; Dureuil, Bertrand; Veber, Benoit; Fellahi, Jean-Luc; Hanouz, Jean-Luc; Dupont, Hervé; Lorne, Emmanuel; Gerard, Jean-Louis; Compère, Vincent

2017-11-22

Blended learning, which combines internet-based platform and lecturing, is used in anaesthesiology and critical care teaching. However, the benefits of this method remain unclear. We conducted a prospective, multicentre, non-randomised work between 2007 and 2014 to study the effect of blended learning on the results of first year anaesthesia and critical care residents in comparison with traditional teaching. Blended learning was implemented in Rouen University Hospital in 2011 and residents affiliated to this university corresponded as the blended learning group. The primary outcome was the resident's results as measured with multiple-choice questions between blended learning and control groups after beginning blended learning (post-interventional stage). The secondary outcomes included residents' results between pre and post-interventional stages and homework's time. Moreover, comparison between control and blended learning group before beginning blended learning (pre-interventional stage) was performed. From 2007 to 2014, 308 residents were included. For the pre-interventional period, the mean score in the blended learning group (n=53) was 176 (CI 95% 163 to 188) whereas the mean score in the control group (n=106) was 167 (CI 95% 160 to 174) (no difference). For the post-interventional period, the mean score in blended learning group (n=54) was 232 on 300 (CI95% 227-237) whereas the mean score in the control group (n=95) is 215 (CI95% 209-220) (P<0.001). In the two groups, comparison between pre and post-interventional stages showed the increase of mean score, stronger for blended learning group (32% and 28% in blended learning and control group, P<0.05). The average time of homework in the blended learning group was 27h (CI 95% 18.2-35.8) and 10h in the control group (CI 95% 2-18) (P<0.05). This work suggests the positive effect of blended learning (associating internet-based learning and flipped classroom) on the anaesthesia and critical care residents' knowledge by increasing their homework's time. Copyright © 2017. Published by Elsevier Masson SAS.

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

NASA Technical Reports Server (NTRS)

Padgett, Mary L. (Editor)

1993-01-01

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

Deep learning and model predictive control for self-tuning mode-locked lasers

NASA Astrophysics Data System (ADS)

Baumeister, Thomas; Brunton, Steven L.; Nathan Kutz, J.

2018-03-01

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\\em deep learning} (DL) architecture with {\\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

Learning and Control Model of the Arm for Loading

NASA Astrophysics Data System (ADS)

Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

Collaborative Testing in Practical Laboratories: An Effective Teaching-Learning Method in Histology.

PubMed

Guo, Yuping; Li, Enzhong

2016-01-01

This article presents an experimental teaching and learning program used in histology with first-year students in the second term in the Faculty of Biology at Huanghuai University, China. Eighty-six students were divided randomly into two groups (n=43 per group). Tests were conducted at the end of each practical laboratory (10 laboratories in total) in which collaborative testing was used in the experimental group and traditional testing in the control group. To assess achievement, a final examination in histology was carried out at the end of the course. To determine students' attitude to the teaching styles, a questionnaire survey was conducted at the end of the term. Results showed that students preferred the collaborative testing format. In the experimental group, students' scores were significantly higher than those of students in the control group in final examinations. These findings indicate that collaborative testing enhances student learning and understanding of the material taught, and suggest that collaborative testing is an effective teaching-learning method in histology.

Training monitoring skills in helicopter pilots.

PubMed

Potter, Brian A; Blickensderfer, Elizabeth L; Boquet, Albert J

2014-05-01

Prior research has indicated that ineffective pilot monitoring has been associated with aircraft accidents. Despite this finding, empirical research concerning pilot monitoring skill training programs is nearly nonexistent. E-learning may prove to be an effective method to foster nontechnical flight skills, including monitoring. This study examined the effect of using e-learning to enhance helicopter aircrew monitoring skill performance. The design was a posttest only field study. Forty-four helicopter pilots completed either an e-learning training module or a control activity and then flew two scenarios in a high-fidelity flight simulator. Learner reactions and knowledge gained were assessed immediately following the e-learning module. Two observer raters assessed behaviors and performance outcomes using recordings of the simulation flights. Subjects who completed the e-learning training module scored almost twice as high as did the control group on the administered knowledge test (experimental group, mean = 92.8%; control group, mean = 47.7%) and demonstrated up to 150% more monitoring behaviors during the simulated flights than the control subjects. In addition, the participating pilots rated the course highly. The results supported the hypothesis that a relatively inexpensive and brief training course implemented through e-learning can foster monitoring skill development among helicopter pilots.

Model learning for robot control: a survey.

PubMed

Nguyen-Tuong, Duy; Peters, Jan

2011-11-01

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Applying Student Team Achievement Divisions (STAD) Model on Material of Basic Programme Branch Control Structure to Increase Activity and Student Result

NASA Astrophysics Data System (ADS)

Akhrian Syahidi, Aulia; Asyikin, Arifin Noor; Asy’ari

2018-04-01

Based on my experience of teaching the material of branch control structure, it is found that the condition of the students is less active causing the low activity of the students on the attitude assessment during the learning process on the material of the branch control structure i.e. 2 students 6.45% percentage of good activity and 29 students percentage 93.55% enough and less activity. Then from the low activity resulted in low student learning outcomes based on a daily re-examination of branch control material, only 8 students 26% percentage reached KKM and 23 students 74% percent did not reach KKM. The purpose of this research is to increase the activity and learning outcomes of students of class X TKJ B SMK Muhammadiyah 1 Banjarmasin after applying STAD type cooperative learning model on the material of branch control structure. The research method used is Classroom Action Research. The study was conducted two cycles with six meetings. The subjects of this study were students of class X TKJ B with a total of 31 students consisting of 23 men and 8 women. The object of this study is the activity and student learning outcomes. Data collection techniques used are test and observation techniques. Data analysis technique used is a percentage and mean. The results of this study indicate that: an increase in activity and learning outcomes of students on the basic programming learning material branch control structure after applying STAD type cooperative learning model.

The effects of computer-supported inquiry-based learning methods and peer interaction on learning stellar parallax

NASA Astrophysics Data System (ADS)

Ruzhitskaya, Lanika

The presented research study investigated the effects of computer-supported inquiry-based learning and peer interaction methods on effectiveness of learning a scientific concept. The stellar parallax concept was selected as a basic, and yet important in astronomy, scientific construct, which is based on a straightforward relationship of several components presented in a simple mathematical equation: d = 1/p. The simplicity of the concept allowed the researchers to explore how the learners construct their conceptual knowledge, build mathematical skills and transfer their knowledge beyond the learning settings. A computer-based tutorial Stellar Parallax Interactive Restricted and Unrestricted Tutorial (SPIRUT) was developed for this study, and was designed to aid students' knowledge construction of the concept either in a learner-controlled or a program-controlled mode. The first investigated method in the study was enhancing engagement by the means of scaffolding for inquiry, which included scripted prompts and called for students' predictions and reflections while working in the learner-controlled or the computer-controlled version of SPIRUT. A second form of enhancing engagement was through peers working cooperatively during the learning activities. The students' level of understanding of the concept was measured by (1) the number of correct answers on a conceptual test with (2) several questions that require knowledge transfer to unfamiliar situations and (3) their ability to calculate the stellar parallax and find distances to stars. The study was conducted in the University of Missouri among 199 non-science major students enrolled in an introductory astronomy course in the fall semester 2010. The participants were divided into two main groups: one was working with SPIRUT and another group was a control group and utilized a paper-based tutorial. The SPIRUT group was further divided into the learner-controlled and the program-controlled subgroups. Students' learning achievements were measured by two post- tests and compared to the students' results on a pre-test. The first post-test was administered right after the treatment with aim to measure the immediate effect of the treatment. The second post-test was administered eight weeks later and was aimed to elicit how much of the constructed knowledge students retained after the treatment. Results of the study revealed that students who learned the concept with SPIRUT constructed greater conceptual knowledge and were able to better transfer it to another situation while their mathematical skills were equally improved as those students who worked with the paper-based tutorial. It was also evident that there was no difference between students' performances after their engagement with the learner-controlled or with the program-controlled version of SPIRUT. It was also found that students who worked independently constructed slightly greater knowledge than students who worked with peers. Albeit, there was no significant difference found of retention of knowledge after any type of treatment.

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

PubMed Central

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. PMID:26065018

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

PubMed

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

Use of e-learning in clinical clerkships: effects on acquisition of dermatological knowledge and learning processes

PubMed Central

Martens, Herm; Nagtzaam, Ivo; Heeneman, Sylvia

2018-01-01

Objectives To obtain a deeper understanding of how the e-learning program, Education in Dermatology (ED), affects the acquisition of dermatological knowledge and the underlying learning processes of medical students in their clinical phase. Methods The study used a mixed method design with a convergent parallel collection of data. Medical students (n=62) from Maastricht University (The Netherlands) were randomized to either a conventional teaching group (control group n=30) or conventional teaching plus the e-learning program (application on smartphone) group (e-learning group n=32). Pre- and post-intervention knowledge test results were analysed using an independent t-test. Individual semi-structured interviews (n=9) were conducted and verbatim-transcribed recordings were analysed using King’s template analysis. Results The e-learning program positively influenced students’ level of knowledge and their process of learning. A significant difference was found in the post-test scores for the control group (M=51.4, SD=6.43) and the e-learning group (M=73.09, SD=5.12); t(60)=-14.75, p<0.000). Interview data showed that the e-learning program stimulated students’ learning as the application promoted the identification and recognition of skin disorders, the use of references, creation of documents and sharing information with colleagues. Conclusions This study demonstrated that use of the e-learning program led to a significant improvement in basic dermatological knowledge. The underlying learning processes indicated that e-learning programs in dermatology filled a vital gap in the understanding of clinical reasoning in dermatology. These results might be useful when developing (clinical) teaching formats with a special focus on visual disciplines.  PMID:29352748

Neural networks applications to control and computations

NASA Technical Reports Server (NTRS)

Luxemburg, Leon A.

1994-01-01

Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

An Examination of Strategy Implementation During Abstract Nonlinguistic Category Learning in Aphasia

PubMed Central

Kiran, Swathi

2015-01-01

Purpose Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive–linguistic assessments. Results Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Conclusions Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities. PMID:25908438

Serendipitous Offline Learning in a Neuromorphic Robot.

PubMed

Stewart, Terrence C; Kleinhans, Ashley; Mundy, Andrew; Conradt, Jörg

2016-01-01

We demonstrate a hybrid neuromorphic learning paradigm that learns complex sensorimotor mappings based on a small set of hard-coded reflex behaviors. A mobile robot is first controlled by a basic set of reflexive hand-designed behaviors. All sensor data is provided via a spike-based silicon retina camera (eDVS), and all control is implemented via spiking neurons simulated on neuromorphic hardware (SpiNNaker). Given this control system, the robot is capable of simple obstacle avoidance and random exploration. To train the robot to perform more complex tasks, we observe the robot and find instances where the robot accidentally performs the desired action. Data recorded from the robot during these times is then used to update the neural control system, increasing the likelihood of the robot performing that task in the future, given a similar sensor state. As an example application of this general-purpose method of training, we demonstrate the robot learning to respond to novel sensory stimuli (a mirror) by turning right if it is present at an intersection, and otherwise turning left. In general, this system can learn arbitrary relations between sensory input and motor behavior.

The effectiveness of module based on guided inquiry method to improve students’ logical thinking ability

NASA Astrophysics Data System (ADS)

Ash-Shiddieqy, M. H.; Suparmi, A.; Sunarno, W.

2018-04-01

The purpose of this research is to understand the effectiveness of module based on guided inquiry method to improve students’ logical thinking ability. This research only evaluate the students’ logical ability after follows the learning activities that used developed physics module based on guided inquiry method. After the learning activities, students This research method uses a test instrument that adapts TOLT instrument. There are samples of 68 students of grade XI taken from SMA Negeri 4 Surakarta.Based on the results of the research can be seen that in the experimental class and control class, the posttest value aspect of probabilistic reasoning has the highest value than other aspects, whereas the posttest value of the proportional reasoning aspect has the lowest value. The average value of N-gain in the experimental class is 0.39, while in the control class is 0.30. Nevertheless, the N-gain values obtained in the experimental class are larger than the control class, so the guided inquiry-based module is considered more effective for improving students’ logical thinking. Based on the data obtained from the research shows the modules available to help teachers and students in learning activities. The developed Physics module is integrated with every syntax present in guided inquiry method, so it can be used to improve students’ logical thinking ability.

Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game.

PubMed

Zhong, Xiangnan; He, Haibo; Wang, Ding; Ni, Zhen

2018-05-01

In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively. The explicit updating rules for these three neural networks are provided based on the data generated during the online learning along the system trajectories. The stability analysis in terms of the neural network approximation errors is discussed based on the Lyapunov approach. Finally, two simulation examples are provided to show the effectiveness of the proposed method.

37: COMPARISON OF TWO METHODS: TBL-BASED AND LECTURE-BASED LEARNING IN NURSING CARE OF PATIENTS WITH DIABETES IN NURSING STUDENTS

PubMed Central

Khodaveisi, Masoud; Qaderian, Khosro; Oshvandi, Khodayar; Soltanian, Ali Reza; Vardanjani, Mehdi molavi

2017-01-01

Background and aims learning plays an important role in developing nursing skills and right care-taking. The Present study aims to evaluate two learning methods based on team –based learning and lecture-based learning in learning care-taking of patients with diabetes in nursing students. Method In this quasi-experimental study, 64 students in term 4 in nursing college of Bukan and Miandoab were included in the study based on knowledge and performance questionnaire including 15 questions based on knowledge and 5 questions based on performance on care-taking in patients with diabetes were used as data collection tool whose reliability was confirmed by cronbach alpha (r=0.83) by the researcher. To compare the mean score of knowledge and performance in each group in pre-test step and post-test step, pair –t test and to compare mean of scores in two groups of control and intervention, the independent t- test was used. Results There was not significant statistical difference between two groups in pre terms of knowledge and performance score (p=0.784). There was significant difference between the mean of knowledge scores and diabetes performance in the post-test in the team-based learning group and lecture-based learning group (p=0.001). There was significant difference between the mean score of knowledge of diabetes care in pre-test and post-test in base learning groups (p=0.001). Conclusion In both methods team-based and lecture-based learning approaches resulted in improvement in learning in students, but the rate of learning in the team-based learning approach is greater compared to that of lecture-based learning and it is recommended that this method be used as a higher education method in the education of students.

Understanding and Taking Control of Surgical Learning Curves.

PubMed

Gofton, Wade T; Papp, Steven R; Gofton, Tyson; Beaulé, Paul E

2016-01-01

As surgical techniques continue to evolve, surgeons will have to integrate new skills into their practice. A learning curve is associated with the integration of any new procedure; therefore, it is important for surgeons who are incorporating a new technique into their practice to understand what the reported learning curve might mean for them and their patients. A learning curve should not be perceived as negative because it can indicate progress; however, surgeons need to understand how to optimize the learning curve to ensure progress with minimal patient risk. It is essential for surgeons who are implementing new procedures or skills to define potential learning curves, examine how a reported learning curve may relate to an individual surgeon's in-practice learning and performance, and suggest methods in which an individual surgeon can modify his or her specific learning curve in order to optimize surgical outcomes and patient safety. A defined personal learning contract may be a practical method for surgeons to proactively manage their individual learning curve and provide evidence of their efforts to safely improve surgical practice.

An Evaluation of Argument Mapping as a Method of Enhancing Critical Thinking Performance in E-Learning Environments

ERIC Educational Resources Information Center

Dwyer, Christopher P.; Hogan, Michael J.; Stewart, Ian

2012-01-01

The current research examined the effects of a critical thinking (CT) e-learning course taught through argument mapping (AM) on measures of CT ability. Seventy-four undergraduate psychology students were allocated to either an AM-infused CT e-learning course or a no instruction control group and were tested both before and after an 8-week…

Effects of case-based learning on communication skills, problem-solving ability, and learning motivation in nursing students.

PubMed

Yoo, Moon-Sook; Park, Hyung-Ran

2015-06-01

The purpose of this study was to explore the effects of case-based learning on communication skills, problem-solving ability, and learning motivation in sophomore nursing students. In this prospective, quasi-experimental study, we compared the pretest and post-test scores of an experimental group and a nonequivalent, nonsynchronized control group. Both groups were selected using convenience sampling, and consisted of students enrolled in a health communication course in the fall semesters of 2011 (control group) and 2012 (experimental group) at a nursing college in Suwon, South Korea. The two courses covered the same material, but in 2011 the course was lecture-based, while in 2012, lectures were replaced by case-based learning comprising five authentic cases of patient-nurse communication. At post-test, the case-based learning group showed significantly greater communication skills, problem-solving ability, and learning motivation than the lecture-based learning group. This finding suggests that case-based learning is an effective learning and teaching method. © 2014 Wiley Publishing Asia Pty Ltd.

The Effect of STEM Learning through the Project of Designing Boat Model toward Student STEM Literacy

NASA Astrophysics Data System (ADS)

Tati, T.; Firman, H.; Riandi, R.

2017-09-01

STEM Learning focusses on development of STEM-literate society, the research about implementation of STEM learning to develope students’ STEM literacy is still limited. This study is aimed to examine the effect of implementation STEM learning through the project of designing boat model on students STEM literacy in energy topic. The method of this study was a quasi-experiment with non-randomized pretest-posttest control group design. There were two classes involved, the experiment class used Project Based Learning with STEM approach and control class used Project-Based Learning without STEM approach. A STEM Literacy test instrument was developed to measure students STEM literacy which consists of science literacy, mathematics literacy, and technology-engineering literacy. The analysis showed that there were significant differences on improvement science literacy, mathematics technology-engineering between experiment class and control class with effect size more than 0.8 (large effect). The difference of improvement of STEM literacy between experiment class and control class is caused by the existence of design engineering activity which required students to apply the knowledge from every field of STEM. The challenge that was faced in STEM learning through design engineering activity was how to give the students practice to integrate STEM field in solving the problems. In additional, most of the students gave positive response toward implementation of STEM learning through design boat model project.

Foodborne outbreak simulation to teach field epidemiology: the Moroccan Field Epidemiology Training Program.

PubMed

Jroundi, Imane; Belarbi, Abdellatif

2016-11-01

Morocco in 2010 launched a new field epidemiology training program to enhance the skills of health professionals in charge of epidemiological surveillance and to investigate outbreaks; including foodborne diseases that represent a very substantial burden of disease. To apply an active learning method to teach outbreak investigation within a controled environment for field epidemiology trainees program at the Moroccan National school of public Health. A scenario describing digestive symptoms evoking a restaurant-associated foodborne outbreak that would affect the school staff was designed for the residents to investigate, to assess their organizational capacity and application of all stages of epidemiological investigation. Nine Residents applied study design, database management and statistical analysis to investigate the foodborne outbreak, to estimate attack rates, classify cases and controls, to identify the contaminated foods and pathogens and to issue preventive recommendations for the control and the prevention of further transmission. The overall resident's satisfaction of the learning method was 67%. A simulation of an outbreak investigation within an academic setting is an active learning method to be used in the curriculum for introducing the residents on field epidemiology program to the principles and practices of outbreak investigation before their implication in a real situation.

The influence of inquiry learning model on additives theme with ethnoscience content to cultural awareness of students

NASA Astrophysics Data System (ADS)

Sudarmin, S.; Selia, E.; Taufiq, M.

2018-03-01

The purpose of this research is to determine the influence of inquiry learning model on additives theme with ethnoscience content to cultural awareness of students and how the students’ responses to learning. The method applied in this research is a quasi-experimental with non-equivalent control group design. The sampling technique applied in this research is the technique of random sampling. The samples were eight grade students of one of junior high schools in Semarang. The results of this research were (1) thestudents’ cultural awareness of the experiment class is better than the control class (2) inquiry learning model with ethnoscience content strongly influencing the cultural awareness of students by 78% and (3) students gave positive responses to inquiry learning model with ethnoscience content. The conclusions of this research are inquiry-learning model with ethnoscience content has positive influence on students’ cultural awareness.

Instilling positive beliefs about disabilities: pilot testing a novel experiential learning activity for rehabilitation students.

PubMed

Silverman, Arielle M; Pitonyak, Jennifer S; Nelson, Ian K; Matsuda, Patricia N; Kartin, Deborah; Molton, Ivan R

2018-05-01

To develop and test a novel impairment simulation activity to teach beginning rehabilitation students how people adapt to physical impairments. Masters of Occupational Therapy students (n = 14) and Doctor of Physical Therapy students (n = 18) completed the study during the first month of their program. Students were randomized to the experimental or control learning activity. Experimental students learned to perform simple tasks while simulating paraplegia and hemiplegia. Control students viewed videos of others completing tasks with these impairments. Before and after the learning activities, all students estimated average self-perceived health, life satisfaction, and depression ratings among people with paraplegia and hemiplegia. Experimental students increased their estimates of self-perceived health, and decreased their estimates of depression rates, among people with paraplegia and hemiplegia after the learning activity. The control activity had no effect on these estimates. Impairment simulation can be an effective way to teach rehabilitation students about the adaptations that people make to physical impairments. Positive impairment simulations should allow students to experience success in completing activities of daily living with impairments. Impairment simulation is complementary to other pedagogical methods, such as simulated clinical encounters using standardized patients. Implication of Rehabilitation It is important for rehabilitation students to learn how people live well with disabilities. Impairment simulations can improve students' assessments of quality of life with disabilities. To be beneficial, impairment simulations must include guided exposure to effective methods for completing daily tasks with disabilities.

The Effect of Visual of a Courseware towards Pre-University Students' Learning in Literature

NASA Astrophysics Data System (ADS)

Masri, Mazyrah; Wan Ahmad, Wan Fatimah; Nordin, Shahrina Md.; Sulaiman, Suziah

This paper highlights the effect of visual of a multimedia courseware, Black Cat Courseware (BC-C), developed for learning literature at a pre-university level in University Teknologi PETRONAS (UTP). The contents of the courseware are based on a Black Cat story which is covered in an English course at the university. The objective of this paper is to evaluate the usability and effectiveness of BC-C. A total of sixty foundation students were involved in the study. Quasi-experimental design was employed, forming two groups: experimental and control groups. The experimental group had to interact with BC-C as part of the learning activities while the control group used the conventional learning methods. The results indicate that the experimental group achieved a statistically significant compared to the control group in understanding the Black Cat story. The study result also proves that the effect of visual increases the students' performances in literature learning at a pre-university level.

Autonomous Motion Learning for Intra-Vehicular Activity Space Robot

NASA Astrophysics Data System (ADS)

Watanabe, Yutaka; Yairi, Takehisa; Machida, Kazuo

Space robots will be needed in the future space missions. So far, many types of space robots have been developed, but in particular, Intra-Vehicular Activity (IVA) space robots that support human activities should be developed to reduce human-risks in space. In this paper, we study the motion learning method of an IVA space robot with the multi-link mechanism. The advantage point is that this space robot moves using reaction force of the multi-link mechanism and contact forces from the wall as space walking of an astronaut, not to use a propulsion. The control approach is determined based on a reinforcement learning with the actor-critic algorithm. We demonstrate to clear effectiveness of this approach using a 5-link space robot model by simulation. First, we simulate that a space robot learn the motion control including contact phase in two dimensional case. Next, we simulate that a space robot learn the motion control changing base attitude in three dimensional case.

An H(∞) control approach to robust learning of feedforward neural networks.

PubMed

Jing, Xingjian

2011-09-01

A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise. Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H(∞) "noise" attenuation level. Several existing BP-type algorithms are shown to be special cases of the new H(∞)-learning algorithm. Theoretical analysis and several examples are provided to show the advantages of the new method. Copyright © 2011 Elsevier Ltd. All rights reserved.

The profile of students’ self-regulated learning at vocational high school

NASA Astrophysics Data System (ADS)

Ciptaningtyas, Asih; Pratiwi, Hasih; Mardiyana

2018-05-01

Self-regulated learning is a power in the individual through the individualization process. Self-regulated learning will occur when the student is active to control himself from everything done, plan something, evaluate, and deeply reflect what he has experienced. This study aims to determine the profile of students’ self-regulated learning in SMK Giripuro, Sumpiuh, Banyumas Regency. This study is a qualitative research with questionnaire and interview methods. This study used triangulation method technique to obtain from the questionnaire and interview to get valid data. The subjects in this study are three 10th Grade students who have different self-regulated learning in SMK Giripuro Sumpiuh. The results showed that the high self-regulated learning student has characteristics: 1) independent of others, 2) believe in their abilities, 3) awareness in learning, and 4) be able to reflect on their learning. Medium self-regulated learning student has characteristics: 1) independent of others, 2) believe in their abilities, 3) awareness in learning, and 4) do not reflect on learning. Low self-regulated learning student has characteristics: 1) dependent on others, 2) do not believe in their abilities, 3) lack awareness of learning, and 4) do not reflect on learning.

