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.

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.

Project-Based Learning in Programmable Logic Controller

NASA Astrophysics Data System (ADS)

Seke, F. R.; Sumilat, J. M.; Kembuan, D. R. E.; Kewas, J. C.; Muchtar, H.; Ibrahim, N.

2018-02-01

Project-based learning is a learning method that uses project activities as the core of learning and requires student creativity in completing the project. The aims of this study is to investigate the influence of project-based learning methods on students with a high level of creativity in learning the Programmable Logic Controller (PLC). This study used experimental methods with experimental class and control class consisting of 24 students, with 12 students of high creativity and 12 students of low creativity. The application of project-based learning methods into the PLC courses combined with the level of student creativity enables the students to be directly involved in the work of the PLC project which gives them experience in utilizing PLCs for the benefit of the industry. Therefore, it’s concluded that project-based learning method is one of the superior learning methods to apply on highly creative students to PLC courses. This method can be used as an effort to improve student learning outcomes and student creativity as well as to educate prospective teachers to become reliable educators in theory and practice which will be tasked to create qualified human resources candidates in order to meet future industry needs.

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Effects of Locus of Control and Learner-Control on Web-Based Language Learning

ERIC Educational Resources Information Center

Chang, Mei-Mei; Ho, Chiung-Mei

2009-01-01

The study explored the effects of students' locus of control and types of control over instruction on their self-efficacy and performance in a web-based language learning environment. A web-based interactive instructional program focusing on the comprehension of news articles for English language learners was developed in two versions: learner-…

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

NASA Astrophysics Data System (ADS)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

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.

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.

Case-based e-learning to improve the attitude of medical students towards occupational health, a randomised controlled trial.

PubMed

Smits, P B A; de Graaf, L; Radon, K; de Boer, A G; Bos, N R; van Dijk, F J H; Verbeek, J H A M

2012-04-01

Undergraduate medical teaching in occupational health (OH) is a challenge in universities around the world. Case-based e-learning with an attractive clinical context could improve the attitude of medical students towards OH. The study question is whether case-based e-learning for medical students is more effective in improving knowledge, satisfaction and a positive attitude towards OH than non-case-based textbook learning. Participants, 141 second year medical students, were randomised to either case-based e-learning or text-based learning. Outcome measures were knowledge, satisfaction and attitude towards OH, measured at baseline, directly after the intervention, after 1 week and at 3-month follow-up. Of the 141 participants, 130 (92%) completed the questionnaires at short-term follow-up and 41 (29%) at 3-month follow-up. At short-term follow-up, intervention and control groups did not show a significant difference in knowledge nor satisfaction but attitude towards OH was significantly more negative in the intervention group (F=4.041, p=0.047). At 3-month follow-up, there were no significant differences between intervention and control groups for knowledge, satisfaction and attitude. We found a significant decrease in favourable attitude during the internship in the experimental group compared with the control group. There were no significant differences in knowledge or satisfaction between case-based e-learning and text-based learning. The attitude towards OH should be further investigated as an outcome of educational programmes.

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.

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.

Comparing Problem-Based Learning Students to Students in a Lecture-Based Curriculum: Learning Strategies and the Relation with Self-Study Time

ERIC Educational Resources Information Center

Wijnen, Marit; Loyens, Sofie M. M.; Smeets, Guus; Kroeze, Maarten; van der Molen, Henk

2017-01-01

In educational theory, deep processing (i.e., connecting different study topics together) and self-regulation (i.e., taking control over one's own learning process) are considered effective learning strategies. These learning strategies can be influenced by the learning environment. Problem-based learning (PBL), a student-centered educational…

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

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.

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.

The effectiveness of research-based physics learning module with predict-observe-explain strategies to improve the student’s competence

NASA Astrophysics Data System (ADS)

Usmeldi

2018-05-01

The preliminary study shows that many students are difficult to master the concept of physics. There are still many students who have not mastery learning physics. Teachers and students still use textbooks. Students rarely do experiments in the laboratory. One model of learning that can improve students’ competence is a research-based learning with Predict- Observe-Explain (POE) strategies. To implement this learning, research-based physics learning modules with POE strategy are used. The research aims to find out the effectiveness of implementation of research-based physics learning modules with POE strategy to improving the students’ competence. The research used a quasi-experimental with pretest-posttest group control design. Data were collected using observation sheets, achievement test, skill assessment sheets, questionnaire of attitude and student responses to learning implementation. The results of research showed that research-based physics learning modules with POE strategy was effective to improve the students’ competence, in the case of (1) mastery learning of physics has been achieved by majority of students, (2) improving the students competency of experimental class including high category, (3) there is a significant difference between the average score of students’ competence of experimental class and the control class, (4) the average score of the students competency of experimental class is higher than the control class, (5) the average score of the students’ responses to the learning implementation is very good category, this means that most students can implement research-based learning with POE strategies.

Punishment Learning in U.S. Veterans With Posttraumatic Stress Disorder.

PubMed

Sawyer, Alice T; Liverant, Gabrielle I; Jun, Janie J; Lee, Daniel J; Cohen, Andrew L; Dutra, Sunny J; Pizzagalli, Diego A; Sloan, Denise M

2016-08-01

Learning processes have been implicated in the development and course of posttraumatic stress disorder (PTSD); however, little is currently known about punishment-based learning in PTSD. The current study investigated impairments in punishment-based learning in U.S. veterans. We expected that veterans with PTSD would demonstrate greater punishment-based learning compared to a non-PTSD control group. We compared a PTSD group with and without co-occurring depression (n = 27) to a control group (with and without trauma exposure) without PTSD or depression (n = 29). Participants completed a computerized probabilistic punishment-based learning task. Compared to the non-PTSD control group, veterans with PTSD showed significantly greater punishment-based learning. Specifically, there was a significant Block × Group interaction, F(1, 54) = 4.12, p = .047, η(2) = .07. Veterans with PTSD demonstrated greater change in response bias for responding toward a less frequently punished stimulus across blocks. The observed hypersensitivity to punishment in individuals with PTSD may contribute to avoidant responses that are not specific to trauma cues. Copyright © 2016 International Society for Traumatic Stress Studies No claim to original US government works.

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.

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.

Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous

NASA Technical Reports Server (NTRS)

Karr, C. L.; Freeman, L. M.; Meredith, D. L.

1990-01-01

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

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.

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results

NASA Technical Reports Server (NTRS)

Jani, Yashvant

1992-01-01

The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.

A Reactive Blended Learning Proposal for an Introductory Control Engineering Course

ERIC Educational Resources Information Center

Mendez, Juan A.; Gonzalez, Evelio J.

2010-01-01

As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element--a Fuzzy Logic based controller--is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the…

Power to the People: Using Learner Control to Improve Trainee Reactions and Learning in Web-Based Instructional Environments

ERIC Educational Resources Information Center

Orvis, Karin A.; Fisher, Sandra L.; Wasserman, Michael E.

2009-01-01

This experimental study investigated the mechanisms by which learner control influences learning in an e-learning environment. The authors hypothesized that learner control would enhance learning indirectly through its effect on trainee reactions and learner engagement (in particular, off-task attention), such that learners who were more satisfied…

Indirect decentralized repetitive control

NASA Technical Reports Server (NTRS)

Lee, Soo Cheol; Longman, Richard W.

1993-01-01

Learning control refers to controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

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

The effectiveness of a clinically integrated e-learning course in evidence-based medicine: a cluster randomised controlled trial.

PubMed

Kulier, Regina; Coppus, Sjors F P J; Zamora, Javier; Hadley, Julie; Malick, Sadia; Das, Kausik; Weinbrenner, Susanne; Meyerrose, Berrit; Decsi, Tamas; Horvath, Andrea R; Nagy, Eva; Emparanza, Jose I; Arvanitis, Theodoros N; Burls, Amanda; Cabello, Juan B; Kaczor, Marcin; Zanrei, Gianni; Pierer, Karen; Stawiarz, Katarzyna; Kunz, Regina; Mol, Ben W J; Khan, Khalid S

2009-05-12

To evaluate the educational effects of a clinically integrated e-learning course for teaching basic evidence-based medicine (EBM) among postgraduates compared to a traditional lecture-based course of equivalent content. We conducted a cluster randomised controlled trial in the Netherlands and the UK involving postgraduate trainees in six obstetrics and gynaecology departments. Outcomes (knowledge gain and change in attitude towards EBM) were compared between the clinically integrated e-learning course (intervention) and the traditional lecture based course (control). We measured change from pre- to post-intervention scores using a validated questionnaire assessing knowledge (primary outcome) and attitudes (secondary outcome). There were six clusters involving teaching of 61 postgraduate trainees (28 in the intervention and 33 in the control group). The intervention group achieved slightly higher scores for knowledge gain compared to the control, but these results were not statistically significant (difference in knowledge gain: 3.5 points, 95% CI -2.7 to 9.8, p = 0.27). The attitudinal changes were similar for both groups. A clinically integrated e-learning course was at least as effective as a traditional lecture based course and was well accepted. Being less costly than traditional teaching and allowing for more independent learning through materials that can be easily updated, there is a place for incorporating e-learning into postgraduate EBM curricula that offer on-the-job training for just-in-time learning. ACTRN12609000022268.

Active-learning versus teacher-centered instruction for learning acids and bases

NASA Astrophysics Data System (ADS)

Acar Sesen, Burcin; Tarhan, Leman

2011-07-01

Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of 'acids and bases'. Sample The sample of this study was 45 high-school students (average age 17 years) from two different classes, which were randomly assigned to the experimental (n = 21) and control groups (n = 25), in a high school in Turkey. Design and methods A pre-test consisting of 25 items was applied to both experimental and control groups before the treatment in order to identify student prerequisite knowledge about their proficiency for learning 'acids and bases'. A one-way analysis of variance (ANOVA) was conducted to compare the pre-test scores for groups and no significant difference was found between experimental (ME = 40.14) and control groups (MC = 41.92) in terms of mean scores (F 1,43 = 2.66, p > 0.05). The experimental group was taught using an active-learning curriculum developed by the authors and the control group was taught using traditional course content based on teacher-centered instruction. After the implementation, 'Acids and Bases Achievement Test' scores were collected for both groups. Results ANOVA results showed that students' 'Acids and Bases Achievement Test' post-test scores differed significantly in terms of groups (F 1,43 = 102.53; p < 0.05). Additionally, in this study 54 misconceptions, 14 of them not reported in the literature before, were observed in the following terms: 'acid and base theories'; 'metal and non-metal oxides'; 'acid and base strengths'; 'neutralization'; 'pH and pOH'; 'hydrolysis'; 'acid-base equilibrium'; 'buffers'; 'indicators'; and 'titration'. Based on the achievement test and individual interview results, it was found that high-school students in the experimental group had fewer misconceptions and understood the concepts more meaningfully than students in control group. Conclusion The study revealed that active-learning implementation is more effective at improving students' learning achievement and preventing misconceptions.

Towards Greater Learner Control: Web Supported Project-Based Learning

ERIC Educational Resources Information Center

Guthrie, Cameron

2010-01-01

Project-based learning has been suggested as an appropriate pedagogy to prepare students in information systems for the realities of the business world. Web-based resources have been used to support such pedagogy with mixed results. The paper argues that the design of web-based learning support to cater to different learning styles may give…

Improve Outcomes Study subjects Chemistry Teaching and Learning Strategies through independent study with the help of computer-based media

NASA Astrophysics Data System (ADS)

Sugiharti, Gulmah

2018-03-01

This study aims to see the improvement of student learning outcomes by independent learning using computer-based learning media in the course of STBM (Teaching and Learning Strategy) Chemistry. Population in this research all student of class of 2014 which take subject STBM Chemistry as many as 4 class. While the sample is taken by purposive as many as 2 classes, each 32 students, as control class and expriment class. The instrument used is the test of learning outcomes in the form of multiple choice with the number of questions as many as 20 questions that have been declared valid, and reliable. Data analysis techniques used one-sided t test and improved learning outcomes using a normalized gain test. Based on the learning result data, the average of normalized gain values for the experimental class is 0,530 and for the control class is 0,224. The result of the experimental student learning result is 53% and the control class is 22,4%. Hypothesis testing results obtained t count> ttable is 9.02> 1.6723 at the level of significance α = 0.05 and db = 58. This means that the acceptance of Ha is the use of computer-based learning media (CAI Computer) can improve student learning outcomes in the course Learning Teaching Strategy (STBM) Chemistry academic year 2017/2018.

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.

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.

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.

Learning fuzzy logic control system

NASA Technical Reports Server (NTRS)

Lung, Leung Kam

1994-01-01

The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore, the LFLC has better performance in rise time, settling time and steady state error than to the conventional PI controller. This abstract accurately represents the content of the candidate's thesis. I recommend its publication.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

Approximate reasoning-based learning and control for proximity operations and docking in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Jani, Yashvant; Lea, Robert N.

1991-01-01

A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.

The Acquisition of Integrated Science Process Skills in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Saat, Rohaida Mohd

2004-01-01

Web-based learning is becoming prevalent in science learning. Some use specially designed programs, while others use materials available on the Internet. This qualitative case study examined the process of acquisition of integrated science process skills, particularly the skill of controlling variables, in a web-based learning environment among…

A Comparison between Paper-Based and Online Learning in Higher Education

ERIC Educational Resources Information Center

Emerson, Lisa; MacKay, Bruce

2011-01-01

To date researchers have had difficulty establishing reliable conclusions in studies comparing traditional forms of learning (eg paper-based or classroom based) vs online learning in relation to student learning outcomes; no consistent results have emerged, and many studies have not been controlled for factors other than lesson mode. This paper…

Simultaneous vibration control and energy harvesting using actor-critic based reinforcement learning

NASA Astrophysics Data System (ADS)

Loong, Cheng Ning; Chang, C. C.; Dimitrakopoulos, Elias G.

2018-03-01

Mitigating excessive vibration of civil engineering structures using various types of devices has been a conspicuous research topic in the past few decades. Some devices, such as electromagnetic transducers, which have a capability of exerting control forces while simultaneously harvesting energy, have been proposed recently. These devices make possible a self-regenerative system that can semi-actively mitigate structural vibration without the need of external energy. Integrating mechanical, electrical components, and control algorithms, these devices open up a new research domain that needs to be addressed. In this study, the feasibility of using an actor-critic based reinforcement learning control algorithm for simultaneous vibration control and energy harvesting for a civil engineering structure is investigated. The actor-critic based reinforcement learning control algorithm is a real-time, model-free adaptive technique that can adjust the controller parameters based on observations and reward signals without knowing the system characteristics. It is suitable for the control of a partially known nonlinear system with uncertain parameters. The feasibility of implementing this algorithm on a building structure equipped with an electromagnetic damper will be investigated in this study. Issues related to the modelling of learning algorithm, initialization and convergence will be presented and discussed.

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.

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.

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.

Molecular substrates of action control in cortico-striatal circuits.

PubMed

Shiflett, Michael W; Balleine, Bernard W

2011-09-15

The purpose of this review is to describe the molecular mechanisms in the striatum that mediate reward-based learning and action control during instrumental conditioning. Experiments assessing the neural bases of instrumental conditioning have uncovered functional circuits in the striatum, including dorsal and ventral striatal sub-regions, involved in action-outcome learning, stimulus-response learning, and the motivational control of action by reward-associated cues. Integration of dopamine (DA) and glutamate neurotransmission within these striatal sub-regions is hypothesized to enable learning and action control through its role in shaping synaptic plasticity and cellular excitability. The extracellular signal regulated kinase (ERK) appears to be particularly important for reward-based learning and action control due to its sensitivity to combined DA and glutamate receptor activation and its involvement in a range of cellular functions. ERK activation in striatal neurons is proposed to have a dual role in both the learning and performance factors that contribute to instrumental conditioning through its regulation of plasticity-related transcription factors and its modulation of intrinsic cellular excitability. Furthermore, perturbation of ERK activation by drugs of abuse may give rise to behavioral disorders such as addiction. Copyright © 2011 Elsevier Ltd. All rights reserved.

[The effects of case-based learning using video on clinical decision making and learning motivation in undergraduate nursing students].

PubMed

Yoo, Moon-Sook; Park, Jin-Hee; Lee, Si-Ra

2010-12-01

The purpose of this study was to examine the effects of case-base learning (CBL) using video on clinical decision-making and learning motivation. This research was conducted between June 2009 and April 2010 as a nonequivalent control group non-synchronized design. The study population was 44 third year nursing students who enrolled in a college of nursing, A University in Korea. The nursing students were divided into the CBL and the control group. The intervention was the CBL with three cases using video. The controls attended a traditional live lecture on the same topics. With questionnaires objective clinical decision-making, subjective clinical decision-making, and learning motivation were measured before the intervention, and 10 weeks after the intervention. Significant group differences were observed in clinical decision-making and learning motivation. The post-test scores of clinical decision-making in the CBL group were statistically higher than the control group. Learning motivation was also significantly higher in the CBL group than in the control group. These results indicate that CBL using video is effective in enhancing clinical decision-making and motivating students to learn by encouraging self-directed learning and creating more interest and curiosity in learning.

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

An instructional intervention to encourage effective deep collaborative learning in undergraduate veterinary students.

PubMed

Khosa, Deep K; Volet, Simone E; Bolton, John R

2010-01-01

In recent years, veterinary education has received an increased amount of attention directed at the value and application of collaborative case-based learning. The benefit of instilling deep learning practices in undergraduate veterinary students has also emerged as a powerful tool in encouraging continued professional education. However, research into the design and application of instructional strategies to encourage deep, collaborative case-based learning in veterinary undergraduates has been limited. This study focused on delivering an instructional intervention (via a 20-minute presentation and student handout) to foster productive, collaborative case-based learning in veterinary education. The aim was to instigate and encourage deep learning practices in a collaborative case-based assignment and to assess the impact of the intervention on students' group learning. Two cohorts of veterinary students were involved in the study. One cohort was exposed to an instructional intervention, and the other provided the control for the study. The instructional strategy was grounded in the collaborative learning literature and prior empirical studies with veterinary students. Results showed that the intervention cohort spent proportionally more time on understanding case content material than did the control cohort and rated their face-to-face discussions as more useful in achieving their learning outcomes than did their control counterparts. In addition, the perceived difficulty of the assignment evolved differently for the control and intervention students from start to end of the assignment. This study provides encouraging evidence that veterinary students can change and enhance the way they interact in a group setting to effectively engage in collaborative learning practices.

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.

Citrus Quality Control: An NMR/MRI Problem-Based Experiment

ERIC Educational Resources Information Center

Erhart, Sarah E.; McCarrick, Robert M.; Lorigan, Gary A.; Yezierski, Ellen J.

2016-01-01

An experiment seated in an industrial context can provide an engaging framework and unique learning opportunity for an upper-division physical chemistry laboratory. An experiment that teaches NMR/MRI through a problem-based quality control of citrus products was developed. In this experiment, using a problem-based learning (PBL) approach, students…

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.

Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course

ERIC Educational Resources Information Center

Mendez, J. A.; Gonzalez, E. J.

2011-01-01

This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The…

Effects of team-based learning on self-regulated online learning.

PubMed

Whittaker, Alice A

2015-04-10

Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p < 0.001) of self-regulated learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

The problem with outcomes-based curricula in medical education: insights from educational theory.

PubMed

Rees, Charlotte E

2004-06-01

Educators across the world are charged with the responsibility of producing core learning outcomes for medical curricula. However, much educational theory exists which deliberates the value of learning outcomes in education. This paper aims to discuss the problems surrounding outcomes-based curricula in medical education, using insights from educational theory. The paper begins with a discussion of the traditions, values and ideologies of medical curricula. It continues by analysing the issue of control within the curriculum and argues that curriculum designers and teachers control product-orientated curricula, leading to student disempowerment. The paper debates outcomes-based curricula from an ideological perspective and argues that learning outcomes cannot specify exactly what is to be achieved as a result of learning. The paper argues that medical schools should adopt a model for co-operative control of the curriculum, thus empowering learners. The paper also suggests that medical educators should determine the value of precise learning outcomes before blindly adopting an outcomes-based model.

The Curse of Planning: Dissecting multiple reinforcement learning systems by taxing the central executive

PubMed Central

Otto, A. Ross; Gershman, Samuel J.; Markman, Arthur B.; Daw, Nathaniel D.

2013-01-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. Along these lines, a flexible but computationally expensive model-based reinforcement learning system has been contrasted with a less flexible but more efficient model-free reinforcement learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Based on the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrate that having human decision-makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement learning strategy. Further, we show that across trials, people negotiate this tradeoff dynamically as a function of concurrent executive function demands and their choice latencies reflect the computational expenses of the strategy employed. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources. PMID:23558545

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.