The most successful method in teaching nursing students infection control - E-learning or lecture?

PubMed

Reime, Marit Hegg; Harris, Anette; Aksnes, June; Mikkelsen, Jane

2008-10-01

Approximately 33% of all health care-associated infections are preventable. It is therefore important to provide training for nursing students about this topic. In collaboration with the local hospital, the Department of Nursing evaluated a newly developed e-learning program on infection control normally used among employees in the hospital but now tried in the setting of bachelor students. The students received learning goals for the course and were divided into two groups: one group used the e-learning program, and the other group had 3-h-long lectures. After the course they took a multiple-choice test. In addition, three focus groups were established. The students were satisfied with both teaching approaches. The lectures provided a good introduction to the recommended reading. The e-learning program was rated as good on design, academic content, and the integrated tests were motivating for their learning. Specific learning goals were found to be useful. Gender and age, depending on the teaching approach used, were significant in determining the results of the test, the same were the number of sources used in preparing for the test. E-learning has to be viewed as a resource in the same way as a lecture. It is important that the students are competent in ICT, because they will need to use this tool in their clinical practice. In addition, a degree level course needs to use many different teaching methods to achieve goals related to in-depth and superficial learning.

A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.

The Final Count Down: A Review of Three Decades of Flight Controller Training Methods for Space Shuttle Mission Operations

NASA Technical Reports Server (NTRS)

Dittemore, Gary D.; Bertels, Christie

2011-01-01

Operations of human spaceflight systems is extremely complex, therefore the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center, in Houston, Texas manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. As the space shuttle program ends in 2011, a review of how training for STS-1 was conducted compared to STS-134 will show multiple changes in training of shuttle flight controller over a thirty year period. This paper will additionally give an overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams have been trained certified over the life span of the space shuttle. The training methods for developing flight controllers have evolved significantly over the last thirty years, while the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. These changes have been driven by many factors including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. A primary method used for training Space Shuttle flight control teams is by running mission simulations of the orbit, ascent, and entry phases, to truly "train like you fly." The reader will learn what it is like to perform a simulation as a shuttle flight controller. Finally, the paper will reflect on the lessons learned in training for the shuttle program, and how those could be applied to future human spaceflight endeavors.

Remote-online case-based learning: A comparison of remote-online and face-to-face, case-based learning - a randomized controlled trial.

PubMed

Nicklen, Peter; Keating, Jenny L; Paynter, Sophie; Storr, Michael; Maloney, Stephen

2016-01-01

Case-based learning (CBL) is an educational approach where students work in small, collaborative groups to solve problems. Computer assisted learning (CAL) is the implementation of computer technology in education. The purpose of this study was to compare the effects of a remote-online CBL (RO-CBL) with traditional face-to-face CBL on learning the outcomes of undergraduate physiotherapy students. Participants were randomized to either the control (face-to-face CBL) or to the CAL intervention (RO-CBL). The entire 3rd year physiotherapy cohort (n = 41) at Monash University, Victoria, Australia, were invited to participate in the randomized controlled trial. Outcomes included a postintervention multiple-choice test evaluating the knowledge gained from the CBL, a self-assessment of learning based on examinable learning objectives and student satisfaction with the CBL. In addition, a focus group was conducted investigating perceptions and responses to the online format. Thirty-eight students (control n = 19, intervention n = 19) participated in two CBL sessions and completed the outcome assessments. CBL median scores for the postintervention multiple-choice test were comparable (Wilcoxon rank sum P = 0.61) (median/10 [range] intervention group: 9 [8-10] control group: 10 [7-10]). Of the 15 examinable learning objectives, eight were significantly in favor of the control group, suggesting a greater perceived depth of learning. Eighty-four percent of students (16/19) disagreed with the statement "I enjoyed the method of CBL delivery." Key themes identified from the focus group included risks associated with the implementation of, challenges of communicating in, and flexibility offered, by web-based programs. RO-CBL appears to provide students with a comparable learning experience to traditional CBL. Procedural and infrastructure factors need to be addressed in future studies to counter student dissatisfaction and decreased perceived depth of learning.

Adaptive learning and control for MIMO system based on adaptive dynamic programming.

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

Efficiency Improvement of Action Acquisition in Two-Link Robot Arm Using Fuzzy ART with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kotani, Naoki; Taniguchi, Kenji

An efficient learning method using Fuzzy ART with Genetic Algorithm is proposed. The proposed method reduces the number of trials by using a policy acquired in other tasks because a reinforcement learning needs a lot of the number of trials until an agent acquires appropriate actions. Fuzzy ART is an incremental unsupervised learning algorithm in responce to arbitrary sequences of analog or binary input vectors. Our proposed method gives a policy by crossover or mutation when an agent observes unknown states. Selection controls the category proliferation problem of Fuzzy ART. The effectiveness of the proposed method was verified with the simulation of the reaching problem for the two-link robot arm. The proposed method achieves a reduction of both the number of trials and the number of states.

Talker Identification across Source Mechanisms: Experiments with Laryngeal and Electrolarynx Speech

ERIC Educational Resources Information Center

Perrachione, Tyler K.; Stepp, Cara E.; Hillman, Robert E.; Wong, Patrick C. M.

2014-01-01

Purpose: The purpose of this study was to determine listeners' ability to learn talker identity from speech produced with an electrolarynx, explore source and filter differentiation in talker identification, and describe acoustic-phonetic changes associated with electrolarynx use. Method: Healthy adult control listeners learned to identify…

Reinforcement interval type-2 fuzzy controller design by online rule generation and q-value-aided ant colony optimization.

PubMed

Juang, Chia-Feng; Hsu, Chia-Hung

2009-12-01

This paper proposes a new reinforcement-learning method using online rule generation and Q-value-aided ant colony optimization (ORGQACO) for fuzzy controller design. The fuzzy controller is based on an interval type-2 fuzzy system (IT2FS). The antecedent part in the designed IT2FS uses interval type-2 fuzzy sets to improve controller robustness to noise. There are initially no fuzzy rules in the IT2FS. The ORGQACO concurrently designs both the structure and parameters of an IT2FS. We propose an online interval type-2 rule generation method for the evolution of system structure and flexible partitioning of the input space. Consequent part parameters in an IT2FS are designed using Q -values and the reinforcement local-global ant colony optimization algorithm. This algorithm selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of which are updated using reinforcement signals. The ORGQACO design method is applied to the following three control problems: 1) truck-backing control; 2) magnetic-levitation control; and 3) chaotic-system control. The ORGQACO is compared with other reinforcement-learning methods to verify its efficiency and effectiveness. Comparisons with type-1 fuzzy systems verify the noise robustness property of using an IT2FS.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

PubMed

Kallenberg, Michiel; Petersen, Kersten; Nielsen, Mads; Ng, Andrew Y; Pengfei Diao; Igel, Christian; Vachon, Celine M; Holland, Katharina; Winkel, Rikke Rass; Karssemeijer, Nico; Lillholm, Martin

2016-05-01

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Intelligent automated control of life support systems using proportional representations.

PubMed

Wu, Annie S; Garibay, Ivan I

2004-06-01

Effective automatic control of Advanced Life Support Systems (ALSS) is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. As a result, such systems can be difficult to control using deliberative and deterministic methods. We investigate the performance of two machine learning algorithms, a genetic algorithm (GA) and a stochastic hill-climber (SH), on the problem of learning how to control an ALSS, and compare the impact of two different types of problem representations on the performance of both algorithms. We perform experiments on three ALSS optimization problems using five strategies with multiple variations of a proportional representation for a total of 120 experiments. Results indicate that although a proportional representation can effectively boost GA performance, it does not necessarily have the same effect on other algorithms such as SH. Results also support previous conclusions that multivector control strategies are an effective method for control of coupled dynamical systems.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

PubMed Central

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

PubMed

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

Robustification and Optimization in Repetitive Control For Minimum Phase and Non-Minimum Phase Systems

NASA Astrophysics Data System (ADS)

Prasitmeeboon, Pitcha

Repetitive control (RC) is a control method that specifically aims to converge to zero tracking error of a control systems that execute a periodic command or have periodic disturbances of known period. It uses the error of one period back to adjust the command in the present period. In theory, RC can completely eliminate periodic disturbance effects. RC has applications in many fields such as high-precision manufacturing in robotics, computer disk drives, and active vibration isolation in spacecraft. The first topic treated in this dissertation develops several simple RC design methods that are somewhat analogous to PID controller design in classical control. From the early days of digital control, emulation methods were developed based on a Forward Rule, a Backward Rule, Tustin's Formula, a modification using prewarping, and a pole-zero mapping method. These allowed one to convert a candidate controller design to discrete time in a simple way. We investigate to what extent they can be used to simplify RC design. A particular design is developed from modification of the pole-zero mapping rules, which is simple and sheds light on the robustness of repetitive control designs. RC convergence requires less than 90 degree model phase error at all frequencies up to Nyquist. A zero-phase cutoff filter is normally used to robustify to high frequency model error when this limit is exceeded. The result is stabilization at the expense of failure to cancel errors above the cutoff. The second topic investigates a series of methods to use data to make real time updates of the frequency response model, allowing one to increase or eliminate the frequency cutoff. These include the use of a moving window employing a recursive discrete Fourier transform (DFT), and use of a real time projection algorithm from adaptive control for each frequency. The results can be used directly to make repetitive control corrections that cancel each error frequency, or they can be used to update a repetitive control FIR compensator. The aim is to reduce the final error level by using real time frequency response model updates to successively increase the cutoff frequency, each time creating the improved model needed to produce convergence zero error up to the higher cutoff. Non-minimum phase systems present a difficult design challenge to the sister field of Iterative Learning Control. The third topic investigates to what extent the same challenges appear in RC. One challenge is that the intrinsic non-minimum phase zero mapped from continuous time is close to the pole of repetitive controller at +1 creating behavior similar to pole-zero cancellation. The near pole-zero cancellation causes slow learning at DC and low frequencies. The Min-Max cost function over the learning rate is presented. The Min-Max can be reformulated as a Quadratically Constrained Linear Programming problem. This approach is shown to be an RC design approach that addresses the main challenge of non-minimum phase systems to have a reasonable learning rate at DC. Although it was illustrated that using the Min-Max objective improves learning at DC and low frequencies compared to other designs, the method requires model accuracy at high frequencies. In the real world, models usually have error at high frequencies. The fourth topic addresses how one can merge the quadratic penalty to the Min-Max cost function to increase robustness at high frequencies. The topic also considers limiting the Min-Max optimization to some frequencies interval and applying an FIR zero-phase low-pass filter to cutoff the learning for frequencies above that interval.

Model-Based and Model-Free Pavlovian Reward Learning: Revaluation, Revision and Revelation

PubMed Central

Dayan, Peter; Berridge, Kent C.

2014-01-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation. PMID:24647659

Model-based and model-free Pavlovian reward learning: revaluation, revision, and revelation.

PubMed

Dayan, Peter; Berridge, Kent C

2014-06-01

Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.

Addressing the Movement of a Freescale Robotic Car Using Neural Network

NASA Astrophysics Data System (ADS)

Horváth, Dušan; Cuninka, Peter

2016-12-01

This article deals with the management of a Freescale small robotic car along the predefined guide line. Controlling of the direction of movement of the robot is performed by neural networks, and scales (memory) of neurons are calculated by Hebbian learning from the truth tables as learning with a teacher. Reflexive infrared sensors serves as inputs. The results are experiments, which are used to compare two methods of mobile robot control - tracking lines.

e-Learning in Surgical Education: A Systematic Review.

PubMed

Jayakumar, Nithish; Brunckhorst, Oliver; Dasgupta, Prokar; Khan, Muhammad Shamim; Ahmed, Kamran

2015-01-01

e-Learning involves the delivery of educational content through web-based methods. Owing to work-hour restrictions and changing practice patterns in surgery, e-learning can offer an effective alternative to traditional teaching. Our aims were to (1) identify current modalities of e-learning, (2) assess the efficacy of e-learning as an intervention in surgical education through a systematic review of the literature, and (3) discuss the relevance of e-learning as an educational tool in surgical education. This is the first such systematic review in this field. A systematic search of MEDLINE and EMBASE was conducted for relevant articles published until July 2014, using a predefined search strategy. The database search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 38 articles were found which met the inclusion criteria. In these studies, e-learning was used as an intervention in 3 different ways: (1) to teach cases through virtual patients (18/38); (2) to teach theoretical knowledge through online tutorials, or other means (18/38); and (3) to teach surgical skills (2/38). Nearly all of the studies reviewed report significant knowledge gain from e-learning; however, 2 in 3 studies did not use a control group. e-Learning has emerged as an effective mode of teaching with particular relevance for surgical education today. Published studies have demonstrated the efficacy of this method; however, future work must involve well-designed randomized controlled trials comparing e-learning against standard teaching. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Effectiveness of Using the iPad in Learning to Acquire the Mental and Performance Skills in Teaching Social Studies Curriculum

ERIC Educational Resources Information Center

Alajmi, Maadi Mahdi; Al-Hadiah, Hanan Abdullah

2017-01-01

This study aims to examine the effectiveness of using the iPad in learning to acquire the mental and performance skills in teaching the social studies. Using experimental design method, the study compared two groups: (a) experimental, taught using the iPad, and (b) control group, taught using the traditional learning without iPad. A total of 48…

Lifelong Transfer Learning for Heterogeneous Teams of Agents in Sequential Decision Processes

DTIC Science & Technology

2016-06-01

making (SDM) tasks in dynamic environments with simulated and physical robots . 15. SUBJECT TERMS Sequential decision making, lifelong learning, transfer...sequential decision-making (SDM) tasks in dynamic environments with both simple benchmark tasks and more complex aerial and ground robot tasks. Our work...and ground robots in the presence of disturbances: We applied our methods to the problem of learning controllers for robots with novel disturbances in

Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

PubMed

Chen, Chunlin; Dong, Daoyi; Qi, Bo; Petersen, Ian R; Rabitz, Herschel

2017-06-01

Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

Online gaming for learning optimal team strategies in real time

NASA Astrophysics Data System (ADS)

Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

2010-04-01

This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beaver, Justin M; Borges, Raymond Charles; Buckner, Mark A

Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems were designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. The centralization of control and the movement towards open systems and standards has improved the efficiency of industrial control, but has also exposed legacy SCADA systems to security threats that they were not designed to mitigate. This work explores the viability of machine learning methods in detecting the new threat scenarios of command and data injection. Similar to network intrusion detection systems in the cyber security domain, the command and control communications in amore » critical infrastructure setting are monitored, and vetted against examples of benign and malicious command traffic, in order to identify potential attack events. Multiple learning methods are evaluated using a dataset of Remote Terminal Unit communications, which included both normal operations and instances of command and data injection attack scenarios.« less

Neuroprosthetic Decoder Training as Imitation Learning.

PubMed

Merel, Josh; Carlson, David; Paninski, Liam; Cunningham, John P

2016-05-01

Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.

Effects of distance learning on clinical management of LUTS in primary care: a randomised trial.

PubMed

Wolters, René; Wensing, Michel; Klomp, Maarten; Lagro-Jansen, Toine; Weel, Chris van; Grol, Richard

2005-11-01

To determine the effect of a distance learning programme on general practice management of men with lower urinary tract symptoms (LUTS). A cluster randomised controlled trial was performed. General practitioners (GPs) were randomised to a distance learning programme accompanied with educational materials or to a control group only receiving mailed clinical guidelines on LUTS. Clinical management was considered as outcome. Sixty-three GPs registered care management of 187 patients older than 50 years attending the practice because of LUTS. The intervention group showed a lower referral rate to a urologist (OR: 0.08 (95% CI: 0.02-0.40)), but no effect on PSA testing or prescription of medication. PSA testing tended to be requested more frequently by intervention group GPs. Secondary analysis showed patients in the intervention group received more educational materials (OR: 75.6 (95% CI: 13.60-419.90)). The educational programme had impact on clinical management without changing PSA testing. Distance learning is an promising method for continuing education. Activating distance learning packages are a potentially effective method for improving professional performance. Emotional matters as PSA testing probably need a more complex approach.

Research on teaching methods.

PubMed

Oermann, M H

1990-01-01

Research on teaching methods in nursing education was categorized into studies on media, CAI, and other nontraditional instructional strategies. While the research differed, some generalizations may be made from the findings. Multimedia, whether it is used for individual or group instruction, is at least as effective as traditional instruction (lecture and lecture-discussion) in promoting cognitive learning, retention of knowledge, and performance. Further study is needed to identify variables that may influence learning and retention. While learner attitudes toward mediated instruction tended to be positive, investigators failed to control for the effect of novelty. Control over intervening variables was lacking in the majority of studies as well. Research indicated that CAI is as effective as other teaching methods in terms of knowledge gain and retention. Attitudes toward CAI tended to be favorable, with similar problems in measurement as those evidenced in studies of media. Chang (1986) also recommends that future research examine the impact of computer-video interactive instruction on students, faculty, and settings. Research is needed on experimental teaching methods, strategies for teaching problem solving and clinical judgment, and ways of improving the traditional lecture and discussion. Limited research in these areas makes generalizations impossible. There is a particular need for research on how to teach students the diagnostic reasoning process and encourage critical thinking, both in terms of appropriate teaching methods and the way in which those strategies should be used. It is interesting that few researchers studied lecture and lecture-discussion except as comparable teaching methods for research on other strategies. Additional research questions may be generated on lecture and discussion in relation to promoting concept learning, an understanding of nursing and other theories, transfer of knowledge, and development of cognitive skills. Few studies attempted to identify variables that may influence learning, particularly characteristics of the learner. Only six investigators addressed learning styles and their interactions with the teaching method and outcomes (Gillies, 1984; Goldsmith, 1984; Kirchhoff & Holzemer, 1979; Kissinger & Munjas, 1981; Norris, 1986; Stein et al., 1972). Research in the future needs to focus on the relationship of different learner characteristics, attributes of the teaching method, and learning outcomes. In addition, initial learning, retention, transfer to practice, and instructional time should be studied. Characteristics of the teacher and setting and relationship to the methodologies used and outcomes of instruction need investigation. Research should attempt to identify optimal conditions for learning and ways in which methods should be used for particular students, subject matter, and points in the nursing curriculum...

Manifold traversing as a model for learning control of autonomous robots

NASA Technical Reports Server (NTRS)

Szakaly, Zoltan F.; Schenker, Paul S.

1992-01-01

This paper describes a recipe for the construction of control systems that support complex machines such as multi-limbed/multi-fingered robots. The robot has to execute a task under varying environmental conditions and it has to react reasonably when previously unknown conditions are encountered. Its behavior should be learned and/or trained as opposed to being programmed. The paper describes one possible method for organizing the data that the robot has learned by various means. This framework can accept useful operator input even if it does not fully specify what to do, and can combine knowledge from autonomous, operator assisted and programmed experiences.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

PubMed Central

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition.

PubMed

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-06-13

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle).

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

PubMed Central

Zheng, Shuai; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A

2017-01-01

Background Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort. Traditional machine-based approaches lack the capability to take user feedbacks for improving the extraction algorithm in real time. Objective Our goal was to provide a generic information extraction framework that can support diverse clinical reports and enables a dynamic interaction between a human and a machine that produces highly accurate results. Methods A clinical information extraction system IDEAL-X has been built on top of online machine learning. It processes one document at a time, and user interactions are recorded as feedbacks to update the learning model in real time. The updated model is used to predict values for extraction in subsequent documents. Once prediction accuracy reaches a user-acceptable threshold, the remaining documents may be batch processed. A customizable controlled vocabulary may be used to support extraction. Results Three datasets were used for experiments based on report styles: 100 cardiac catheterization procedure reports, 100 coronary angiographic reports, and 100 integrated reports—each combines history and physical report, discharge summary, outpatient clinic notes, outpatient clinic letter, and inpatient discharge medication report. Data extraction was performed by 3 methods: online machine learning, controlled vocabularies, and a combination of these. The system delivers results with F1 scores greater than 95%. Conclusions IDEAL-X adopts a unique online machine learning–based approach combined with controlled vocabularies to support data extraction for clinical reports. The system can quickly learn and improve, thus it is highly adaptable. PMID:28487265

Biomechanics and Developmental Neuromotor Control.

ERIC Educational Resources Information Center

Zernicke, Ronald F.; Schneider, Klaus

1993-01-01

By applying the principles and methods of mechanics to the musculoskeletal system, new insights can be discovered about control of human limb dynamics in both adults and infants. Reviews previous research on how infants gain control of their limbs and learn to reach in the first year of life. (MDM)

Improving Aerospace Engineering Students' Achievements by an Open Aero Control Experiment Apparatus

ERIC Educational Resources Information Center

Zeng, QingHua; Zhang, WeiHua; Huang, ZheZhi; Dong, RongHua

2014-01-01

This paper describes the development of an aero control experiment apparatus (ACEA) for use in aerospace control practical courses. The ACEA incorporates a systematic multihierarchy learning and teaching method, and was designed to improve aerospace engineering students' understanding of unmanned aerial vehicle (UAV) control systems. It offers a…

Reward-Based Spatial Learning in Teens With Bulimia Nervosa

PubMed Central

Cyr, Marilyn; Wang, Zhishun; Tau, Gregory Z.; Zhao, Guihu; Friedl, Eve; Stefan, Mihaela; Terranova, Kate; Marsh, Rachel

2016-01-01

Objective To assess the functioning of mesolimbic and fronto-striatal areas involved in reward-based spatial learning in teenaged girls with bulimia nervosa (BN) that might be involved in the development and maintenance of maladaptive behaviors characteristic of the disorder. Method We compared functional magnetic resonance imaging blood oxygen level dependent response in 27 adolescent girls with BN to that of 27 healthy, age-matched control participants during a reward-based learning task that required learning to use extra-maze cues to navigate a virtual 8-arm radial maze to find hidden rewards. We compared groups in their patterns of brain activation associated with reward-based spatial learning versus a control condition in which rewards were unexpected because they were allotted pseudo-randomly to experimentally prevent learning. Results Both groups learned to navigate the maze to find hidden rewards, but group differences in brain activity associated with maze navigation and reward processing were detected in fronto-striatal regions and right anterior hippocampus. Unlike healthy adolescents, those with BN did not engage right inferior frontal gyrus during maze navigation, activated right anterior hippocampus during the receipt of unexpected rewards (control condition), and deactivated left superior frontal gyrus and right anterior hippocampus during expected reward receipt (learning condition). These patterns of hippocampal activation in the control condition were significantly associated with the frequency of binge-eating episodes. Conclusion Adolescents with BN displayed abnormal functioning of anterior hippocampus and fronto-striatal regions during reward-based spatial learning. These findings suggest that an imbalance in control and reward circuits may arise early in the course of BN. Clinical trial registration information An fMRI Study of Self-regulation in Adolescents With Bulimia Nervosa; https://clinicaltrials.gov/ct2/show/NCT00345943; NCT00345943. PMID:27806864

Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.

PubMed

Li, Lingli; Fan, Wenliang; Li, Jun; Li, Quanlin; Wang, Jin; Fan, Yang; Ye, Tianhe; Guo, Jialun; Li, Sen; Zhang, Youpeng; Cheng, Yongbiao; Tang, Yong; Zeng, Hanqing; Yang, Lian; Zhu, Zhaohui

2018-03-29

To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification. 45 VED patients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VED patients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VED patients from healthy controls. Compared to healthy control subjects, VED patients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VED patients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%. Cortical volume and white matter (WM) microstructural changes were observed in VED patients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VED patients and healthy control subjects, as shown by machine learning analyses. • Multimodal magnetic resonance imaging helps clinicians to assess patients with VED. • VED patients show cerebral structural alterations related to their clinical symptoms. • Machine learning analyses discriminated VED patients from controls with an excellent performance. • Machine learning classification provided a preliminary demonstration of DTI's clinical use.

Hexacopter trajectory control using a neural network

NASA Astrophysics Data System (ADS)

Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

2013-10-01

The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

Pathological gamblers are more vulnerable to the illusion of control in a standard associative learning task

PubMed Central

Orgaz, Cristina; Estévez, Ana; Matute, Helena

2013-01-01

An illusion of control is said to occur when a person believes that he or she controls an outcome that is uncontrollable. Pathological gambling has often been related to an illusion of control, but the assessment of the illusion has generally used introspective methods in domain-specific (i.e., gambling) situations. The illusion of control of pathological gamblers, however, could be a more general problem, affecting other aspects of their daily life. Thus, we tested them using a standard associative learning task which is known to produce illusions of control in most people under certain conditions. The results showed that the illusion was significantly stronger in pathological gamblers than in a control undiagnosed sample. This suggests (1) that the experimental tasks used in basic associative learning research could be used to detect illusions of control in gamblers in a more indirect way, as compared to introspective and domain-specific questionnaires; and (2), that in addition to gambling-specific problems, pathological gamblers may have a higher-than-normal illusion of control in their daily life. PMID:23785340

Body painting to promote self-active learning of hand anatomy for preclinical medical students.

PubMed

Jariyapong, Pitchanee; Punsawad, Chuchard; Bunratsami, Suchirat; Kongthong, Paranyu

2016-01-01

Background The purpose of this study was to use the body painting method to teach hand anatomy to a group of preclinical medical students. Methods Students reviewed hand anatomy using the traditional method and body painting exercise. Feedback and retention of the anatomy-related information were examined by a questionnaire and multiple-choice questions, respectively, immediately and 1 month after the painting exercise. Results Students agreed that the exercise was advantageous and helped facilitate self-active learning after in-class anatomy lessons. While there was no significant difference in knowledge retention between the control and experimental groups, the students appreciated the exercise in which they applied body paint to the human body to learn anatomy. Conclusion The body painting was an efficient tool for aiding the interactive learning of medical students and increasing the understanding of gross anatomy.

Memory consolidation in aging and MCI after 1 week

PubMed Central

Walsh, Christine M; Wilkins, Sarah; Bettcher, Brianne Magouirk; Butler, Christopher R; Miller, Bruce L; Kramer, Joel H

2014-01-01

Objective To assess consolidation in amnestic mild cognitive (aMCI) impairment, controlling for differences in initial learning and using a protracted delay period for recall. Methods Fifteen individuals with MCI were compared to fifteen healthy older adult controls on a story learning task. Subjects were trained to criteria to equalize initial learning across subjects. Recall was tested at both the 30-minute typically used delay and a 1-week delay used to target consolidation. Results Using repeated measures ANOVAs adjusted for age, we found group × time point interactions across the entire task between the final trial and 30-minute delay, and again between the 30-minute and 1-week delay periods, with MCI having greater declines in recall as compared to controls. Significant group main effects were also found, with MCI recalling less than controls. Conclusion Consolidation was impaired in aMCI as compared to controls. Our findings indicate that MCI-related performance typically measured at 30 minutes underestimates MCI-associated memory deficits. This is the first study to isolate consolidation by controlling for initial learning differences and using a protracted delay period to target consolidation in an MCI sample. PMID:24219610

Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints.

PubMed

Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai

2015-07-01

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

Overlay improvements using a real time machine learning algorithm

NASA Astrophysics Data System (ADS)

Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

2014-04-01

While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

Fast calculation of the `ILC norm' in iterative learning control

NASA Astrophysics Data System (ADS)

Rice, Justin K.; van Wingerden, Jan-Willem

2013-06-01

In this paper, we discuss and demonstrate a method for the exploitation of matrix structure in computations for iterative learning control (ILC). In Barton, Bristow, and Alleyne [International Journal of Control, 83(2), 1-8 (2010)], a special insight into the structure of the lifted convolution matrices involved in ILC is used along with a modified Lanczos method to achieve very fast computational bounds on the learning convergence, by calculating the 'ILC norm' in ? computational complexity. In this paper, we show how their method is equivalent to a special instance of the sequentially semi-separable (SSS) matrix arithmetic, and thus can be extended to many other computations in ILC, and specialised in some cases to even faster methods. Our SSS-based methodology will be demonstrated on two examples: a linear time-varying example resulting in the same ? complexity as in Barton et al., and a linear time-invariant example where our approach reduces the computational complexity to ?, thus decreasing the computation time, for an example, from the literature by a factor of almost 100. This improvement is achieved by transforming the norm computation via a linear matrix inequality into a check of positive definiteness - which allows us to further exploit the almost-Toeplitz properties of the matrix, and additionally provides explicit upper and lower bounds on the norm of the matrix, instead of the indirect Ritz estimate. These methods are now implemented in a MATLAB toolbox, freely available on the Internet.

Realization of Comfortable Massage by Using Iterative Learning Control Based on EEG

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Recently the massage chair is used by a lot of people because they are able to use it easily at home. However a present massage chair only realizes the massage motion. Moreover the massage chair can not consider the user’s condition and massage force. On the other hand, the professional masseur is according to presume the mental condition by patient’s reaction. Then this paper proposes the method of applying masseur’s procedure for the massage chair using iterative learning control based on EEG. And massage force is estimated by acceleration sensor. The realizability of the proposed method is verified by the experimental works using the massage chair.

Effectiveness of creative and productive instructional method towards students' learning achievement in steel structure course

NASA Astrophysics Data System (ADS)

Sugiyanto, Pribadi, Supriyanto, Bambang

2017-09-01

The purpose of this study was to investigate the effectiveness of Creative & Productive instructional method compared with conventional method. This research was a quasi-experimental study involving all Civil Engineering students at Universitas Negeri Malang who were taking a course of Steel Structure. The students were randomly assigned to two different treatment groups, 30 students in experimental group and 37 students in the control group. It was assumed that these groups were equal in all relevant aspects; they differed only in the treatment administered. We used the t-test to test the hypothesis. The results of this research suggest that: (l) the use of Creative & Productive instructional method can significantly improve students' learning achievement, (2) the use of Creative & Productive instructional method can significantly improve students' retention, (3) students' motivation has a significant effect on their learning achievement, and (4) students' motivation has a significant effect on their retention.

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Ricketts, Daniel; Kotaru, Raj; Thomas, Robert; Noga, Donald F. (Technical Monitor); Kankam, Mark D. (Technical Monitor)

2000-01-01

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Applying team-based learning of diagnostics for undergraduate students: assessing teaching effectiveness by a randomized controlled trial study.

PubMed

Zeng, Rui; Xiang, Lian-Rui; Zeng, Jing; Zuo, Chuan

2017-01-01

We aimed to introduce team-based learning (TBL) as one of the teaching methods for diagnostics and to compare its teaching effectiveness with that of the traditional teaching methods. We conducted a randomized controlled trial on diagnostics teaching involving 111 third-year medical undergraduates, using TBL as the experimental intervention, compared with lecture-based learning as the control, for teaching the two topics of symptomatology. Individual Readiness Assurance Test (IRAT)-baseline and Group Readiness Assurance Test (GRAT) were performed in members of each TBL subgroup. The scores in Individual Terminal Test 1 (ITT1) immediately after class and Individual Terminal Test 2 (ITT2) 1 week later were compared between the two groups. The questionnaire and interview were also implemented to survey the attitude of students and teachers toward TBL. There was no significant difference between the two groups in ITT1 (19.85±4.20 vs 19.70±4.61), while the score of the TBL group was significantly higher than that of the control group in ITT2 (19.15±3.93 vs 17.46±4.65). In the TBL group, the scores of the two terminal tests after the teaching intervention were significantly higher than the baseline test score of individuals. IRAT-baseline, ITT1, and ITT2 scores of students at different academic levels in the TBL teaching exhibited significant differences, but the ITT1-IRAT-baseline and ITT2-IRAT-baseline indicated no significant differences among the three subgroups. Our TBL in symptomatology approach was highly accepted by students in the improvement of interest and self-directed learning and resulted in an increase in knowledge acquirements, which significantly improved short-term test scores compared with lecture-based learning. TBL is regarded as an effective teaching method worthy of promoting.

Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

Cogging effect minimization in PMSM position servo system using dual high-order periodic adaptive learning compensation.

PubMed

Luo, Ying; Chen, Yangquan; Pi, Youguo

2010-10-01

Cogging effect which can be treated as a type of position-dependent periodic disturbance, is a serious disadvantage of the permanent magnetic synchronous motor (PMSM). In this paper, based on a simulation system model of PMSM position servo control, the cogging force, viscous friction, and applied load in the real PMSM control system are considered and presented. A dual high-order periodic adaptive learning compensation (DHO-PALC) method is proposed to minimize the cogging effect on the PMSM position and velocity servo system. In this DHO-PALC scheme, more than one previous periods stored information of both the composite tracking error and the estimate of the cogging force is used for the control law updating. Asymptotical stability proof with the proposed DHO-PALC scheme is presented. Simulation is implemented on the PMSM servo system model to illustrate the proposed method. When the constant speed reference is applied, the DHO-PALC can achieve a faster learning convergence speed than the first-order periodic adaptive learning compensation (FO-PALC). Moreover, when the designed reference signal changes periodically, the proposed DHO-PALC can obtain not only faster convergence speed, but also much smaller final error bound than the FO-PALC. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Teaching Parents About Responsive Feeding Through a Vicarious Learning Video: A Pilot Randomized Controlled Trial.

PubMed

Ledoux, Tracey; Robinson, Jessica; Baranowski, Tom; O'Connor, Daniel P

2018-04-01

The American Academy of Pediatrics and World Health Organization recommend responsive feeding (RF) to promote healthy eating behaviors in early childhood. This project developed and tested a vicarious learning video to teach parents RF practices. A RF vicarious learning video was developed using community-based participatory research methods. Fifty parents of preschoolers were randomly assigned to watch Happier Meals or a control video about education. Knowledge and beliefs about RF practices were measured 1 week before and immediately after intervention. Experimental group participants also completed measures of narrative engagement and video acceptability. Seventy-four percent of the sample was White, 90% had at least a college degree, 96% were married, and 88% made >$50,000/year. RF knowledge increased ( p = .03) and positive beliefs about some unresponsive feeding practices decreased ( ps < .05) more among experimental than control parents. Knowledge and belief changes were associated with video engagement ( ps < .05). Parents perceived Happier Meals as highly relevant, applicable, and informative. Community-based participatory research methods were instrumental in developing this vicarious learning video, with preliminary evidence of effectiveness in teaching parents about RF. Happier Meals is freely available for parents or community health workers to use when working with families to promote healthy eating behaviors in early childhood.

Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

PubMed

Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

2014-07-01

In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

List Memory in Young Adults with Language Learning Disability

ERIC Educational Resources Information Center

Sheng, Li; Byrd, Courtney T.; McGregor, Karla K.; Zimmerman, Hannah; Bludau, Kadee

2015-01-01

Purpose: The purpose of this study was to characterize the verbal memory limitations of young adults with language learning disability (LLD). Method: Sixteen young adults with LLD and 34 age- and education-matched controls with typical language participated in a Deese-Roediger-McDermott (DRM; Deese, 1959; Roediger & McDermott, 1995) list…

Advancing Virtual Patient Simulations through Design Research and InterPLAY: Part I--Design and Development

ERIC Educational Resources Information Center

Hirumi, Atsusi; Kleinsmith, Andrea; Johnsen, Kyle; Kubovec, Stacey; Eakins, Michael; Bogert, Kenneth; Rivera-Gutierrez, Diego J.; Reyes, Ramsamooj Javier; Lok, Benjamin; Cendan, Juan

2016-01-01

Systematic reviews and meta-analyses of randomized controlled studies conclude that virtual patient simulations are consistently associated with higher learning outcomes compared to other educational methods. However, we cannot assume that students will learn from simply exposing students to the simulations. The instructional features that are…

Problem-Based Learning and High School Macroeconomics: A Comparative Study of Instructional Methods

ERIC Educational Resources Information Center

Maxwell, Nan L.; Mergendoller, John R.; Bellisimo, Yolanda

2005-01-01

The authors examined the potential differences between problem-based learning (PBL) and traditional instructional approaches in building knowledge of macroeconomic concepts and principles in high school students. Using data from 252 economics students at 11 high schools and controlling for individual characteristics, most notably verbal ability,…

The Effect of Project Based Learning on Seventh Grade Students' Academic Achievement

ERIC Educational Resources Information Center

Kizkapan, Oktay; Bektas, Oktay

2017-01-01

The purpose of this study is to investigate whether there is a significant effect of project based learning approach on seventh grade students' academic achievement in the structure and properties of matter. In the study, according to the characteristics of quantitative research methods, pretest-posttest control group quasi-experimental design was…

Effects of Experiential-Based Videos in Multi-Disciplinary Learning

ERIC Educational Resources Information Center

Jabbar, Khalid Bin Abdul; Ong, Alex; Choy, Jeanette; Lim, Lisa

2013-01-01

This study examined the use of authentic experiential-based videos in self-explanation activities on 32 polytechnic students' learning and motivation, using a mixed method quasi-experimental design. The control group analysed a set of six pre-recorded videos of a subject performing the standing broad jump (SBJ). The experimental group captured…

Learned Resourcefulness and the Long-Term Benefits of a Chronic Pain Management Program

ERIC Educational Resources Information Center

Kennett, Deborah J.; O'Hagan, Fergal T.; Cezer, Diego

2008-01-01

A concurrent mixed methods approach was used to understand how learned resourcefulness empowers individuals. After completing Rosenbaum's Self-Control Schedule (SCS) measuring resourcefulness, 16 past clients of a multimodal pain clinic were interviewed about the kinds of pain-coping strategies they were practicing from the program. Constant…

Problem-Based Learning in Secondary Education: Evaluation by an Experiment

ERIC Educational Resources Information Center

De Witte, Kristof; Rogge, Nicky

2016-01-01

The effectiveness of problem-based learning (PBL) in terms of increasing students' educational attainments has been extensively studied for higher education students and in nonexperimental settings. This paper tests the effectiveness of PBL as an alternative instruction method in secondary education. In a controlled experiment at the class level,…

The Enhancement of Students' Teacher Mathematical Reasoning Ability through Reflective Learning

ERIC Educational Resources Information Center

Rohana

2015-01-01

This study aims to examine the enhancement of mathematical reasoning ability through reflective learning. This study used quasi-experimental method with nonequivalent pretest and posttest control group design. The subject of this study were students of Mathematics Education Program in one of private universities in Palembang, South Sumatera,…

Showercap Mindmap: A Spatial Activity for Learning Physiology Terminology and Location

ERIC Educational Resources Information Center

Vanags, Thea; Budimlic, Mira; Herbert, Elissa; Montgomery, Melena M.; Vickers, Tracy

2012-01-01

Students struggle with the volume and complexity of physiology terminology. We compared first-year undergraduate psychology students' learning of physiological terms using two teaching methods: one verbal (control group; n = 16) and one spatial and multisensory (experimental group; n = 19). The experimental group used clear plastic shower caps to…

Implementation of Cooperative Learning Model in Preschool

ERIC Educational Resources Information Center

Akçay, Nilüfer Okur

2016-01-01

In this study, the effectivity of jigsaw method, one of the cooperative learning models, on teaching the concepts related to sense organs and their functions to four-five year-old children in nursery class was analyzed. The study is in the semi-experimental design consisting of experimental and control groups and pretest and posttest. The sample…

Sources of Strength: Women and Culture. A Teacher's Guide.

ERIC Educational Resources Information Center

Hunter, Lisa K.; And Others

The document presents teaching methods, content, and learning activities for units in multicultural women's studies for secondary students. The major objective is to help students answer the question, "How much control can a person exercise over her/his own life?" Students learn about the ways women have lived their lives and perceived…

Auditory Learning Using a Portable Real-Time Vocoder: Preliminary Findings

ERIC Educational Resources Information Center

Casserly, Elizabeth D.; Pisoni, David B.

2015-01-01

Purpose: Although traditional study of auditory training has been in controlled laboratory settings, interest has been increasing in more interactive options. The authors examine whether such interactive training can result in short-term perceptual learning, and the range of perceptual skills it impacts. Method: Experiments 1 (N = 37) and 2 (N =…

Effects of Toy Crane Design-Based Learning on Simple Machines

ERIC Educational Resources Information Center

Korur, Fikret; Efe, Gülfem; Erdogan, Fisun; Tunç, Berna

2017-01-01

The aim of this 2-group study was to investigate the following question: Are there significant differences between scaffolded design-based learning controlled using 7 forms and teacher-directed instruction methods for the toy crane project on grade 7 students' posttest scores on the simple machines achievement test, attitude toward simple…

Authoring Tools and Methods for Adaptive Training and Education in Support of the US Army Learning Model: Research Outline

DTIC Science & Technology

2015-10-01

higher effect sizes than others when comparing any intervention (e.g., computer trainers, human tutors, group learning) to a control . It is difficult... control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) October 2015 2. REPORT TYPE Special Report 3...ABSTRACT While human tutoring and mentoring are common teaching tools, current US Army standards for training and education are group instruction and

Leading an IT Organization Out of Control

ERIC Educational Resources Information Center

Jackson, Gregory A.

2011-01-01

With the era of control ending for campus IT organizations, leaders need to learn to use some known management approaches and methods in radically different ways. In this article, the author begins with some examples of how technology change, organizational change, and contextual change are eroding centralized control over campus information…

A Control Systems Concept Inventory Test Design and Assessment

ERIC Educational Resources Information Center

Bristow, M.; Erkorkmaz, K.; Huissoon, J. P.; Jeon, Soo; Owen, W. S.; Waslander, S. L.; Stubley, G. D.

2012-01-01

Any meaningful initiative to improve the teaching and learning in introductory control systems courses needs a clear test of student conceptual understanding to determine the effectiveness of proposed methods and activities. The authors propose a control systems concept inventory. Development of the inventory was collaborative and iterative. The…

Accelerating Imitation Learning in Relational Domains via Transfer by Initialization

DTIC Science & Technology

2013-08-28

Warcraft , regulation of traffic lights, logistics, and a variety of planning domains. A supervised learning method for imitation learning was recently...some information about the world (traffic density at a signal, distance to the goal etc.). We assume a functional parametrization over the policy and...strategy (RTS) game engine written in C based off the Warcraft series of games. Like all RTS games, it allows multiple agents to be controlled

A Fully Automated Drosophila Olfactory Classical Conditioning and Testing System for Behavioral Learning and Memory Assessment

PubMed Central

Jiang, Hui; Hanna, Eriny; Gatto, Cheryl L.; Page, Terry L.; Bhuva, Bharat; Broadie, Kendal

2016-01-01

Background Aversive olfactory classical conditioning has been the standard method to assess Drosophila learning and memory behavior for decades, yet training and testing are conducted manually under exceedingly labor-intensive conditions. To overcome this severe limitation, a fully automated, inexpensive system has been developed, which allows accurate and efficient Pavlovian associative learning/memory analyses for high-throughput pharmacological and genetic studies. New Method The automated system employs a linear actuator coupled to an odorant T-maze with airflow-mediated transfer of animals between training and testing stages. Odorant, airflow and electrical shock delivery are automatically administered and monitored during training trials. Control software allows operator-input variables to define parameters of Drosophila learning, short-term memory and long-term memory assays. Results The approach allows accurate learning/memory determinations with operational fail-safes. Automated learning indices (immediately post-training) and memory indices (after 24 hours) are comparable to traditional manual experiments, while minimizing experimenter involvement. Comparison with Existing Methods The automated system provides vast improvements over labor-intensive manual approaches with no experimenter involvement required during either training or testing phases. It provides quality control tracking of airflow rates, odorant delivery and electrical shock treatments, and an expanded platform for high-throughput studies of combinational drug tests and genetic screens. The design uses inexpensive hardware and software for a total cost of ~$500US, making it affordable to a wide range of investigators. Conclusions This study demonstrates the design, construction and testing of a fully automated Drosophila olfactory classical association apparatus to provide low-labor, high-fidelity, quality-monitored, high-throughput and inexpensive learning and memory behavioral assays. PMID:26703418

The Use of Conceptual Change Text toward Students’ Argumentation Skills in Learning Sound

NASA Astrophysics Data System (ADS)

Sari, B. P.; Feranie, S.; Winarno, N.

2017-09-01

This research aim is to investigate the effect of Conceptual Change Text toward students’ argumentation skills in learning sound concept. The participant comes from one of International school in Bandung, Indonesia. The method that used in this research is a quasi-experimental design with one control group (N=21) and one experimental group (N=21) were involves in this research. The learning model that used in both classes is demonstration model which included teacher explanation and examples, the difference only in teaching materials. In experiment group learn with Conceptual Change Text, while control group learn with conventional book which is used in school. The results showed that Conceptual Change Text instruction was better than the conventional book to improved students’ argumentation skills of sound concept. Based on this results showed that Conceptual Change Text instruction can be an alternative tool to improve students’ argumentation skills significantly.

Plastic Surgery Inclusion in the Undergraduate Medical Curriculum: Perception, Challenges, and Career Choice—A Comparative Study

PubMed Central

Vaughan, R.; Thomas, S.

2017-01-01

Objective The undergraduate medical curriculum has been overcrowded with core learning outcomes with no formal exposure to plastic surgery. The aim of this study was to compare medical students from two educational settings for the basic understanding, preferred learning method, and factors influencing a career choice in plastic surgery. Design and Setting A prospective cohort study based on a web-based anonymous questionnaire sent to final year medical students at Birmingham University (United Kingdom), McGill University (Canada), and a control group (non-medical staff). The questions were about plastic surgery: (1) source of information and basic understanding; (2) undergraduate curriculum inclusion and preferred learning methods; (3) factors influencing a career choice. A similar questionnaire was sent to non-medical staff (control group). The data was analysed based on categorical outcomes (Chi-square χ2) and level of significance p ≤ 0.05. Results Questionnaire was analysed for 243 students (Birmingham, n = 171/332, 52%) (McGill n = 72/132, 54%). Birmingham students (14%) considered the word “plastic” synonymous with “cosmetic” more than McGill students (4%, p < 0.025). Teaching was the main source of knowledge for McGill students (39%, p < 0.001) while Birmingham students and control group chose the media (70%, p < 0.001). McGill students (67%) more than Birmingham (49%, p < 0.010) considered curriculum inclusion. The preferred learning method was lectures for McGill students (61%, p < 0.01) but an optional module for Birmingham (61%). A similar proportion (18%) from both student groups considered a career in plastic surgery. Conclusions Medical students recognised the need for plastic surgery inclusion in the undergraduate curriculum. There was a difference for plastic surgery source of information, operations, and preferred method of learning for students. The study highlighted the urgent need to reform plastic surgery undergraduate teaching in collaboration with national educational bodies worldwide. PMID:28630768

The Final Count Down: A Review of Three Decades of Flight Controller Training Methods for Space Shuttle Mission Operations

NASA Technical Reports Server (NTRS)

Dittermore, Gary; Bertels, Christie

2011-01-01

Operations of human spaceflight systems is extremely complex; therefore, the training and certification of operations personnel is a critical piece of ensuring mission success. Mission Control Center (MCC-H), at the Lyndon B. Johnson Space Center in Houston, Texas, manages mission operations for the Space Shuttle Program, including the training and certification of the astronauts and flight control teams. An overview of a flight control team s makeup and responsibilities during a flight, and details on how those teams are trained and certified, reveals that while the training methodology for developing flight controllers has evolved significantly over the last thirty years the core goals and competencies have remained the same. In addition, the facilities and tools used in the control center have evolved. Changes in methodology and tools have been driven by many factors, including lessons learned, technology, shuttle accidents, shifts in risk posture, and generational differences. Flight controllers share their experiences in training and operating the space shuttle. The primary training method throughout the program has been mission simulations of the orbit, ascent, and entry phases, to truly train like you fly. A review of lessons learned from flight controller training suggests how they could be applied to future human spaceflight endeavors, including missions to the moon or to Mars. The lessons learned from operating the space shuttle for over thirty years will help the space industry build the next human transport space vehicle.

Combining metric episodes with semantic event concepts within the Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS)

NASA Astrophysics Data System (ADS)

Kelley, Troy D.; McGhee, S.

2013-05-01

This paper describes the ongoing development of a robotic control architecture that inspired by computational cognitive architectures from the discipline of cognitive psychology. The Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS) combines symbolic and sub-symbolic representations of knowledge into a unified control architecture. The new architecture leverages previous work in cognitive architectures, specifically the development of the Adaptive Character of Thought-Rational (ACT-R) and Soar. This paper details current work on learning from episodes or events. The use of episodic memory as a learning mechanism has, until recently, been largely ignored by computational cognitive architectures. This paper details work on metric level episodic memory streams and methods for translating episodes into abstract schemas. The presentation will include research on learning through novelty and self generated feedback mechanisms for autonomous systems.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

PubMed

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

Measuring the surgical 'learning curve': methods, variables and competency.

PubMed

Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

2014-03-01

To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

REVIEW: Internal models in sensorimotor integration: perspectives from adaptive control theory

NASA Astrophysics Data System (ADS)

Tin, Chung; Poon, Chi-Sang

2005-09-01

Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

Machine Learning Methods for Production Cases Analysis

NASA Astrophysics Data System (ADS)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

Assessing the Impact of Voice-Over Screen-Captured Presentations Delivered Online on Dental Students' Learning.