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.

Reward-Based Spatial Learning in Teens With Bulimia Nervosa.

PubMed

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

2016-11-01

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. 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. 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 the fronto-striatal regions and right anterior hippocampus. Unlike healthy adolescents, those with BN did not engage the right inferior frontal gyrus during maze navigation, activated the right anterior hippocampus during the receipt of unexpected rewards (control condition), and deactivated the 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. Adolescents with BN displayed abnormal functioning of the 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/; NCT00345943. Copyright © 2016 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

NASA Technical Reports Server (NTRS)

Assad, C.; Hartmann, M.; Paulin, M. G.

2001-01-01

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

Medical Students' and Tutors' Experiences of Directed and Self-Directed Learning Programs in Evidence-Based Medicine: A Qualitative Evaluation Accompanying a Randomized Controlled Trial

ERIC Educational Resources Information Center

Bradley, Peter; Oterholt, Christina; Nordheim, Lena; Bjorndal, Arild

2005-01-01

This qualitative study aims to interpret the results of a randomized controlled trial comparing two educational programs (directed learning and self-directed learning) in evidence-based medicine (EBM) for medical students at the University of Oslo from 2002 to 2003. There is currently very little comparative educational research in this field. In…

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.

Implementing Project Based Learning in Computer Classroom

ERIC Educational Resources Information Center

Asan, Askin; Haliloglu, Zeynep

2005-01-01

Project-based learning offers the opportunity to apply theoretical and practical knowledge, and to develop the student's group working, and collaboration skills. In this paper we presented a design of effective computer class that implements the well-known and highly accepted project-based learning paradigm. A pre-test/post-test control group…

Intelligent control based on fuzzy logic and neural net theory

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

Agent-based traffic management and reinforcement learning in congested intersection network.

DOT National Transportation Integrated Search

2012-08-01

This study evaluates the performance of traffic control systems based on reinforcement learning (RL), also called approximate dynamic programming (ADP). Two algorithms have been selected for testing: 1) Q-learning and 2) approximate dynamic programmi...

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.

Robotic Mission to Mars: Hands-on, minds-on, web-based learning

NASA Astrophysics Data System (ADS)

Mathers, Naomi; Goktogen, Ali; Rankin, John; Anderson, Marion

2012-11-01

Problem-based learning has been demonstrated as an effective methodology for developing analytical skills and critical thinking. The use of scenario-based learning incorporates problem-based learning whilst encouraging students to collaborate with their colleagues and dynamically adapt to their environment. This increased interaction stimulates a deeper understanding and the generation of new knowledge. The Victorian Space Science Education Centre (VSSEC) uses scenario-based learning in its Mission to Mars, Mission to the Orbiting Space Laboratory and Primary Expedition to the M.A.R.S. Base programs. These programs utilize methodologies such as hands-on applications, immersive-learning, integrated technologies, critical thinking and mentoring to engage students in Science, Technology, Engineering and Mathematics (STEM) and highlight potential career paths in science and engineering. The immersive nature of the programs demands specialist environments such as a simulated Mars environment, Mission Control and Space Laboratory, thus restricting these programs to a physical location and limiting student access to the programs. To move beyond these limitations, VSSEC worked with its university partners to develop a web-based mission that delivered the benefits of scenario-based learning within a school environment. The Robotic Mission to Mars allows students to remotely control a real rover, developed by the Australian Centre for Field Robotics (ACFR), on the VSSEC Mars surface. After completing a pre-mission training program and site selection activity, students take on the roles of scientists and engineers in Mission Control to complete a mission and collect data for further analysis. Mission Control is established using software developed by the ACRI Games Technology Lab at La Trobe University using the principles of serious gaming. The software allows students to control the rover, monitor its systems and collect scientific data for analysis. This program encourages students to work scientifically and explores the interaction between scientists and engineers. This paper presents the development of the program, including the involvement of university students in the development of the rover, the software, and the collation of the scientific data. It also presents the results of the trial phase of this program including the impact on student engagement and learning outcomes.

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.

A knowledge-base generating hierarchical fuzzy-neural controller.

PubMed

Kandadai, R M; Tien, J M

1997-01-01

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. First, we modify Berenji and Khedkar's (1992) GARIC architecture to enable it to automatically generate a knowledge base; a pseudosupervised learning scheme using reinforcement learning and error backpropagation is employed. Next, we further extend this architecture to a hierarchical controller that is able to generate its own knowledge base. Example applications are provided to underscore its viability.

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.

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.

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.

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…

Closed-loop and robust control of quantum systems.

PubMed

Chen, Chunlin; Wang, Lin-Cheng; Wang, Yuanlong

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.

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.

Intelligent fault-tolerant controllers

NASA Technical Reports Server (NTRS)

Huang, Chien Y.

1987-01-01

A system with fault tolerant controls is one that can detect, isolate, and estimate failures and perform necessary control reconfiguration based on this new information. Artificial intelligence (AI) is concerned with semantic processing, and it has evolved to include the topics of expert systems and machine learning. This research represents an attempt to apply AI to fault tolerant controls, hence, the name intelligent fault tolerant control (IFTC). A generic solution to the problem is sought, providing a system based on logic in addition to analytical tools, and offering machine learning capabilities. The advantages are that redundant system specific algorithms are no longer needed, that reasonableness is used to quickly choose the correct control strategy, and that the system can adapt to new situations by learning about its effects on system dynamics.

The Greek Challenge in Work-Based Learning

ERIC Educational Resources Information Center

Taousanidis, Nikolaos I.; Antoniadou, Myrofora A.

2008-01-01

Work-based learning is generated, controlled and used within a community of practice and brings new understanding to pedagogical principles as the role of worker becomes also that of learner. This paper presents a series of opportunities of this type of learning, which even enables students to work at a distance, using open-learning techniques, as…

Presentation-Practice-Production and Task-Based Learning in the Light of Second Language Learning Theories.

ERIC Educational Resources Information Center

Ritchie, Graeme

2003-01-01

Features of presentation-practice-production (PPP) and task-based learning (TBL) models for language teaching are discussed with reference to language learning theories. Pre-selection of target structures, use of controlled repetition, and explicit grammar instruction in a PPP lesson are given. Suggests TBL approaches afford greater learning…

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…

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.

Metacognition in Upper-Division Biology Students: Awareness Does Not Always Lead to Control

ERIC Educational Resources Information Center

Dye, Kathryn Morris; Stanton, Julie Dangremond

2017-01-01

Students with awareness and control of their own thinking can learn more and perform better than students who are not metacognitive. Metacognitive regulation is how you control your thinking in order to learn. It includes the skill of evaluation, which is the ability to appraise your approaches to learning and then modify future plans based on…

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.

Model-Free Optimal Tracking Control via Critic-Only Q-Learning.

PubMed

Luo, Biao; Liu, Derong; Huang, Tingwen; Wang, Ding

2016-10-01

Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine nonlinear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.

Future applications of artificial intelligence to Mission Control Centers

NASA Technical Reports Server (NTRS)

Friedland, Peter

1991-01-01

Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.

Characterizing Rule-Based Category Learning Deficits in Patients with Parkinson's Disease

ERIC Educational Resources Information Center

Filoteo, J. Vincent; Maddox, W. Todd; Ing, A. David; Song, David D.

2007-01-01

Parkinson's disease (PD) patients and normal controls were tested in three category learning experiments to determine if previously observed rule-based category learning impairments in PD patients were due to deficits in selective attention or working memory. In Experiment 1, optimal categorization required participants to base their decision on a…

Development and Evaluation of Mechatronics Learning System in a Web-Based Environment

ERIC Educational Resources Information Center

Shyr, Wen-Jye

2011-01-01

The development of remote laboratory suitable for the reinforcement of undergraduate level teaching of mechatronics is important. For the reason, a Web-based mechatronics learning system, called the RECOLAB (REmote COntrol LABoratory), for remote learning in engineering education has been developed in this study. The web-based environment is an…

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.

The influence of focused-attention meditation states on the cognitive control of sequence learning.

PubMed

Chan, Russell W; Immink, Maarten A; Lushington, Kurt

2017-10-01

Cognitive control processes influence how motor sequence information is utilised and represented. Since cognitive control processes are shared amongst goal-oriented tasks, motor sequence learning and performance might be influenced by preceding cognitive tasks such as focused-attention meditation (FAM). Prior to a serial reaction time task (SRTT), participants completed either a single-session of FAM, a single-session of FAM followed by delay (FAM+) or no meditation (CONTROL). Relative to CONTROL, FAM benefitted performance in early, random-ordered blocks. However, across subsequent sequence learning blocks, FAM+ supported the highest levels of performance improvement resulting in superior performance at the end of the SRTT. Performance following FAM+ demonstrated greater reliance on embedded sequence structures than FAM. These findings illustrate that increased top-down control immediately after FAM biases the implementation of stimulus-based planning. Introduction of a delay following FAM relaxes top-down control allowing for implementation of response-based planning resulting in sequence learning benefits. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Slim Chance: A Weight Control Program for the Learning Disabled.

ERIC Educational Resources Information Center

Rotatori, Anthony J.; And Others

1982-01-01

A school-based diet program for learning disabled children stresses modifying eating behavior by learning alternative ways of interacting with the environment. Techniques stress the importance of self regulation, strategies for self monitoring, the establishment of realistic weight goals, use of stimulus control procedures, increased physical…

A Laboratory Testbed for Embedded Fuzzy Control

ERIC Educational Resources Information Center

Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

2011-01-01

This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses…

The Influence of Guided Error-Based Learning on Motor Skills Self-Efficacy and Achievement.

PubMed

Chien, Kuei-Pin; Chen, Sufen

2018-01-01

The authors investigated the role of errors in motor skills teaching, specifically the influence of errors on skills self-efficacy and achievement. The participants were 75 undergraduate students enrolled in pétanque courses. The experimental group (guided error-based learning, n = 37) received a 6-week period of instruction based on the students' errors, whereas the control group (correct motion instruction, n = 38) received a 6-week period of instruction emphasizing correct motor skills. The experimental group had significantly higher scores in motor skills self-efficacy and outcomes than did the control group. Novices' errors reflect their schema in motor skills learning, which provides a basis for instructors to implement student-centered instruction and to facilitate the learning process. Guided error-based learning can effectively enhance beginners' skills self-efficacy and achievement in precision sports such as pétanque.

Feedback control by online learning an inverse model.

PubMed

Waegeman, Tim; Wyffels, Francis; Schrauwen, Francis

2012-10-01

A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made.

Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

PubMed

Hiremath, Shivayogi V; Chen, Weidong; Wang, Wei; Foldes, Stephen; Yang, Ying; Tyler-Kabara, Elizabeth C; Collinger, Jennifer L; Boninger, Michael L

2015-01-01

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

A Study on the Mobile Learning of English and American Literature Based on WeChat Public Account

ERIC Educational Resources Information Center

Dai, Guiyu; Liu, Yang; Cui, Shanmeng

2018-01-01

This paper uses Edgar Dale's Audio-visual Learning Theory and Jean Piaget's Constructionist Learning Theory as the theoretical framework to conduct two control experimental tests and a questionnaire research to investigate students' impression and expectations toward WeChat public account based mobile learning mode as well as its validity,…

A self-learning rule base for command following in dynamical systems

NASA Technical Reports Server (NTRS)

Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

1992-01-01

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

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.

A proof-of-principle simulation for closed-loop control based on preexisting experimental thalamic DBS-enhanced instrumental learning.

PubMed

Wang, Ching-Fu; Yang, Shih-Hung; Lin, Sheng-Huang; Chen, Po-Chuan; Lo, Yu-Chun; Pan, Han-Chi; Lai, Hsin-Yi; Liao, Lun-De; Lin, Hui-Ching; Chen, Hsu-Yan; Huang, Wei-Chen; Huang, Wun-Jhu; Chen, You-Yin

Deep brain stimulation (DBS) has been applied as an effective therapy for treating Parkinson's disease or essential tremor. Several open-loop DBS control strategies have been developed for clinical experiments, but they are limited by short battery life and inefficient therapy. Therefore, many closed-loop DBS control systems have been designed to tackle these problems by automatically adjusting the stimulation parameters via feedback from neural signals, which has been reported to reduce the power consumption. However, when the association between the biomarkers of the model and stimulation is unclear, it is difficult to develop an optimal control scheme for other DBS applications, i.e., DBS-enhanced instrumental learning. Furthermore, few studies have investigated the effect of closed-loop DBS control for cognition function, such as instrumental skill learning, and have been implemented in simulation environments. In this paper, we proposed a proof-of-principle design for a closed-loop DBS system, cognitive-enhancing DBS (ceDBS), which enhanced skill learning based on in vivo experimental data. The ceDBS acquired local field potential (LFP) signal from the thalamic central lateral (CL) nuclei of animals through a neural signal processing system. A strong coupling of the theta oscillation (4-7 Hz) and the learning period was found in the water reward-related lever-pressing learning task. Therefore, the theta-band power ratio, which was the averaged theta band to averaged total band (1-55 Hz) power ratio, could be used as a physiological marker for enhancement of instrumental skill learning. The on-line extraction of the theta-band power ratio was implemented on a field-programmable gate array (FPGA). An autoregressive with exogenous inputs (ARX)-based predictor was designed to construct a CL-thalamic DBS model and forecast the future physiological marker according to the past physiological marker and applied DBS. The prediction could further assist the design of a closed-loop DBS controller. A DBS controller based on a fuzzy expert system was devised to automatically control DBS according to the predicted physiological marker via a set of rules. The simulated experimental results demonstrate that the ceDBS based on the closed-loop control architecture not only reduced power consumption using the predictive physiological marker, but also achieved a desired level of physiological marker through the DBS controller. Copyright © 2017 Elsevier Inc. All rights reserved.

A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims.

PubMed

Doroodgar, Barzin; Liu, Yugang; Nejat, Goldie

2014-12-01

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing a human operator to cooperate and share such tasks with a rescue robot as navigation, exploration, and victim identification. In this paper, we present a unique hierarchical reinforcement learning-based semi-autonomous control architecture for rescue robots operating in cluttered and unknown urban search and rescue (USAR) environments. The aim of the controller is to enable a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A direction-based exploration technique is integrated in the controller to expand the search area of the robot via the classification of regions and the rubble piles within these regions. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed HRL-based semi-autonomous controller to unknown cluttered scenes with different sizes and varying types of configurations.

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.

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…

The Effects of Team-Based Learning on Social Studies Knowledge Acquisition in High School

ERIC Educational Resources Information Center

Wanzek, Jeanne; Vaughn, Sharon; Kent, Shawn C.; Swanson, Elizabeth A.; Roberts, Greg; Haynes, Martha; Fall, Anna-Mária; Stillman-Spisak, Stephanie J.; Solis, Michael

2014-01-01

This randomized control trial examined the efficacy of team-based learning implemented within 11th-grade social studies classes. A randomized blocked design was implemented with 26 classes randomly assigned to treatment or comparison. In the treatment classes teachers implemented team-based learning practices to support students in engaging in…

Learning to Obtain Reward, but Not Avoid Punishment, Is Affected by Presence of PTSD Symptoms in Male Veterans: Empirical Data and Computational Model

PubMed Central

Myers, Catherine E.; Moustafa, Ahmed A.; Sheynin, Jony; VanMeenen, Kirsten M.; Gilbertson, Mark W.; Orr, Scott P.; Beck, Kevin D.; Pang, Kevin C. H.; Servatius, Richard J.

2013-01-01

Post-traumatic stress disorder (PTSD) symptoms include behavioral avoidance which is acquired and tends to increase with time. This avoidance may represent a general learning bias; indeed, individuals with PTSD are often faster than controls on acquiring conditioned responses based on physiologically-aversive feedback. However, it is not clear whether this learning bias extends to cognitive feedback, or to learning from both reward and punishment. Here, male veterans with self-reported current, severe PTSD symptoms (PTSS group) or with few or no PTSD symptoms (control group) completed a probabilistic classification task that included both reward-based and punishment-based trials, where feedback could take the form of reward, punishment, or an ambiguous “no-feedback” outcome that could signal either successful avoidance of punishment or failure to obtain reward. The PTSS group outperformed the control group in total points obtained; the PTSS group specifically performed better than the control group on reward-based trials, with no difference on punishment-based trials. To better understand possible mechanisms underlying observed performance, we used a reinforcement learning model of the task, and applied maximum likelihood estimation techniques to derive estimated parameters describing individual participants’ behavior. Estimations of the reinforcement value of the no-feedback outcome were significantly greater in the control group than the PTSS group, suggesting that the control group was more likely to value this outcome as positively reinforcing (i.e., signaling successful avoidance of punishment). This is consistent with the control group’s generally poorer performance on reward trials, where reward feedback was to be obtained in preference to the no-feedback outcome. Differences in the interpretation of ambiguous feedback may contribute to the facilitated reinforcement learning often observed in PTSD patients, and may in turn provide new insight into how pathological behaviors are acquired and maintained in PTSD. PMID:24015254

Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

ERIC Educational Resources Information Center

Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

2010-01-01

Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

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.

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.

Fixed Point Learning Based Intelligent Traffic Control System

NASA Astrophysics Data System (ADS)

Zongyao, Wang; Cong, Sui; Cheng, Shao

2017-10-01

Fixed point learning has become an important tool to analyse large scale distributed system such as urban traffic network. This paper presents a fixed point learning based intelligence traffic network control system. The system applies convergence property of fixed point theorem to optimize the traffic flow density. The intelligence traffic control system achieves maximum road resources usage by averaging traffic flow density among the traffic network. The intelligence traffic network control system is built based on decentralized structure and intelligence cooperation. No central control is needed to manage the system. The proposed system is simple, effective and feasible for practical use. The performance of the system is tested via theoretical proof and simulations. The results demonstrate that the system can effectively solve the traffic congestion problem and increase the vehicles average speed. It also proves that the system is flexible, reliable and feasible for practical use.

Effectiveness of an e-learning course in evidence-based medicine for foundation (internship) training.

PubMed

Hadley, Julie; Kulier, Regina; Zamora, Javier; Coppus, Sjors F P J; Weinbrenner, Susanne; Meyerrose, Berrit; Decsi, Tamas; Horvath, Andrea R; Nagy, Eva; Emparanza, Jose I; Arvanitis, Theodoros N; Burls, Amanda; Cabello, Juan B; Kaczor, Marcin; Zanrei, Gianni; Pierer, Karen; Kunz, Regina; Wilkie, Veronica; Wall, David; Mol, Ben Wj; Khan, Khalid S

2010-07-01

To evaluate the educational effectiveness of a clinically integrated e-learning course for teaching basic evidence-based medicine (EBM) among postgraduate medical trainees compared to a traditional lecture-based course of equivalent content. We conducted a cluster randomized controlled trial to compare a clinically integrated e-learning EBM course (intervention) to a lecture-based course (control) among postgraduate trainees at foundation or internship level in seven teaching hospitals in the UK West Midlands region. Knowledge gain among participants was measured with a validated instrument using multiple choice questions. Change in knowledge was compared between groups taking into account the cluster design and adjusted for covariates at baseline using generalized estimating equations (GEE) model. There were seven clusters involving teaching of 237 trainees (122 in the intervention and 115 in the control group). The total number of postgraduate trainees who completed the course was 88 in the intervention group and 72 in the control group. After adjusting for baseline knowledge, there was no difference in the amount of improvement in knowledge of EBM between the two groups. The adjusted post course difference between the intervention group and the control group was only 0.1 scoring points (95% CI -1.2-1.4). An e-learning course in EBM was as effective in improving knowledge as a standard lecture-based course. The benefits of an e-learning approach need to be considered when planning EBM curricula as it allows standardization of teaching materials and is a potential cost-effective alternative to standard lecture-based teaching.

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.

Novel associative-memory-based self-learning neurocontrol model

NASA Astrophysics Data System (ADS)

Chen, Ke

1992-09-01

Intelligent control is an important field of AI application, which is closely related to machine learning, and the neurocontrol is a kind of intelligent control that controls actions of a physical system or a plant. Linear associative memory model is a good analytic tool for artificial neural networks. In this paper, we present a novel self-learning neurocontrol on the basis of the linear associative memory model to support intelligent control. Using our self-learning neurocontrol model, the learning process is viewed as an extension of one of J. Piaget's developmental stages. After a particular linear associative model developed by us is presented, a brief introduction to J. Piaget's cognitive theory is described as the basis of our self-learning style control. It follows that the neurocontrol model is presented, which usually includes two learning stages, viz. primary learning and high-level learning. As a demonstration of our neurocontrol model, an example is also presented with simulation techniques, called that `bird' catches an aim. The tentative experimental results show that the learning and controlling performance of this approach is surprisingly good. In conclusion, future research is pointed out to improve our self-learning neurocontrol model and explore other areas of application.