PubMed

Schönwetter, Dieter J; Gareau-Wilson, Nicole; Cunha, Rodrigo Sanches; Mello, Isabel

2016-02-01

The traditional lecturing method is still one of the most common forms of delivering content to students in dental education, but innovative learning technologies have the potential to improve the effectiveness and quality of teaching dental students. What challenges instructors is the extent to which these learning tools have a direct impact on student learning outcomes. The aim of this study was to assess the impact of a voice-over screen-captured learning tool by identifying a positive, nil, or negative impact on student learning as well as student engagement (affective, behavioral, and cognitive) when compared to the traditional face-to-face lecture. Extraneous variables thought to impact student learning were controlled by the use of baseline measures as well as random assignment of second-year dental students to one of two teaching conditions: voice-over screen-captured presentation delivered online and the traditional classroom lecture. A total of 28 students enrolled in the preclinical course in endodontics at a Canadian dental school participated in the study, 14 in each of the two teaching conditions. The results showed that, in most cases, the students who experienced the online lecture had somewhat higher posttest scores and perceived satisfaction levels than those in the face-to-face lecture group, but the differences did not achieve statistical significance except for their long-term recognition test scores. This study found that the students had comparable learning outcomes whether they experienced the face-to-face or the online lecture, but that the online lecture had a more positive impact on their long-term learning. The controls for extraneous variables used in this study suggest ways to improve research into the comparative impact of traditional and innovative teaching methods on student learning outcomes.

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

PubMed

Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela

2018-01-01

Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short- and long-term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short-term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype. Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

Neuroprosthetic Decoder Training as Imitation Learning

PubMed Central

Merel, Josh; Paninski, Liam; Cunningham, John P.

2016-01-01

Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user’s intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user’s intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector. PMID:27191387

Effect of Play-based Therapy on Meta-cognitive and Behavioral Aspects of Executive Function: A Randomized, Controlled, Clinical Trial on the Students With Learning Disabilities.

PubMed

Karamali Esmaili, Samaneh; Shafaroodi, Narges; Hassani Mehraban, Afsoon; Parand, Akram; Zarei, Masoume; Akbari-Zardkhaneh, Saeed

2017-01-01

Although the effect of educational methods on executive function (EF) is well known, training this function by a playful method is debatable. The current study aimed at investigating if a play-based intervention is effective on metacognitive and behavioral skills of EF in students with specific learning disabilities. In the current randomized, clinical trial, 49 subjects within the age range of 7 to 11 years with specific learning disabilities were randomly assigned into the intervention (25 subjects; mean age 8.5±1.33 years) and control (24 subjects; mean age 8.7±1.03 years) groups. Subjects in the intervention group received EF group training based on playing activities; subjects in the control group received no intervention. The behavior rating inventory of executive function (BRIEF) was administered to evaluate the behavioral and cognitive aspects of EF. The duration of the intervention was 6 hours per week for 9 weeks. Multivariate analysis of covariance was used to compare mean changes (before and after) in the BRIEF scores between the groups. The assumptions of multivariate analysis of covariance were examined. After controlling pre-test conditions, the intervention and control groups scored significantly differently on both the metacognition (P=0.002; effect size=0.20) and behavior regulation indices (P=0.01; effect size=0.12) of BRIEF. Play-based therapy is effective on the metacognitive and behavioral aspects of EF in students with specific learning disabilities. Professionals can use play-based therapy rather than educational approaches in clinical practice to enhance EF skills.

Evolving a Behavioral Repertoire for a Walking Robot.

PubMed

Cully, A; Mouret, J-B

2016-01-01

Numerous algorithms have been proposed to allow legged robots to learn to walk. However, most of these algorithms are devised to learn walking in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which combines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of controllers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution introduced a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

The development of a primary dental care outreach course.

PubMed

Waterhouse, P; Maguire, A; Tabari, D; Hind, V; Lloyd, J

2008-02-01

The aim of this work was to develop the first north-east based primary dental care outreach (PDCO) course for clinical dental undergraduate students at Newcastle University. The process of course design will be described and involved review of the existing Bachelor of Dental Surgery (BDS) degree course in relation to previously published learning outcomes. Areas were identified where the existing BDS course did not meet fully these outcomes. This was followed by setting the PDCO course aims and objectives, intended learning outcomes, curriculum and structure. The educational strategy and methods of teaching and learning were subsequently developed together with a strategy for overall quality control of the teaching and learning experience. The newly developed curriculum was aligned with appropriate student assessment methods, including summative, formative and ipsative elements.

A Policy Representation Using Weighted Multiple Normal Distribution

NASA Astrophysics Data System (ADS)

Kimura, Hajime; Aramaki, Takeshi; Kobayashi, Shigenobu

In this paper, we challenge to solve a reinforcement learning problem for a 5-linked ring robot within a real-time so that the real-robot can stand up to the trial and error. On this robot, incomplete perception problems are caused from noisy sensors and cheap position-control motor systems. This incomplete perception also causes varying optimum actions with the progress of the learning. To cope with this problem, we adopt an actor-critic method, and we propose a new hierarchical policy representation scheme, that consists of discrete action selection on the top level and continuous action selection on the low level of the hierarchy. The proposed hierarchical scheme accelerates learning on continuous action space, and it can pursue the optimum actions varying with the progress of learning on our robotics problem. This paper compares and discusses several learning algorithms through simulations, and demonstrates the proposed method showing application for the real robot.

REWARD/PUNISHMENT REVERSAL LEARNING IN OLDER SUICIDE ATTEMPTERS

PubMed Central

Dombrovski, Alexandre Y.; Clark, Luke; Siegle, Greg J.; Butters, Meryl A.; Ichikawa, Naho; Sahakian, Barbara; Szanto, Katalin

2011-01-01

Objective Suicide rates are very high in old age, and the contribution of cognitive risk factors remains poorly understood. Suicide may be viewed as an outcome of an altered decision process. We hypothesized that impairment in a component of affective decision-making – reward/punishment-based learning – is associated with attempted suicide in late-life depression. We expected that suicide attempters would discount past reward/punishment history, focusing excessively on the most recent rewards and punishments. Further, we hypothesized that this impairment could be dissociated from executive abilities such as forward planning. Method We assessed reward/punishment-based learning using the Probabilistic Reversal Learning task in 65 individuals aged 60 and older: suicide attempters, suicide ideators, non-suicidal depressed elderly, and non-depressed controls. We used a reinforcement learning computational model to decompose reward/punishment processing over time. The Stockings of Cambridge test served as a control measure of executive function. Results Suicide attempters but not suicide ideators showed impaired probabilistic reversal learning compared to both non-suicidal depressed elderly and to non-depressed controls, after controlling for effects of education, global cognitive function, and substance use. Model-based analyses revealed that suicide attempters discounted previous history to a higher degree, compared to controls, basing their choice largely on reward/punishment received on the last trial. Groups did not differ in their performance on the Stockings of Cambridge. Conclusions Older suicide attempters display impaired reward/punishment-based learning. We propose a hypothesis that older suicide attempters make overly present-focused decisions, ignoring past experiences. Modification of this ‘myopia for the past’ may have therapeutic potential. PMID:20231320

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

PubMed Central

Sajid, Muhammad R.; Abothenain, Fayha; Salam, Yezan; AlJayar, Dina; Obeidat, Akef

2016-01-01

Objectives To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. Methods This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. Results A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Conclusions Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.  PMID:27591930

Internet-Based Assessment of Oncology Health Care Professional Learning Style and Optimization of Materials for Web-Based Learning: Controlled Trial With Concealed Allocation

PubMed Central

Micheel, Christine M; Anderson, Ingrid A; Lee, Patricia; Chen, Sheau-Chiann; Justiss, Katy; Giuse, Nunzia B; Ye, Fei; Kusnoor, Sheila V

2017-01-01

Background Precision medicine has resulted in increasing complexity in the treatment of cancer. Web-based educational materials can help address the needs of oncology health care professionals seeking to understand up-to-date treatment strategies. Objective This study aimed to assess learning styles of oncology health care professionals and to determine whether learning style-tailored educational materials lead to enhanced learning. Methods In all, 21,465 oncology health care professionals were invited by email to participate in the fully automated, parallel group study. Enrollment and follow-up occurred between July 13 and September 7, 2015. Self-enrolled participants took a learning style survey and were assigned to the intervention or control arm using concealed alternating allocation. Participants in the intervention group viewed educational materials consistent with their preferences for learning (reading, listening, and/or watching); participants in the control group viewed educational materials typical of the My Cancer Genome website. Educational materials covered the topic of treatment of metastatic estrogen receptor-positive (ER+) breast cancer using cyclin-dependent kinases 4/6 (CDK4/6) inhibitors. Participant knowledge was assessed immediately before (pretest), immediately after (posttest), and 2 weeks after (follow-up test) review of the educational materials. Study statisticians were blinded to group assignment. Results A total of 751 participants enrolled in the study. Of these, 367 (48.9%) were allocated to the intervention arm and 384 (51.1%) were allocated to the control arm. Of those allocated to the intervention arm, 256 (69.8%) completed all assessments. Of those allocated to the control arm, 296 (77.1%) completed all assessments. An additional 12 participants were deemed ineligible and one withdrew. Of the 552 participants, 438 (79.3%) self-identified as multimodal learners. The intervention arm showed greater improvement in posttest score compared to the control group (0.4 points or 4.0% more improvement on average; P=.004) and a higher follow-up test score than the control group (0.3 points or 3.3% more improvement on average; P=.02). Conclusions Although the study demonstrated more learning with learning style-tailored educational materials, the magnitude of increased learning and the largely multimodal learning styles preferred by the study participants lead us to conclude that future content-creation efforts should focus on multimodal educational materials rather than learning style-tailored content. PMID:28743680

Spiral and Project-Based Learning with Peer Assessment in a Computer Science Project Management Course

NASA Astrophysics Data System (ADS)

Jaime, Arturo; Blanco, José Miguel; Domínguez, César; Sánchez, Ana; Heras, Jónathan; Usandizaga, Imanol

2016-06-01

Different learning methods such as project-based learning, spiral learning and peer assessment have been implemented in science disciplines with different outcomes. This paper presents a proposal for a project management course in the context of a computer science degree. Our proposal combines three well-known methods: project-based learning, spiral learning and peer assessment. Namely, the course is articulated during a semester through the structured (progressive and incremental) development of a sequence of four projects, whose duration, scope and difficulty of management increase as the student gains theoretical and instrumental knowledge related to planning, monitoring and controlling projects. Moreover, the proposal is complemented using peer assessment. The proposal has already been implemented and validated for the last 3 years in two different universities. In the first year, project-based learning and spiral learning methods were combined. Such a combination was also employed in the other 2 years; but additionally, students had the opportunity to assess projects developed by university partners and by students of the other university. A total of 154 students have participated in the study. We obtain a gain in the quality of the subsequently projects derived from the spiral project-based learning. Moreover, this gain is significantly bigger when peer assessment is introduced. In addition, high-performance students take advantage of peer assessment from the first moment, whereas the improvement in poor-performance students is delayed.

Impact of guided reciprocal peer questioning on nursing students' self-esteem and learning.

PubMed

Lakdizaji, Sima; Abdollahzadeh, Farahnaz; Hassankhanih, Hadi; Kalantari, Manizhe

2013-07-01

Self-esteem is essential for clinical judgments. Nursing students in clinical environments should make a bridge between theoretical education and clinical function. This study was aimed to survey the effect of guided questioning in peer groups on nursing students' self-esteem and clinical learning. In this quasi-experimental study, all nursing students in semester 4 (60) were selected. The autumn semester students (n = 28) were chosen as the control group, and the spring semester students (n = 32) as the experimental group. The experimental group underwent the course of cardiac medical surgical training by the Guided Reciprocal Peer Questioning. The control group was trained by lecture. After confirmation of the validity and reliability of tools including Rosenberg Self-esteem Scale and the researcher-made questionnaire, data were collected and analyzed by SPSS version 17.0. There was no significant difference concerning demographic and educational characteristics between the two groups. Mean score differences of self-esteem and learning were not significant before teaching, while they were significantly promoted after teaching in the experimental (P < 0.001) and control (P < 0.05) groups. Promotion in the experimental group was more considerable than in the control group. As revealed by the results, inquiry method, due to its more positive impact on self-esteem and students' learning, can be applied alone or in combination with the other methods. Conducting this study for other students and for theoretical courses is suggested.

Learning from demonstration: Teaching a myoelectric prosthesis with an intact limb via reinforcement learning.

PubMed

Vasan, Gautham; Pilarski, Patrick M

2017-07-01

Prosthetic arms should restore and extend the capabilities of someone with an amputation. They should move naturally and be able to perform elegant, coordinated movements that approximate those of a biological arm. Despite these objectives, the control of modern-day prostheses is often nonintuitive and taxing. Existing devices and control approaches do not yet give users the ability to effect highly synergistic movements during their daily-life control of a prosthetic device. As a step towards improving the control of prosthetic arms and hands, we introduce an intuitive approach to training a prosthetic control system that helps a user achieve hard-to-engineer control behaviours. Specifically, we present an actor-critic reinforcement learning method that for the first time promises to allow someone with an amputation to use their non-amputated arm to teach their prosthetic arm how to move through a wide range of coordinated motions and grasp patterns. We evaluate our method during the myoelectric control of a multi-joint robot arm by non-amputee users, and demonstrate that by using our approach a user can train their arm to perform simultaneous gestures and movements in all three degrees of freedom in the robot's hand and wrist based only on information sampled from the robot and the user's above-elbow myoelectric signals. Our results indicate that this learning-from-demonstration paradigm may be well suited to use by both patients and clinicians with minimal technical knowledge, as it allows a user to personalize the control of his or her prosthesis without having to know the underlying mechanics of the prosthetic limb. These preliminary results also suggest that our approach may extend in a straightforward way to next-generation prostheses with precise finger and wrist control, such that these devices may someday allow users to perform fluid and intuitive movements like playing the piano, catching a ball, and comfortably shaking hands.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

PubMed

Yoo, Youngjin; Tang, Lisa Y W; Brosch, Tom; Li, David K B; Kolind, Shannon; Vavasour, Irene; Rauscher, Alexander; MacKay, Alex L; Traboulsee, Anthony; Tam, Roger C

2018-01-01

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t -test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Comparing two methods of education (virtual versus traditional) on learning of Iranian dental students: a post-test only design study

PubMed Central

2014-01-01

Background The importance of using technologies such as e-learning in different disciplines is discussed in the literature. Researchers have measured the effectiveness of e-learning in a number of fields. Considering the lack of research on the effectiveness of online learning in dental education particularly in Iran, the advantages of these learning methods and the positive university atmosphere regarding the use of online learning. This study, therefore, aims to compare the effects of two methods of teaching (virtual versus traditional) on student learning. Methods This post-test only design study approached 40, fifth year dental students of Shiraz University of Medical Sciences. From this group, 35 students agreed to participate. These students were randomly allocated into two groups, experimental (virtual learning) and comparison (traditional learning). To ensure similarity between groups, we compared GPAs of all participants by the Mann–Whitney U test (P > 0.05). The experimental group received a virtual learning environment courseware package specifically designed for this study, whereas the control group received the same module structured in a traditional lecture form. The virtual learning environment consisted of online and offline materials. Two identical valid, reliable post-tests that consisted of 40 multiple choice questions (MCQs) and 4 essay questions were administered immediately (15 min) after the last session and two months later to assess for knowledge retention. Data were analyzed by SPSS version 20. Results A comparison of the mean knowledge score of both groups showed that virtual learning was more effective than traditional learning (effect size = 0.69). Conclusion The newly designed virtual learning package is feasible and will result in more effective learning in comparison with lecture-based training. However further studies are needed to generalize the findings of this study. PMID:24597923

Creative Digital Worksheet Base on Mobile Learning

NASA Astrophysics Data System (ADS)

Wibawa, S. C.; Cholifah, R.; Utami, A. W.; Nurhidayat, A. I.

2018-01-01

The student is required to understand and act in the classroom and it is very important for selecting the media learning to determine the learning outcome. An instructional media is needed to help students achieve the best learning outcome. The objectives of this study are (1) to make Android-based student worksheet, (2) to know the students’ response on Android-based student worksheet in multimedia subject, (3) to determine the student result using Android-based student worksheet. The method used was Research and Development (R&D) using post-test-only in controlled quasi-experimental group design. The subjects of the study were 2 classes, a control class and an experimental class. The results showed (1) Android-based student worksheet was categorized very good as percentage of 85%; (2) the students’ responses was categorized very good as percentage of 86.42%; (3) the experimental class results were better than control class. The average result on cognitive tests on the experimental class was 89.97 and on control class was 78.31; whether the average result on psychomotor test on the experimental class was 89.90 and on the control class was 79.83. In conclusion, student result using Android-based student worksheet was better than those without it.

Application of experiential learning model using simple physical kit to increase attitude toward physics student senior high school in fluid

NASA Astrophysics Data System (ADS)

Johari, A. H.; Muslim

2018-05-01

Experiential learning model using simple physics kit has been implemented to get a picture of improving attitude toward physics senior high school students on Fluid. This study aims to obtain a description of the increase attitudes toward physics senior high school students. The research method used was quasi experiment with non-equivalent pretest -posttest control group design. Two class of tenth grade were involved in this research 28, 26 students respectively experiment class and control class. Increased Attitude toward physics of senior high school students is calculated using an attitude scale consisting of 18 questions. Based on the experimental class test average of 86.5% with the criteria of almost all students there is an increase and in the control class of 53.75% with the criteria of half students. This result shows that the influence of experiential learning model using simple physics kit can improve attitude toward physics compared to experiential learning without using simple physics kit.

Representing high-dimensional data to intelligent prostheses and other wearable assistive robots: A first comparison of tile coding and selective Kanerva coding.

PubMed

Travnik, Jaden B; Pilarski, Patrick M

2017-07-01

Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches. Specifically, we examine the effect that high-dimensional sensory data has on the computation time and prediction performance of a true-online temporal-difference learning prediction method as embedded within a resource-limited upper-limb prosthesis control system. We present results comparing tile coding, the dominant linear representation for real-time prosthetic machine learning, with a newly proposed modification to Kanerva coding that we call selective Kanerva coding. In addition to showing promising results for selective Kanerva coding, our results confirm potential limitations to tile coding as the number of sensory input dimensions increases. To our knowledge, this study is the first to explicitly examine representations for realtime machine learning prosthetic devices in general terms. This work therefore provides an important step towards forming an efficient prosthesis-eye view of the world, wherein prompt and accurate representations of high-dimensional data may be provided to machine learning control systems within artificial limbs and other assistive rehabilitation technologies.

Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments

PubMed Central

Smith, Alex M. C.; Yang, Chenguang; Ma, Hongbin; Culverhouse, Phil; Cangelosi, Angelo; Burdet, Etienne

2015-01-01

In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing. PMID:26029916

Mixing Problem Based Learning and Conventional Teaching Methods in an Analog Electronics Course

ERIC Educational Resources Information Center

Podges, J. M.; Kommers, P. A. M.; Winnips, K.; van Joolingen, W. R.

2014-01-01

This study, undertaken at the Walter Sisulu University of Technology (WSU) in South Africa, describes how problem-based learning (PBL) affects the first year 'analog electronics course', when PBL and the lecturing mode is compared. Problems were designed to match real-life situations. Data between the experimental group and the control group that…

Addressing the Needs of Students with Learning Disabilities during Their Interaction with the Web

ERIC Educational Resources Information Center

Curcic, Svjetlana

2011-01-01

Purpose: The purpose of this study is to examine the effectiveness of instruction in information problem solving within the world wide web (the web) environment. The participants were 20 seventh and eighth grade students with a learning disability (LD) in reading. An experimental pretest-posttest control group method was used to investigate the…

Is It the Intervention or the Students? Using Linear Regression to Control for Student Characteristics in Undergraduate STEM Education Research

ERIC Educational Resources Information Center

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due…

Virtual Project Management: Examining the Roles and Functions of Online Instructors in Creating Learning Applications with Value

ERIC Educational Resources Information Center

Barrett, Bob

2012-01-01

While many students and instructors are transitioning from the brick-and-mortar classrooms to virtual classrooms, labs, and simulations, this requires a higher-level of expertise, control, and perseverance by the instructor. Traditional methods of teaching, leading, managing, and organizing learn activities has changed in terms of the virtual…

Learning Auditory Discrimination with Computer-Assisted Instruction: A Comparison of Two Different Performance Objectives.

ERIC Educational Resources Information Center

Steinhaus, Kurt A.

A 12-week study of two groups of 14 college freshmen music majors was conducted to determine which group demonstrated greater achievement in learning auditory discrimination using computer-assisted instruction (CAI). The method employed was a pre-/post-test experimental design using subjects randomly assigned to a control group or an experimental…

Preconditions for Post-Employment Learning: Preliminary Results from Ongoing Research

ERIC Educational Resources Information Center

Salter, Linda

2011-01-01

This article describes the first phase of a two-phase, mixed-method study. The study, now in progress, explores how and to what extent willingness to engage in learning in mature adulthood is influenced by prior experiences and specific individual personality variables, such as perceived locus of control and degree of self-efficacy. Study…

Health-Related Fitness Knowledge Development through Project-Based Learning

ERIC Educational Resources Information Center

Hastle, Peter A.; Chen, Senlin; Guarino, Anthony J.

2017-01-01

Purpose: The purpose of this study was to examine the process and outcome of an intervention using the project-based learning (PBL) model to increase students' health-related fitness (HRF) knowledge. Method: The participants were 185 fifth-grade students from three schools in Alabama (PBL group: n = 109; control group: n = 76). HRF knowledge was…

Academic Performance in ADHD when Controlled for Comorbid Learning Disorders, Family Income, and Parental Education in Brazil

ERIC Educational Resources Information Center

Carmine Pastura, Giuseppe Mario; Mattos, Paulo; Campos Araujo, Alexandra Prufer de Queiroz

2009-01-01

Objective: Scholastic achievement in a nonclinical sample of ADHD children and adolescents was evaluated taking into consideration variables such as comorbid learning disorders, family income, and parental education which may also be associated with poor academic performance. Method: After screening for ADHD in 396 students, the authors compared…

A Randomized Controlled Trial Validating the Impact of the LASER Model of Science Education on Student Achievement and Teacher Instruction

ERIC Educational Resources Information Center

Kaldon, Carolyn R.; Zoblotsky, Todd A.

2014-01-01

Previous research has linked inquiry-based science instruction (i.e., science instruction that engages students in doing science rather than just learning about science) with greater gains in student learning than text-book based methods (Vanosdall, Klentschy, Hedges & Weisbaum, 2007; Banilower, 2007; Ferguson 2009; Bredderman, 1983;…

The Effectiveness of Using Interactive Multimedia in Improving the Concept of Fashion Design and Its Application in The Making of Digital Fashion Design

NASA Astrophysics Data System (ADS)

Wiana, W.

2018-02-01

This research is related to the effort to design a more representative learning system to improve the learning result of digital fashion design, through the development of interactive multimedia based on motion graphic. This research is aimed to know the effect of interactive multimedia application based on motion graphic to increase the mastery of the concept and skill of the students to making fashion designing in digital format. The research method used is quasi experiment with research design of Non-equivalent Control Group Design. The lectures are conducted in two different classes, namely class A as the Experimental Class and class B as the Control Class. From the calculation result after interpreted using Normalize Gain, there is an increase of higher learning result in student with interactive learning based on motion graphic, compared with student achievement on conventional learning. In this research, interactive multimedia learning based on motion graphic is effective toward the improvement of student learning in concept mastering indicator and on the aspect of making fashion design in digital format.

Significances of Multimedia Technologies Training

NASA Astrophysics Data System (ADS)

Zhang, Fulei

The use of multimedia technologies in education has enabled teachers to simulate final outcomes and assist s-tudents in applying knowledge learned from textbooks, thereby compensating for the deficiency of traditional teach- ing methods. It is important to examine how effective these technologies are in practical use. This study developed online learning-teaching resource platforms using Flash multimedia, providing interactive and integrated features in an easy-to-use user interface, in order to discuss Computer-Aided Drawing (CAD). The study utilized a teaching experiment with a non-equivalent pretest-posttest control group design to test and discuss students' professional cognition, operating skill cognition, and level of learning satisfaction during the learning process. No significant differences emerged between the groups in regards to professional cognition or operation skills cognition. However, a significant difference in learning satisfaction was noted, indicating that the coursework with multimedia Flash produced greater satisfaction than with traditional learning methods. Results are explained in detail and recommendations for further research provided.

Operant Conditioning in Honey Bees (Apis mellifera L.): The Cap Pushing Response.