Performance improvement in remote manipulation with time delay by means of a learning system.

NASA Technical Reports Server (NTRS)

Freedy, A.; Weltman, G.

1973-01-01

A teleoperating system is presented that involves shared control between a human operator and a general-purpose computer-based learning machine. This setup features a trainable control network termed the autonomous control subsystem (ACS) which is able to observe the operator's control actions, learn the task at hand, and take appropriate control actions. A working ACS system is described that has been put in operation for the purpose of exploring the uses of a remote intelligence of this type. The expansion of the present system into a multifunctional learning machine capable of a greater degree of autonomy is also discussed.

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.

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

Rule-Based Category Learning in Children: The Role of Age and Executive Functioning

PubMed Central

Rabi, Rahel; Minda, John Paul

2014-01-01

Rule-based category learning was examined in 4–11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy being used to solve the categorization task, demonstrating that the use of the task appropriate strategy increased with age. When children and adults who identified the correct categorization rule were compared, the performance deficit was no longer evident. Executive functions were also measured. While both working memory and inhibitory control were related to rule-based categorization and improved with age, working memory specifically was found to marginally mediate the age-related improvements in categorization. When analyses focused only on the sample of children, results showed that working memory ability and inhibitory control were associated with categorization performance and strategy use. The current findings track changes in categorization performance across childhood, demonstrating at which points performance begins to mature and resemble that of adults. Additionally, findings highlight the potential role that working memory and inhibitory control may play in rule-based category learning. PMID:24489658

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.

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.

Students' Critical Thinking Skills in Chemistry Learning Using Local Culture-Based 7E Learning Cycle Model

ERIC Educational Resources Information Center

Suardana, I. Nyoman; Redhana, I. Wayan; Sudiatmika, A. A. Istri Agung Rai; Selamat, I. Nyoman

2018-01-01

This research aimed at describing the effectiveness of the local culture-based 7E learning cycle model in improving students' critical thinking skills in chemistry learning. It was an experimental research with post-test only control group design. The population was the eleventh-grade students of senior high schools in Singaraja, Indonesia. The…

A Game-Based Learning Approach to Improving Students' Learning Achievements in a Nutrition Course

ERIC Educational Resources Information Center

Yien, Jui-Mei; Hung, Chun-Ming; Hwang, Gwo-Jen; Lin, Yueh-Chiao

2011-01-01

The aim of this study was to explore the influence of applying a game-based learning approach to nutrition education. The quasi-experimental nonequivalent-control group design was adopted in a four-week learning activity. The participants included sixty-six third graders in two classes of an elementary school. One of the classes was assigned to be…

Informal Learning: Student Achievement and Motivation in Science through Museum-Based Learning

ERIC Educational Resources Information Center

Holmes, Julie A.

2011-01-01

This study examined changes in student motivation and achievement in science during a visit to a university children's science museum. The study was based on the pretest-posttest control comparison group design with four treatment groups: control, exhibit, lesson and exhibit/lesson. The sample consisted of 228 sixth-grade students from a Louisiana…

The effect of conceptual metaphors through guided inquiry on student's conceptual change

NASA Astrophysics Data System (ADS)

Menia, Meli; Mudzakir, Ahmad; Rochintaniawati, Diana

2017-05-01

The purpose of this study was to identify student's conceptual change of global warming after integrated science learning based guided inquiry through conceptual metaphors. This study used a quasi-experimental with a nonequivalent control group design. The subject was students of two classes of one of MTsN Salido. Data was collected using conceptual change test (pretest and posttest), observation sheet to observe the learning processes, questionnaire sheet to identify students responses, and interview to identifyteacher'srespons of science learning with conceptual metaphors. The results showed that science learning based guided inquiry with conceptual metaphors is better than science learning without conceptual metaphors. The average of posttest experimental class was 79,40 and control class was 66,09. The student's conceptual change for two classes changed significantly byusing mann whitney U testwith P= 0,003(P less than sig. value, P< 0,05). This means that there was differenceson student's conceptual changebeetwen integrated science learning based guided inquiry with conceptual metaphors class and integrated science learning without conceptual metaphors class. The study also showed that teachers and studentsgive positive responsesto implementation of integrated science learning based guided inquiry with conceptual metaphors.

Altered brain activation in a reversal learning task unmasks adaptive changes in cognitive control in writer's cramp.

PubMed

Zeuner, Kirsten E; Knutzen, Arne; Granert, Oliver; Sablowsky, Simone; Götz, Julia; Wolff, Stephan; Jansen, Olav; Dressler, Dirk; Schneider, Susanne A; Klein, Christine; Deuschl, Günther; van Eimeren, Thilo; Witt, Karsten

2016-01-01

Previous receptor binding studies suggest dopamine function is altered in the basal ganglia circuitry in task-specific dystonia, a condition characterized by contraction of agonist and antagonist muscles while performing specific tasks. Dopamine plays a role in reward-based learning. Using fMRI, this study compared 31 right-handed writer's cramp patients to 35 controls in reward-based learning of a probabilistic reversal-learning task. All subjects chose between two stimuli and indicated their response with their left or right index finger. One stimulus response was rewarded 80%, the other 20%. After contingencies reversal, the second stimulus response was rewarded in 80%. We further linked the DRD2/ANKK1-TaqIa polymorphism, which is associated with 30% reduction of the striatal dopamine receptor density with reward-based learning and assumed impaired reversal learning in A + subjects. Feedback learning in patients was normal. Blood-oxygen level dependent (BOLD) signal in controls increased with negative feedback in the insula, rostral cingulate cortex, middle frontal gyrus and parietal cortex (pFWE < 0.05). In comparison to controls, patients showed greater increase in BOLD activity following negative feedback in the dorsal anterior cingulate cortex (BA32). The genetic status was not correlated with the BOLD activity. The Brodmann area 32 (BA32) is part of the dorsal anterior cingulate cortex (dACC) that plays an important role in coordinating and integrating information to guide behavior and in reward-based learning. The dACC is connected with the basal ganglia-thalamo-loop modulated by dopaminergic signaling. This finding suggests disturbed integration of reinforcement history in decision making and implicate that the reward system might contribute to the pathogenesis in writer's cramp.

Are Marketing Students in Control in Problem-Based Learning?

ERIC Educational Resources Information Center

Geitz, Gerry; Joosten-ten Brinke, Desirée; Kirschner, Paul A.

2016-01-01

This study investigated to what extent self-efficacy, learning behavior, and performance outcomes relate to each other and how they can be positively influenced by students asking for and seeking feedback within a problem-based learning (PBL) environment in order to meet today's requirements of marketing graduates. An experimental…

Remote Labs and Game-Based Learning for Process Control

ERIC Educational Resources Information Center

Zualkernan, Imran A.; Husseini, Ghaleb A.; Loughlin, Kevin F.; Mohebzada, Jamshaid G.; El Gaml, Moataz

2013-01-01

Social networking platforms and computer games represent a natural informal learning environment for the current generation of learners in higher education. This paper explores the use of game-based learning in the context of an undergraduate chemical engineering remote laboratory. Specifically, students are allowed to manipulate chemical…

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

Lessons Learned from the Node 1 Temperature and Humidity Control Subsystem Design

NASA Technical Reports Server (NTRS)

Williams, David E.

2010-01-01

Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Temperature and Humidity Control (THC) subsystem and it will document some of the lessons that have been learned to date for this subsystem and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs. 1

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

Karr, C. L.; Phillips, J. C.

1994-01-01

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

Impairment of probabilistic reward-based learning in schizophrenia.

PubMed

Weiler, Julia A; Bellebaum, Christian; Brüne, Martin; Juckel, Georg; Daum, Irene

2009-09-01

Recent models assume that some symptoms of schizophrenia originate from defective reward processing mechanisms. Understanding the precise nature of reward-based learning impairments might thus make an important contribution to the understanding of schizophrenia and the development of treatment strategies. The present study investigated several features of probabilistic reward-based stimulus association learning, namely the acquisition of initial contingencies, reversal learning, generalization abilities, and the effects of reward magnitude. Compared to healthy controls, individuals with schizophrenia exhibited attenuated overall performance during acquisition, whereas learning rates across blocks were similar to the rates of controls. On the group level, persons with schizophrenia were, however, unable to learn the reversal of the initial reward contingencies. Exploratory analysis of only the subgroup of individuals with schizophrenia who showed significant learning during acquisition yielded deficits in reversal learning with low reward magnitudes only. There was further evidence of a mild generalization impairment of the persons with schizophrenia in an acquired equivalence task. In summary, although there was evidence of intact basic processing of reward magnitudes, individuals with schizophrenia were impaired at using this feedback for the adaptive guidance of behavior.

The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive.

PubMed

Otto, A Ross; Gershman, Samuel J; Markman, Arthur B; Daw, Nathaniel D

2013-05-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.

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.

Implementation of 7e learning cycle model using technology based constructivist teaching (TBCT) approach to improve students' understanding achievment in mechanical wave material

NASA Astrophysics Data System (ADS)

Warliani, Resti; Muslim, Setiawan, Wawan

2017-05-01

This study aims to determine the increase in the understanding achievement in senior high school students through the Learning Cycle 7E with technology based constructivist teaching approach (TBCT). This study uses a pretest-posttest control group design. The participants were 67 high school students of eleventh grade in Garut city with two class in control and experiment class. Experiment class applying the Learning Cycle 7E through TBCT approach and control class applying the 7E Learning Cycle through Constructivist Teaching approach (CT). Data collection tools from mechanical wave concept test with totally 22 questions with reability coefficient was found 0,86. The findings show the increase of the understanding achievement of the experiment class is in the amount of 0.51 was higher than the control class that is in the amount of 0.33.

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

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.

Designing the Architecture of Hierachical Neural Networks Model Attention, Learning and Goal-Oriented Behavior

DTIC Science & Technology

1993-12-31

19,23,25,26,27,28,32,33,35,41]) - A new cost function is postulated and an algorithm that employs this cost function is proposed for the learning of...updates the controller parameters from time to time [53]. The learning control algorithm consist of updating the parameter estimates as used in the...proposed cost function with the other learning type algorithms , such as based upon learning of iterative tasks [Kawamura-85], variable structure

Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

NASA Astrophysics Data System (ADS)

Abdollahi, Rohollah; Farhangi, Reza; Yarahmadi, Ali

2014-08-01

This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

DPT Student Perceptions of the Physical Therapist Assistant's Role: Effect of Collaborative Case-Based Learning Compared to Traditional Content Delivery and Clinical Experience.

PubMed

Colgrove, Yvonne M; VanHoose, Lisa D

2017-01-01

Doctor of physical therapy (DPT) student learning about role delineation of physical therapist assistants (PTAs) is essential to ethical and legal practice. Survey assessment of three DPT student cohorts compared collaborative interprofessional case-based learning with PTA students to traditional curriculum delivery strategies. Control cohorts were assessed one time. The intervention group was assessed pre-intervention, immediately post-intervention, and after completing a full-time clinical experience. The case-based learning covered 46% of survey content, allowing for the assessment of content-specific material and potential learning through collaboration. Following the educational intervention, the intervention group improved significantly in areas inside and outside the case-based study content, outscoring both control groups on 25-34% of the survey items. Following the clinical experience, the intervention group declined answer accuracy for patient evaluation and treatment implementation, suggesting unlearning. Improvement in the administrative section was observed after the clinical experience. Perceptions of the tasks within the PTA role were diminished while tasks outside the scope of practice appeared clarified following the clinical experience. While case-based collaborative intraprofessional learning proves effective in student learning about the PTA role, changes following the clinical experience raise questions about the influence of the clinical environment on learning and the practical application of recently learned knowledge.

Grounding cognitive control in associative learning.

PubMed

Abrahamse, Elger; Braem, Senne; Notebaert, Wim; Verguts, Tom

2016-07-01

Cognitive control covers a broad range of cognitive functions, but its research and theories typically remain tied to a single domain. Here we outline and review an associative learning perspective on cognitive control in which control emerges from associative networks containing perceptual, motor, and goal representations. Our review identifies 3 trending research themes that are shared between the domains of conflict adaptation, task switching, response inhibition, and attentional control: Cognitive control is context-specific, can operate in the absence of awareness, and is modulated by reward. As these research themes can be envisaged as key characteristics of learning, we propose that their joint emergence across domains is not coincidental but rather reflects a (latent) growth of interest in learning-based control. Associative learning has the potential for providing broad-scaled integration to cognitive control theory, and offers a promising avenue for understanding cognitive control as a self-regulating system without postulating an ill-defined set of homunculi. We discuss novel predictions, theoretical implications, and immediate challenges that accompany an associative learning perspective on cognitive control. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Individual Differences: Implications for Web-Based Learning Design

ERIC Educational Resources Information Center

Alomyan, Hesham

2004-01-01

In the past ten years the Web has attracted many educators for purposes of teaching and learning. The main advantage of the Web lies in its non-linear interaction. That is, students can have more control over their learning paths. However, this freedom of control may cause problems for some students, such as disorientation, cognitive overload and…

Laboratory Control System's Effects on Student Achievement and Attitudes

ERIC Educational Resources Information Center

Cicek, Fatma Gozalan; Taspinar, Mehmet

2016-01-01

Problem Statement: The current study investigates whether the learning environment designed based on the laboratory control system affects the academic achievement, the attitude toward the learning-teaching process and the retention of the students in computer education. Purpose of Study: The study aims to identify the laboratory control system…

Impaired associative learning in schizophrenia: behavioral and computational studies

PubMed Central

Diwadkar, Vaibhav A.; Flaugher, Brad; Jones, Trevor; Zalányi, László; Ujfalussy, Balázs; Keshavan, Matcheri S.

2008-01-01

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia. PMID:19003486

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

Effects of Ventral Striatum Lesions on Stimulus-Based versus Action-Based Reinforcement Learning.

PubMed

Rothenhoefer, Kathryn M; Costa, Vincent D; Bartolo, Ramón; Vicario-Feliciano, Raquel; Murray, Elisabeth A; Averbeck, Bruno B

2017-07-19

Learning the values of actions versus stimuli may depend on separable neural circuits. In the current study, we evaluated the performance of rhesus macaques with ventral striatum (VS) lesions on a two-arm bandit task that had randomly interleaved blocks of stimulus-based and action-based reinforcement learning (RL). Compared with controls, monkeys with VS lesions had deficits in learning to select rewarding images but not rewarding actions. We used a RL model to quantify learning and choice consistency and found that, in stimulus-based RL, the VS lesion monkeys were more influenced by negative feedback and had lower choice consistency than controls. Using a Bayesian model to parse the groups' learning strategies, we also found that VS lesion monkeys defaulted to an action-based choice strategy. Therefore, the VS is involved specifically in learning the value of stimuli, not actions. SIGNIFICANCE STATEMENT Reinforcement learning models of the ventral striatum (VS) often assume that it maintains an estimate of state value. This suggests that it plays a general role in learning whether rewards are assigned based on a chosen action or stimulus. In the present experiment, we examined the effects of VS lesions on monkeys' ability to learn that choosing a particular action or stimulus was more likely to lead to reward. We found that VS lesions caused a specific deficit in the monkeys' ability to discriminate between images with different values, whereas their ability to discriminate between actions with different values remained intact. Our results therefore suggest that the VS plays a specific role in learning to select rewarded stimuli. Copyright © 2017 the authors 0270-6474/17/376902-13$15.00/0.

Stress enhances model-free reinforcement learning only after negative outcome

PubMed Central

Lee, Daeyeol

2017-01-01

Previous studies found that stress shifts behavioral control by promoting habits while decreasing goal-directed behaviors during reward-based decision-making. It is, however, unclear how stress disrupts the relative contribution of the two systems controlling reward-seeking behavior, i.e. model-free (or habit) and model-based (or goal-directed). Here, we investigated whether stress biases the contribution of model-free and model-based reinforcement learning processes differently depending on the valence of outcome, and whether stress alters the learning rate, i.e., how quickly information from the new environment is incorporated into choices. Participants were randomly assigned to either a stress or a control condition, and performed a two-stage Markov decision-making task in which the reward probabilities underwent periodic reversals without notice. We found that stress increased the contribution of model-free reinforcement learning only after negative outcome. Furthermore, stress decreased the learning rate. The results suggest that stress diminishes one’s ability to make adaptive choices in multiple aspects of reinforcement learning. This finding has implications for understanding how stress facilitates maladaptive habits, such as addictive behavior, and other dysfunctional behaviors associated with stress in clinical and educational contexts. PMID:28723943

Stress enhances model-free reinforcement learning only after negative outcome.

PubMed

Park, Heyeon; Lee, Daeyeol; Chey, Jeanyung

2017-01-01

Previous studies found that stress shifts behavioral control by promoting habits while decreasing goal-directed behaviors during reward-based decision-making. It is, however, unclear how stress disrupts the relative contribution of the two systems controlling reward-seeking behavior, i.e. model-free (or habit) and model-based (or goal-directed). Here, we investigated whether stress biases the contribution of model-free and model-based reinforcement learning processes differently depending on the valence of outcome, and whether stress alters the learning rate, i.e., how quickly information from the new environment is incorporated into choices. Participants were randomly assigned to either a stress or a control condition, and performed a two-stage Markov decision-making task in which the reward probabilities underwent periodic reversals without notice. We found that stress increased the contribution of model-free reinforcement learning only after negative outcome. Furthermore, stress decreased the learning rate. The results suggest that stress diminishes one's ability to make adaptive choices in multiple aspects of reinforcement learning. This finding has implications for understanding how stress facilitates maladaptive habits, such as addictive behavior, and other dysfunctional behaviors associated with stress in clinical and educational contexts.

The Effect of Problem-Based Learning on Undergraduate Students' Learning about Solutions and Their Physical Properties and Scientific Processing Skills

ERIC Educational Resources Information Center

Tosun, Cemal; Taskesenligil, Yavuz

2013-01-01

The aim of this study was to investigate the effect of Problem-Based Learning (PBL) on undergraduate students' learning about solutions and their physical properties, and on their scientific processing skills. The quasi experimental study was carried out through non-equivalent control and comparison groups pre-post test design. The data were…

Effects of Locus of Control on Behavioral Intention and Learning Performance of Energy Knowledge in Game-Based Learning

ERIC Educational Resources Information Center

Yang, Jie Chi; Lin, Yi Lung; Liu, Yi-Chun

2017-01-01

Game-based learning has been gradually adopted in energy education as an effective learning tool because digital games have the potential to increase energy literacy and encourage behavior change. However, not every learner can benefit from this support. There is a need to examine how human factors affect learners' reactions to digital games for…

Model-based hierarchical reinforcement learning and human action control

PubMed Central

Botvinick, Matthew; Weinstein, Ari

2014-01-01

Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822

Learning Control: Sense-Making, CNC Machines, and Changes in Vocational Training for Industrial Work

ERIC Educational Resources Information Center

Berner, Boel

2009-01-01

The paper explores how novices in school-based vocational training make sense of computerized numerical control (CNC) machines. Based on two ethnographic studies in Swedish schools, one from the early 1980s and one from 2006, it analyses change and continuity in the cognitive, social, and emotional processes of learning how to become a machine…

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.

Scaffolding in Problem-Based Learning for Low-Achieving Learners

ERIC Educational Resources Information Center

Haruehansawasin, Sanit; Kiattikomol, Paiboon

2018-01-01

This research investigates scaffolding approaches for supporting low-achieving learners in a problem-based learning environment. The study was conducted in a vocational school with 3 different approaches to scaffolding using 3 groups in addition to a control group. The area of focus was a learning module using computer spreadsheets. The results…

Scaffolding Teachers Integrate Social Media into a Problem-Based Learning Approach?