PubMed

Abramson, Charles I; Dinges, Christopher W; Wells, Harrington

2016-01-01

The honey bee has been an important model organism for studying learning and memory. More recently, the honey bee has become a valuable model to understand perception and cognition. However, the techniques used to explore psychological phenomena in honey bees have been limited to only a few primary methodologies such as the proboscis extension reflex, sting extension reflex, and free flying target discrimination-tasks. Methods to explore operant conditioning in bees and other invertebrates are not as varied as with vertebrates. This may be due to the availability of a suitable response requirement. In this manuscript we offer a new method to explore operant conditioning in honey bees: the cap pushing response (CPR). We used the CPR to test for difference in learning curves between novel auto-shaping and more traditional explicit-shaping. The CPR protocol requires bees to exhibit a novel behavior by pushing a cap to uncover a food source. Using the CPR protocol we tested the effects of both explicit-shaping and auto-shaping techniques on operant conditioning. The goodness of fit and lack of fit of these data to the Rescorla-Wagner learning-curve model, widely used in classical conditioning studies, was tested. The model fit well to both control and explicit-shaping results, but only for a limited number of trials. Learning ceased rather than continuing to asymptotically approach the physiological most accurate possible. Rate of learning differed between shaped and control bee treatments. Learning rate was about 3 times faster for shaped bees, but for all measures of proficiency control and shaped bees reached the same level. Auto-shaped bees showed one-trial learning rather than the asymptotic approach to a maximal efficiency. However, in terms of return-time, the auto-shaped bees' learning did not carry over to the covered-well test treatments.

Peer Teaching to Foster Learning in Physiology.

PubMed

Srivastava, Tripti K; Waghmare, Lalitbhushan S; Mishra, Ved Prakash; Rawekar, Alka T; Quazi, Nazli; Jagzape, Arunita T

2015-08-01

Peer teaching is an effective tool to promote learning and retention of knowledge. By preparing to teach, students are encouraged to construct their own learning program, so that they can explain effectively to fellow learners. Peer teaching is introduced in present study to foster learning and pedagogical skills amongst first year medical under-graduates in physiology with a Hypothesis that teaching is linked to learning on part of the teacher. Non-randomized, Interventional study, with mixed methods design. Cases experienced peer teaching whereas controls underwent tutorials for four consecutive classes. Quantitative Evaluation was done through pre/post test score analysis for Class average normalized gain and tests of significance, difference in average score in surprise class test after one month and percentage of responses in closed ended items of feedback questionnaire. Qualitative Evaluation was done through categorization of open ended items and coding of reflective statements. The average pre and post test score was statistically significant within cases (p = 0.01) and controls (p = 0.023). The average post test scores was more for cases though not statistically significant. The class average normalized gain (g) for Tutorials was 49% and for peer teaching 53%. Surprise test had average scoring of 36 marks (out of 50) for controls and 41 marks for cases. Analysed section wise, the average score was better for Long answer question (LAQ) in cases. Section wise analysis suggested that through peer teaching, retention was better for descriptive answers as LAQ has better average score in cases. Feedback responses were predominantly positive for efficacy of peer teaching as a learning method. The reflective statements were sorted into reflection in action, reflection on action, claiming evidence, describing experience, and recognizing discrepancies. Teaching can stimulate further learning as it involves interplay of three processes: metacognitive awareness; deliberate practice, and self-explanation. Coupled with immediate feedback and reflective exercises, learning can be measurably enhanced along with improved teaching skills.

Operant Conditioning in Honey Bees (Apis mellifera L.): The Cap Pushing Response

PubMed Central

Abramson, Charles I.; Dinges, Christopher W.; Wells, Harrington

2016-01-01

The honey bee has been an important model organism for studying learning and memory. More recently, the honey bee has become a valuable model to understand perception and cognition. However, the techniques used to explore psychological phenomena in honey bees have been limited to only a few primary methodologies such as the proboscis extension reflex, sting extension reflex, and free flying target discrimination-tasks. Methods to explore operant conditioning in bees and other invertebrates are not as varied as with vertebrates. This may be due to the availability of a suitable response requirement. In this manuscript we offer a new method to explore operant conditioning in honey bees: the cap pushing response (CPR). We used the CPR to test for difference in learning curves between novel auto-shaping and more traditional explicit-shaping. The CPR protocol requires bees to exhibit a novel behavior by pushing a cap to uncover a food source. Using the CPR protocol we tested the effects of both explicit-shaping and auto-shaping techniques on operant conditioning. The goodness of fit and lack of fit of these data to the Rescorla-Wagner learning-curve model, widely used in classical conditioning studies, was tested. The model fit well to both control and explicit-shaping results, but only for a limited number of trials. Learning ceased rather than continuing to asymptotically approach the physiological most accurate possible. Rate of learning differed between shaped and control bee treatments. Learning rate was about 3 times faster for shaped bees, but for all measures of proficiency control and shaped bees reached the same level. Auto-shaped bees showed one-trial learning rather than the asymptotic approach to a maximal efficiency. However, in terms of return-time, the auto-shaped bees’ learning did not carry over to the covered-well test treatments. PMID:27626797

Comparing two methods of education (virtual versus traditional) on learning of Iranian dental students: a post-test only design study.

PubMed

Moazami, Fariborz; Bahrampour, Ehsan; Azar, Mohammad Reza; Jahedi, Farzad; Moattari, Marzieh

2014-03-05

The importance of using technologies such as e-learning in different disciplines is discussed in the literature. Researchers have measured the effectiveness of e-learning in a number of fields.Considering the lack of research on the effectiveness of online learning in dental education particularly in Iran, the advantages of these learning methods and the positive university atmosphere regarding the use of online learning. This study, therefore, aims to compare the effects of two methods of teaching (virtual versus traditional) on student learning. This post-test only design study approached 40, fifth year dental students of Shiraz University of Medical Sciences. From this group, 35 students agreed to participate. These students were randomly allocated into two groups, experimental (virtual learning) and comparison (traditional learning). To ensure similarity between groups, we compared GPAs of all participants by the Mann-Whitney U test (P > 0.05). The experimental group received a virtual learning environment courseware package specifically designed for this study, whereas the control group received the same module structured in a traditional lecture form. The virtual learning environment consisted of online and offline materials. Two identical valid, reliable post-tests that consisted of 40 multiple choice questions (MCQs) and 4 essay questions were administered immediately (15 min) after the last session and two months later to assess for knowledge retention. Data were analyzed by SPSS version 20. A comparison of the mean knowledge score of both groups showed that virtual learning was more effective than traditional learning (effect size = 0.69). The newly designed virtual learning package is feasible and will result in more effective learning in comparison with lecture-based training. However further studies are needed to generalize the findings of this study.

Pavlovian to instrumental transfer of control in a human learning task.

PubMed

Nadler, Natasha; Delgado, Mauricio R; Delamater, Andrew R

2011-10-01

Pavlovian learning tasks have been widely used as tools to understand basic cognitive and emotional processes in humans. The present studies investigated one particular task, Pavlovian-to-instrumental transfer (PIT), with human participants in an effort to examine potential cognitive and emotional effects of Pavlovian cues upon instrumentally trained performance. In two experiments, subjects first learned two separate instrumental response-outcome relationships (i.e., R1-O1 and R2-O2) and then were exposed to various stimulus-outcome relationships (i.e., S1-O1, S2-O2, S3-O3, and S4-) before the effects of the Pavlovian stimuli on instrumental responding were assessed during a non-reinforced test. In Experiment 1, instrumental responding was established using a positive-reinforcement procedure, whereas in Experiment 2, a quasi-avoidance learning task was used. In both cases, the Pavlovian stimuli exerted selective control over instrumental responding, whereby S1 and S2 selectively elevated the instrumental response with which it shared an outcome. In addition, in Experiment 2, S3 exerted a nonselective transfer of control effect, whereby both responses were elevated over baseline levels. These data identify two ways, one specific and one general, in which Pavlovian processes can exert control over instrumental responding in human learning paradigms, suggesting that this method may serve as a useful tool in the study of basic cognitive and emotional processes in human learning.

Pavlovian to Instrumental Transfer of Control in a Human Learning Task

PubMed Central

Nadler, Natasha; Delgado, Mauricio R.; Delamater, Andrew R.

2011-01-01

Pavlovian learning tasks have been widely used as tools to understand basic cognitive and emotional processes in humans. The present studies investigated one particular task, Pavlovian-to-instrumental transfer (PIT), with human participants in an effort to examine potential cognitive and emotional effects of Pavlovian cues upon instrumentally-trained performance. In two experiments subjects first learned two separate instrumental response-outcome relationships (R1-O1, R2-O2) and then were exposed to various stimulus-outcome relationships (S1-O1, S2-O2, S3-O3, S4-) before the effects of the Pavlovian stimuli on instrumental responding were assessed during a nonreinforced test. In Experiment 1 instrumental responding was established using a positive reinforcement procedure whereas in Experiment 2 a quasi-avoidance learning task was used. In both cases the Pavlovian stimuli exerted selective control over instrumental responding, whereby S1 & S2 selectively elevated the instrumental response with which it shared an outcome. In addition, in Experiment 2, S3 exerted a nonselective transfer of control effect, whereby both responses were elevated over baseline levels. These data identify two ways, one specific and one general, in which Pavlovian processes can exert control over instrumental responding in human learning paradigms, and suggest that this method may serve as a useful tool in the study of basic cognitive and emotional processes in human learning. PMID:21534664

Adaptive filter design using recurrent cerebellar model articulation controller.

PubMed

Lin, Chih-Min; Chen, Li-Yang; Yeung, Daniel S

2010-07-01

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.

Design and Control of Large Collections of Learning Agents

NASA Technical Reports Server (NTRS)

Agogino, Adrian

2001-01-01

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

The Efficacy of Three Learning Methods Collaborative, Context-Based Learning and Traditional, on Learning, Attitude and Behaviour of Undergraduate Nursing Students: Integrating Theory and Practice

PubMed Central

Hasanpour-Dehkordi, Ali

2016-01-01

Introduction Communication skills training, responsibility, respect, and self-awareness are important indexes of changing learning behaviours in modern approaches. Aim The aim of this study was to investigate the efficacy of three learning approaches, collaborative, context-based learning (CBL), and traditional, on learning, attitude, and behaviour of undergraduate nursing students. Materials and Methods This study was a clinical trial with pretest and post-test of control group. The participants were senior nursing students. The samples were randomly assigned to three groups; CBL, collaborative, and traditional. To gather data a standard questionnaire of students’ behaviour and attitude was administered prior to and after the intervention. Also, the rate of learning was investigated by a researcher-developed questionnaire prior to and after the intervention in the three groups. Results In CBL and collaborative training groups, the mean score of behaviour and attitude increased after the intervention. But no significant association was obtained between the mean scores of behaviour and attitude prior to and after the intervention in the traditional group. However, the mean learning score increased significantly in the CBL, collaborative, and traditional groups after the study in comparison to before the study. Conclusion Both CBL and collaborative approaches were useful in terms of increased respect, self-awareness, self-evaluation, communication skills and responsibility as well as increased motivation and learning score in comparison to traditional method. PMID:27190926

Application of Model Project Based Learning on Integrated Science in Water Pollution

NASA Astrophysics Data System (ADS)

Yamin, Y.; Permanasari, A.; Redjeki, S.; Sopandi, W.

2017-09-01

The function of this research was to analyze the influence model Project Based Learning (PjBl) on integrated science about the concept mastery for junior high school students. Method used for this research constitutes the quasi of experiment method. Population and sample for this research are the students junior high school in Bandung as many as two classes to be experiment and control class. The instrument that used for this research is the test concept mastery, assessment questionnaire of product and the questionnaire responses of the student about learning integrated science. Based on the result of this research get some data that with accomplishment the model of PjBl. Learning authority of integrated science can increase the concept mastery for junior high school students. The highest increase in the theme of pollution water is in the concept of mixtures and the separation method. The students give a positive response in learning of integrated science for the theme of pollution of the water used model PjBL with questionnaire of the opinion aspect in amount of 83.5%, the anxiety of the students in amount of 95.5%, the profit learning model of PjBL in amount of 96.25% and profit learning of integrated science in amount of 95.75%.

Improving self-regulated learning junior high school students through computer-based learning

NASA Astrophysics Data System (ADS)

Nurjanah; Dahlan, J. A.

2018-05-01

This study is back grounded by the importance of self-regulated learning as an affective aspect that determines the success of students in learning mathematics. The purpose of this research is to see how the improvement of junior high school students' self-regulated learning through computer based learning is reviewed in whole and school level. This research used a quasi-experimental research method. This is because individual sample subjects are not randomly selected. The research design used is Pretest-and-Posttest Control Group Design. Subjects in this study were students of grade VIII junior high school in Bandung taken from high school (A) and middle school (B). The results of this study showed that the increase of the students' self-regulated learning who obtain learning with computer-based learning is higher than students who obtain conventional learning. School-level factors have a significant effect on increasing of the students' self-regulated learning.

Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding.

PubMed

Gao, Wei; Kwong, Sam; Jia, Yuheng

2017-08-25

In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in High Efficiency Video Coding (HEVC). First, a support vector machine (SVM) based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level Rate-Distortion (R-D) model. The legacy "chicken-and-egg" dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model based utility function is proved, and Nash bargaining solution (NBS) is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level Quantization parameter (QP) change. Lastly, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

Effects of Mobile Phone-Based App Learning Compared to Computer-Based Web Learning on Nursing Students: Pilot Randomized Controlled Trial

PubMed Central

2015-01-01

Objectives This study aimed to determine the effect of mobile-based discussion versus computer-based discussion on self-directed learning readiness, academic motivation, learner-interface interaction, and flow state. Methods This randomized controlled trial was conducted at one university. Eighty-six nursing students who were able to use a computer, had home Internet access, and used a mobile phone were recruited. Participants were randomly assigned to either the mobile phone app-based discussion group (n = 45) or a computer web-based discussion group (n = 41). The effect was measured at before and after an online discussion via self-reported surveys that addressed academic motivation, self-directed learning readiness, time distortion, learner-learner interaction, learner-interface interaction, and flow state. Results The change in extrinsic motivation on identified regulation in the academic motivation (p = 0.011) as well as independence and ability to use basic study (p = 0.047) and positive orientation to the future in self-directed learning readiness (p = 0.021) from pre-intervention to post-intervention was significantly more positive in the mobile phone app-based group compared to the computer web-based discussion group. Interaction between learner and interface (p = 0.002), having clear goals (p = 0.012), and giving and receiving unambiguous feedback (p = 0.049) in flow state was significantly higher in the mobile phone app-based discussion group than it was in the computer web-based discussion group at post-test. Conclusions The mobile phone might offer more valuable learning opportunities for discussion teaching and learning methods in terms of self-directed learning readiness, academic motivation, learner-interface interaction, and the flow state of the learning process compared to the computer. PMID:25995965

Closing the Certification Gaps in Adaptive Flight Control Software

NASA Technical Reports Server (NTRS)

Jacklin, Stephen A.

2008-01-01

Over the last five decades, extensive research has been performed to design and develop adaptive control systems for aerospace systems and other applications where the capability to change controller behavior at different operating conditions is highly desirable. Although adaptive flight control has been partially implemented through the use of gain-scheduled control, truly adaptive control systems using learning algorithms and on-line system identification methods have not seen commercial deployment. The reason is that the certification process for adaptive flight control software for use in national air space has not yet been decided. The purpose of this paper is to examine the gaps between the state-of-the-art methodologies used to certify conventional (i.e., non-adaptive) flight control system software and what will likely to be needed to satisfy FAA airworthiness requirements. These gaps include the lack of a certification plan or process guide, the need to develop verification and validation tools and methodologies to analyze adaptive controller stability and convergence, as well as the development of metrics to evaluate adaptive controller performance at off-nominal flight conditions. This paper presents the major certification gap areas, a description of the current state of the verification methodologies, and what further research efforts will likely be needed to close the gaps remaining in current certification practices. It is envisioned that closing the gap will require certain advances in simulation methods, comprehensive methods to determine learning algorithm stability and convergence rates, the development of performance metrics for adaptive controllers, the application of formal software assurance methods, the application of on-line software monitoring tools for adaptive controller health assessment, and the development of a certification case for adaptive system safety of flight.

Iterative learning control with applications in energy generation, lasers and health care.

PubMed

Rogers, E; Tutty, O R

2016-09-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

Males and Females Respond Differently to Controllability and Antidepressant Treatment

PubMed Central

Leuner, Benedetta; Mendolia-Loffredo, Sabrina; Shors, Tracey J.

2012-01-01

Background Women are much more likely to suffer from stress-related mental illness than men; yet few, if any, animal models for such sex differences exist. Previously, we reported that exposure to an acute stressor enhances learning in male rats yet severely impairs learning in female rats. Here, we tested whether these opposite effects in males versus females could be prevented by establishing control over the stressor or by antidepressant treatment. Methods Learning was assessed using the hippocampal-dependent task of trace eyeblink conditioning. In the first experiment, groups of male and female rats were exposed to controllable or uncontrollable stress and trained. In a second experiment, they were exposed to an uncontrollable stressor after chronic treatment with the antidepressant fluoxetine (Prozac). In a final experiment, females were exposed to uncontrollable stress after acute treatment with fluoxetine. Results Establishing control over the stressful experience eliminated the detrimental effect of stress on learning in females as well as the enhancing effect of stress in males. Moreover, chronic but not acute treatment with fluoxetine prevented the learning deficit in females after exposure to stress. Treatment with fluoxetine did not alter the male response to stress. Conclusions These data indicate that males and females not only respond in opposite directions to the same stressful event but also respond differently to controllability and antidepressant treatments. PMID:15601607

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

PubMed

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Videotaped Feedback Method to Enhance Learning in Preclinical Operative Dentistry: An Experimental Study.

PubMed

Shah, Dipali Yogesh; Dadpe, Ashwini Manish; Kalra, Dheeraj Deepak; Garcha, Vikram P

2015-12-01

The aim of this study was to investigate if a videotaped feedback method enhanced teaching and learning outcomes in a preclinical operative laboratory setting for novice learners. In 2013, 60 dental students at a dental school in India were randomly assigned to two groups: control (n=30) and experimental (n=30). The control group prepared a Class II tooth preparation for amalgam after receiving a video demonstration of the exercise. The experimental group received the same video demonstration as the control group, but they also participated in a discussion and analysis of the control groups' videotaped performance and then performed the same exercise. The self-evaluation scores (SS) and examiner evaluation scores (ES) of the two groups were compared using the unpaired t-test. The experimental group also used a five-point Likert scale to rate each item on the feedback form. The means of SS (13.65±2.43) and ES (14.75±1.97) of the experimental group were statistically higher than the means of SS (11.55±2.09) and ES (11.60±1.82) of the control group. Most students in the experimental group perceived that this technique enhanced their learning experience. Within the limits of this study, the videotaped feedback using both ideal and non-ideal examples enhanced the students' performance.

Intact Associative Learning in Patients with Schizophrenia: Evidence from a Go/NoGo Paradigm

PubMed Central

Woolard, Austin A.; Kose, Samet; Woodward, Neil D.; Verbruggen, Frederick; Logan, Gordon D.; Heckers, Stephan

2010-01-01

Objective Schizophrenia is associated with deficits in executive control and associative learning. In the present study, we investigated the effect of associative learning during a Go/NoGo task in healthy controls subjects and patients with schizophrenia. Methods Thirty patients with schizophrenia and 30 age-and-gender matched healthy control subjects performed 15 blocks of training and 3 blocks of test trials. The trials consisted of responding to words denoting either living or non-living objects. In the training condition, subjects were instructed to respond by pressing the space bar (Go-task) to one of the word types (living or non-living objects), but not the other. In the test phase, the Go/NoGo mapping was reversed. Subjects were instructed to respond as quickly and as accurately as possible. Reaction times (RT) and accuracy were recorded for each trial and all subjects were debriefed upon completion of the test trials. Results Patients with schizophrenia had significantly longer Go RTs when compared to the control group, during both training and test trials. However, the two groups did not differ on any measure of associative learning. Conclusions Our findings suggest that associative learning is intact in schizophrenia patients during the performance of a relational Go/NoGo paradigm. PMID:20226631

Rose garden promises of intelligent tutoring systems: Blossom or thorn

NASA Technical Reports Server (NTRS)

Shute, Valerie J.

1991-01-01

Intelligent tutoring systems (ITS) have been in existence for over a decade. However, few controlled evaluation studies have been conducted comparing the effectiveness of these systems to more traditional instruction methods. Two main promises of ITSs are examined: (1) Engender more effective and efficient learning in relation to traditional formats; and (2) Reduce the range of learning outcome measures where a majority of individuals are elevated to high performance levels. Bloom (1984) has referred to these as the two sigma problem; to achieve two standard deviation improvements with tutoring over traditional instruction methods. Four ITSs are discussed in relation to the two promises. These tutors have undergone systematic, controlled evaluations: (1) The LISP tutor (Anderson Farrell and Sauers, 1984); (2) Smithtown (Shute and Glaser, in press); (3) Sherlock (Lesgold, Lajoie, Bunzo and Eggan, 1990); and (4) The Pascal ITS (Bonar, Cunningham, Beatty and Well, 1988). Results show that these four tutors do accelerate learning with no degradation in final outcome. Suggestions for improvements to the design and evaluation of ITSs are discussed.

Learning outcomes in a simulation game for associate degree nursing students.

PubMed

Clark-C

1977-01-01

Learning outcomes of a simulation game designed to have one-to-one correspondence between behavioral objectives and game plays is reported. The behavioral objectives were core concepts in psychiatric mental health nursing taught to associate degree nursing students. Decisions to use the simulation game method method grew out of difficulties inherent in the community college nursing program, as well as the need for self-paced, efficient, learner-centered learning and evaluative tools. After the trial and revision of the game, a number of research hypotheses were tested. Simulation gaming was found to be an effective mode of learning, and students who acted as teachers for other students learned significantly more than those who were taught. Some of the recommendations for further research were to study varied nursing populations, to add a control group, to test the long-range learning effects of playing the game, to decrease experimenter bias, to study transfer of learning to actual nurse-patient situations and changes in attitudes toward psychiatric patients, and to develop more simulation games for nursing education.

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

Learning to Fail in Aphasia: An Investigation of Error Learning in Naming

PubMed Central

Middleton, Erica L.; Schwartz, Myrna F.

2013-01-01

Purpose To determine if the naming impairment in aphasia is influenced by error learning and if error learning is related to type of retrieval strategy. Method Nine participants with aphasia and ten neurologically-intact controls named familiar proper noun concepts. When experiencing tip-of-the-tongue naming failure (TOT) in an initial TOT-elicitation phase, participants were instructed to adopt phonological or semantic self-cued retrieval strategies. In the error learning manipulation, items evoking TOT states during TOT-elicitation were randomly assigned to a short or long time condition where participants were encouraged to continue to try to retrieve the name for either 20 seconds (short interval) or 60 seconds (long). The incidence of TOT on the same items was measured on a post test after 48-hours. Error learning was defined as a higher rate of recurrent TOTs (TOT at both TOT-elicitation and post test) for items assigned to the long (versus short) time condition. Results In the phonological condition, participants with aphasia showed error learning whereas controls showed a pattern opposite to error learning. There was no evidence for error learning in the semantic condition for either group. Conclusion Error learning is operative in aphasia, but dependent on the type of strategy employed during naming failure. PMID:23816662

Probabilistic Category Learning in Developmental Dyslexia: Evidence from Feedback and Paired-Associate Weather Prediction Tasks

PubMed Central

Gabay, Yafit; Vakil, Eli; Schiff, Rachel; Holt, Lori L.

2015-01-01

Objective Developmental dyslexia is presumed to arise from specific phonological impairments. However, an emerging theoretical framework suggests that phonological impairments may be symptoms stemming from an underlying dysfunction of procedural learning. Method We tested procedural learning in adults with dyslexia (n=15) and matched-controls (n=15) using two versions of the Weather Prediction Task: Feedback (FB) and Paired-associate (PA). In the FB-based task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of response. In the PA-based learning task, participants viewed the cue and its associated outcome simultaneously without overt response or feedback. In both versions, participants trained across 150 trials. Learning was assessed in a subsequent test without presentation of the outcome, or corrective feedback. Results The Dyslexia group exhibited impaired learning compared with the Control group on both the FB and PA versions of the weather prediction task. Conclusions The results indicate that the ability to learn by feedback is not selectively impaired in dyslexia. Rather it seems that the probabilistic nature of the task, shared by the FB and PA versions of the weather prediction task, hampers learning in those with dyslexia. Results are discussed in light of procedural learning impairments among participants with dyslexia. PMID:25730732

Improvement of drug dose calculations by classroom teaching or e-learning: a randomised controlled trial in nurses

PubMed Central

Simonsen, Bjoerg O; Daehlin, Gro K; Johansson, Inger; Farup, Per G

2014-01-01

Introduction Insufficient skills in drug dose calculations increase the risk for medication errors. Even experienced nurses may struggle with such calculations. Learning flexibility and cost considerations make e-learning interesting as an alternative to classroom teaching. This study compared the learning outcome and risk of error after a course in drug dose calculations for nurses with the two methods. Methods In a randomised controlled open study, nurses from hospitals and primary healthcare were randomised to either e-learning or classroom teaching. Before and after a 2-day course, the nurses underwent a multiple choice test in drug dose calculations: 14 tasks with four alternative answers (score 0–14), and a statement regarding the certainty of each answer (score 0–3). High risk of error was being certain that incorrect answer was correct. The results are given as the mean (SD). Results 16 men and 167 women participated in the study, aged 42.0 (9.5) years with a working experience of 12.3 (9.5) years. The number of correct answers after e-learning was 11.6 (2.0) and after classroom teaching 11.9 (2.0) (p=0.18, NS); improvement were 0.5 (1.6) and 0.9 (2.2), respectively (p=0.07, NS). Classroom learning was significantly superior to e-learning among participants with a pretest score below 9. In support of e-learning was evaluation of specific value for the working situation. There was no difference in risk of error between groups after the course (p=0.77). Conclusions The study showed no differences in learning outcome or risk of error between e-learning and classroom teaching in drug dose calculations. The overall learning outcome was small. Weak precourse knowledge was associated with better outcome after classroom teaching. PMID:25344483

The impact of E-learning in medical education.

PubMed

Ruiz, Jorge G; Mintzer, Michael J; Leipzig, Rosanne M

2006-03-01

The authors provide an introduction to e-learning and its role in medical education by outlining key terms, the components of e-learning, the evidence for its effectiveness, faculty development needs for implementation, evaluation strategies for e-learning and its technology, and how e-learning might be considered evidence of academic scholarship. E-learning is the use of Internet technologies to enhance knowledge and performance. E-learning technologies offer learners control over content, learning sequence, pace of learning, time, and often media, allowing them to tailor their experiences to meet their personal learning objectives. In diverse medical education contexts, e-learning appears to be at least as effective as traditional instructor-led methods such as lectures. Students do not see e-learning as replacing traditional instructor-led training but as a complement to it, forming part of a blended-learning strategy. A developing infrastructure to support e-learning within medical education includes repositories, or digital libraries, to manage access to e-learning materials, consensus on technical standardization, and methods for peer review of these resources. E-learning presents numerous research opportunities for faculty, along with continuing challenges for documenting scholarship. Innovations in e-learning technologies point toward a revolution in education, allowing learning to be individualized (adaptive learning), enhancing learners' interactions with others (collaborative learning), and transforming the role of the teacher. The integration of e-learning into medical education can catalyze the shift toward applying adult learning theory, where educators will no longer serve mainly as the distributors of content, but will become more involved as facilitators of learning and assessors of competency.

Reinforcement learning state estimator.

PubMed

Morimoto, Jun; Doya, Kenji

2007-03-01

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

Model-based iterative learning control of Parkinsonian state in thalamic relay neuron

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Li, Huiyan; Xue, Zhiqin; Deng, Bin; Wei, Xile

2014-09-01

Although the beneficial effects of chronic deep brain stimulation on Parkinson's disease motor symptoms are now largely confirmed, the underlying mechanisms behind deep brain stimulation remain unclear and under debate. Hence, the selection of stimulation parameters is full of challenges. Additionally, due to the complexity of neural system, together with omnipresent noises, the accurate model of thalamic relay neuron is unknown. Thus, the iterative learning control of the thalamic relay neuron's Parkinsonian state based on various variables is presented. Combining the iterative learning control with typical proportional-integral control algorithm, a novel and efficient control strategy is proposed, which does not require any particular knowledge on the detailed physiological characteristics of cortico-basal ganglia-thalamocortical loop and can automatically adjust the stimulation parameters. Simulation results demonstrate the feasibility of the proposed control strategy to restore the fidelity of thalamic relay in the Parkinsonian condition. Furthermore, through changing the important parameter—the maximum ionic conductance densities of low-threshold calcium current, the dominant characteristic of the proposed method which is independent of the accurate model can be further verified.

Neural-Network-Development Program

NASA Technical Reports Server (NTRS)

Phillips, Todd A.

1993-01-01

NETS, software tool for development and evaluation of neural networks, provides simulation of neural-network algorithms plus computing environment for development of such algorithms. Uses back-propagation learning method for all of networks it creates. Enables user to customize patterns of connections between layers of network. Also provides features for saving, during learning process, values of weights, providing more-precise control over learning process. Written in ANSI standard C language. Machine-independent version (MSC-21588) includes only code for command-line-interface version of NETS 3.0.

Machine learning derived risk prediction of anorexia nervosa.

PubMed

Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

2016-01-20

Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

The Formation Experiment in the Age of Hypermedia and Distance Learning

ERIC Educational Resources Information Center

Giest, Hartmut

2004-01-01

Searching for an adequate method to investigate human development (especially the development of theoretical thinking) Vygotsky and his collaborators developed the causal genetic method. The basic idea of this method consists in the investigation of psychic functions and structures by their formation under controlled conditions (for instance via a…

Autonomous learning in gesture recognition by using lobe component analysis

NASA Astrophysics Data System (ADS)

Lu, Jian; Weng, Juyang

2007-02-01

Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

Interactive videodisc instruction is an alternative method for learning and performing a critical nursing skill.

PubMed

DeAmicis, P A

1997-01-01

A study was conducted to compare the effectiveness of interactive videodisc instruction (IVDI) with the traditional lecture/demonstration as an alternative method for learning and performing a critical nursing skill. Students were assigned randomly to a treatment group that worked in small groups to complete the IVDI on intravenous therapy skills and a control group receiving the same content in a classroom lecture/demonstration format. After the instruction, each subject performed a re-demonstration of the learned skills using specific guidelines. Results revealed that although the IVDI group scored higher on the overall re-demonstration, there was no significant difference in the ability of the two groups to effectively perform this critical nursing skill. These findings support the use of IVDI as an alternative self-paced, independent study method for learning psychomotor skills and are consistent with previous studies, which indicate that working in small groups on the computer has a positive effect on self-efficacy and achievement.

Body painting to promote self-active learning of hand anatomy for preclinical medical students.

PubMed

Jariyapong, Pitchanee; Punsawad, Chuchard; Bunratsami, Suchirat; Kongthong, Paranyu

2016-01-01

The purpose of this study was to use the body painting method to teach hand anatomy to a group of preclinical medical students. Students reviewed hand anatomy using the traditional method and body painting exercise. Feedback and retention of the anatomy-related information were examined by a questionnaire and multiple-choice questions, respectively, immediately and 1 month after the painting exercise. Students agreed that the exercise was advantageous and helped facilitate self-active learning after in-class anatomy lessons. While there was no significant difference in knowledge retention between the control and experimental groups, the students appreciated the exercise in which they applied body paint to the human body to learn anatomy. The body painting was an efficient tool for aiding the interactive learning of medical students and increasing the understanding of gross anatomy.

Algorithm for personal identification in distance learning system based on registration of keyboard rhythm

NASA Astrophysics Data System (ADS)

Nikitin, P. V.; Savinov, A. N.; Bazhenov, R. I.; Sivandaev, S. V.

2018-05-01

The article describes the method of identifying a person in distance learning systems based on a keyboard rhythm. An algorithm for the organization of access control is proposed, which implements authentication, identification and verification of a person using the keyboard rhythm. Authentication methods based on biometric personal parameters, including those based on the keyboard rhythm, due to the inexistence of biometric characteristics without a particular person, are able to provide an advanced accuracy and inability to refuse authorship and convenience for operators of automated systems, in comparison with other methods of conformity checking. Methods of permanent hidden keyboard monitoring allow detecting the substitution of a student and blocking the key system.

Associations between Verbal Learning Slope and Neuroimaging Markers across the Cognitive Aging Spectrum.

PubMed

Gifford, Katherine A; Phillips, Jeffrey S; Samuels, Lauren R; Lane, Elizabeth M; Bell, Susan P; Liu, Dandan; Hohman, Timothy J; Romano, Raymond R; Fritzsche, Laura R; Lu, Zengqi; Jefferson, Angela L

2015-07-01

A symptom of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is a flat learning profile. Learning slope calculation methods vary, and the optimal method for capturing neuroanatomical changes associated with MCI and early AD pathology is unclear. This study cross-sectionally compared four different learning slope measures from the Rey Auditory Verbal Learning Test (simple slope, regression-based slope, two-slope method, peak slope) to structural neuroimaging markers of early AD neurodegeneration (hippocampal volume, cortical thickness in parahippocampal gyrus, precuneus, and lateral prefrontal cortex) across the cognitive aging spectrum [normal control (NC); (n=198; age=76±5), MCI (n=370; age=75±7), and AD (n=171; age=76±7)] in ADNI. Within diagnostic group, general linear models related slope methods individually to neuroimaging variables, adjusting for age, sex, education, and APOE4 status. Among MCI, better learning performance on simple slope, regression-based slope, and late slope (Trial 2-5) from the two-slope method related to larger parahippocampal thickness (all p-values<.01) and hippocampal volume (p<.01). Better regression-based slope (p<.01) and late slope (p<.01) were related to larger ventrolateral prefrontal cortex in MCI. No significant associations emerged between any slope and neuroimaging variables for NC (p-values ≥.05) or AD (p-values ≥.02). Better learning performances related to larger medial temporal lobe (i.e., hippocampal volume, parahippocampal gyrus thickness) and ventrolateral prefrontal cortex in MCI only. Regression-based and late slope were most highly correlated with neuroimaging markers and explained more variance above and beyond other common memory indices, such as total learning. Simple slope may offer an acceptable alternative given its ease of calculation.

Mind map learning for advanced engineering study: case study in system dynamics

NASA Astrophysics Data System (ADS)

Woradechjumroen, Denchai

2018-01-01

System Dynamics (SD) is one of the subjects that were use in learning Automatic Control Systems in dynamic and control field. Mathematical modelling and solving skills of students for engineering systems are expecting outcomes of the course which can be further used to efficiently study control systems and mechanical vibration; however, the fundamental of the SD includes strong backgrounds in Dynamics and Differential Equations, which are appropriate to the students in governmental universities that have strong skills in Mathematics and Scientifics. For private universities, students are weak in the above subjects since they obtained high vocational certificate from Technical College or Polytechnic School, which emphasize the learning contents in practice. To enhance their learning for improving their backgrounds, this paper applies mind maps based problem based learning to relate the essential relations of mathematical and physical equations. With the advantages of mind maps, each student is assigned to design individual mind maps for self-leaning development after they attend the class and learn overall picture of each chapter from the class instructor. Four problems based mind maps learning are assigned to each student. Each assignment is evaluated via mid-term and final examinations, which are issued in terms of learning concepts and applications. In the method testing, thirty students are tested and evaluated via student learning backgrounds in the past. The result shows that well-design mind maps can improve learning performance based on outcome evaluation. Especially, mind maps can reduce time-consuming and reviewing for Mathematics and Physics in SD significantly.

Basic and supplementary sensory feedback in handwriting

PubMed Central

Danna, Jérémy; Velay, Jean-Luc

2015-01-01

The mastering of handwriting is so essential in our society that it is important to try to find new methods for facilitating its learning and rehabilitation. The ability to control the graphic movements clearly impacts on the quality of the writing. This control allows both the programming of letter formation before movement execution and the online adjustments during execution, thanks to diverse sensory feedback (FB). New technologies improve existing techniques or enable new methods to supply the writer with real-time computer-assisted FB. The possibilities are numerous and various. Therefore, two main questions arise: (1) What aspect of the movement is concerned and (2) How can we best inform the writer to help them correct their handwriting? In a first step, we report studies on FB naturally used by the writer. The purpose is to determine which information is carried by each sensory modality, how it is used in handwriting control and how this control changes with practice and learning. In a second step, we report studies on supplementary FB provided to the writer to help them to better control and learn how to write. We suggest that, depending on their contents, certain sensory modalities will be more appropriate than others to assist handwriting motor control. We emphasize particularly the relevance of auditory modality as online supplementary FB on handwriting movements. Using real-time supplementary FB to assist in the handwriting process is probably destined for a brilliant future with the growing availability and rapid development of tablets. PMID:25750633

The Effect of ICT Assisted Project Based Learning Approach on Prospective ICT Integration Skills of Teacher Candidates

ERIC Educational Resources Information Center

Pilten, Pusat; Pilten, Gulhiz; Sahinkaya, Nihan

2017-01-01

The purpose of the present research is studying the effects of information and communication technologies (ICT) assisted project based learning practices on ICT integration skills of pre-service classroom teachers. The research adopted a mixed method. The quantitative dimension of the research was designed with pre-test-post-test control groups.…

Can Animations Effectively Substitute for Traditional Teaching Methods? Part II: Potential for Differentiated Learning

ERIC Educational Resources Information Center

Gregorius, Roberto Ma.; Santos, Rhodora; Dano, Judith B.; Gutierrez, Jose J.

2010-01-01

Animations were prepared using Adobe Flash MX and tested on elementary (3rd-5th grade) and secondary chemistry students. A pre- and post-test study was used to compare the learning gains of students who received the animations with those who received textbook reading time and discussion in class. The control and experimental groups were further…

Understanding the Impact of Training on Performance

DTIC Science & Technology

2014-05-01

learning argue that this method encourages metacognitive activity and self -regulation of learning; which, in turn, can aid the development of adaptable...Frese, M. (2005). Self -regulation in error management training: Emotion control and metacognition as mediators of performance effects. Journal...on aptitude by treatment interactions that indicate the effectiveness of training interventions can differ depending on trainee aptitude for self

Game-Based Learning as a Vehicle to Teach First Aid Content: A Randomized Experiment

ERIC Educational Resources Information Center

Charlier, Nathalie; De Fraine, Bieke

2013-01-01

Background: Knowledge of first aid (FA), which constitutes lifesaving treatments for injuries or illnesses, is important for every individual. In this study, we have set up a group-randomized controlled trial to assess the effectiveness of a board game for learning FA. Methods: Four class groups (120 students) were randomly assigned to 2…

Evaluating the Impact of Action Plans on Trainee Compliance with Learning Objectives

ERIC Educational Resources Information Center

Aumann, Michael J.

2013-01-01

This mixed methods research study evaluated the use of technology-based action plans as a way to help improve compliance with the learning objectives of an online training event. It explored how the action planning strategy impacted subjects in a treatment group and compared them to subjects in a control group who did not get the action plan. The…

The Effect of Project Based Learning on the Statistical Literacy Levels of Student 8th Grade

ERIC Educational Resources Information Center

Koparan, Timur; Güven, Bülent

2014-01-01

This study examines the effect of project based learning on 8th grade students' statistical literacy levels. A performance test was developed for this aim. Quasi-experimental research model was used in this article. In this context, the statistics were taught with traditional method in the control group and it was taught using project based…

The Effect on the 8th Grade Students' Attitude towards Statistics of Project Based Learning

ERIC Educational Resources Information Center

Koparan, Timur; Güven, Bülent

2014-01-01

This study investigates the effect of the project based learning approach on 8th grade students' attitude towards statistics. With this aim, an attitude scale towards statistics was developed. Quasi-experimental research model was used in this study. Following this model in the control group the traditional method was applied to teach statistics…

Impaired Statistical Learning in Developmental Dyslexia

PubMed Central

Thiessen, Erik D.; Holt, Lori L.

2015-01-01

Purpose Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. Method DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. Results As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. Conclusion Results are discussed in light of procedural learning impairments among participants with DD. PMID:25860795

Preliminary investigation into application of problem-based learning in the practical teaching of diagnostics

PubMed Central

Rui, Zeng; Rong-Zheng, Yue; Hong-Yu, Qiu; Jing, Zeng; Xue-Hong, Wan; Chuan, Zuo

2015-01-01

Background Problem-based learning (PBL) is a pedagogical approach based on problems. Specifically, it is a student-centered, problem-oriented teaching method that is conducted through group discussions. The aim of our study is to explore the effects of PBL in diagnostic teaching for Chinese medical students. Methods A prospective, randomized controlled trial was conducted. Eighty junior clinical medical students were randomly divided into two groups. Forty students were allocated to a PBL group and another 40 students were allocated to a control group using the traditional teaching method. Their scores in the practice skills examination, ability to write and analyze medical records, and results on the stage test and behavior observation scale were compared. A questionnaire was administered in the PBL group after class. Results There were no significant differences in scores for writing medical records, content of interviewing, physical examination skills, and stage test between the two groups. However, compared with the control group, the PBL group had significantly higher scores on case analysis, interviewing skills, and behavioral observation scales. Conclusion The questionnaire survey revealed that PBL could improve interest in learning, cultivate an ability to study independently, improve communication and analytical skills, and good team cooperation spirit. However, there were some shortcomings in systematization of imparting knowledge. PBL has an obvious advantage in teaching with regard to diagnostic practice. PMID:25848334

Effect of an EBM course in combination with case method learning sessions: an RCT on professional performance, job satisfaction, and self-efficacy of occupational physicians.

PubMed

Hugenholtz, Nathalie I R; Schaafsma, Frederieke G; Nieuwenhuijsen, Karen; van Dijk, Frank J H

2008-10-01

An intervention existing of an evidence-based medicine (EBM) course in combination with case method learning sessions (CMLSs) was designed to enhance the professional performance, self-efficacy and job satisfaction of occupational physicians. A cluster randomized controlled trial was set up and data were collected through questionnaires at baseline (T0), directly after the intervention (T1) and 7 months after baseline (T2). The data of the intervention group [T0 (n = 49), T1 (n = 31), T2 (n = 29)] and control group [T0 (n = 49), T1 (n = 28), T2 (n = 28)] were analysed in mixed model analyses. Mean scores of the perceived value of the CMLS were calculated in the intervention group. The overall effect of the intervention over time comparing the intervention with the control group was statistically significant for professional performance (p < 0.001). Job satisfaction and self-efficacy changes were small and not statistically significant between the groups. The perceived value of the CMLS to gain new insights and to improve the quality of their performance increased with the number of sessions followed. An EBM course in combination with case method learning sessions is perceived as valuable and offers evidence to enhance the professional performance of occupational physicians. However, it does not seem to influence their self-efficacy and job satisfaction.

A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment.

PubMed

Hung, Shao-Ming; Givigi, Sidney N

2017-01-01

In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multiplier. However, to do so requires autonomous coordination among the UAVs, similar to swarming behaviors seen in animals and insects. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. In particular, Peng's Q(λ) with a variable learning rate is employed by the followers to learn a control policy that facilitates flocking in a leader-follower topology. The problem is structured as a Markov decision process, where the agents are modeled as small fixed-wing UAVs that experience stochasticity due to disturbances such as winds and control noises, as well as weight and balance issues. Learned policies are compared to ones solved using stochastic optimal control (i.e., dynamic programming) by evaluating the average cost incurred during flight according to a cost function. Simulation results demonstrate the feasibility of the proposed learning approach at enabling agents to learn how to flock in a leader-follower topology, while operating in a nonstationary stochastic environment.

Effects of competitive learning tools on medical students: A case study

PubMed Central

2018-01-01

Objective Competitive learning techniques are being successfully used in courses of different disciplines. However, there is still a significant gap in analyzing their effects in medical students competing individually. The authors conducted this study to assess the effectiveness of the use of a competitive learning tool on the academic achievement and satisfaction of medical students. Methods The authors collected data from a Human Immunology course in medical students (n = 285) and conducted a nonrandomized (quasi-experimental) control group pretest-posttest design. They used the Mann-Whitney U-test to measure the strength of the association between two variables and to compare the two student groups. Results The improvement and academic outcomes of the experimental group students were significantly higher than those of the control group students. The students using the competitive learning tool had better academic performance, and they were satisfied with this type of learning. The study, however, had some limitations. The authors did not make a random assignment to the control and experimental groups and the groups were not completely homogenous. Conclusion The use of competitive learning techniques motivates medical students, improves their academic outcomes and may foster the cooperation among students and provide a pleasant classroom environment. The authors are planning further studies with a more complete evaluation of cognitive learning styles or incorporating chronometry as well as team-competition. PMID:29518123

Application of the statistical process control method for prospective patient safety monitoring during the learning phase: robotic kidney transplantation with regional hypothermia (IDEAL phase 2a-b).

PubMed

Sood, Akshay; Ghani, Khurshid R; Ahlawat, Rajesh; Modi, Pranjal; Abaza, Ronney; Jeong, Wooju; Sammon, Jesse D; Diaz, Mireya; Kher, Vijay; Menon, Mani; Bhandari, Mahendra

2014-08-01

Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption phase. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

IMPLICIT DUAL CONTROL BASED ON PARTICLE FILTERING AND FORWARD DYNAMIC PROGRAMMING.

PubMed

Bayard, David S; Schumitzky, Alan

2010-03-01

This paper develops a sampling-based approach to implicit dual control. Implicit dual control methods synthesize stochastic control policies by systematically approximating the stochastic dynamic programming equations of Bellman, in contrast to explicit dual control methods that artificially induce probing into the control law by modifying the cost function to include a term that rewards learning. The proposed implicit dual control approach is novel in that it combines a particle filter with a policy-iteration method for forward dynamic programming. The integration of the two methods provides a complete sampling-based approach to the problem. Implementation of the approach is simplified by making use of a specific architecture denoted as an H-block. Practical suggestions are given for reducing computational loads within the H-block for real-time applications. As an example, the method is applied to the control of a stochastic pendulum model having unknown mass, length, initial position and velocity, and unknown sign of its dc gain. Simulation results indicate that active controllers based on the described method can systematically improve closed-loop performance with respect to other more common stochastic control approaches.

Assessment of Web-Based Authentication Methods in the U.S.: Comparing E-Learning Systems to Internet Healthcare Information Systems

ERIC Educational Resources Information Center

Mattord, Herbert J.

2012-01-01

Organizations continue to rely on password-based authentication methods to control access to many Web-based systems. This research study developed a benchmarking instrument intended to assess authentication methods used in Web-based information systems (IS). It developed an Authentication Method System Index (AMSI) to analyze collected data from…

Encoder-Decoder Optimization for Brain-Computer Interfaces

PubMed Central

Merel, Josh; Pianto, Donald M.; Cunningham, John P.; Paninski, Liam

2015-01-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages. PMID:26029919

Encoder-decoder optimization for brain-computer interfaces.

PubMed

Merel, Josh; Pianto, Donald M; Cunningham, John P; Paninski, Liam

2015-06-01

Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.

Language, learning, and memory in children with and without single-suture craniosynostosis.

PubMed

Kapp-Simon, Kathleen A; Wallace, Erin; Collett, Brent R; Cradock, Mary Michaeleen; Crerand, Canice E; Speltz, Matthew L

2016-05-01

OBJECTIVE The language and memory functions of children with and without single-suture craniosynostosis (SSC) were compared at school age (mean 7.45 years, standard deviation [SD] 0.54 years). The children in this cohort were originally recruited in infancy and prior to cranial surgery for those with SSC. METHODS Individual evaluations of 179 school-aged children with SSC and 183 controls were conducted (70% of the original cohort) using standardized measures of language, learning, and memory. Parents participated in an interview about specialized education interventions and school progress. Parents and teachers completed questionnaires about language development. RESULTS Children with SSC (cases) obtained lower scores than controls on all measures. The adjusted differences in language, learning, and memory scores were modest, with SD ranging from 0.0 to -0.4 (p values ranged from 0.001 to 0.99). Censored normal regression was used to account for intervention services received prior to the school-age evaluation; this increased case-control differences (SD range 0.1 to -0.5, p value range 0.001 to 0.50). Mean scores for cases in each SSC diagnostic group were lower than those for controls, with the greatest differences observed among children with unilateral coronal craniosynostosis. CONCLUSIONS Children with SSC continue to show poorer performance than controls on language, learning, and memory tasks at early elementary school age, even when controlling for known confounders, although mean differences are small. Multidisciplinary care, including direct psychological assessment, for children with SSC should extend through school age with a specific focus on language and conceptual learning, as these are areas of potential risk. Future research is needed to investigate language, memory, and learning for this population during the middle to high school years.

Impact of self-assessment questions and learning styles in Web-based learning: a randomized, controlled, crossover trial.

PubMed

Cook, David A; Thompson, Warren G; Thomas, Kris G; Thomas, Matthew R; Pankratz, V Shane

2006-03-01

To determine the effect of self-assessment questions on learners' knowledge and format preference in a Web-based course, and investigate associations between learning styles and outcomes. The authors conducted a randomized, controlled, crossover trial in the continuity clinics of the Mayo-Rochester internal medicine residency program during the 2003-04 academic year. Case-based self-assessment questions were added to Web-based modules covering topics in ambulatory internal medicine. Participants completed two modules with questions and two modules without questions, with sequence randomly assigned. Outcomes included knowledge assessed after each module, format preference, and learning style assessed using the Index of Learning Styles. A total of 121 of 146 residents (83%) consented. Residents had higher test scores when using the question format (mean +/- standard error, 78.9% +/- 1.0) than when using the standard format (76.2% +/- 1.0, p = .006). Residents preferring the question format scored higher (79.7% +/- 1.1) than those preferring standard (69.5% +/- 2.3, p < .001). Learning styles did not affect scores except that visual-verbal "intermediate" learners (80.6% +/- 1.4) and visual learners (77.5% +/- 1.3) did better than verbal learners (70.9% +/- 3.0, p = .003 and p = .033, respectively). Sixty-five of 78 residents (83.3%, 95% CI 73.2-90.8%) preferred the question format. Learning styles were not associated with preference (p > .384). Although the question format took longer than the standard format (60.4 +/- 3.6 versus 44.3 +/- 3.3 minutes, p < .001), 55 of 77 residents (71.4%, 60.0-81.2%) reported that it was more efficient. Instructional methods that actively engage learners improve learning outcomes. These findings hold implications for both Web-based learning and "traditional" educational activities. Future research, in both Web-based learning and other teaching modalities, should focus on further defining the effectiveness of selected instructional methods in specific learning contexts.