ERIC Educational Resources Information Center

Buus, Lillian

2012-01-01

At Aalborg University (AAU) we are known to work with problem-based learning (PBL) in a particular way designated "The Aalborg PBL model." In PBL the focus is on participant control, knowledge sharing, collaboration among participants, which makes it interesting to consider the integration of social media in the learning that takes…

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…

Examining Metacognitive Processes in Exploratory Computer-Based Learning Environments Using Activity Log Analysis

ERIC Educational Resources Information Center

Chang, Yoo Kyung

2010-01-01

Metacognition is widely studied for its influence on the effectiveness of learning. With Exploratory Computer-Based Learning Environments (ECBLE), metacognition is found to be especially important because these environments require adaptive metacognitive control by the learners due to their open-ended structure that allows for multiple learning…

Effects of Activity Based Blended Learning Strategy on Prospective of Teachers' Achievement and Motivation

ERIC Educational Resources Information Center

Abdelraheem, Ahmed Yousif; Ahmed, Abdelrahman Mohammed

2015-01-01

The study investigates the effect of Activity based Blended Learning strategy and Conventional Blended Learning strategy on students' achievement and motivation. Two groups namely, experimental and control group from Sultan Qaboos University were selected randomly for the study. To assess students' achievement in the different groups, pre- and…

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.

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.

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.

Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

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.

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.

Improving Transfer of Learning through Designed Context-Based Instructional Materials

ERIC Educational Resources Information Center

Bahtaji, Michael Allan A.

2015-01-01

This study investigates the outcome of designed source-text materials in context-based physics learning using validated test questions in mechanics. Two groups of students received context-based instruction (experimental group) and one group received content-based instruction (control group). These three groups of students are only different with…

Universal Design for Learning and Elementary School Science: Exploring the Efficacy, Use, and Perceptions of a Web-Based Science Notebook

ERIC Educational Resources Information Center

Rappolt-Schlichtmann, Gabrielle; Daley, Samantha G.; Lim, Seoin; Lapinski, Scott; Robinson, Kristin H.; Johnson, Mindy

2013-01-01

Science notebooks can play a critical role in activity-based science learning, but the tasks of recording, organizing, analyzing, and interpreting data create barriers that impede science learning for many students. This study (a) assessed in a randomized controlled trial the potential for a web-based science notebook designed using the Universal…

Two Stages Cooperative Learning by Ability Indicators

ERIC Educational Resources Information Center

Wu, YuLung

2013-01-01

The teaching system in Taiwan is currently based on large classes where teachers cannot control student situations totally. In E-Learning System, a teacher who reviews a student's learning situation must examine the students' learning records according to different items, and further organize and define the students' current learning situations,…

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 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.

Understanding the essential elements of work-based learning and its relevance to everyday clinical practice.

PubMed

Williams, Caroline

2010-09-01

To critically review the work-based learning literature and explore the implications of the findings for the development of work-based learning programmes. With NHS budgets under increasing pressure, and challenges to the impact of classroom-based learning on patient outcomes, work-based learning is likely to come under increased scrutiny as a potential solution. Evidence from higher education institutions suggests that work-based learning can improve practice, but in many cases it is perceived as little more than on-the-job training to perform tasks. The CINAHL database was searched using the keywords work-based learning, work-place learning and practice-based learning. Those articles that had a focus on post-registration nursing were selected and critically reviewed. Using the review of the literature, three key issues were explored. Work-based learning has the potential to change practice. Learning how to learn and critical reflection are key features. For effective work-based learning nurses need to take control of their own learning, receive support to critically reflect on their practice and be empowered to make changes to that practice. A critical review of the literature has identified essential considerations for the implementation of work-based learning. A change in culture from classroom to work-based learning requires careful planning and consideration of learning cultures. To enable effective work-based learning, nurse managers need to develop a learning culture in their workplace. They should ensure that skilled facilitation is provided to support staff with critical reflection and effecting changes in practice. CONTRIBUTION TO NEW KNOWLEDGE: This paper has identified three key issues that need to be considered in the development of work-based learning programmes. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

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.

Comparing Learning Gains: Audio Versus Text-based Instructor Communication in a Blended Online Learning Environment

NASA Astrophysics Data System (ADS)

Shimizu, Dominique

Though blended course audio feedback has been associated with several measures of course satisfaction at the postsecondary and graduate levels compared to text feedback, it may take longer to prepare and positive results are largely unverified in K-12 literature. The purpose of this quantitative study was to investigate the time investment and learning impact of audio communications with 228 secondary students in a blended online learning biology unit at a central Florida public high school. A short, individualized audio message regarding the student's progress was given to each student in the audio group; similar text-based messages were given to each student in the text-based group on the same schedule; a control got no feedback. A pretest and posttest were employed to measure learning gains in the three groups. To compare the learning gains in two types of feedback with each other and to no feedback, a controlled, randomized, experimental design was implemented. In addition, the creation and posting of audio and text feedback communications were timed in order to assess whether audio feedback took longer to produce than text only feedback. While audio feedback communications did take longer to create and post, there was no difference between learning gains as measured by posttest scores when student received audio, text-based, or no feedback. Future studies using a similar randomized, controlled experimental design are recommended to verify these results and test whether the trend holds in a broader range of subjects, over different time frames, and using a variety of assessment types to measure student learning.

Effects of a Web-based course on nursing skills and knowledge learning.

PubMed

Lu, Der-Fa; Lin, Zu-Chun; Li, Yun-Ju

2009-02-01

The purpose of the study was to assess the effectiveness of supplementing traditional classroom teaching with Web-based learning design when teaching intramuscular injection nursing skills. Four clusters of nursing students at a junior college in eastern Taiwan were randomly assigned to experimental and control groups. A total of 147 students (80 in the experimental group, 67 in the control group) completed the study. All participants received the same classroom lectures and skill demonstration. The experimental group interacted using a Web-based course and were able to view the content on demand. The students and instructor interacted via a chatroom, the bulletin board, and e-mail. Participants in the experimental group had significantly higher scores on both intramuscular injection knowledge and skill learning. A Web-based design can be an effective supplementing learning tool for teaching nursing knowledge and skills.

Lessons Learned from the Node 1 Atmosphere Control and Storage and Water Recovery and Management Subsystem Design

NASA Technical Reports Server (NTRS)

Williams, David E.

2010-01-01

Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Atmosphere Control and Storage (ACS) and Water Recovery and Management (WRM) subsystems and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs.

Lessons Learned from the Node 1 Atmosphere Control and Storage and Water Recovery and Management Subsystem Design

NASA Technical Reports Server (NTRS)

Williams, David E.

2011-01-01

Node 1 flew to the International Space Station (ISS) on Flight 2A during December 1998. To date the National Aeronautics and Space Administration (NASA) has learned a lot of lessons from this module based on its history of approximately two years of acceptance testing on the ground and currently its twelve years on-orbit. This paper will provide an overview of the ISS Environmental Control and Life Support (ECLS) design of the Node 1 Atmosphere Control and Storage (ACS) and Water Recovery and Management (WRM) subsystems and it will document some of the lessons that have been learned to date for these subsystems based on problems prelaunch, problems encountered on-orbit, and operational problems/concerns. It is hoped that documenting these lessons learned from ISS will help in preventing them in future Programs.

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.

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

PubMed

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

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.

Academic Achievement from Using the Learning Medium Via a Tablet Device Based on Multiple Intelligences in Grade 1 Elementary Student.

PubMed

Nuallaong, Winitra; Nuallaong, Thanya; Preechadirek, Nongluck

2015-04-01

To measure academic achievement of the multiple intelligence-based learning medium via a tablet device. This is a quasi-experimental research study (non-randomized control group pretest-posttest design) in 62 grade 1 elementary students (33 males and 29 females). Thirty-one students were included in an experimental group using purposive sampling by choosing a student who had highest multiple intelligence test scores in logical-mathematic. Then, this group learned by the new learning medium via a tablet which the application matched to logical-mathematic multiple intelligence. Another 31 students were included in a control group using simple random sampling and then learning by recitation. Both groups did pre-test and post-test vocabulary. Thirty students in the experimental group and 24 students in the control group increased post-test scores (odds ratio = 8.75). Both groups made significant increasing in post-test scores. The experimental group increased 9.07 marks (95% CI 8.20-9.93) significantly higher than the control group which increased 4.39 marks (95% CI 3.06-5.72) (t = -6.032, df = 51.481, p < 0.001). Although learning from either multiple intelligence-based learning medium via a tablet or recitation can contribute academic achievement, learningfrom the new medium contributed more achievement than recitation. The new learning medium group had higher post-test scores 8.75 times than the recitation group. Therefore, the new learning medium is more effective than the traditional recitation in terms of academic achievement. This study has limitations because samples came from the same school. However, the previous study in Thailand did notfind a logical-mathematical multiple intelligence difference among schools. In the future, long-term research to find how the new learning medium affects knowledge retention will support the advantage for life-long learning.

Impairments in learning by monetary rewards and alcohol-associated rewards in detoxified alcoholic patients.

PubMed

Jokisch, Daniel; Roser, Patrik; Juckel, Georg; Daum, Irene; Bellebaum, Christian

2014-07-01

Excessive alcohol consumption has been linked to structural and functional brain changes associated with cognitive, emotional, and behavioral impairments. It has been suggested that neural processing in the reward system is also affected by alcoholism. The present study aimed at further investigating reward-based associative learning and reversal learning in detoxified alcohol-dependent patients. Twenty-one detoxified alcohol-dependent patients and 26 healthy control subjects participated in a probabilistic learning task using monetary and alcohol-associated rewards as feedback stimuli indicating correct responses. Performance during acquisition and reversal learning in the different feedback conditions was analyzed. Alcohol-dependent patients and healthy control subjects showed an increase in learning performance over learning blocks during acquisition, with learning performance being significantly lower in alcohol-dependent patients. After changing the contingencies, alcohol-dependent patients exhibited impaired reversal learning and showed, in contrast to healthy controls, different learning curves for different types of rewards with no increase in performance for high monetary and alcohol-associated feedback. The present findings provide evidence that dysfunctional processing in the reward system in alcohol-dependent patients leads to alterations in reward-based learning resulting in a generally reduced performance. In addition, the results suggest that alcohol-dependent patients are, in particular, more impaired in changing an established behavior originally reinforced by high rewards. Copyright © 2014 by the Research Society on Alcoholism.

Comparison between project-based learning and discovery learning toward students' metacognitive strategies on global warming concept

NASA Astrophysics Data System (ADS)

Tumewu, Widya Anjelia; Wulan, Ana Ratna; Sanjaya, Yayan

2017-05-01

The purpose of this study was to know comparing the effectiveness of learning using Project-based learning (PjBL) and Discovery Learning (DL) toward students metacognitive strategies on global warming concept. A quasi-experimental research design with a The Matching-Only Pretest-Posttest Control Group Design was used in this study. The subjects were students of two classes 7th grade of one of junior high school in Bandung City, West Java of 2015/2016 academic year. The study was conducted on two experimental class, that were project-based learning treatment on the experimental class I and discovery learning treatment was done on the experimental class II. The data was collected through questionnaire to know students metacognitive strategies. The statistical analysis showed that there were statistically significant differences in students metacognitive strategies between project-based learning and discovery learning.

Design issues for a reinforcement-based self-learning fuzzy controller

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Dauherity, Walter

1993-01-01

Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

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.

Dynamic Scaffolding of Socially Regulated Learning in a Computer-Based Learning Environment

ERIC Educational Resources Information Center

Molenaar, Inge; Roda, Claudia; van Boxtel, Carla; Sleegers, Peter

2012-01-01

The aim of this study is to test the effects of dynamically scaffolding social regulation of middle school students working in a computer-based learning environment. Dyads in the scaffolding condition (N=56) are supported with computer-generated scaffolds and students in the control condition (N=54) do not receive scaffolds. The scaffolds are…

A Project-Based Laboratory for Learning Embedded System Design with Industry Support

ERIC Educational Resources Information Center

Lee, Chyi-Shyong; Su, Juing-Huei; Lin, Kuo-En; Chang, Jia-Hao; Lin, Gu-Hong

2010-01-01

A project-based laboratory for learning embedded system design with support from industry is presented in this paper. The aim of this laboratory is to motivate students to learn the building blocks of embedded systems and practical control algorithms by constructing a line-following robot using the quadratic interpolation technique to predict the…

Remote Learning for the Manipulation and Control of Robotic Cells

ERIC Educational Resources Information Center

Goldstain, Ofir; Ben-Gal, Irad; Bukchin, Yossi

2007-01-01

This work proposes an approach to remote learning of robotic cells based on internet and simulation tools. The proposed approach, which integrates remote-learning and tele-operation into a generic scheme, is designed to enable students and developers to set-up and manipulate a robotic cell remotely. Its implementation is based on a dedicated…

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…

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…

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.

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

A Sarsa(λ)-based control model for real-time traffic light coordination.

PubMed

Zhou, Xiaoke; Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Assessing the Effectiveness of Case-Based Collaborative Learning via Randomized Controlled Trial.

PubMed

Krupat, Edward; Richards, Jeremy B; Sullivan, Amy M; Fleenor, Thomas J; Schwartzstein, Richard M

2016-05-01

Case-based collaborative learning (CBCL) is a novel small-group approach that borrows from team-based learning principles and incorporates elements of problem-based learning (PBL) and case-based learning. CBCL includes a preclass readiness assurance process and case-based in-class activities in which students respond to focused, open-ended questions individually, discuss their answers in groups of 4, and then reach consensus in larger groups of 16. This study introduces CBCL and assesses its effectiveness in one course at Harvard Medical School. In a 2013 randomized controlled trial, 64 medical and dental student volunteers were assigned randomly to one of four 8-person PBL tutorial groups (control; n = 32) or one of two 16-person CBCL tutorial groups (experimental condition; n = 32) as part of a required first-year physiology course. Outcomes for the PBL and CBCL groups were compared using final exam scores, student responses to a postcourse survey, and behavioral coding of portions of video-recorded class sessions. Overall, the course final exam scores for CBCL and PBL students were not significantly different. However, CBCL students whose mean exam performance in prior courses was below the participant median scored significantly higher than their PBL counterparts on the physiology course final exam. The most common adjectives students used to describe CBCL were "engaging," "fun," and "thought-provoking." Coding of observed behaviors indicated that individual affect was significantly higher in the CBCL groups than in the PBL groups. CBCL is a viable, engaging, active learning method. It may particularly benefit students with lower academic performance.

Online learning and control of attraction basins for the development of sensorimotor control strategies.

PubMed

de Rengervé, Antoine; Andry, Pierre; Gaussier, Philippe

2015-04-01

Imitation and learning from humans require an adequate sensorimotor controller to learn and encode behaviors. We present the Dynamic Muscle Perception-Action(DM-PerAc) model to control a multiple degrees-of-freedom (DOF) robot arm. In the original PerAc model, path-following or place-reaching behaviors correspond to the sensorimotor attractors resulting from the dynamics of learned sensorimotor associations. The DM-PerAc model, inspired by human muscles, permits one to combine impedance-like control with the capability of learning sensorimotor attraction basins. We detail a solution to learn incrementally online the DM-PerAc visuomotor controller. Postural attractors are learned by adapting the muscle activations in the model depending on movement errors. Visuomotor categories merging visual and proprioceptive signals are associated with these muscle activations. Thus, the visual and proprioceptive signals activate the motor action generating an attractor which satisfies both visual and proprioceptive constraints. This visuomotor controller can serve as a basis for imitative behaviors. In addition, the muscle activation patterns can define directions of movement instead of postural attractors. Such patterns can be used in state-action couples to generate trajectories like in the PerAc model. We discuss a possible extension of the DM-PerAc controller by adapting the Fukuyori's controller based on the Langevin's equation. This controller can serve not only to reach attractors which were not explicitly learned, but also to learn the state/action couples to define trajectories.

Evolving fuzzy rules in a learning classifier system

NASA Technical Reports Server (NTRS)

Valenzuela-Rendon, Manuel

1993-01-01

The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

Improving Grasp Skills Using Schema Structured Learning

NASA Technical Reports Server (NTRS)

Platt, Robert; Grupen, ROderic A.; Fagg, Andrew H.

2006-01-01

Abstract In the control-based approach to robotics, complex behavior is created by sequencing and combining control primitives. While it is desirable for the robot to autonomously learn the correct control sequence, searching through the large number of potential solutions can be time consuming. This paper constrains this search to variations of a generalized solution encoded in a framework known as an action schema. A new algorithm, SCHEMA STRUCTURED LEARNING, is proposed that repeatedly executes variations of the generalized solution in search of instantiations that satisfy action schema objectives. This approach is tested in a grasping task where Dexter, the UMass humanoid robot, learns which reaching and grasping controllers maximize the probability of grasp success.

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.

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.

A Study of a Social Annotation Modeling Learning System

ERIC Educational Resources Information Center

Samuel, Roy David; Kim, Chanmin; Johnson, Tristan E.

2011-01-01

The transition from classroom instruction to e-learning raises pedagogical challenges for university instructors. A controlled integration of e-learning tools into classroom instruction may offer learners tangible benefits and improved effectiveness. This design-based research (DBR) study engaged students in e-learning activities integrated into…

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.

Compensation of significant parametric uncertainties using sliding mode online learning

NASA Astrophysics Data System (ADS)

Schnetter, Philipp; Kruger, Thomas

An augmented nonlinear inverse dynamics (NID) flight control strategy using sliding mode online learning for a small unmanned aircraft system (UAS) is presented. Because parameter identification for this class of aircraft often is not valid throughout the complete flight envelope, aerodynamic parameters used for model based control strategies may show significant deviations. For the concept of feedback linearization this leads to inversion errors that in combination with the distinctive susceptibility of small UAS towards atmospheric turbulence pose a demanding control task for these systems. In this work an adaptive flight control strategy using feedforward neural networks for counteracting such nonlinear effects is augmented with the concept of sliding mode control (SMC). SMC-learning is derived from variable structure theory. It considers a neural network and its training as a control problem. It is shown that by the dynamic calculation of the learning rates, stability can be guaranteed and thus increase the robustness against external disturbances and system failures. With the resulting higher speed of convergence a wide range of simultaneously occurring disturbances can be compensated. The SMC-based flight controller is tested and compared to the standard gradient descent (GD) backpropagation algorithm under the influence of significant model uncertainties and system failures.

Learning Based Bidding Strategy for HVAC Systems in Double Auction Retail Energy Markets

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

Sun, Yannan; Somani, Abhishek; Carroll, Thomas E.

In this paper, a bidding strategy is proposed using reinforcement learning for HVAC systems in a double auction market. The bidding strategy does not require a specific model-based representation of behavior, i.e., a functional form to translate indoor house temperatures into bid prices. The results from reinforcement learning based approach are compared with the HVAC bidding approach used in the AEP gridSMART® smart grid demonstration project and it is shown that the model-free (learning based) approach tracks well the results from the model-based behavior. Successful use of model-free approaches to represent device-level economic behavior may help develop similar approaches tomore » represent behavior of more complex devices or groups of diverse devices, such as in a building. Distributed control requires an understanding of decision making processes of intelligent agents so that appropriate mechanisms may be developed to control and coordinate their responses, and model-free approaches to represent behavior will be extremely useful in that quest.« less

Why No Difference? A Controlled Flipped Classroom Study for an Introductory Differential Equations Course

ERIC Educational Resources Information Center

Yong, Darryl; Levy, Rachel; Lape, Nancy

2015-01-01

Flipped classrooms have the potential to improve student learning and metacognitive skills as a result of increased time for active learning and group work and student control over pacing, when compared with traditional lecture-based courses. We are currently running a 4-year controlled study to examine the impact of flipping an Introductory…

Mediating Parent Learning to Promote Social Communication for Toddlers with Autism: Effects from a Randomized Controlled Trial

ERIC Educational Resources Information Center

Schertz, Hannah H.; Odom, Samuel L.; Baggett, Kathleen M.; Sideris, John H.

2018-01-01

A randomized controlled trial was conducted to evaluate effects of the Joint Attention Mediated Learning (JAML) intervention. Toddlers with autism spectrum disorders (ASD) aged 16-30 months (n = 144) were randomized to intervention and community control conditions. Parents, who participated in 32 weekly home-based sessions, followed a mediated…

Working-memory capacity protects model-based learning from stress.

PubMed

Otto, A Ross; Raio, Candace M; Chiang, Alice; Phelps, Elizabeth A; Daw, Nathaniel D

2013-12-24

Accounts of decision-making have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental advances suggest that this classic distinction between habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning, called model-free and model-based learning. Popular neurocomputational accounts of reward processing emphasize the involvement of the dopaminergic system in model-free learning and prefrontal, central executive-dependent control systems in model-based choice. Here we hypothesized that the hypothalamic-pituitary-adrenal (HPA) axis stress response--believed to have detrimental effects on prefrontal cortex function--should selectively attenuate model-based contributions to behavior. To test this, we paired an acute stressor with a sequential decision-making task that affords distinguishing the relative contributions of the two learning strategies. We assessed baseline working-memory (WM) capacity and used salivary cortisol levels to measure HPA axis stress response. We found that stress response attenuates the contribution of model-based, but not model-free, contributions to behavior. Moreover, stress-induced behavioral changes were modulated by individual WM capacity, such that low-WM-capacity individuals were more susceptible to detrimental stress effects than high-WM-capacity individuals. These results enrich existing accounts of the interplay between acute stress, working memory, and prefrontal function and suggest that executive function may be protective against the deleterious effects of acute stress.

Working-memory capacity protects model-based learning from stress

PubMed Central

Otto, A. Ross; Raio, Candace M.; Chiang, Alice; Phelps, Elizabeth A.; Daw, Nathaniel D.

2013-01-01

Accounts of decision-making have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental advances suggest that this classic distinction between habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning, called model-free and model-based learning. Popular neurocomputational accounts of reward processing emphasize the involvement of the dopaminergic system in model-free learning and prefrontal, central executive–dependent control systems in model-based choice. Here we hypothesized that the hypothalamic-pituitary-adrenal (HPA) axis stress response—believed to have detrimental effects on prefrontal cortex function—should selectively attenuate model-based contributions to behavior. To test this, we paired an acute stressor with a sequential decision-making task that affords distinguishing the relative contributions of the two learning strategies. We assessed baseline working-memory (WM) capacity and used salivary cortisol levels to measure HPA axis stress response. We found that stress response attenuates the contribution of model-based, but not model-free, contributions to behavior. Moreover, stress-induced behavioral changes were modulated by individual WM capacity, such that low-WM-capacity individuals were more susceptible to detrimental stress effects than high-WM-capacity individuals. These results enrich existing accounts of the interplay between acute stress, working memory, and prefrontal function and suggest that executive function may be protective against the deleterious effects of acute stress. PMID:24324166

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.

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.

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

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

Aziz, H. M. Abdul; Zhu, Feng; Ukkusuri, Satish V.

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better atmore » higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO 2, NO x, VOC, PM 10) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.« less

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Daugherity, Walter C.

1992-01-01

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

Learning communication from erroneous video-based examples: A double-blind randomised controlled trial.

PubMed

Schmitz, Felix Michael; Schnabel, Kai Philipp; Stricker, Daniel; Fischer, Martin Rudolf; Guttormsen, Sissel

2017-06-01

Appropriate training strategies are required to equip undergraduate healthcare students to benefit from communication training with simulated patients. This study examines the learning effects of different formats of video-based worked examples on initial communication skills. First-year nursing students (N=36) were randomly assigned to one of two experimental groups (correct v. erroneous examples) or to the control group (no examples). All the groups were provided an identical introduction to learning materials on breaking bad news; the experimental groups also received a set of video-based worked examples. Each example was accompanied by a self-explanation prompt (considering the example's correctness) and elaborated feedback (the true explanation). Participants presented with erroneous examples broke bad news to a simulated patient significantly more appropriately than students in the control group. Additionally, they tended to outperform participants who had correct examples, while participants presented with correct examples tended to outperform the control group. The worked example effect was successfully adapted for learning in the provider-patient communication domain. Implementing video-based worked examples with self-explanation prompts and feedback can be an effective strategy to prepare students for their training with simulated patients, especially when examples are erroneous. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparing the Effects of Self-Controlled and Examiner-Controlled Feedback on Learning in Children With Developmental Coordination Disorder.

PubMed

Zamani, Mohamad Hosein; Fatemi, Rouholah; Soroushmoghadam, Keyvan

2015-12-01

Feedback can improve task learning in children with developmental coordination disorder (DCD). However, the frequency and type of feedback may play different role in learning and needs to more investigations. The aim of this study was to evaluate the acquisition and retention of new feedback skills in children with DCD under different frequency of self-control and control examiner feedback. In this quasi-experimental study with pretest-posttest design, participants based on their retention were divided into four feedback groups: self-controlled feedback groups with frequencies of 50% and75%, experimenter controls with frequencies of 50% and 75%. The study sample consisted of 24 boys with DCD aged between 9 to 11 years old in Ahvaz City, Iran. Then subjects practiced 30 throwing (6 blocks of 5 attempts) in eighth session. Acquisition test immediately after the last training session, and then the retention test were taken. Data were analyzed using the paired t-test, ANOVA and Tukey tests. The results showed no significant difference between groups in the acquisition phase (P > 0.05). However,in the retention session, group of self-control showed better performance than the control tester group (P < 0.05). Based on the current findings, self-control feedback with high frequency leads to more learning in DCD children. The results of this study can be used in rehabilitation programs to improve performance and learning in children with DCD.

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

Research on Student Thought Processes during Computer-Based Instruction.

ERIC Educational Resources Information Center

Clark, Richard E.

1984-01-01

Reviews cognitive research related to computer-based instruction in the areas of motivation; the relationship between computer-assisted instruction and learning; learner control; transfer of learning; hemispheric dominance; and anxiety. Design professionals are urged to consider congitive views. (MBR)

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…

Conceptual Understanding of Acids and Bases Concepts and Motivation to Learn Chemistry

ERIC Educational Resources Information Center

Cetin-Dindar, Ayla; Geban, Omer

2017-01-01

The purpose of this study was to investigate the effect of 5E learning cycle model oriented instruction (LCMI) on 11th-grade students' conceptual understanding of acids and bases concepts and student motivation to learn chemistry. The study, which lasted for 7 weeks, involved two groups: An experimental group (LCMI) and a control group (the…

Proposal of a Framework for Internet Based Licensing of Learning Objects

ERIC Educational Resources Information Center

Santos, Osvaldo A.; Ramos, Fernando M. S.

2004-01-01

This paper presents a proposal of a framework whose main objective is to manage the delivery and rendering of learning objects in a digital rights controlled environment. The framework is based on a digital licensing scheme that requires each learning object to have the proper license in order to be rendered by a trusted player. A conceptual model…

Mathematics Learning Assisted Geogebra using Technologically Aligned Classroom (TAC) to Improve Communication Skills of Vocasional High School Student

NASA Astrophysics Data System (ADS)

Yuliardi, R.; Nurjanah

2017-09-01

The purpose of this study to analyze mathematical communication skill’s student to resolve geometry transformation problems through computer Assisted Geogebra using Technologically Aligned Classroom (TAC). The population in this study were students from one of Vocasional High School Student in West Java. Selection of sample by purposed random sampling, the experimental class is taught Technologically Aligned Classroom (TAC) with GeoGebra, while the control class is taught by conventional learning. This study was quasi-experimental with pretest and posttest control group design. Based on the results; (1) The enhancement of student mathematical communication skills through TAC was higher than the conventional learning; (2) based on gender, there were no differences of mathematical communication skilss student who exposed with TAC and conventional learning; (3) based on KAM test, there was significant enhancement of students’ communication skills among ability of high, middle, and low KAM. The differences occur between high KAM and middle KAM, and also between high KAM and low KAM. Based on this result, mathematics learning Assisted Geogebra using Technologically Aligned Classroom (TAC) can be applied in the process of Mathematics Learning in Vocasional High School.

Rational metareasoning and the plasticity of cognitive control.

PubMed

Lieder, Falk; Shenhav, Amitai; Musslick, Sebastian; Griffiths, Thomas L

2018-04-01

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people's ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.

Rational metareasoning and the plasticity of cognitive control

PubMed Central

Shenhav, Amitai; Musslick, Sebastian; Griffiths, Thomas L.

2018-01-01

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure. PMID:29694347

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

PubMed

Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

2008-08-01

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

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.

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.

Learning Grasp Context Distinctions that Generalize

NASA Technical Reports Server (NTRS)

Platt, Robert; Grupen, Roderic A.; Fagg, Andrew H.

2006-01-01

Control-based approaches to grasp synthesis create grasping behavior by sequencing and combining control primitives. In the absence of any other structure, these approaches must evaluate a large number of feasible control sequences as a function of object shape, object pose, and task. This work explores a new approach to grasp synthesis that limits consideration to variations on a generalized localize-reach-grasp control policy. A new learning algorithm, known as schema structured learning, is used to learn which instantiations of the generalized policy are most likely to lead to a successful grasp in different problem contexts. Two experiments are described where Dexter, a bimanual upper torso, learns to select an appropriate grasp strategy as a function of object eccentricity and orientation. In addition, it is shown that grasp skills learned in this way can generalize to new objects. Results are presented showing that after learning how to grasp a small, representative set of objects, the robot's performance quantitatively improves for similar objects that it has not experienced before.

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes.

PubMed

Fravolini, M L; Fabietti, P G

2014-01-01

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.

Creating a peaceful school learning environment: a controlled study of an elementary school intervention to reduce violence.

PubMed

Twemlow, S W; Fonagy, P; Sacco, F C; Gies, M L; Evans, R; Ewbank, R

2001-05-01

The impact of a manual-based antiviolence program on the learning climate in an elementary school over 4 years was compared with the outcome in a control school. The two schools were matched for demographic characteristics. The intervention in the experimental school was based on zero tolerance for bullying; the control school received only regular psychiatric consultation. Disciplinary and academic achievement data were collected in both schools. The experimental school showed significant reductions in discipline referrals and increases in scores on standardized academic achievement measures. A low-cost antiviolence intervention that does not focus on individual pathology or interfere with the educational process may improve the learning environment in elementary schools.

Design of robust iterative learning control schemes for systems with polytopic uncertainties and sector-bounded nonlinearities

NASA Astrophysics Data System (ADS)

Boski, Marcin; Paszke, Wojciech

2017-01-01

This paper deals with designing of iterative learning control schemes for uncertain systems with static nonlinearities. More specifically, the nonlinear part is supposed to be sector bounded and system matrices are assumed to range in the polytope of matrices. For systems with such nonlinearities and uncertainties the repetitive process setting is exploited to develop a linear matrix inequality based conditions for computing the feedback and feedforward (learning) controllers. These controllers guarantee acceptable dynamics along the trials and ensure convergence of the trial-to-trial error dynamics, respectively. Numerical examples illustrate the theoretical results and confirm effectiveness of the designed control scheme.

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.

Non-linguistic learning and aphasia: Evidence from a paired associate and feedback-based task

PubMed Central

Vallila-Rohter, Sofia; Kiran, Swathi

2013-01-01

Though aphasia is primarily characterized by impairments in the comprehension and/or expression of language, research has shown that patients with aphasia also show deficits in cognitive-linguistic domains such as attention, executive function, concept knowledge and memory (Helm-Estabrooks, 2002 for review). Research in aphasia suggests that cognitive impairments can impact the online construction of language, new verbal learning, and transactional success (Freedman & Martin, 2001; Hula & McNeil, 2008; Ramsberger, 2005). In our research, we extend this hypothesis to suggest that general cognitive deficits influence progress with therapy. The aim of our study is to explore learning, a cognitive process that is integral to relearning language, yet underexplored in the field of aphasia rehabilitation. We examine non-linguistic category learning in patients with aphasia (n=19) and in healthy controls (n=12), comparing feedback and non-feedback based instruction. Participants complete two computer-based learning tasks that require them to categorize novel animals based on the percentage of features shared with one of two prototypes. As hypothesized, healthy controls showed successful category learning following both methods of instruction. In contrast, only 60% of our patient population demonstrated successful non-linguistic category learning. Patient performance was not predictable by standardized measures of cognitive ability. Results suggest that general learning is affected in aphasia and is a unique, important factor to consider in the field of aphasia rehabilitation. PMID:23127795

A novel model of motor learning capable of developing an optimal movement control law online from scratch.

PubMed

Shimansky, Yury P; Kang, Tao; He, Jiping

2004-02-01

A computational model of a learning system (LS) is described that acquires knowledge and skill necessary for optimal control of a multisegmental limb dynamics (controlled object or CO), starting from "knowing" only the dimensionality of the object's state space. It is based on an optimal control problem setup different from that of reinforcement learning. The LS solves the optimal control problem online while practicing the manipulation of CO. The system's functional architecture comprises several adaptive components, each of which incorporates a number of mapping functions approximated based on artificial neural nets. Besides the internal model of the CO's dynamics and adaptive controller that computes the control law, the LS includes a new type of internal model, the minimal cost (IM(mc)) of moving the controlled object between a pair of states. That internal model appears critical for the LS's capacity to develop an optimal movement trajectory. The IM(mc) interacts with the adaptive controller in a cooperative manner. The controller provides an initial approximation of an optimal control action, which is further optimized in real time based on the IM(mc). The IM(mc) in turn provides information for updating the controller. The LS's performance was tested on the task of center-out reaching to eight randomly selected targets with a 2DOF limb model. The LS reached an optimal level of performance in a few tens of trials. It also quickly adapted to movement perturbations produced by two different types of external force field. The results suggest that the proposed design of a self-optimized control system can serve as a basis for the modeling of motor learning that includes the formation and adaptive modification of the plan of a goal-directed movement.

Drive Control Scheme of Electric Power Assisted Wheelchair Based on Neural Network Learning of Human Wheelchair Operation Characteristics

NASA Astrophysics Data System (ADS)

Tanohata, Naoki; Seki, Hirokazu

This paper describes a novel drive control scheme of electric power assisted wheelchairs based on neural network learning of human wheelchair operation characteristics. “Electric power assisted wheelchair” which enhances the drive force of the operator by employing electric motors is expected to be widely used as a mobility support system for elderly and disabled people. However, some handicapped people with paralysis of the muscles of one side of the body cannot maneuver the wheelchair as desired because of the difference in the right and left input force. Therefore, this study proposes a neural network learning system of such human wheelchair operation characteristics and a drive control scheme with variable distribution and assistance ratios. Some driving experiments will be performed to confirm the effectiveness of the proposed control system.

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.

Orbital frontal cortex updates state-induced value change for decision-making.

PubMed

Baltz, Emily T; Yalcinbas, Ege A; Renteria, Rafael; Gremel, Christina M

2018-06-13

Recent hypotheses have posited that orbital frontal cortex (OFC) is important for using inferred consequences to guide behavior. Less clear is OFC's contribution to goal-directed or model-based behavior, where the decision to act is controlled by previous experience with the consequence or outcome. Investigating OFC's role in learning about changed outcomes separate from decision-making is not trivial and often the two are confounded. Here we adapted an incentive learning task to mice, where we investigated processes controlling experience-based outcome updating independent from inferred action control. We found chemogenetic OFC attenuation did not alter the ability to perceive motivational state-induced changes in outcome value but did prevent the experience-based updating of this change. Optogenetic inhibition of OFC excitatory neuron activity selectively when experiencing an outcome change disrupted the ability to update, leaving mice unable to infer the appropriate behavior. Our findings support a role for OFC in learning that controls decision-making. © 2018, Baltz et al.

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.

Autonomous reinforcement learning with experience replay.

PubMed

Wawrzyński, Paweł; Tanwani, Ajay Kumar

2013-05-01

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

Influence of Web Based Cooperative Learning Strategy and Achiever Motivation on Student Study Outcome

ERIC Educational Resources Information Center

Hariadi, Bambang; Wurijanto, Tutut

2016-01-01

The research aimed at examining the effect of instructional strategy (web-based STAD and text-based STAD) and achiever motivation toward student learning outcomes. The research implied quasi-experimental design with nonequivalent control group factorial version. The subjects were undergraduate students of Information Systems of academic year…

Comparison of Computer-Based Versus Counselor-Based Occupational Information Systems with Disadvantaged Vocational Students

ERIC Educational Resources Information Center

Maola, Joseph; Kane, Gary

1976-01-01

Subjects, who were Occupational Work Experience students, were randomly assigned to individual guidance from either a computerized occupational information system, to a counselor-based information system or to a control group. Results demonstrate a hierarchical learning effect: The computer group learned more than the counseled group, which…

Travels towards Problem Based Learning in Medical Education (VPBL).

ERIC Educational Resources Information Center

Bowdish, Bruce E.; Chauvin, Sheila W.; Kreisman, Norman; Britt, Mike

2003-01-01

Reports results of an investigation of the effectiveness of a prototype virtual problem-based learning (VPBL) exercise delivered via the World Wide Web to first year medical students. Compares the VPBL and a text-based version of the same exercise on students' achievement and examines instructional design issues including learner control and…

Learning in the tutorial group: a balance between individual freedom and institutional control.

PubMed

McAllister, Anita; Aanstoot, Janna; Hammarström, Inger Lundeborg; Samuelsson, Christina; Johannesson, Eva; Sandström, Karin; Berglind, Ulrika

2014-01-01

The study investigates factors in problem-based learning tutorial groups which promote or inhibit learning. The informants were tutors and students from speech-language pathology and physiotherapy programmes. Semi-structured focus-group interviews and individual interviews were used. Results revealed three themes: Responsibility. Time and Support. Under responsibility, the delicate balance between individual and institutional responsibility and control was shown. Time included short and long-term perspectives on learning. Under support, supporting documents, activities and personnel resources were mentioned. In summary, an increased control by the program and tutors decreases student's motivation to assume responsibility for learning. Support in tutorial groups needs to adapt to student progression and to be well aligned to tutorial work to have the intended effect. A lifelong learning perspective may help students develop a meta-awareness regarding learning that could make tutorial work more meaningful.

Computer-Assisted Learning in Elementary Reading: A Randomized Control Trial

ERIC Educational Resources Information Center

Shannon, Lisa Cassidy; Styers, Mary Koenig; Wilkerson, Stephanie Baird; Peery, Elizabeth

2015-01-01

This study evaluated the efficacy of Accelerated Reader, a computer-based learning program, at improving student reading. Accelerated Reader is a progress-monitoring, assessment, and practice tool that supports classroom instruction and guides independent reading. Researchers used a randomized controlled trial to evaluate the program with 344…

Mobile-Based Video Learning Outcomes in Clinical Nursing Skill Education: A Randomized Controlled Trial.

PubMed

Lee, Nam-Ju; Chae, Sun-Mi; Kim, Haejin; Lee, Ji-Hye; Min, Hyojin Jennifer; Park, Da-Eun

2016-01-01

Mobile devices are a regular part of daily life among the younger generations. Thus, now is the time to apply mobile device use to nursing education. The purpose of this study was to identify the effects of a mobile-based video clip on learning motivation, competence, and class satisfaction in nursing students using a randomized controlled trial with a pretest and posttest design. A total of 71 nursing students participated in this study: 36 in the intervention group and 35 in the control group. A video clip of how to perform a urinary catheterization was developed, and the intervention group was able to download it to their own mobile devices for unlimited viewing throughout 1 week. All of the students participated in a practice laboratory to learn urinary catheterization and were blindly tested for their performance skills after participation in the laboratory. The intervention group showed significantly higher levels of learning motivation and class satisfaction than did the control. Of the fundamental nursing competencies, the intervention group was more confident in practicing catheterization than their counterparts. Our findings suggest that video clips using mobile devices are useful tools that educate student nurses on relevant clinical skills and improve learning outcomes.

Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level

NASA Astrophysics Data System (ADS)

Fakhrazari, Amin; Boroushaki, Mehrdad

2008-06-01

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Huang, Wei

2014-01-01

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control. PMID:24592183

Instructional Approaches on Science Performance, Attitude and Inquiry Ability in a Computer-Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Chen, Ching-Huei; Chen, Chia-Ying

2012-01-01

This study examined the effects of an inquiry-based learning (IBL) approach compared to that of a problem-based learning (PBL) approach on learner performance, attitude toward science and inquiry ability. Ninety-six students from three 7th-grade classes at a public school were randomly assigned to two experimental groups and one control group. All…

The Effect of Learning Cycle Constructivist-Based Approach on Students' Academic Achievement and Attitude towards Chemistry in Secondary Schools in North-Eastern Part of Nigeria

ERIC Educational Resources Information Center

Jack, Gladys Uzezi

2017-01-01

This study investigated the effect of learning cycle constructivist-based approach on secondary schools students' academic achievement and their attitude towards chemistry. The design used was a pre-test, post-test non randomized control group quasi experimental research design. The design consisted of two instructional groups (learning cycle…

The Moderating Role of Self-Regulated Learning in Job Characteristics and Attitudes towards Web-Based Continuing Learning in the Airlines Workplace

ERIC Educational Resources Information Center

Lin, Xiao-fan; Liang, Jyh-Chong; Tsai, Chin-Chung; Hu, Qintai

2018-01-01

With the increasing importance of adult and continuing education, the present study aimed to examine the factors that influence continuing web-based learning at work. Three questionnaires were utilised to investigate the association of the job characteristics from Karasek et al.'s (1998) job demand-control-support model and the self-regulated…

Fast Brain Plasticity during Word Learning in Musically-Trained Children.