Impact of e-learning on nurses' and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis.

PubMed

Lahti, Mari; Hätönen, Heli; Välimäki, Maritta

2014-01-01

To review the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction related to e-learning. We conducted a systematic review and meta-analysis of randomized controlled trials (RCT) to assess the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction. Electronic databases including MEDLINE (1948-2010), CINAHL (1981-2010), Psychinfo (1967-2010) and Eric (1966-2010) were searched in May 2010 and again in December 2010. All RCT studies evaluating the effectiveness of e-learning and differentiating between traditional learning methods among nurses were included. Data was extracted related to the purpose of the trial, sample, measurements used, index test results and reference standard. An extraction tool developed for Cochrane reviews was used. Methodological quality of eligible trials was assessed. 11 trials were eligible for inclusion in the analysis. We identified 11 randomized controlled trials including a total of 2491 nurses and student nurses'. First, the random effect size for four studies showed some improvement associated with e-learning compared to traditional techniques on knowledge. However, the difference was not statistically significant (p=0.39, MD 0.44, 95% CI -0.57 to 1.46). Second, one study reported a slight impact on e-learning on skills, but the difference was not statistically significant, either (p=0.13, MD 0.03, 95% CI -0.09 to 0.69). And third, no results on nurses or student nurses' satisfaction could be reported as the statistical data from three possible studies were not available. Overall, there was no statistical difference between groups in e-learning and traditional learning relating to nurses' or student nurses' knowledge, skills and satisfaction. E-learning can, however, offer an alternative method of education. In future, more studies following the CONSORT and QUOROM statements are needed to evaluate the effects of these interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance.

PubMed

Xu, Bin; Sun, Fuchun

2018-02-01

This paper addresses the dynamic surface control of uncertain nonlinear systems on the basis of composite intelligent learning and disturbance observer in presence of unknown system nonlinearity and time-varying disturbance. The serial-parallel estimation model with intelligent approximation and disturbance estimation is built to obtain the prediction error and in this way the composite law for weights updating is constructed. The nonlinear disturbance observer is developed using intelligent approximation information while the disturbance estimation is guaranteed to converge to a bounded compact set. The highlight is that different from previous work directly toward asymptotic stability, the transparency of the intelligent approximation and disturbance estimation is included in the control scheme. The uniformly ultimate boundedness stability is analyzed via Lyapunov method. Through simulation verification, the composite intelligent learning with disturbance observer can efficiently estimate the effect caused by system nonlinearity and disturbance while the proposed approach obtains better performance with higher accuracy.

Reinforcement learning solution for HJB equation arising in constrained optimal control problem.

PubMed

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong

2015-11-01

The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fault-tolerant optimised tracking control for unknown discrete-time linear systems using a combined reinforcement learning and residual compensation methodology

NASA Astrophysics Data System (ADS)

Han, Ke-Zhen; Feng, Jian; Cui, Xiaohong

2017-10-01

This paper considers the fault-tolerant optimised tracking control (FTOTC) problem for unknown discrete-time linear system. A research scheme is proposed on the basis of data-based parity space identification, reinforcement learning and residual compensation techniques. The main characteristic of this research scheme lies in the parity-space-identification-based simultaneous tracking control and residual compensation. The specific technical line consists of four main contents: apply subspace aided method to design observer-based residual generator; use reinforcement Q-learning approach to solve optimised tracking control policy; rely on robust H∞ theory to achieve noise attenuation; adopt fault estimation triggered by residual generator to perform fault compensation. To clarify the design and implementation procedures, an integrated algorithm is further constructed to link up these four functional units. The detailed analysis and proof are subsequently given to explain the guaranteed FTOTC performance of the proposed conclusions. Finally, a case simulation is provided to verify its effectiveness.

SVM-based tree-type neural networks as a critic in adaptive critic designs for control.

PubMed

Deb, Alok Kanti; Jayadeva; Gopal, Madan; Chandra, Suresh

2007-07-01

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.

WWC Review of the Report "Learning the Control of Variables Strategy in Higher and Lower Achieving Classrooms: Contributions of Explicit Instruction and Experimentation"

ERIC Educational Resources Information Center

What Works Clearinghouse, 2012

2012-01-01

The study reviewed in this paper examined three separate methods for teaching the "control of variables strategy" ("CVS"), a procedure for conducting a science experiment so that only one variable is tested and all others are held constant, or "controlled." The study analyzed data from a randomized controlled trial of…

Effects of endurance, resistance, and concurrent exercise on learning and memory after morphine withdrawal in rats.

PubMed

Zarrinkalam, Ebrahim; Heidarianpour, Ali; Salehi, Iraj; Ranjbar, Kamal; Komaki, Alireza

2016-07-15

Continuous morphine consumption contributes to the development of cognitive disorders. This work investigates the impacts of different types of exercise on learning and memory in morphine-dependent rats. Forty morphine-dependent rats were randomly divided into five groups: sedentary-dependent (Sed-D), endurance exercise-dependent (En-D), strength exercise-dependent (St-D), and combined (concurrent) exercise-dependent (Co-D). Healthy rats were used as controls (Con). After 10weeks of regular exercise (endurance, strength, and concurrent; each five days per week), spatial and aversive learning and memory were assessed using the Morris water maze and shuttle box tests. The results showed that morphine addiction contributes to deficits in spatial learning and memory. Furthermore, each form of exercise training restored spatial learning and memory performance in morphine-dependent rats to levels similar to those of healthy controls. Aversive learning and memory during the acquisition phase were not affected by morphine addiction or exercise, but were significantly decreased by morphine dependence. Only concurrent training returned the time spent in the dark compartment in the shuttle box test to control levels. These findings show that different types of exercise exert similar effects on spatial learning and memory, but show distinct effects on aversive learning and memory. Further, morphine dependence-induced deficits in cognitive function were blocked by exercise. Therefore, different exercise regimens may represent practical treatment methods for cognitive and behavioral impairments associated with morphine-related disease. Copyright © 2016 Elsevier Inc. All rights reserved.

The effect of problem-based learning with cooperative-learning strategies in surgery clerkships.

PubMed

Turan, Sevgi; Konan, Ali; Kılıç, Yusuf Alper; Özvarış, Şevkat Bahar; Sayek, Iskender

2012-01-01

Cooperative learning is used often as part of the problem-based learning (PBL) process. But PBL does not demand that students work together until all individuals master the material or share the rewards for their work together. A cooperative learning and assessment structure was introduced in a PBL course in 10-week surgery clerkship, and the difference was evaluated between this method and conventional PBL in an acute abdominal pain module. An experimental design was used. No significant differences in achievement were found between the study and control group. Both the study and control group students who scored low on the pretest made the greatest gains at the end of the education. Students in the cooperative learning group felt that cooperation helped them learn, it was fun to study and expressed satisfaction, but they complained about the amount of time the groups had to work together, difficulties of group work, and noise during the sessions. This study evaluated the impact of a cooperative learning technique (student team learning [STL]) in PBL and found no differences. The study confirms that a relationship exists between allocated study time and achievement, and student's satisfaction about using this technique. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Enhancing students' science literacy using solar cell learning multimedia containing science and nano technology

NASA Astrophysics Data System (ADS)

Eliyawati, Sunarya, Yayan; Mudzakir, Ahmad

2017-05-01

This research attempts to enhance students' science literacy in the aspects of students' science content, application context, process, and students' attitude using solar cell learning multimedia containing science and nano technology. The quasi-experimental method with pre-post test design was used to achieve these objectives. Seventy-two students of class XII at a high school were employed as research's subject. Thirty-six students were in control class and another thirty-six were in experiment class. Variance test (t-test) was performed on the average level of 95% to identify the differences of students' science literacy in both classes. As the result, there were significant different of learning outcomes between experiment class and control class. Almost half of students (41.67%) in experiment class are categorized as high. Therefore, the learning using solar cell learning multimedia can improve students' science literacy, especially in the students' science content, application context, and process aspects with n-gain(%) 59.19 (medium), 63.04 (medium), and 52.98 (medium). This study can be used to develop learning multimedia in other science context.

Design of Intelligent Robot as A Tool for Teaching Media Based on Computer Interactive Learning and Computer Assisted Learning to Improve the Skill of University Student

NASA Astrophysics Data System (ADS)

Zuhrie, M. S.; Basuki, I.; Asto B, I. G. P.; Anifah, L.

2018-01-01

The focus of the research is the teaching module which incorporates manufacturing, planning mechanical designing, controlling system through microprocessor technology and maneuverability of the robot. Computer interactive and computer-assisted learning is strategies that emphasize the use of computers and learning aids (computer assisted learning) in teaching and learning activity. This research applied the 4-D model research and development. The model is suggested by Thiagarajan, et.al (1974). 4-D Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with an objective to produce a tool of learning in the form of intelligent robot modules and kit based on Computer Interactive Learning and Computer Assisted Learning. From the data of the Indonesia Robot Contest during the period of 2009-2015, it can be seen that the modules that have been developed confirm the fourth stage of the research methods of development; disseminate method. The modules which have been developed for students guide students to produce Intelligent Robot Tool for Teaching Based on Computer Interactive Learning and Computer Assisted Learning. Results of students’ responses also showed a positive feedback to relate to the module of robotics and computer-based interactive learning.

Off-policy reinforcement learning for H∞ control design.

PubMed

Luo, Biao; Wu, Huai-Ning; Huang, Tingwen

2015-01-01

The H∞ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H∞ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.

Cognitive control over learning: Creating, clustering and generalizing task-set structure

PubMed Central

Collins, Anne G.E.; Frank, Michael J.

2013-01-01

Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID:23356780

An advanced teaching scheme for integrating problem-based learning in control education

NASA Astrophysics Data System (ADS)

Juuso, Esko K.

2018-03-01

Engineering education needs to provide both theoretical knowledge and problem-solving skills. Many topics can be presented in lectures and computer exercises are good tools in teaching the skills. Learning by doing is combined with lectures to provide additional material and perspectives. The teaching scheme includes lectures, computer exercises, case studies, seminars and reports organized as a problem-based learning process. In the gradually refining learning material, each teaching method has its own role. The scheme, which has been used in teaching two 4th year courses, is beneficial for overall learning progress, especially in bilingual courses. The students become familiar with new perspectives and are ready to use the course material in application projects.

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

NASA Technical Reports Server (NTRS)

Jacklin, Stephen A.; Schumann, Johann; Guenther, Kurt; Bosworth, John

2006-01-01

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Learning basic laparoscopic skills: a randomized controlled study comparing box trainer, virtual reality simulator, and mental training.

PubMed

Mulla, Mubashir; Sharma, Davendra; Moghul, Masood; Kailani, Obeda; Dockery, Judith; Ayis, Salma; Grange, Philippe

2012-01-01

The objectives of this study were (1) to compare different methods of learning basic laparoscopic skills using box trainer (BT), virtual reality simulator (VRS) and mental training (MT); and (2) to determine the most effective method of learning laparoscopic skills. Randomized controlled trial. King's College, London. 41 medical students were included in the study. After randomization, they were divided into 5 groups. Group 1 was the control group without training; group 2 was box trained; group 3 was also box trained with an additional practice session; group 4 was VRS trained; and group 5 was solely mentally trained. The task was to cut out a circle marked on a stretchable material. All groups were assessed after 1 week on both BT and VRS. Four main parameters were assessed, namely time, precision, accuracy, and performance. Time: On BT assessment, the box-trained group with additional practice group 3 was the fastest, and the mental-trained group 5 was the slowest. On VRS assessment, the time difference between group 3 and the control group 1 was statistically significant. Precision: On BT assessment, the box-trained groups 2 and 3 scored high, and mental trained were low on precision. On VRS assessment, the VRS-trained group ranked at the top, and the MT group was at the bottom on precision. Accuracy: On BT assessment, the box-trained group 3 was best and the mental-trained group was last. On VRS assessment, the VRS-trained group 4 scored high closely followed by box-trained groups 2 and 3. Performance: On BT assessment, the box-trained group 3 ranked above all the other groups, and the mental-trained group ranked last. On VRS assessment, the VRS group 4 scored best, followed closely by box-trained groups 2 and 3. The skills learned on box training were reproducible on both VRS and BT. However, not all the skills learned on VRS were transferable to BT. Furthermore, VRS was found to be a reliable and the most convenient method of assessment. MT alone cannot replace conventional training. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

An implementation of 7E Learning Cycle Model to Improve Student Self-esteem

NASA Astrophysics Data System (ADS)

Firdaus, F.; Priatna, N.; Suhendra, S.

2017-09-01

One of the affective factors that affect student learning outcomes is student self-esteem in mathematics, learning achievement and self-esteem influence each other. The purpose of this research is to know whether self-esteem students who get 7E learning cycle model is better than students who get conventional learning. This research method is a non-control group design. Based on the results obtained that the normal and homogeneous data so that the t test and from the test results showed there are significant differences in self-esteem students learning with 7E learning cycle model compared with students who get conventional learning. The implications of the results of this study are that students should be required to conduct many discussions, presentations and evaluations on classroom activities as these learning stages can improve students’ self-esteem especially pride in the results achieved.

PixelLearn

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri; Bornstein, Benjamin; Tang, Nghia; Roden, Joseph

2006-01-01

PixelLearn is an integrated user-interface computer program for classifying pixels in scientific images. Heretofore, training a machine-learning algorithm to classify pixels in images has been tedious and difficult. PixelLearn provides a graphical user interface that makes it faster and more intuitive, leading to more interactive exploration of image data sets. PixelLearn also provides image-enhancement controls to make it easier to see subtle details in images. PixelLearn opens images or sets of images in a variety of common scientific file formats and enables the user to interact with several supervised or unsupervised machine-learning pixel-classifying algorithms while the user continues to browse through the images. The machinelearning algorithms in PixelLearn use advanced clustering and classification methods that enable accuracy much higher than is achievable by most other software previously available for this purpose. PixelLearn is written in portable C++ and runs natively on computers running Linux, Windows, or Mac OS X.

Enhancing students’ mathematical representation and selfefficacy through situation-based learning assisted by geometer’s sketchpad program

NASA Astrophysics Data System (ADS)

Sowanto; Kusumah, Y. S.

2018-05-01

This research was conducted based on the problem of a lack of students’ mathematical representation ability as well as self-efficacy in accomplishing mathematical tasks. To overcome this problem, this research used situation-based learning (SBL) assisted by geometer’s sketchpad program (GSP). This research investigated students’ improvement of mathematical representation ability who were taught under situation-based learning (SBL) assisted by geometer’s sketchpad program (GSP) and regular method that viewed from the whole students’ prior knowledge (high, average, and low level). In addition, this research investigated the difference of students’ self-efficacy after learning was given. This research belongs to quasi experiment research using non-equivalent control group design with purposive sampling. The result of this research showed that students’ enhancement in their mathematical representation ability taught under SBL assisted by GSP was better than the regular method. Also, there was no interaction between learning methods and students prior knowledge in student’ enhancement of mathematical representation ability. There was significant difference of students’ enhancement of mathematical representation ability taught under SBL assisted by GSP viewed from students’ prior knowledge. Furthermore, there was no significant difference in terms of self-efficacy between those who were taught by SBL assisted by GSP with the regular method.

Cooperative learning in third graders' jigsaw groups for mathematics and science with and without questioning training.

PubMed

Souvignier, Elmar; Kronenberger, Julia

2007-12-01

There is much support for using cooperative methods, since important instructional aspects, such as elaboration of new information, can easily be realized by methods like 'jigsaw'. However, the impact of providing students with additional help like a questioning training and potential limitations of the method concerning the (minimum) age of the students have rarely been investigated. The study investigated the effects of cooperative methods at elementary school level. Three conditions of instruction were compared: jigsaw, jigsaw with a supplementary questioning training and teacher-guided instruction. Nine third grade classes from three schools with 208 students participated in the study. In each school, all the three instructional conditions were realized in three different classes. All classes studied three units on geometry and one unit on astronomy using the assigned instructional method. Each learning unit comprised six lessons. For each unit, an achievement test was administered as pre-test, post-test and delayed test. In the math units, no differences between the three conditions could be detected. In the astronomy unit, students benefited more from teacher-guided instruction. Differential analyses revealed that 'experts' learned more than students in teacher-guided instruction, whereas 'novices' were outperformed by the students in the control classes. Even third graders used the jigsaw method with satisfactory learning results. The modest impact of the questioning training and the low learning gains of the cooperative classes in the astronomy unit as well as high discrepancies between learning outcomes of experts and novices show that explicit instruction of explaining skills in combination with well-structured material are key issues in using the jigsaw method with younger students.

Comparing problem-based learning and lecture as methods to teach whole-systems design to engineering students

NASA Astrophysics Data System (ADS)

Dukes, Michael Dickey

The objective of this research is to compare problem-based learning and lecture as methods to teach whole-systems design to engineering students. A case study, Appendix A, exemplifying successful whole-systems design was developed and written by the author in partnership with the Rocky Mountain Institute. Concepts to be tested were then determined, and a questionnaire was developed to test students' preconceptions. A control group of students was taught using traditional lecture methods, and a sample group of students was taught using problem-based learning methods. After several weeks, the students were given the same questionnaire as prior to the instruction, and the data was analyzed to determine if the teaching methods were effective in correcting misconceptions. A statistically significant change in the students' preconceptions was observed in both groups on the topic of cost related to the design process. There was no statistically significant change in the students' preconceptions concerning the design process, technical ability within five years, and the possibility of drastic efficiency gains with current technologies. However, the results were inconclusive in determining that problem-based learning is more effective than lecture as a method for teaching the concept of whole-systems design, or vice versa.

Error amplification to promote motor learning and motivation in therapy robotics.

PubMed

Shirzad, Navid; Van der Loos, H F Machiel

2012-01-01

To study the effects of different feedback error amplification methods on a subject's upper-limb motor learning and affect during a point-to-point reaching exercise, we developed a real-time controller for a robotic manipulandum. The reaching environment was visually distorted by implementing a thirty degrees rotation between the coordinate systems of the robot's end-effector and the visual display. Feedback error amplification was provided to subjects as they trained to learn reaching within the visually rotated environment. Error amplification was provided either visually or through both haptic and visual means, each method with two different amplification gains. Subjects' performance (i.e., trajectory error) and self-reports to a questionnaire were used to study the speed and amount of adaptation promoted by each error amplification method and subjects' emotional changes. We found that providing haptic and visual feedback promotes faster adaptation to the distortion and increases subjects' satisfaction with the task, leading to a higher level of attentiveness during the exercise. This finding can be used to design a novel exercise regimen, where alternating between error amplification methods is used to both increase a subject's motor learning and maintain a minimum level of motivational engagement in the exercise. In future experiments, we will test whether such exercise methods will lead to a faster learning time and greater motivation to pursue a therapy exercise regimen.

Environmental Control System Installer/Servicer (Residential Air Conditioning Mechanic). V-TECS Guide.

ERIC Educational Resources Information Center

Meyer, Calvin F.; Benson, Robert T.

This guide provides job relevant tasks, performance objectives, performance guides, resources, learning activitites, evaluation standards, and achievement testing in the occupation of environmental control system installer/servicer (residential air conditioning mechanic). It is designed to be used with any chosen teaching method. The course…

Health Instruction Packages: Consumer--Birth Control.

ERIC Educational Resources Information Center

Pries, Rose Mary; And Others

Designed for the general public, these three learning modules utilize text, illustrations, and exercises to describe various methods of birth control. The first module, "All about Contraception for the Teenage New Mother" by Rose Mary Pries, discusses the desirability of planned pregnancy and reviews the effectiveness and side effects of…

The effects of concept and vee mappings under three learning modes on Jamaican eighth graders' knowledge of nutrition and plant reproduction

NASA Astrophysics Data System (ADS)

Ugwu, Okechukwu; Soyibo, Kola

2004-01-01

The first objective of this study was to investigate if the experimental students' post-test knowledge of nutrition and plant reproduction would be improved more significantly than that of their control group counterparts based on their treatment, attitudes to science, self-esteem, gender and socio-economic background. Treatment involved teaching the experimental students under three learning modes--pure cooperative, cooperative-competitive and individualistic whole class interpersonal competitive condition--using concept and vee mappings and the lecture method. The control groups received the same treatment but were not exposed to concept and vee mappings. This study's second objective was to determine which of the three learning modes would produce the highest post-test mean gain in the subjects' knowledge of the two biology concepts. The study's sample comprised 932 eighth graders (12-13-year-olds) in 14 co-educational comprehensive high schools randomly selected from two Jamaican parishes. An integrated science performance test, an attitudes to science questionnaire and a self-esteem questionnaire were used to collect data. The results indicated that the experimental students (a) under the three learning modes, (b) with high, moderate, and low attitudes to science, and (c) with high, moderate, and low self-esteem, performed significantly better than their control group counterparts. The individualist whole class learning mode engendered the highest mean gain on the experimental students' knowledge, while the cooperative-competitive learning mode generated the highest mean gain for the control group students.

Studies of learned helplessness in honey bees (Apis mellifera ligustica).

PubMed

Dinges, Christopher W; Varnon, Christopher A; Cota, Lisa D; Slykerman, Stephen; Abramson, Charles I

2017-04-01

The current study reports 2 experiments investigating learned helplessness in the honey bee (Apis mellifera ligustica). In Experiment 1, we used a traditional escape method but found the bees' activity levels too high to observe changes due to treatment conditions. The bees were not able to learn in this traditional escape procedure; thus, such procedures may be inappropriate to study learned helplessness in honey bees. In Experiment 2, we used an alternative punishment, or passive avoidance, method to investigate learned helplessness. Using a master and yoked design where bees were trained as either master or yoked and tested as either master or yoked, we found that prior training with unavoidable and inescapable shock in the yoked condition interfered with avoidance and escape behavior in the later master condition. Unlike control bees, learned helplessness bees failed to restrict their movement to the safe compartment following inescapable shock. Unlike learned helplessness studies in other animals, no decrease in general activity was observed. Furthermore, we did not observe a "freezing" response to inescapable aversive stimuli-a phenomenon, thus far, consistently observed in learned helplessness tests with other species. The bees, instead, continued to move back and forth between compartments despite punishment in the incorrect compartment. These findings suggest that, although traditional escape methods may not be suitable, honey bees display learned helplessness in passive avoidance procedures. Thus, regardless of behavioral differences from other species, honey bees can be a unique invertebrate model organism for the study of learned helplessness. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Is literature search training for medical students and residents effective? a literature review.

PubMed

Just, Melissa L

2012-10-01

This literature review examines the effectiveness of literature searching skills instruction for medical students or residents, as determined in studies that either measure learning before and after an intervention or compare test and control groups. The review reports on the instruments used to measure learning and on their reliability and validity, where available. Finally, a summary of learning outcomes is presented. Fifteen studies published between 1998 and 2011 were identified for inclusion in the review. The selected studies all include a description of the intervention, a summary of the test used to measure learning, and the results of the measurement. Instruction generally resulted in improvement in clinical question writing, search strategy construction, article selection, and resource usage. Although the findings of most of the studies indicate that the current instructional methods are effective, the study designs are generally weak, there is little evidence that learning persists over time, and few validated methods of skill measurement have been developed.

Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data

NASA Astrophysics Data System (ADS)

Stoecklein, Daniel; Lore, Kin Gwn; Davies, Michael; Sarkar, Soumik; Ganapathysubramanian, Baskar

2017-04-01

A new technique for shaping microfluid flow, known as flow sculpting, offers an unprecedented level of passive fluid flow control, with potential breakthrough applications in advancing manufacturing, biology, and chemistry research at the microscale. However, efficiently solving the inverse problem of designing a flow sculpting device for a desired fluid flow shape remains a challenge. Current approaches struggle with the many-to-one design space, requiring substantial user interaction and the necessity of building intuition, all of which are time and resource intensive. Deep learning has emerged as an efficient function approximation technique for high-dimensional spaces, and presents a fast solution to the inverse problem, yet the science of its implementation in similarly defined problems remains largely unexplored. We propose that deep learning methods can completely outpace current approaches for scientific inverse problems while delivering comparable designs. To this end, we show how intelligent sampling of the design space inputs can make deep learning methods more competitive in accuracy, while illustrating their generalization capability to out-of-sample predictions.