PubMed

Dittinger, Eva; Chobert, Julie; Ziegler, Johannes C; Besson, Mireille

2017-01-01

Children learn new words every day and this ability requires auditory perception, phoneme discrimination, attention, associative learning and semantic memory. Based on previous results showing that some of these functions are enhanced by music training, we investigated learning of novel words through picture-word associations in musically-trained and control children (8-12 year-old) to determine whether music training would positively influence word learning. Results showed that musically-trained children outperformed controls in a learning paradigm that included picture-sound matching and semantic associations. Moreover, the differences between unexpected and expected learned words, as reflected by the N200 and N400 effects, were larger in children with music training compared to controls after only 3 min of learning the meaning of novel words. In line with previous results in adults, these findings clearly demonstrate a correlation between music training and better word learning. It is argued that these benefits reflect both bottom-up and top-down influences. The present learning paradigm might provide a useful dynamic diagnostic tool to determine which perceptive and cognitive functions are impaired in children with learning difficulties.

Fast Brain Plasticity during Word Learning in Musically-Trained Children

PubMed Central

Dittinger, Eva; Chobert, Julie; Ziegler, Johannes C.; Besson, Mireille

2017-01-01

Children learn new words every day and this ability requires auditory perception, phoneme discrimination, attention, associative learning and semantic memory. Based on previous results showing that some of these functions are enhanced by music training, we investigated learning of novel words through picture-word associations in musically-trained and control children (8–12 year-old) to determine whether music training would positively influence word learning. Results showed that musically-trained children outperformed controls in a learning paradigm that included picture-sound matching and semantic associations. Moreover, the differences between unexpected and expected learned words, as reflected by the N200 and N400 effects, were larger in children with music training compared to controls after only 3 min of learning the meaning of novel words. In line with previous results in adults, these findings clearly demonstrate a correlation between music training and better word learning. It is argued that these benefits reflect both bottom-up and top-down influences. The present learning paradigm might provide a useful dynamic diagnostic tool to determine which perceptive and cognitive functions are impaired in children with learning difficulties. PMID:28553213

Dual-Processes in Learning and Judgment: Evidence from the Multiple Cue Probability Learning Paradigm

ERIC Educational Resources Information Center

Rolison, Jonathan J.; Evans, Jonathan St. B. T.; Dennis, Ian; Walsh, Clare R.

2012-01-01

Multiple cue probability learning (MCPL) involves learning to predict a criterion based on a set of novel cues when feedback is provided in response to each judgment made. But to what extent does MCPL require controlled attention and explicit hypothesis testing? The results of two experiments show that this depends on cue polarity. Learning about…

The Implications of Cognitive Psychology for Computer-Based Learning Tools.

ERIC Educational Resources Information Center

Kozma, Robert B.

1987-01-01

Defines cognitive computer tools as software programs that use the control capabilities of computers to amplify, extend, or enhance human cognition; suggests seven ways in which computers can aid learning; and describes the "Learning Tool," a software package for the Apple Macintosh microcomputer that is designed to aid learning of…

Dialogue and Structure: Enabling Learner Self-Regulation in Technology-Enhanced Learning Environments

ERIC Educational Resources Information Center

Andrade, Maureen Snow

2014-01-01

Distance learning that incorporates technology-enhanced learning environments provides a solution to the ever-increasing global demand for higher education. To be successful in these contexts, learners must be self-regulated, or have the ability to control the factors affecting their learning. Based on the theories of transactional distance,…

Resource-Based Learning and Class Organisation for Adult EFL Learners.

ERIC Educational Resources Information Center

Gewirtz, Agatha

1979-01-01

A list is presented of special factors pertaining to English as a foreign language class in England that provide strong arguments for organizing them along resource-based learning situations. Students can be in control of their studies, engaging in independent, individual work. (SW)

The impact of inquiry-based learning on the critical thinking dispositions of pre-service science teachers

NASA Astrophysics Data System (ADS)

Arsal, Zeki

2017-07-01

In the study, the impact of inquiry-based learning on pre-service teachers' critical thinking dispositions was investigated. The sample of the study comprised of 56 pre-service teachers in the science education teacher education programme at the public university in the north of Turkey. In the study, quasi-experimental design with an experimental and a control group were applied to find out the impact of inquiry-based learning on the critical thinking dispositions of the pre-service teachers in the teacher education programme. The results showed that the pre-service teachers in the experimental group did not show statistically significant greater progress in terms of critical thinking dispositions than those in the control group. Teacher educators who are responsible for pedagogical courses in the teacher education programme should consider that the inquiry-based learning could not be effective method to improve pre-service teachers' critical thinking dispositions. The results are discussed in relation to potential impact on science teacher education and implications for future research.

Cognitive control predicts use of model-based reinforcement learning.

PubMed

Otto, A Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D

2015-02-01

Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information--in the service of overcoming habitual, stimulus-driven responses--in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.

Preserved complex emotion-based learning in amnesia.

PubMed

Turnbull, Oliver H; Evans, Cathryn E Y

2006-01-01

An important role for emotion in decision-making has recently been highlighted by disruptions in problem solving abilities after lesion to the frontal lobes. Such complex decision-making skills appear to be based on a class of memory ability (emotion-based learning) that may be anatomically independent of hippocampally mediated episodic memory systems. There have long been reports of intact emotion-based learning in amnesia, arguably dating back to the classic report of Claparede. However, all such accounts relate to relatively simple patterns of emotional valence learning, rather than the more complex contingency patterns of emotional experience, which characterise everyday life. A patient, SL, who had a profound anterograde amnesia following posterior cerebral artery infarction, performed a measure of complex emotion-based learning (the Iowa Gambling Task) on three separate occasions. Despite his severe episodic memory impairment, he showed normal levels of performance on the Gambling Task, at levels comparable or better than controls-including learning that persisted across substantial periods of time (weeks). Thus, emotion-based learning systems appear able to encode, and sustain, more sophisticated patterns of valence learning than have previously been reported.

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.

Stimulus-response learning in long-term cocaine users: acquired equivalence and probabilistic category learning.

PubMed

Vadhan, Nehal P; Myers, Catherine E; Rubin, Eric; Shohamy, Daphna; Foltin, Richard W; Gluck, Mark A

2008-01-11

The purpose of this study was to examine stimulus-response (S-R) learning in active cocaine users. Twenty-two cocaine-dependent participants (20 males and 2 females) and 21 non-drug using control participants (19 males and 2 females) who were similar in age and education were administered two computerized learning tasks. The Acquired Equivalence task initially requires learning of simple antecedent-consequent discriminations, but later requires generalization of this learning when the stimuli are presented in novel recombinations. The Weather Prediction task requires the prediction of a dichotomous outcome based on different stimuli combinations when the stimuli predict the outcome only probabilistically. On the Acquired Equivalence task, cocaine users made significantly more errors than control participants when required to learn new discriminations while maintaining previously learned discriminations, but performed similarly to controls when required to generalize this learning. No group differences were seen on the Weather Prediction task. Cocaine users' learning of stimulus discriminations under conflicting response demands was impaired, but their ability to generalize this learning once they achieved criterion was intact. This performance pattern is consistent with other laboratory studies of long-term cocaine users that demonstrated that established learning interfered with new learning on incremental learning tasks, relative to healthy controls, and may reflect altered dopamine transmission in the basal ganglia of long-term cocaine users.

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

PubMed

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

2017-07-25

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. 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. 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. 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). 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. ©Christine M Micheel, Ingrid A Anderson, Patricia Lee, Sheau-Chiann Chen, Katy Justiss, Nunzia B Giuse, Fei Ye, Sheila V Kusnoor, Mia A Levy. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.07.2017.

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.

Problem-based learning versus lecture-based learning in postgraduate medical education.

PubMed

Smits, Paul B; de Buisonjé, Cathelijn D; Verbeek, Jos H; van Dijk, Frank J; Metz, Jaap C; ten Cate, Olle J

2003-08-01

The objective of this study was to investigate the effectiveness of problem-based learning in comparison with lecture-based learning in a postgraduate medical training program concerning the management of mental health problems for occupational health physicians. A randomized controlled trial in 1999, with a mean follow-up of 14 months after the educational intervention, was used involving postgraduate medical education and training for occupational health physicians in The Netherlands, with 118 physicians in training as occupational health physicians. The experimental program was based on the principles of problem-based learning; the control program used the traditional lecture-based approach. Both programs were aimed at improving knowledge of and performance in the occupational management of work-related mental health problems. As the main outcome measures, knowledge tests consisting of true-or-false and open-answer questions and performance in practice based on self-reports and performance indicators were used. Satisfaction with the course was rated by the participants. In both groups, knowledge had increased equally directly after the programs and decreased equally after the follow-up. The gain in knowledge remained positive. The performance indicator scores also increased in both groups, but significantly more so in the problem-based group. The problem-based group was less satisfied with the course. Both forms of postgraduate medical training are effective. In spite of less favorable evaluations, the problem-based program appeared to be more effective than the lecture-based program in improving performance. Both programs, however, were equally effective in improving knowledge levels.

The impact of simulation-based learning on students' English for Nursing Purposes (ENP) reading proficiency: a quasi-experimental study.

PubMed

Chang, Hsiao-Yun Annie; Chan, Luke; Siren, Betty

2013-06-01

This is a report of a study which evaluated simulation-based learning as a teaching strategy for improving participants' ENP reading proficiency in the senior college program of students whose first language is Chinese, not English. Simulation-based learning is known to be one of most effective teaching strategies in the healthcare professional curricula, which brings a clinical setting into the classroom. However, developing English reading skills for English written nursing journals through simulation-based learning in the nursing curricula, is largely unknown. We used a quasi-experimental approach with nonequivalent control group design to collect the causal connections between intervention and outcomes. 101 students were enrolled in this study (response rate 92.6%) of these 48 students volunteered for the intervention group, and 53 students for the control group. The findings indicated that the intervention group had significantly higher mean scores in ENP reading proficiency with unknown words in the article (p=.004), vocabulary (p<.001), and comprehension (p<.001) compared to the control group. Also, the intervention students showed more improvement in their English reading, both from quantitative and qualitative findings. Simulation-based learning may have some advantages in improving the English reading ability on English written nursing journals among nursing students. However, the benefits to the students of this study is still to be determined, and further exploration is needed with well designed research and a universal method of outcome measurement. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

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.

Self-regulated learning strategies used in surgical clerkship and the relationship with clinical achievement.

PubMed

Turan, Sevgi; Konan, Ali

2012-01-01

Self-regulated learning indicates students' skills in controlling their own learning. Self-regulated learning, which a context-specific process, emphasizes autonomy and control. Students gain more autonomy with respect to learning in the clinical years. Examining the self-regulated learning skills of students in this period will provide important clues about the level at which students are ready to use these skills in real-life conditions. The self-regulated learning strategies used by medical students in surgical clerkship were investigated in this study and their relation with clinical achievement was analyzed. The study was conducted during the surgery clerkship of medical students. The participation rate was 94% (309 students). Motivated Strategies for Learning Questionnaire (MSLQ), a case-based examination, Objective Structured Clinical Examination (OSCE), and tutor evaluations for assessing achievement were used. The relationship between the Motivated Strategies for Learning Questionnaire scores of the students and clinical achievement was analyzed with multilinear regression analysis. The findings showed that students use self-regulated learning skills at medium levels during their surgery clerkship. A relationship between these skills and OSCE scores and tutor evaluations was determined. OSCE scores of the students were observed to increase in conjunction with increased self-efficacy levels. However, as students' beliefs regarding control over learning increased, OSCE scores decreased. No significant relationship was defined between self-regulated learning skills and case-based examination scores. We observed that a greater self-efficacy for learning resulted in higher OSCE scores. Conversely, students who believe that learning is a result of their own effort had lower OSCE scores. Copyright © 2012 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

PubMed Central

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-01-01

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697

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.

Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

PubMed

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-02-08

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.

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

A composite self tuning strategy for fuzzy control of dynamic systems

NASA Technical Reports Server (NTRS)

Shieh, C.-Y.; Nair, Satish S.

1992-01-01

The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

Dopamine selectively remediates 'model-based' reward learning: a computational approach.

PubMed

Sharp, Madeleine E; Foerde, Karin; Daw, Nathaniel D; Shohamy, Daphna

2016-02-01

Patients with loss of dopamine due to Parkinson's disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from 'model-free' learning. The other, 'model-based' learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson's disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson's disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson's disease may be related to an inability to pursue reward based on complete representations of the environment. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Picmonic(®) Learning System: enhancing memory retention of medical sciences, using an audiovisual mnemonic Web-based learning platform.

PubMed

Yang, Adeel; Goel, Hersh; Bryan, Matthew; Robertson, Ron; Lim, Jane; Islam, Shehran; Speicher, Mark R

2014-01-01

Medical students are required to retain vast amounts of medical knowledge on the path to becoming physicians. To address this challenge, multimedia Web-based learning resources have been developed to supplement traditional text-based materials. The Picmonic(®) Learning System (PLS; Picmonic, Phoenix, AZ, USA) is a novel multimedia Web-based learning platform that delivers audiovisual mnemonics designed to improve memory retention of medical sciences. A single-center, randomized, subject-blinded, controlled study was conducted to compare the PLS with traditional text-based material for retention of medical science topics. Subjects were randomly assigned to use two different types of study materials covering several diseases. Subjects randomly assigned to the PLS group were given audiovisual mnemonics along with text-based materials, whereas subjects in the control group were given the same text-based materials with key terms highlighted. The primary endpoints were the differences in performance on immediate, 1 week, and 1 month delayed free-recall and paired-matching tests. The secondary endpoints were the difference in performance on a 1 week delayed multiple-choice test and self-reported satisfaction with the study materials. Differences were calculated using unpaired two-tailed t-tests. PLS group subjects demonstrated improvements of 65%, 161%, and 208% compared with control group subjects on free-recall tests conducted immediately, 1 week, and 1 month after study of materials, respectively. The results of performance on paired-matching tests showed an improvement of up to 331% for PLS group subjects. PLS group subjects also performed 55% greater than control group subjects on a 1 week delayed multiple choice test requiring higher-order thinking. The differences in test performance between the PLS group subjects and the control group subjects were statistically significant (P<0.001), and the PLS group subjects reported higher overall satisfaction with the material. The data of this pilot site demonstrate marked improvements in the retention of disease topics when using the PLS compared with traditional text-based materials. The use of the PLS in medical education is supported.

Impaired implicit learning and feedback processing after stroke.

PubMed

Lam, J M; Globas, C; Hosp, J A; Karnath, H-O; Wächter, T; Luft, A R

2016-02-09

The ability to learn is assumed to support successful recovery and rehabilitation therapy after stroke. Hence, learning impairments may reduce the recovery potential. Here, the hypothesis is tested that stroke survivors have deficits in feedback-driven implicit learning. Stroke survivors (n=30) and healthy age-matched control subjects (n=21) learned a probabilistic classification task with brain activation measured using functional magnetic resonance imaging in a subset of these individuals (17 stroke and 10 controls). Stroke subjects learned slower than controls to classify cues. After being rewarded with a smiley face, they were less likely to give the same response when the cue was repeated. Stroke subjects showed reduced brain activation in putamen, pallidum, thalamus, frontal and prefrontal cortices and cerebellum when compared with controls. Lesion analysis identified those stroke survivors as learning-impaired who had lesions in frontal areas, putamen, thalamus, caudate and insula. Lesion laterality had no effect on learning efficacy or brain activation. These findings suggest that stroke survivors have deficits in reinforcement learning that may be related to dysfunctional processing of feedback-based decision-making, reward signals and working memory. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Metacognition in Upper-Division Biology Students: Awareness Does Not Always Lead to Control

PubMed Central

Dye, Kathryn Morris; Stanton, Julie Dangremond

2017-01-01

Students with awareness and control of their own thinking can learn more and perform better than students who are not metacognitive. Metacognitive regulation is how you control your thinking in order to learn. It includes the skill of evaluation, which is the ability to appraise your approaches to learning and then modify future plans based on those appraisals. We asked when, why, and how upper-division biology students evaluated their approaches to learning. We used self-evaluation assignments to identify students with potentially high metacognition and conducted semistructured interviews to collect rich qualitative data from them. Through content analysis, we found that students evaluated their approaches to learning when their courses presented novel challenges. Most students evaluated in response to an unsatisfactory grade. While evaluating study strategies, many students considered performance and learning simultaneously. We gained insights on the barriers students face when they try to change their approaches to learning based on their evaluations. A few students continued to use ineffective study strategies even though they were aware of the ineffectiveness of those strategies. A desire to avoid feeling uncomfortable was the primary reason they avoided strategies that they knew were more effective. We examined the behavioral change literature to help interpret these findings. PMID:28495935

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.

Cousins Virtual Jane and Virtual Joe, Extraordinary Virtual Helpers

ERIC Educational Resources Information Center

Blignaut, Seugnet; Nagel, Lynette

2009-01-01

Higher education institutions deliver web-based learning with varied success. The success rate of distributed online courses remains low. Factors such as ineffective course facilitation and insufficient communication contribute to the unfulfilled promises of web-based learning. Students consequently feel unmotivated. Instructor control and in the…

Cognitive Support for Learning Computer-Based Tasks Using Animated Demonstration

ERIC Educational Resources Information Center

Chen, Chun-Ying

2016-01-01

This study investigated the influence of cognitive support for learning computer-based tasks using animated demonstration (AD) on instructional efficiency. Cognitive support included (1) segmentation and learner control introducing interactive devices that allow content sequencing through a navigational menu, and content pacing through stop and…

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.

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Enhancing Competence and Autonomy in Computer-Based Instruction Using a Skill-Challenge Balancing Strategy

ERIC Educational Resources Information Center

Kim, Jieun; Ryu, Hokyoung; Katuk, Norliza; Wang, Ruili; Choi, Gyunghyun

2014-01-01

The present study aims to show if a skill-challenge balancing (SCB) instruction strategy can assist learners to motivationally engage in computer-based learning. Csikszentmihalyi's flow theory (self-control, curiosity, focus of attention, and intrinsic interest) was applied to an account of the optimal learning experience in SCB-based learning…

Educational Intervention on Undergraduate Cancer Awareness and Self-Directed Learning.

PubMed

Hwang, Lih-Lian

2018-06-01

Traditional lecture-based learning (LBL) can increase cancer awareness in undergraduates. However, because of the rapidly changing knowledge base in medicine, undergraduates must develop skills required for lifelong self-directed learning (SDL). Problem-based learning (PBL) has been suggested as an SDL approach. This study used a nonequivalent control group with a pretest-posttest design for comparing PBL and LBL for their effectiveness in increasing cancer awareness and SDL among nonmedicine or nonnursing major undergraduates in a health-related general education course. Experimental groups 1 and 2 were instructed using PBL while the control group was instructed using LBL. Cancer educational programs were offered to experimental group 1 and the control group but not to experimental group 2. Among the 325 undergraduates who completed a questionnaire regarding cancer awareness and SDL in the pretest, 223 completed the 12-week follow-up survey of the posttest. Cancer awareness significantly improved between the pretest and posttest in the control group (P < 0.001). No significant difference in cancer awareness improvement was observed between experimental group 1 and the control group (P = 0.934). Cancer awareness improvement in experimental group 2 was significantly less than in the control group (P = 0.010). No statistically significant change in SDL was observed in the control group during the study (P = 0.897). However, the SDL of experimental groups 1 and 2 improved more significantly than that of the control group (P = 0.049 and 0.023, respectively). Therefore, PBL is an effective method of increasing cancer awareness and SDL in undergraduates.

Comparing self-guided learning and educator-guided learning formats for simulation-based clinical training.

PubMed

Brydges, Ryan; Carnahan, Heather; Rose, Don; Dubrowski, Adam

2010-08-01

In this paper, we tested the over-arching hypothesis that progressive self-guided learning offers equivalent learning benefit vs. proficiency-based training while limiting the need to set proficiency standards. We have shown that self-guided learning is enhanced when students learn on simulators that progressively increase in fidelity during practice. Proficiency-based training, a current gold-standard training approach, requires achievement of a criterion score before students advance to the next learning level. Baccalaureate nursing students (n = 15/group) practised intravenous catheterization using simulators that differed in fidelity (i.e. students' perceived realism). Data were collected in 2008. Proficiency-based students advanced from low- to mid- to high-fidelity after achieving a proficiency criterion at each level. Progressive students self-guided their progression from low- to mid- to high-fidelity. Yoked control students followed an experimenter-defined progressive practice schedule. Open-ended students moved freely between the simulators. One week after practice, blinded experts evaluated students' skill transfer on a standardized patient simulation. Group differences were examined using analyses of variance. Proficiency-based students scored highest on the high-fidelity post-test (effect size = 1.22). An interaction effect showed that the Progressive and Open-ended groups maintained their performance from post-test to transfer test, whereas the Proficiency-based and Yoked control groups experienced a significant decrease (P < 0.05). Surprisingly, most Open-ended students (73%) chose the progressive practice schedule. Progressive training and proficiency-based training resulted in equivalent transfer test performance, suggesting that progressive students effectively self-guided when to transition between simulators. Students' preference for the progressive practice schedule indicates that educators should consider this sequence for simulation-based training.

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

NASA Astrophysics Data System (ADS)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Extra! Extra! Learn All about It

ERIC Educational Resources Information Center

Curry, Kristen; Moore, Jerilou; Sumrall, William J.

2007-01-01

When students investigate science, they model, imitate, and perform science much as scientists do. Learning science in this way is best, according to the locus of control research. Based on this research, students need to develop an internal belief that they can control science outcomes and become a part of science through their own hands-on…

New Knowledge Derived from Learned Knowledge: Functional-Anatomic Correlates of Stimulus Equivalence

ERIC Educational Resources Information Center

Schlund, Michael W.; Hoehn-Saric, Rudolf; Cataldo, Michael F.

2007-01-01

Forming new knowledge based on knowledge established through prior learning is a central feature of higher cognition that is captured in research on stimulus equivalence (SE). Numerous SE investigations show that reinforcing behavior under control of distinct sets of arbitrary conditional relations gives rise to stimulus control by new, "derived"…

Robotics-Control Technology. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This document contains the materials required for presenting an 8-day competency-based technology learning activity (TLA) designed to introduce students in grades 6-10 to advances and career opportunities in the field of robotics-control technology. The guide uses hands-on exploratory experiences into which activities to help students develop…

The Effects of Study Tasks in a Computer-Based Chemistry Learning Environment

NASA Astrophysics Data System (ADS)

Urhahne, Detlef; Nick, Sabine; Poepping, Anna Christin; Schulz, Sarah Jayne

2013-12-01

The present study examines the effects of different study tasks on the acquisition of knowledge about acids and bases in a computer-based learning environment. Three different task formats were selected to create three treatment conditions: learning with gap-fill and matching tasks, learning with multiple-choice tasks, and learning only from text and figures without any additional tasks. Participants were 196 ninth-grade students who learned with a self-developed multimedia program in a pretest-posttest control group design. Research results reveal that gap-fill and matching tasks were most effective in promoting knowledge acquisition, followed by multiple-choice tasks, and no tasks at all. The findings are in line with previous research on this topic. The effects can possibly be explained by the generation-recognition model, which predicts that gap-fill and matching tasks trigger more encompassing learning processes than multiple-choice tasks. It is concluded that instructional designers should incorporate more challenging study tasks for enhancing the effectiveness of computer-based learning environments.

Teaching veterinary radiography by e-learning versus structured tutorial: a randomized, single-blinded controlled trial.

PubMed

Vandeweerd, Jean-Michel E F; Davies, John C; Pinchbeck, Gina L; Cotton, Jo C

2007-01-01

Case-based e-learning may allow effective teaching of veterinary radiology in the field of equine orthopedics. The objective of this study was to investigate the effectiveness of a new case-based e-learning tool, compared with a standard structured tutorial, in altering students' knowledge and skills about interpretation of radiographs of the digit in the horse. It was also designed to assess students' attitudes toward the two educational interventions. A randomized, single-blinded, controlled trial of 96 fourth-year undergraduate veterinary students, involving an educational intervention of either structured tutorial or case-based e-learning, was performed. A multiple-choice examination based on six learning outcomes was carried out in each group after the session, followed by an evaluation of students' attitudes toward their session on a seven-point scale. Text blanks were available to students to allow them to comment on the educational interventions and on their learning outcomes. Students also rated, on a Likert scale from 1 to 7, their performance for each specific learning outcome and their general ability to use a systematic approach in interpreting radiographs. Data were analyzed using the Mann-Whitney test, the t-test, and the equivalence test. There was no significant difference in student achievement on course tests. The results of the survey suggest positive student attitudes toward the e-learning tool and illustrate the difference between objective ratings and subjective assessments by students in testing a new educational intervention.

Understanding Work-Related Learning: The Case of ICT Workers

ERIC Educational Resources Information Center

Gijbels, David; Raemdonck, Isabel; Vervecken, Dries; Van Herck, Jonas

2012-01-01

Purpose: A central issue in the field of workplace learning is how work-related learning can be stimulated so that a powerful learning work environment is created. This paper seeks to further enlarge understanding on this issue. Based on the demand-control-support the aim is to investigate the influence of job-characteristics on the work-related…

Assessing College-Level Learning Difficulties and "At Riskness" for Learning Disabilities and ADHD: Development and Validation of the Learning Difficulties Assessment

ERIC Educational Resources Information Center

Kane, Steven T.; Walker, John H.; Schmidt, George R.

2011-01-01

This article describes the development and validation of the "Learning Difficulties Assessment" (LDA), a normed and web-based survey that assesses perceived difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. The LDA is designed to…

How the Young Generation Uses Digital Textbooks via Mobile Learning Terminals: Measurement of Elementary School Students in China

ERIC Educational Resources Information Center

Sun, Zhong; Jiang, Yuzhen

2015-01-01

Digital textbooks that offer multimedia features, interactive controls, e-annotation and learning process tracking are gaining increasing attention in today's mobile learning era, particularly with the rapid development of mobile learning terminals such as Apple's iPad series and Android-based models. Accordingly, this study explores how…

Reward-based spatial learning in unmedicated adults with obsessive-compulsive disorder.

PubMed

Marsh, Rachel; Tau, Gregory Z; Wang, Zhishun; Huo, Yuankai; Liu, Ge; Hao, Xuejun; Packard, Mark G; Peterson, Bradley S; Simpson, H Blair

2015-04-01

The authors assessed the functioning of mesolimbic and striatal areas involved in reward-based spatial learning in unmedicated adults with obsessive-compulsive disorder (OCD). Functional MRI blood-oxygen-level-dependent response was compared in 33 unmedicated adults with OCD and 33 healthy, age-matched comparison subjects during a reward-based learning task that required learning to use extramaze cues to navigate a virtual eight-arm radial maze to find hidden rewards. The groups were compared 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 pseudorandomly to experimentally prevent learning. Both groups learned to navigate the maze to find hidden rewards, but group differences in neural activity during navigation and reward processing were detected in mesolimbic and striatal areas. During navigation, the OCD group, unlike the healthy comparison group, exhibited activation in the left posterior hippocampus. Unlike healthy subjects, participants in the OCD group did not show activation in the left ventral putamen and amygdala when anticipating rewards or in the left hippocampus, amygdala, and ventral putamen when receiving unexpected rewards (control condition). Signal in these regions decreased relative to baseline during unexpected reward receipt among those in the OCD group, and the degree of activation was inversely associated with doubt/checking symptoms. Participants in the OCD group displayed abnormal recruitment of mesolimbic and ventral striatal circuitry during reward-based spatial learning. Whereas healthy comparison subjects exhibited activation in this circuitry in response to the violation of reward expectations, unmedicated OCD participants did not and instead over-relied on the posterior hippocampus during learning. Thus, dopaminergic innervation of reward circuitry may be altered, and future study of anterior/posterior hippocampal dysfunction in OCD is warranted.

On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\\infty}$ Control.

PubMed

Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen

2018-04-01

In this paper, based on the adaptive critic learning technique, the control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.

E-learning courses in epilepsy--concept, evaluation, and experience with the e-learning course "genetics of epilepsies".

PubMed

Wehrs, Verena Hézser-V; Pfäfflin, Margarete; May, Theodor W

2007-05-01

To evaluate the efficacy of the e-learning course "Genetics of Epilepsies" and to assess the experiences of the participants and e-moderators with this new approach. Prospective, controlled study with waiting group (control group, n = 18) and e-learning group (n = 20). The control group got the same reference literature list as the e-learning group. Both groups were assessed twice: The e-learning group before and after the course; the control group was assessed at the same times. increase in knowledge about genetics of epilepsies using questionnaires based on items formulated by experts (internal consistency, Cronbach's alpha = 0.86). Main hypothesis: greater increase of knowledge in the e-learning group compared to control group. assessment of the educational course and learning environment by participants and by tutors/e-moderators. Significant time x group interaction and group effect (ANOVA, each p < 0.01) with regard to knowledge. At baseline, the groups did not differ with respect to knowledge about genetics of epilepsy. In contrast to the control group, the increase of knowledge in the e-learning group was highly significant (p < 0.001). The majority of the participants of the e-learning course was content with their personal learning process (75% agree, 15% strongly agree). Most of them reported a gain in competence in the treatment and counseling of people with epilepsy (38.9% agree, 50% strongly agree). All participants would recommend this course to others and all but one participant are interested in other e-learning courses. The study indicates e-learning courses are an appropriate tool to improve knowledge of physicians in genetics of epilepsy.

Dopamine selectively remediates ‘model-based’ reward learning: a computational approach

PubMed Central

Sharp, Madeleine E.; Foerde, Karin; Daw, Nathaniel D.

2016-01-01

Patients with loss of dopamine due to Parkinson’s disease are impaired at learning from reward. However, it remains unknown precisely which aspect of learning is impaired. In particular, learning from reward, or reinforcement learning, can be driven by two distinct computational processes. One involves habitual stamping-in of stimulus-response associations, hypothesized to arise computationally from ‘model-free’ learning. The other, ‘model-based’ learning, involves learning a model of the world that is believed to support goal-directed behaviour. Much work has pointed to a role for dopamine in model-free learning. But recent work suggests model-based learning may also involve dopamine modulation, raising the possibility that model-based learning may contribute to the learning impairment in Parkinson’s disease. To directly test this, we used a two-step reward-learning task which dissociates model-free versus model-based learning. We evaluated learning in patients with Parkinson’s disease tested ON versus OFF their dopamine replacement medication and in healthy controls. Surprisingly, we found no effect of disease or medication on model-free learning. Instead, we found that patients tested OFF medication showed a marked impairment in model-based learning, and that this impairment was remediated by dopaminergic medication. Moreover, model-based learning was positively correlated with a separate measure of working memory performance, raising the possibility of common neural substrates. Our results suggest that some learning deficits in Parkinson’s disease may be related to an inability to pursue reward based on complete representations of the environment. PMID:26685155

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.

Impact of interactive online units on learning science among students with learning disabilities and English learners

NASA Astrophysics Data System (ADS)

Terrazas-Arellanes, Fatima E.; Gallard M., Alejandro J.; Strycker, Lisa A.; Walden, Emily D.

2018-03-01

The purpose of this study was to document the design, classroom implementation, and effectiveness of interactive online units to enhance science learning over 3 years among students with learning disabilities, English learners, and general education students. Results of a randomised controlled trial with 2,303 middle school students and 71 teachers across 13 schools in two states indicated that online units effectively deepened science knowledge across all three student groups. Comparing all treatment and control students on pretest-to-posttest improvement on standards-based content-specific assessments, there were statistically significant mean differences (17% improvement treatment vs. 6% control; p < .001); no significant interactions were found between treatment condition and learning disability or English learner status, indicating that these two groups performed similarly to their peers; students with learning disabilities had significantly lower assessment scores overall. Teachers and students were moderately satisfied with the units.

Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning

PubMed Central

Gulati, Tanuj; Ramanathan, Dhakshin; Wong, Chelsea; Ganguly, Karunesh

2017-01-01

Brain-Machine Interfaces can allow neural control over assistive devices. They also provide an important platform to study neural plasticity. Recent studies indicate that optimal engagement of learning is essential for robust neuroprosthetic control. However, little is known about the neural processes that may consolidate a neuroprosthetic skill. Based on the growing body of evidence linking slow-wave activity (SWA) during sleep to consolidation, we examined if there is ‘offline’ processing after neuroprosthetic learning. Using a rodent model, here we show that after successful learning, task-related units specifically experienced increased locking and coherency to SWA during sleep. Moreover, spike-spike coherence among these units was significantly enhanced. These changes were not present with poor skill acquisition or after control awake periods, demonstrating specificity of our observations to learning. Interestingly, time spent in SWA predicted performance gains. Thus, SWA appears to play a role in offline processing after neuroprosthetic learning. PMID:24997761

A lateralized avian hippocampus: preferential role of the left hippocampal formation in homing pigeon sun compass-based spatial learning.

PubMed

Gagliardo, Anna; Vallortigara, Giorgio; Nardi, Daniele; Bingman, Verner P

2005-11-01

The hippocampal formation (HF) plays a crucial role in amniote spatial cognition. There are also indications of functional lateralization in the contribution of the left and right HF in processes that enable birds to navigate space. The experiments described in this study were designed to examine left and right HF differences in a task of sun compass-based spatial learning in homing pigeons (Columba livia). Control, left (HFL) and right (HFR) HF lesioned pigeons were trained in an outdoor arena to locate a food reward using their sun compass in the presence or absence of alternative feature cues. Subsequent to training, the pigeons were subjected to test sessions to determine if they learned to represent the goal location with their sun compass and the relative importance of the sun compass vs. feature cues. Under all test conditions, the control pigeons demonstrated preferential use of the sun compass in locating the goal. By contrast, the HFL pigeons demonstrated no ability to locate the goal by the sun compass but an ability to use the feature cues. The behaviour of the HFR pigeons demonstrated that an intact left HF is sufficient to support sun compass-based learning, but in conflict situations and in contrast to controls, they often relied on feature cues. In conclusion, only the left HF is capable of supporting sun compass-based learning. However, preferential use of the sun compass for learning requires an intact right HF. The data support the hypothesis that the left and right HF make different but complementary contributions toward avian spatial cognition.

Implicit sequence-specific motor learning after sub-cortical stroke is associated with increased prefrontal brain activations: An fMRI study

PubMed Central

Meehan, Sean K.; Randhawa, Bubblepreet; Wessel, Brenda; Boyd, Lara A.

2010-01-01

Implicit motor learning is preserved after stroke, but how the brain compensates for damage to facilitate learning is unclear. We used a random effects analysis to determine how stroke alters patterns of brain activity during implicit sequence-specific motor learning as compared to general improvements in motor control. Nine healthy participants and 9 individuals with chronic, right focal sub-cortical stroke performed a continuous joystick-based tracking task during an initial fMRI session, over 5 days of practice, and a retention test during a separate fMRI session. Sequence-specific implicit motor learning was differentiated from general improvements in motor control by comparing tracking performance on a novel, repeated tracking sequences during early practice and again at the retention test. Both groups demonstrated implicit sequence-specific motor learning at the retention test, yet substantial differences were apparent. At retention, healthy control participants demonstrated increased BOLD response in left dorsal premotor cortex (BA 6) but decreased BOLD response left dorsolateral prefrontal cortex (DLPFC; BA 9) during repeated sequence tracking. In contrast, at retention individuals with stroke did not show this reduction in DLPFC during repeated tracking. Instead implicit sequence-specific motor learning and general improvements in motor control were associated with increased BOLD response in the left middle frontal gyrus BA 8, regardless of sequence type after stroke. These data emphasize the potential importance of a prefrontal-based attentional network for implicit motor learning after stroke. The present study is the first to highlight the importance of the prefrontal cortex for implicit sequence-specific motor learning after stroke. PMID:20725908

Modelling and Optimizing Mathematics Learning in Children

ERIC Educational Resources Information Center

Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus

2013-01-01

This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…

School-Based Service-Learning for Promoting Citizenship in Young People: A Systematic Review

DTIC Science & Technology

2005-09-06

nonequivalent pre- and post-test design with control group was utilized but participants were not randomized to groups . The sample...other methodology. She notes the limitations of the research chosen for the review (i.e., most studies lack a control group , do not track effects over...experimental and control groups Pre- and post- test design Surveys “Service-learning”12 Intervention groups : Service-learning

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.

Role of cerebellum in learning postural tasks.

PubMed

Ioffe, M E; Chernikova, L A; Ustinova, K I

2007-01-01

For a long time, the cerebellum has been known to be a structure related to posture and equilibrium control. According to the anatomic structure of inputs and internal structure of the cerebellum, its role in learning was theoretically reasoned and experimentally proved. The hypothesis of an inverse internal model based on feedback-error learning mechanism combines feedforward control by the cerebellum and feedback control by the cerebral motor cortex. The cerebellar cortex is suggested to acquire internal models of the body and objects in the external world. During learning of a new tool the motor cortex receives feedback from the realized movement while the cerebellum produces only feedforward command. To realize a desired movement without feedback of the realized movement, the cerebellum needs to form an inverse model of the hand/arm system. This suggestion was supported by FMRi data. The role of cerebellum in learning new postural tasks mainly concerns reorganization of natural synergies. A learned postural pattern in dogs has been shown to be disturbed after lesions of the cerebral motor cortex or cerebellar nuclei. In humans, learning voluntary control of center of pressure position is greatly disturbed after cerebellar lesions. However, motor cortex and basal ganglia are also involved in the feedback learning postural tasks.

Discovery Learning, Representation, and Explanation within a Computer-Based Simulation: Finding the Right Mix

ERIC Educational Resources Information Center

Rieber, Lloyd P.; Tzeng, Shyh-Chii; Tribble, Kelly

2004-01-01

The purpose of this research was to explore how adult users interact and learn during an interactive computer-based simulation supplemented with brief multimedia explanations of the content. A total of 52 college students interacted with a computer-based simulation of Newton's laws of motion in which they had control over the motion of a simple…

Exploring Design Features to Enhance Computer-Based Assessment: Learners' Views on Using a Confidence-Indicator Tool and Computer-Based Feedback

ERIC Educational Resources Information Center

Nix, Ingrid; Wyllie, Ali

2011-01-01

Many institutions encourage formative computer-based assessment (CBA), yet competing priorities mean that learners are necessarily selective about what they engage in. So how can we motivate them to engage? Can we facilitate learners to take more control of shaping their learning experience? To explore this, the Learning with Interactive…

Measuring strategic control in artificial grammar learning.

PubMed

Norman, Elisabeth; Price, Mark C; Jones, Emma

2011-12-01

In response to concerns with existing procedures for measuring strategic control over implicit knowledge in artificial grammar learning (AGL), we introduce a more stringent measurement procedure. After two separate training blocks which each consisted of letter strings derived from a different grammar, participants either judged the grammaticality of novel letter strings with respect to only one of these two grammars (pure-block condition), or had the target grammar varying randomly from trial to trial (novel mixed-block condition) which required a higher degree of conscious flexible control. Random variation in the colour and font of letters was introduced to disguise the nature of the rule and reduce explicit learning. Strategic control was observed both in the pure-block and mixed-block conditions, and even among participants who did not realise the rule was based on letter identity. This indicated detailed strategic control in the absence of explicit learning. Copyright © 2011 Elsevier Inc. All rights reserved.

Open Integrated Personal Learning Environment: Towards a New Conception of the ICT-Based Learning Processes

NASA Astrophysics Data System (ADS)

Conde, Miguel Ángel; García-Peñalvo, Francisco José; Casany, Marià José; Alier Forment, Marc

Learning processes are changing related to technological and sociological evolution, taking this in to account, a new learning strategy must be considered. Specifically what is needed is to give an effective step towards the eLearning 2.0 environments consolidation. This must imply the fusion of the advantages of the traditional LMS (Learning Management System) - more formative program control and planning oriented - with the social learning and the flexibility of the web 2.0 educative applications.

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

NASA Astrophysics Data System (ADS)

Li, Zhifu; Hu, Yueming; Li, Di

2016-08-01

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Neural network-based model reference adaptive control system.

PubMed

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

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.

Intelligent control of an IPMC actuated manipulator using emotional learning-based controller

NASA Astrophysics Data System (ADS)

Shariati, Azadeh; Meghdari, Ali; Shariati, Parham

2008-08-01

In this research an intelligent emotional learning controller, Takagi- Sugeno- Kang (TSK) is applied to govern the dynamics of a novel Ionic-Polymer Metal Composite (IPMC) actuated manipulator. Ionic-Polymer Metal Composites are active actuators that show very large deformation in existence of low applied voltage. In this research, a new IPMC actuator is considered and applied to a 2-dof miniature manipulator. This manipulator is designed for miniature tasks. The control system consists of a set of neurofuzzy controller whose parameters are adapted according to the emotional learning rules, and a critic with task to assess the present situation resulted from the applied control action in terms of satisfactory achievement of the control goals and provides the emotional signal (the stress). The controller modifies its characteristics so that the critic's stress decreased.