Improving the Critic Learning for Event-Based Nonlinear $H_{\\infty }$ Control Design.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

In this paper, we aim at improving the critic learning criterion to cope with the event-based nonlinear H ∞ state feedback control design. First of all, the H ∞ control problem is regarded as a two-player zero-sum game and the adaptive critic mechanism is used to achieve the minimax optimization under event-based environment. Then, based on an improved updating rule, the event-based optimal control law and the time-based worst-case disturbance law are obtained approximately by training a single critic neural network. The initial stabilizing control is no longer required during the implementation process of the new algorithm. Next, the closed-loop system is formulated as an impulsive model and its stability issue is handled by incorporating the improved learning criterion. The infamous Zeno behavior of the present event-based design is also avoided through theoretical analysis on the lower bound of the minimal intersample time. Finally, the applications to an aircraft dynamics and a robot arm plant are carried out to verify the efficient performance of the present novel design method.

Iterative learning control with applications in energy generation, lasers and health care

PubMed Central

Tutty, O. R.

2016-01-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

Teaching Organic Chemistry via Student-Directed Learning: A Technique that Promotes Independence and Responsibility in the Student

NASA Astrophysics Data System (ADS)

Katz, Marlene

1996-05-01

One of the frustrations of teaching is the failure of talented students due to lack of effort on their part. We have to admit that Organic chemistry presents many challenges to students. At the same time we are aware that students often defeat themselves by a combination of procrastination and cramming. The Student-Directed Learning (SDL) method discourages this student strategy. Instead SDL fosters increased self-confidence, independence, and an awareness of the student's role in the teaching/learning process. This method incorporates four criteria for acceptance of responsibility: student ownership, student-active learning, student accountability, and student control. With SDL the course content is reorganized to make it more accessible to students. Learning modules are centered around "The Big Ideas". Each big idea is connected to its usefulness in pharmaceutical science, or is identified as a foundation idea for understanding subsequent course material. The class session is changed from traditional lecture to continuous dialogue between teacher and learners. Reading quizzes emphasize the importance of conscientious preparation for class. Structured retesting is offered to increase student self-confidence and learning. The extra effort required by the SDL method is more than compensated for by the improved grades, ACS exam scores, and student attitudes towards the course.

The Effect of Mastery Learning Model with Reflective Thinking Activities on Medical Students' Academic Achievement: An Experimental Study

ERIC Educational Resources Information Center

Elaldi, Senel

2016-01-01

This study aimed to determine the effect of mastery learning model supported with reflective thinking activities on the fifth grade medical students' academic achievement. Mixed methods approach was applied in two samples (n = 64 and n = 6). Quantitative part of the study was based on a pre-test-post-test control group design with an experiment…

Working Memory Deficits in ADHD: The Contribution of Age, Learning/Language Difficulties, and Task Parameters

ERIC Educational Resources Information Center

Sowerby, Paula; Seal, Simon; Tripp, Gail

2011-01-01

Objective: To further define the nature of working memory (WM) impairments in children with combined-type ADHD. Method: A total of 40 Children with ADHD and an age and gender-matched control group (n = 40) completed two measures of visuo-spatial WM and two measures of verbal WM. The effects of age and learning/language difficulties on performance…

Pharmacy Students’ Performance and Perceptions in a Flipped Teaching Pilot on Cardiac Arrhythmias

PubMed Central

Ip, Eric J.; Lopes, Ingrid; Rajagopalan, Vanishree

2014-01-01

Objective. To implement the flipped teaching method in a 3-class pilot on cardiac arrhythmias and to assess the impact of the intervention on academic performance and student perceptions. Design. An intervention group of 101 first-year pharmacy students, who took the class with the flipped teaching method, were supplied with prerecorded lectures prior to their 3 classes (1 class in each of the following subjects: basic sciences, pharmacology, and therapeutics) on cardiac arrhythmias. Class time was focused on active-learning and case-based exercises. Students then took a final examination that included questions on cardiac arrhythmias. The examination scores of the intervention group were compared to scores of the Spring 2011 control group of 105 first-year students who took the class with traditional teaching methods. An online survey was conducted to assess student feedback from the intervention group. Assessment. The mean examination scores of the intervention group were significantly higher than the mean examination scores of the control group for the cardiac arrhythmia classes in pharmacology (with 89.6 ± 2.0% vs 56.8 ± 2.2%, respectively) and therapeutics (89.2 ± 1.4% vs 73.7 ± 2.1%, respectively). The survey indicated higher student satisfaction for flipped classes with highly rated learning objectives, recordings, and in-class activities. Conclusion. Use of the flipped teaching method in a 3-class pilot on cardiac arrhythmias improved examination scores for 2 of the 3 classes (pharmacology and therapeutics). Student satisfaction was influenced by the quality of the learning objectives, prerecorded lectures, and inclass active-learning activities. PMID:25657372

The role of multisensor data fusion in neuromuscular control of a sagittal arm with a pair of muscles using actor-critic reinforcement learning method.

PubMed

Golkhou, V; Parnianpour, M; Lucas, C

2004-01-01

In this study, we consider the role of multisensor data fusion in neuromuscular control using an actor-critic reinforcement learning method. The model we use is a single link system actuated by a pair of muscles that are excited with alpha and gamma signals. Various physiological sensor information such as proprioception, spindle sensors, and Golgi tendon organs have been integrated to achieve an oscillatory movement with variable amplitude and frequency, while achieving a stable movement with minimum metabolic cost and coactivation. The system is highly nonlinear in all its physical and physiological attributes. Transmission delays are included in the afferent and efferent neural paths to account for a more accurate representation of the reflex loops. This paper proposes a reinforcement learning method with an Actor-Critic architecture instead of middle and low level of central nervous system (CNS). The Actor in this structure is a two layer feedforward neural network and the Critic is a model of the cerebellum. The Critic is trained by the State-Action-Reward-State-Action (SARSA) method. The Critic will train the Actor by supervisory learning based on previous experiences. The reinforcement signal in SARSA is evaluated based on available alternatives concerning the concept of multisensor data fusion. The effectiveness and the biological plausibility of the present model are demonstrated by several simulations. The system showed excellent tracking capability when we integrated the available sensor information. Addition of a penalty for activation of muscles resulted in much lower muscle coactivation while keeping the movement stable.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Mathematical Problem Solving Ability of Junior High School Students through Ang’s Framework for Mathematical Modelling Instruction

NASA Astrophysics Data System (ADS)

Fasni, N.; Turmudi, T.; Kusnandi, K.

2017-09-01

This research background of this research is the importance of student problem solving abilities. The purpose of this study is to find out whether there are differences in the ability to solve mathematical problems between students who have learned mathematics using Ang’s Framework for Mathematical Modelling Instruction (AFFMMI) and students who have learned using scientific approach (SA). The method used in this research is a quasi-experimental method with pretest-postest control group design. Data analysis of mathematical problem solving ability using Indepent Sample Test. The results showed that there was a difference in the ability to solve mathematical problems between students who received learning with Ang’s Framework for Mathematical Modelling Instruction and students who received learning with a scientific approach. AFFMMI focuses on mathematical modeling. This modeling allows students to solve problems. The use of AFFMMI is able to improve the solving ability.

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

PubMed

Sajid, Muhammad R; Laheji, Abrar F; Abothenain, Fayha; Salam, Yezan; AlJayar, Dina; Obeidat, Akef

2016-09-04

To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.

Adaptive dynamic programming approach to experience-based systems identification and control.

PubMed

Lendaris, George G

2009-01-01

Humans have the ability to make use of experience while selecting their control actions for distinct and changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method "up a level", away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and effectively select designs from a repository of existing controller solutions (perhaps previously developed via application of ADP methods). The resulting approach is called Higher-Level Learning Algorithm. The approach and its rationale are described and some examples of its application are given. The notions of context and context discernment are important to understanding the human abilities noted above. These are first defined, in a manner appropriate to controls and system-identification, and as a foundation relating to the application arena, a historical view of the various phases during development of the controls field is given, organized by how the notion 'context' was, or was not, involved in each phase.

Effect of an EBM course in combination with case method learning sessions: an RCT on professional performance, job satisfaction, and self-efficacy of occupational physicians

PubMed Central

Schaafsma, Frederieke G.; Nieuwenhuijsen, Karen; van Dijk, Frank J. H.

2008-01-01

Objective An intervention existing of an evidence-based medicine (EBM) course in combination with case method learning sessions (CMLSs) was designed to enhance the professional performance, self-efficacy and job satisfaction of occupational physicians. Methods A cluster randomized controlled trial was set up and data were collected through questionnaires at baseline (T0), directly after the intervention (T1) and 7 months after baseline (T2). The data of the intervention group [T0 (n = 49), T1 (n = 31), T2 (n = 29)] and control group [T0 (n = 49), T1 (n = 28), T2 (n = 28)] were analysed in mixed model analyses. Mean scores of the perceived value of the CMLS were calculated in the intervention group. Results The overall effect of the intervention over time comparing the intervention with the control group was statistically significant for professional performance (p < 0.001). Job satisfaction and self-efficacy changes were small and not statistically significant between the groups. The perceived value of the CMLS to gain new insights and to improve the quality of their performance increased with the number of sessions followed. Conclusion An EBM course in combination with case method learning sessions is perceived as valuable and offers evidence to enhance the professional performance of occupational physicians. However, it does not seem to influence their self-efficacy and job satisfaction. PMID:18386046

The effect of active learning on college students' achievement, motivation, and self-efficacy in a human physiology course for non-majors

NASA Astrophysics Data System (ADS)

Wilke, Roger Russell

2000-10-01

This study investigated the effects active learning strategies had on college students' achievement, motivation, and self-efficacy, in a human physiology course for non-majors. A continuum-based active learning instructional model was implemented over the course of a semester to assess the effects on the variables and specific student outcomes of learning mentioned above. In addition analyses were conducted to explore what learner characteristics contributed to the successful implementation of the model such as students' gender, classification, major, grade point average, ACT and SAT scores, motivation, and self-efficacy. A quasi-experimental, Solomon-4 Group design was undertaken on 171 students in a small west-Texas university. Treatment groups were taught using the model while controls were taught using traditional lecture methods. Students were administered a comprehensive physiology content exam, sections of the Motivated Strategies for Learning Questionnaire, and attitude surveys to assess the effects of the continuum-based active learning strategies. Factorial analyses indicated the treatment group acquired significantly more content knowledge and were significantly more self-efficacious than students in the control group. There were no significant differences in motivation. Factorial and modified regression analyses in the aptitude by treatment interaction exploration determined that males in the treatment group performed significantly better on the comprehensive physiology content exam versus males in the control group. While females performed better overall than males, there were no significant differences in achievement between females in the treatment group and those in the control. No significant interactions were found for the other learner characteristics. The results also indicated that students' general cognitive ability as measured by their grade point average, ACT, and SAT scores and their self-efficacy contributed significantly to their achievement. Attitude surveys indicated that students in both the treatment and control groups demonstrated a positive attitude toward active learning, believed it helped them to learn the material, and would choose an active learning course in the future if given the opportunity. This study demonstrated that continuum-based active learning strategies used in this context, improved students' content acquisition and self-efficacy and had wide applicability with a number of learner characteristics.

Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems.

PubMed

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

2017-07-01

This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

PubMed

Li, Yang; Yang, Jianyi

2017-04-24

The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pearson correlation coefficient to about 0.8 on the core set of the PDBbind 2007 database, which is significantly higher than the performance of any conventional scoring function on the same benchmark. A few studies have been made to discuss the performance of machine-learning-based methods, but the reason for this improvement remains unclear. In this study, by systemically controlling the structural and sequence similarity between the training and test proteins of the PDBbind benchmark, we demonstrate that protein structural and sequence similarity makes a significant impact on machine-learning-based methods. After removal of training proteins that are highly similar to the test proteins identified by structure alignment and sequence alignment, machine-learning-based methods trained on the new training sets do not outperform the conventional scoring functions any more. On the contrary, the performance of conventional functions like X-Score is relatively stable no matter what training data are used to fit the weights of its energy terms.

Learning from Simple Ebooks, Online Cases or Classroom Teaching When Acquiring Complex Knowledge. A Randomized Controlled Trial in Respiratory Physiology and Pulmonology

PubMed Central

Worm, Bjarne Skjødt

2013-01-01

Background and Aims E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. Methods 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. Results For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). Conclusions E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method. PMID:24039917

PASCAL vs BASIC

ERIC Educational Resources Information Center

Mundie, David A.

1978-01-01

A comparison between PASCAL and BASIC as general purpose microprocessor languages rates PASCAL above BASIC in such points as program structure, data types, structuring methods, control structures, procedures and functions, and ease in learning. (CMV)

Inhibition of Vicariously Learned Fear in Children Using Positive Modeling and Prior Exposure

PubMed Central

2015-01-01

One of the challenges to conditioning models of fear acquisition is to explain how different individuals can experience similar learning events and only some of them subsequently develop fear. Understanding factors moderating the impact of learning events on fear acquisition is key to understanding the etiology and prevention of fear in childhood. This study investigates these moderators in the context of vicarious (observational) learning. Two experiments tested predictions that the acquisition or inhibition of fear via vicarious learning is driven by associative learning mechanisms similar to direct conditioning. In Experiment 1, 3 groups of children aged 7 to 9 years received 1 of 3 inhibitive information interventions—psychoeducation, factual information, or no information (control)—prior to taking part in a vicarious fear learning procedure. In Experiment 2, 3 groups of children aged 7 to 10 years received 1 of 3 observational learning interventions—positive modeling (immunization), observational familiarity (latent inhibition), or no prevention (control)—before vicarious fear learning. Results indicated that observationally delivered manipulations inhibited vicarious fear learning, while preventions presented via written information did not. These findings confirm that vicarious learning shares some of the characteristics of direct conditioning and can explain why not all individuals will develop fear following a vicarious learning event. They also suggest that the modality of inhibitive learning is important and should match the fear learning pathway for increased chances of inhibition. Finally, the results demonstrate that positive modeling is likely to be a particularly effective method for preventing fear-related observational learning in children. PMID:26653136

Inhibition of vicariously learned fear in children using positive modeling and prior exposure.

PubMed

Askew, Chris; Reynolds, Gemma; Fielding-Smith, Sarah; Field, Andy P

2016-02-01

One of the challenges to conditioning models of fear acquisition is to explain how different individuals can experience similar learning events and only some of them subsequently develop fear. Understanding factors moderating the impact of learning events on fear acquisition is key to understanding the etiology and prevention of fear in childhood. This study investigates these moderators in the context of vicarious (observational) learning. Two experiments tested predictions that the acquisition or inhibition of fear via vicarious learning is driven by associative learning mechanisms similar to direct conditioning. In Experiment 1, 3 groups of children aged 7 to 9 years received 1 of 3 inhibitive information interventions-psychoeducation, factual information, or no information (control)-prior to taking part in a vicarious fear learning procedure. In Experiment 2, 3 groups of children aged 7 to 10 years received 1 of 3 observational learning interventions-positive modeling (immunization), observational familiarity (latent inhibition), or no prevention (control)-before vicarious fear learning. Results indicated that observationally delivered manipulations inhibited vicarious fear learning, while preventions presented via written information did not. These findings confirm that vicarious learning shares some of the characteristics of direct conditioning and can explain why not all individuals will develop fear following a vicarious learning event. They also suggest that the modality of inhibitive learning is important and should match the fear learning pathway for increased chances of inhibition. Finally, the results demonstrate that positive modeling is likely to be a particularly effective method for preventing fear-related observational learning in children. (c) 2016 APA, all rights reserved).

Data quality assurance and control in cognitive research: Lessons learned from the PREDICT-HD study.

PubMed

Westervelt, Holly James; Bernier, Rachel A; Faust, Melanie; Gover, Mary; Bockholt, H Jeremy; Zschiegner, Roland; Long, Jeffrey D; Paulsen, Jane S

2017-09-01

We discuss the strategies employed in data quality control and quality assurance for the cognitive core of Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a long-term observational study of over 1,000 participants with prodromal Huntington disease. In particular, we provide details regarding the training and continual evaluation of cognitive examiners, methods for error corrections, and strategies to minimize errors in the data. We present five important lessons learned to help other researchers avoid certain assumptions that could potentially lead to inaccuracies in their cognitive data. Copyright © 2017 John Wiley & Sons, Ltd.

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls.

PubMed

Kiiski, Hanni; Jollans, Lee; Donnchadha, Seán Ó; Nolan, Hugh; Lonergan, Róisín; Kelly, Siobhán; O'Brien, Marie Claire; Kinsella, Katie; Bramham, Jessica; Burke, Teresa; Hutchinson, Michael; Tubridy, Niall; Reilly, Richard B; Whelan, Robert

2018-05-01

Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded during an oddball task would predict cognitive functioning and information processing speed in Multiple Sclerosis (MS) patients and controls at the individual level. Seventy-eight participants (35 MS patients, 43 healthy age-matched controls) completed visual and auditory 2- and 3-stimulus oddball tasks with 128-channel EEG, and a neuropsychological battery, at baseline (month 0) and at Months 13 and 26. ERPs from 0 to 700 ms and across the whole scalp were transformed into 1728 individual spatio-temporal datapoints per participant. A machine learning method that included penalized linear regression used the entire spatio-temporal ERP to predict composite scores of both cognitive functioning and processing speed at baseline (month 0), and months 13 and 26. The results showed ERPs during the visual oddball tasks could predict cognitive functioning and information processing speed at baseline and a year later in a sample of MS patients and healthy controls. In contrast, ERPs during auditory tasks were not predictive of cognitive performance. These objective neurophysiological indicators of cognitive functioning and processing speed, and machine learning methods that can interrogate high-dimensional data, show promise in outcome prediction.

Foot Pedals for Spacecraft Manual Control

NASA Technical Reports Server (NTRS)

Love, Stanley G.; Morin, Lee M.; McCabe, Mary

2010-01-01

Fifty years ago, NASA decided that the cockpit controls in spacecraft should be like the ones in airplanes. But controls based on the stick and rudder may not be best way to manually control a vehicle in space. A different method is based on submersible vehicles controlled with foot pedals. A new pilot can learn the sub's control scheme in minutes and drive it hands-free. We are building a pair of foot pedals for spacecraft control, and will test them in a spacecraft flight simulator.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration.

PubMed

Siu, Ho Chit; Arenas, Ana M; Sun, Tingxiao; Stirling, Leia A

2018-02-05

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

PubMed Central

Arenas, Ana M.; Sun, Tingxiao

2018-01-01

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. PMID:29401754

Use of e-learning in clinical clerkships: effects on acquisition of dermatological knowledge and learning processes.

PubMed

Fransen, Frederike; Martens, Herm; Nagtzaam, Ivo; Heeneman, Sylvia

2018-01-17

To obtain a deeper understanding of how the e-learning program, Education in Dermatology (ED), affects the acquisition of dermatological knowledge and the underlying learning processes of medical students in their clinical phase. The study used a mixed method design with a convergent parallel collection of data. Medical students (n=62) from Maastricht University (The Netherlands) were randomized to either a conventional teaching group (control group n=30) or conventional teaching plus the e-learning program (application on smartphone) group (e-learning group n=32). Pre- and post-intervention knowledge test results were analysed using an independent t-test. Individual semi-structured interviews (n=9) were conducted and verbatim-transcribed recordings were analysed using King's template analysis. The e-learning program positively influenced students' level of knowledge and their process of learning. A significant difference was found in the post-test scores for the control group (M=51.4, SD=6.43) and the e-learning group (M=73.09, SD=5.12); t(60)=-14.75, p<0.000). Interview data showed that the e-learning program stimulated students' learning as the application promoted the identification and recognition of skin disorders, the use of references, creation of documents and sharing information with colleagues. This study demonstrated that use of the e-learning program led to a significant improvement in basic dermatological knowledge. The underlying learning processes indicated that e-learning programs in dermatology filled a vital gap in the understanding of clinical reasoning in dermatology. These results might be useful when developing (clinical) teaching formats with a special focus on visual disciplines.

The effect of web quest and team-based learning on students’ self-regulation

PubMed Central

BADIYEPEYMAIE JAHROMI, ZOHREH; MOSALANEJAD, LEILI; REZAEE, RITA

2016-01-01

Introduction In this study, the authors aimed to examine the effects of cooperative learning methods using Web Quest and team-based learning on students’ self-direction, self-regulation, and academic achievement. Method This is a comparative study of students taking a course in mental health and psychiatric disorders. In two consecutive years, a group of students were trained using the WebQuest approach as a teaching strategy (n = 38), while the other group was taught using team-based learning (n=39). Data gathering was based on Guglielmino’s self-directed learning readiness scale (SDLRS) and Buford’s self-regulation questionnaire. The data were analyzed by descriptive test using M (IQR), Wilcoxon signed-rank test, and the Mann–Whitney U-test in SPSS software, version 13. p<0.05 was considered as the significance level. Results The results of the Mann–Whitney U test showed that the participants’ self- directed (self-management) and self-regulated learning differed between the two groups (p=0.04 and p=0.01, respectively). Wilcoxon test revealed that self-directed learning indices (self-control and self-management) were differed between the two strategies before and after the intervention. However, the scores related to learning (students’ final scores) were higher in the WebQuest approach than in team-based learning. Conclusion By employing modern educational approaches, students are not only more successful in their studies but also acquire the necessary professional skills for future performance. Further research to compare the effects of new methods of teaching is required. PMID:27104202

The use of an android–based-game in the team assisted individualization to improve students’ creativity and cognitive achievement in chemistry

NASA Astrophysics Data System (ADS)

Sugiyarto, K. H.; Ikhsan, J.; Lukman, I. R.

2018-05-01

The use of information and communication technology (ICT) in learning process resulted in positive impact to students’ output. This research investigated the difference of improvement of students’ creativity and cognitive achievement due to the use of android-based games on Chemistry Nomenclature in learning method of team-assisted individualization (TAI) into the improvement of students’ creativity and cognitive achievement. This was an quasi experiment research with non-equivalent pretest-posttest control group design involving 2 groups of students of X grade of a senior high school in Yogyakarta, Indonesia, SMAN 1 Seyegan, Sleman. The groups were experiment and control which were chosen randomly, involving 32 students in each group. The difference of learning model in the two groups were the use of android-based games within learning model of TAI in the experiment group, but it was only the use of TAI model in control group. The android-based games were developed and validated previously in this investigation, and were excellent in quality for the use in Chemistry learning process, and were reported separately. The data of both students’ creativity and cognitive achievement were measured before and after learning process. Data of students’ creativity were collected with the instruments of questionnaire and observation sheets, and the data of cognitive achievement were collected with a set of test. Statistical analysis of MANOVA was used to analyze data to measure the difference of the improvement of students’ creativity and cognitive achievement between experiment and control groups. The results showed that the improvement of creativity and cognitive achievement of students in the experiment group was higher significantly than that in control group.

Automatic labeling of MR brain images through extensible learning and atlas forests.

PubMed

Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

2017-12-01

Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic atlas datasets and obtain accurate results. © 2017 American Association of Physicists in Medicine.

Applying flow chemistry: methods, materials, and multistep synthesis.

PubMed

McQuade, D Tyler; Seeberger, Peter H

2013-07-05

The synthesis of complex molecules requires control over both chemical reactivity and reaction conditions. While reactivity drives the majority of chemical discovery, advances in reaction condition control have accelerated method development/discovery. Recent tools include automated synthesizers and flow reactors. In this Synopsis, we describe how flow reactors have enabled chemical advances in our groups in the areas of single-stage reactions, materials synthesis, and multistep reactions. In each section, we detail the lessons learned and propose future directions.

The scientific learning approach using multimedia-based maze game to improve learning outcomes

NASA Astrophysics Data System (ADS)

Setiawan, Wawan; Hafitriani, Sarah; Prabawa, Harsa Wara

2016-02-01

The objective of curriculum 2013 is to improve the quality of education in Indonesia, which leads to improving the quality of learning. The scientific approach and supported empowerment media is one approach as massaged of curriculum 2013. This research aims to design a labyrinth game based multimedia and apply in the scientific learning approach. This study was conducted in one of the Vocational School in Subjects of Computer Network on 2 (two) classes of experimental and control. The method used Mix Method Research (MMR) which combines qualitative in multimedia design, and quantitative in the study of learning impact. The results of a survey showed that the general of vocational students like of network topology material (68%), like multimedia (74%), and in particular, like interactive multimedia games and flash (84%). Multimediabased maze game developed good eligibility based on media and material aspects of each value 840% and 82%. Student learning outcomes as a result of using a scientific approach to learning with a multimediabased labyrinth game increase with an average of gain index about (58%) and higher than conventional multimedia with index average gain of 0.41 (41%). Based on these results the scientific approach to learning by using multimediabased labyrinth game can improve the quality of learning and increase understanding of students. Multimedia of learning based labyrinth game, which developed, got a positive response from the students with a good qualification level (75%).