"The Child's World": a creative and visual trigger to stimulate student enquiry in a problem based learning module.

PubMed

Barron, Carol; Lambert, Veronica; Conlon, Joy; Harrington, Tracey

2008-11-01

Despite the abundance of literature on problem based learning (PBL) [Murray, I., Savin-Baden, M., 2000. Staff development in problem-based learning. Teaching in Higher Education 5 (1), 107-126; Johnson, A.K., Tinning, R.S., 2001. Meeting the challenge of problem-based learning: developing the facilitators. Nurse Education Today 21 (3), 161-169; McCourt, C., Thomas, G., 2001. Evaluation of a problem based curriculum in midwifery. Midwifery 17 (4), 323-331; Cooke, M., Moyle, K., 2002. Students' evaluation of problem-based learning. Nurse Education Today 22, 330-339; Haith-Cooper, M., 2003a. An exploration of tutors' experiences of facilitating problem-based learning. Part 1--an educational research methodology combining innovation and philosophical tradition. Nurse Education Today 23, 58-64; Haith-Cooper, M., 2003b. An exploration of tutor' experiences of facilitating problem-based learning. Part 2--implications for the facilitation of problem based learning. Nurse Education Today 23, 65-75; Rowan, C.J., Mc Court, C., Beake, S., 2007. Problem based learning in midwifery--The teacher's perspective. Nurse Education Today 27, 131-138; Rowan, C.J., Mc Court, C., Beake, S., 2008. Problem based learning in midwifery--The students' perspective. Nurse Education Today 28, 93-99] few studies focus on describing "triggers", the process involved in their development and their evaluation from students' perspective. It is clearly documented that well designed, open ended, real life and challenging "triggers" are key to the success of PBL implementation [Roberts, D., Ousey, K., 2004. Problem based learning: developing the triggers. Experiences from a first wave site. Nurse Education in Practice 4, 154-158, Gibson, I., 2005. Designing projects for learning. In: Barrett, T., Mac Labhrainn, I., Fallon, H., (Eds.), Handbook of Enquiry and Problem-based Learning: Irish Case Studies and International Perspectives. AISHE & CELT: NUI Galway. , Barrett, T., 2005. Understanding problem-based learning. In: Barrett, T. Mac Labhrainn, I., Fallon, H., (Eds.), Handbook of Enquiry and Problem-based Learning: Irish Case Studies and International Perspectives. AISHE & CELT, NUI Galway. ]. This paper outlines the planning, implementation and evaluation of a "trigger" developed for a first year undergraduate nursing module. To meet specific module learning outcomes and to stimulate student inquiry through the learning strategy of PBL, a bright and colourful collage, was constructed. This tool was then evaluated using focus group interviews. Students' perspectives centered round a core theme, 'finding a focus and taking control'. Four categories were identified illustrating students progress from 'initial confusion' to engaging with the 'trigger diversity' before confidently 'exploring their own line of inquiry', thus leading to the 'stimulation of their learning'. Consistent with previous research, we also suggest it is customary for students to experience an initial period of ambiguity as they switch from teacher led to student centered learning [Biley, F., 1999. Creating tension: under graduate students nurses' response to a problem-based learning curriculum. Nurse Education Today 19 (7), 586-589]. One challenge in developing "triggers" is that the process is primarily controlled by lecturers. We suggest that a possible way forward would be to also engage students in the development of "triggers".

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

Comparison of the effectiveness of two styles of case-based learning implemented in lectures for developing nursing students' critical thinking ability: A randomized controlled trial.

PubMed

Hong, Shaohua; Yu, Ping

2017-03-01

To explore and compare the effectiveness of two styles of case-based learning methods, unfolding nursing case and usual nursing case, implemented in lectures for developing nursing students' critical thinking ability. 122 undergraduate nursing students in four classes were taught the subject of medical nursing for one year. Two classes were randomly assigned as the experimental group and the other two the control group. The experimental group received the lectures presenting unfolding nursing cases and the control group was taught the usual cases. Nineteen case-based lectures were provided in 8 months in two semesters to each group. The two groups started with a similar level of critical thinking ability as tested by the instrument of Critical Thinking Disposition Inventory-Chinese version (CTDI-CV). After receiving 19 case-based learning lectures for 8 months, both groups of students significantly improved their critical thinking ability. The improvement in the experimental group was significantly higher than that in the control group (with the average total score of 303.77±15.24 vs. 288.34±13.94, p<0.05). The experimental group also had significantly better improvement in six out of seven dimensions whereas the control group showed improvement in only three out of seven dimensions of CTDI-CV. The study suggests the feasibility of implementing case-based learning in lectures. Unfolding nursing cases appear to be significantly more effective than the usual nursing cases in developing undergraduate nursing students' critical thinking ability in the subject of medical nursing. Further research can implement the unfolding nursing cases in other nursing subjects. Copyright © 2016 Elsevier Ltd. All rights reserved.

[A Study on the Cognitive Learning Effectiveness of Scenario-Based Concept Mapping in a Neurological Nursing Course].

PubMed

Pan, Hui-Ching; Hsieh, Suh-Ing; Hsu, Li-Ling

2015-12-01

The multiple levels of knowledge related to the neurological system deter many students from pursuing studies on this topic. Thus, in facing complicated and uncertain medical circumstances, nursing students have diffi-culty adjusting and using basic neurological-nursing knowledge and skills. Scenario-based concept-mapping teaching has been shown to promote the integration of complicated data, clarify related concepts, and increase the effectiveness of cognitive learning. To investigate the effect on the neurological-nursing cognition and learning attitude of nursing students of a scenario-based concept-mapping strategy that was integrated into the neurological nursing unit of a medical and surgical nursing course. This quasi-experimental study used experimental and control groups and a pre-test / post-test design. Sopho-more (2nd year) students in a four-year program at a university of science and technology in Taiwan were convenience sampled using cluster randomization that was run under SPSS 17.0. Concept-mapping lessons were used as the intervention for the experimental group. The control group followed traditional lesson plans only. The cognitive learning outcome was measured using the neurological nursing-learning examination. Both concept-mapping and traditional lessons significantly improved post-test neurological nursing learning scores (p < .001), with no significant difference between the two groups (p = .51). The post-test feedback from the control group mentioned that too much content was taught and that difficulties were experienced in understanding mechanisms and in absorbing knowledge. In contrast, the experimental group held a significantly more positive perspective and learning attitude with regard to the teaching material. Furthermore, a significant number in the experimental group expressed the desire to add more lessons on anatomy, physiology, and pathology. These results indicate that this intervention strategy may help change the widespread fear and refusal of nursing students with regard to neurological lessons and may facilitate interest and positively affect learning in this important subject area. Integrating the concept-mapping strategy and traditional clinical-case lessons into neurological nursing lessons holds the potential to increase post-test scores significantly. Concept mapping helped those in the experimental group adopt views and attitudes toward learning the teaching material that were more positive than those held by their control-group peers. In addition, while 59% of the experimental group and 49% of the control group submitted opinions related to learning attitude in the open-ended questions, positive feedback was greater in the experimental group than in the control group.

Learning Physical Science through Astronomy Activities: A Comparison between Constructivist and Traditional Approaches in Grades 3-6

NASA Astrophysics Data System (ADS)

Ward, R. Bruce; Sadler, Philip M.; Shapiro, Irwin I.

We report on an evaluation of the effectiveness of Project ARIES, an astronomy- based physical science curriculum for upper elementary and middle school children. ARIES students use innovative, simple, and affordable apparatus to carry out a wide range of indoor and outdoor hands-on, discovery- based activities. Student journals and comprehensive teacher materials aid in making the science content accessible to students based on their shared experiences and observations. Approximately 750 Grades 3 6 students in ARIES (or treatment) classrooms are compared with approximately 650 Grades 4 6 students in control classrooms through a series of open-ended assessment measures, using a pretest and posttest format. A detailed analysis by item measures the gain in treatment and control groups. We identify concepts where the ARIES approach is more effective, where both are equally effective, and where neither results in much learning. (The ARIES approach was never less effective.) Although learning is in evidence for both control and treatment groups, overall, the ARIES students achieve roughly four times the gain of their control counterparts. In particular, ARIES students had much greater gains for the concepts that the control students found most difficult.

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…

Flipping the Classroom: An Empirical Study Examining Student Learning

ERIC Educational Resources Information Center

Sparks, Roland J.

2013-01-01

Flipping the classroom is the latest reported teaching technique to improve student learning at all levels. Prior studies showed significant increases in learning by employing this technique. However, an examination of the previous studies indicates significant flaws in the testing procedure controls. Moreover, most studies were based on anecdotal…

The GenTechnique Project: Developing an Open Environment for Learning Molecular Genetics.

ERIC Educational Resources Information Center

Calza, R. E.; Meade, J. T.

1998-01-01

The GenTechnique project at Washington State University uses a networked learning environment for molecular genetics learning. The project is developing courseware featuring animation, hyper-link controls, and interactive self-assessment exercises focusing on fundamental concepts. The first pilot course featured a Web-based module on DNA…

HOW TO LEARN AN UNWRITTEN LANGUAGE.

ERIC Educational Resources Information Center

GUDSCHINSKY, SARAH C.

A PRACTICAL GUIDE FOR THE ANTHROPOLOGY STUDENT CONFRONTED WITH LEARNING A LANGUAGE IN THE FIELD, THIS BOOK FOCUSES ON ACQUIRING EVERYDAY CONVERSATION RATHER THAN DIFFICULT LINGUISTIC PROBLEMS. THE FORM AND CONTENT ARE BASED ON THE FOLLOWING BASIC PREMISES--(1) LEARNING A LANGUAGE CONSISTS OF DISCOVERING AND CONTROLLING AS AUTOMATIC HABITS THE…

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.

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

Improving Classroom Learning Environments by Cultivating Awareness and Resilience in Education (CARE): Results of a Randomized Controlled Trial

ERIC Educational Resources Information Center

Jennings, Patricia A.; Frank, Jennifer L.; Snowberg, Karin E.; Coccia, Michael A.; Greenberg, Mark T.

2013-01-01

Cultivating Awareness and Resilience in Education (CARE for Teachers) is a mindfulness-based professional development program designed to reduce stress and improve teachers' performance and classroom learning environments. A randomized controlled trial examined program efficacy and acceptability among a sample of 50 teachers randomly assigned to…

Influence of Problem Based Learning on Critical Thinking Skills and Competence Class VIII SMPN 1 Gunuang Omeh, 2016/2017

NASA Astrophysics Data System (ADS)

Aswan, D. M.; Lufri, L.; Sumarmin, R.

2018-04-01

This research intends to determine the effect of Problem Based Learning models on students' critical thinking skills and competences. This study was a quasi-experimental research. The population of the study was the students of class VIII SMPN 1 Subdistrict Gunuang Omeh. Random sample selection is done by randomizing the class. Sample class that was chosen VIII3 as an experimental class given that treatment study based on problems and class VIII1 as control class that treatment usually given study. Instrument that used to consist of critical thinking test, cognitive tests, observation sheet of affective and psychomotor. Independent t-test and Mann Whitney U test was used for the analysis. Results showed that there was significant difference (sig <0.05) between control and experimental group. The conclusion of this study was Problem Based Learning models affected the students’ critical thinking skills and competences.

The implementation of multiple intelligences based teaching model to improve mathematical problem solving ability for student of junior high school

NASA Astrophysics Data System (ADS)

Fasni, Nurli; Fatimah, Siti; Yulanda, Syerli

2017-05-01

This research aims to achieve some purposes such as: to know whether mathematical problem solving ability of students who have learned mathematics using Multiple Intelligences based teaching model is higher than the student who have learned mathematics using cooperative learning; to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using Multiple Intelligences based teaching model., to know the improvement of the mathematical problem solving ability of the student who have learned mathematics using cooperative learning; to know the attitude of the students to Multiple Intelligences based teaching model. The method employed here is quasi-experiment which is controlled by pre-test and post-test. The population of this research is all of VII grade in SMP Negeri 14 Bandung even-term 2013/2014, later on two classes of it were taken for the samples of this research. A class was taught using Multiple Intelligences based teaching model and the other one was taught using cooperative learning. The data of this research were gotten from the test in mathematical problem solving, scale questionnaire of the student attitudes, and observation. The results show the mathematical problem solving of the students who have learned mathematics using Multiple Intelligences based teaching model learning is higher than the student who have learned mathematics using cooperative learning, the mathematical problem solving ability of the student who have learned mathematics using cooperative learning and Multiple Intelligences based teaching model are in intermediate level, and the students showed the positive attitude in learning mathematics using Multiple Intelligences based teaching model. As for the recommendation for next author, Multiple Intelligences based teaching model can be tested on other subject and other ability.

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…

Feedback-based probabilistic category learning is selectively impaired in attention/hyperactivity deficit disorder.

PubMed

Gabay, Yafit; Goldfarb, Liat

2017-07-01

Although Attention-Deficit Hyperactivity Disorder (ADHD) is closely linked to executive function deficits, it has recently been attributed to procedural learning impairments that are quite distinct from the former. These observations challenge the ability of the executive function framework solely to account for the diverse range of symptoms observed in ADHD. A recent neurocomputational model emphasizes the role of striatal dopamine (DA) in explaining ADHD's broad range of deficits, but the link between this model and procedural learning impairments remains unclear. Significantly, feedback-based procedural learning is hypothesized to be disrupted in ADHD because of the involvement of striatal DA in this type of learning. In order to test this assumption, we employed two variants of a probabilistic category learning task known from the neuropsychological literature. Feedback-based (FB) and paired associate-based (PA) probabilistic category learning were employed in a non-medicated sample of ADHD participants and neurotypical participants. In the FB task, participants learned associations between cues and outcomes initially by guessing and subsequently through feedback indicating the correctness of the response. In the PA learning task, participants viewed the cue and its associated outcome simultaneously without receiving an overt response or corrective feedback. In both tasks, participants were trained across 150 trials. Learning was assessed in a subsequent test without a presentation of the outcome or corrective feedback. Results revealed an interesting disassociation in which ADHD participants performed as well as control participants in the PA task, but were impaired compared with the controls in the FB task. The learning curve during FB training differed between the two groups. Taken together, these results suggest that the ability to incrementally learn by feedback is selectively disrupted in ADHD participants. These results are discussed in relation to both the ADHD dopaminergic dysfunction model and recent findings implicating procedural learning impairments in those with ADHD. Copyright © 2017 Elsevier Inc. All rights reserved.

The effectivenes of science domain-based science learning integrated with local potency

NASA Astrophysics Data System (ADS)

Kurniawati, Arifah Putri; Prasetyo, Zuhdan Kun; Wilujeng, Insih; Suryadarma, I. Gusti Putu

2017-08-01

This research aimed to determine the significant effect of science domain-based science learning integrated with local potency toward science process skills. The research method used was a quasi-experimental design with nonequivalent control group design. The population of this research was all students of class VII SMP Negeri 1 Muntilan. The sample of this research was selected through cluster random sampling, namely class VII B as an experiment class (24 students) and class VII C as a control class (24 students). This research used a test instrument that was adapted from Agus Dwianto's research. The aspect of science process skills in this research was observation, classification, interpretation and communication. The analysis of data used the one factor anova at 0,05 significance level and normalized gain score. The significance level result of science process skills with one factor anova is 0,000. It shows that the significance level < alpha (0,05). It means that there was significant effect of science domain-based science learning integrated with local potency toward science learning process skills. The results of analysis show that the normalized gain score are 0,29 (low category) in control class and 0,67 (medium category) in experiment class.

Effectiveness of e-learning in continuing medical education for occupational physicians.

PubMed

Hugenholtz, Nathalie I R; de Croon, Einar M; Smits, Paul B; van Dijk, Frank J H; Nieuwenhuijsen, Karen

2008-08-01

Within a clinical context e-learning is comparable to traditional approaches of continuing medical education (CME). However, the occupational health context differs and until now the effect of postgraduate e-learning among occupational physicians (OPs) has not been evaluated. To evaluate the effect of e-learning on knowledge on mental health issues as compared to lecture-based learning in a CME programme for OPs. Within the context of a postgraduate meeting for 74 OPs, a randomized controlled trial was conducted. Test assessments of knowledge were made before and immediately after an educational session with either e-learning or lecture-based learning. In both groups, a significant gain in knowledge on mental health care was found (P < 0.05). However, there was no significant difference between the two educational approaches. The effect of e-learning on OPs' mental health care knowledge is comparable to a lecture-based approach. Therefore, e-learning can be beneficial for the CME of OPs.

Mathematical learning instruction and teacher motivation factors affecting science technology engineering and math (STEM) major choices in 4-year colleges and universities: Multilevel structural equation modeling

NASA Astrophysics Data System (ADS)

Lee, Ahlam

2011-12-01

Using the Educational Longitudinal Study of 2002/06, this study examined the effects of the selected mathematical learning and teacher motivation factors on graduates' science, technology, engineering, and math (STEM) related major choices in 4-year colleges and universities, as mediated by math performance and math self-efficacy. Using multilevel structural equation modeling, I analyzed: (1) the association between mathematical learning instruction factors (i.e., computer, individual, and lecture-based learning activities in mathematics) and students' STEM major choices in 4-year colleges and universities as mediated by math performance and math self-efficacy and (2) the association between school factor, teacher motivation and students' STEM major choices in 4-year colleges and universities via mediators of math performance and math self-efficacy. The results revealed that among the selected learning experience factors, computer-based learning activities in math classrooms yielded the most positive effects on math self-efficacy, which significantly predicted the increase in the proportion of students' STEM major choice as mediated by math self-efficacy. Further, when controlling for base-year math Item Response Theory (IRT) scores, a positive relationship between individual-based learning activities in math classrooms and the first follow-up math IRT scores emerged, which related to the high proportion of students' STEM major choices. The results also indicated that individual and lecture-based learning activities in math yielded positive effects on math self-efficacy, which related to STEM major choice. Concerning between-school levels, teacher motivation yielded positive effects on the first follow up math IRT score, when controlling for base year IRT score. The results from this study inform educators, parents, and policy makers on how mathematics instruction can improve student math performance and encourage more students to prepare for STEM careers. Students should receive all possible opportunities to use computers to enhance their math self-efficacy, be encouraged to review math materials, and concentrate on listening to math teachers' lectures. While all selected math-learning activities should be embraced in math instruction, computer and individual-based learning activities, which reflect student-driven learning, should be emphasized in the high school instruction. Likewise, students should be encouraged to frequently engage in individual-based learning activities to improve their math performance.

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.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Problem-based learning: effects on student’s scientific reasoning skills in science

NASA Astrophysics Data System (ADS)

Wulandari, F. E.; Shofiyah, N.

2018-04-01

This research aimed to develop instructional package of problem-based learning to enhance student’s scientific reasoning from concrete to formal reasoning skills level. The instructional package was developed using the Dick and Carey Model. Subject of this study was instructional package of problem-based learning which was consisting of lesson plan, handout, student’s worksheet, and scientific reasoning test. The instructional package was tried out on 4th semester science education students of Universitas Muhammadiyah Sidoarjo by using the one-group pre-test post-test design. The data of scientific reasoning skills was collected by making use of the test. The findings showed that the developed instructional package reflecting problem-based learning was feasible to be implemented in classroom. Furthermore, through applying the problem-based learning, students could dominate formal scientific reasoning skills in terms of functionality and proportional reasoning, control variables, and theoretical reasoning.

Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial.

PubMed

Buch, Steen Vigh; Treschow, Frederik Philip; Svendsen, Jesper Brink; Worm, Bjarne Skjødt

2014-01-01

This study investigated the effectiveness of two different levels of e-learning when teaching clinical skills to medical students. Sixty medical students were included and randomized into two comparable groups. The groups were given either a video- or text/picture-based e-learning module and subsequently underwent both theoretical and practical examination. A follow-up test was performed 1 month later. The students in the video group performed better than the illustrated text-based group in the practical examination, both in the primary test (P<0.001) and in the follow-up test (P<0.01). Regarding theoretical knowledge, no differences were found between the groups on the primary test, though the video group performed better on the follow-up test (P=0.04). Video-based e-learning is superior to illustrated text-based e-learning when teaching certain practical clinical skills.

Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial

PubMed Central

Buch, Steen Vigh; Treschow, Frederik Philip; Svendsen, Jesper Brink; Worm, Bjarne Skjødt

2014-01-01

Background and aims This study investigated the effectiveness of two different levels of e-learning when teaching clinical skills to medical students. Materials and methods Sixty medical students were included and randomized into two comparable groups. The groups were given either a video- or text/picture-based e-learning module and subsequently underwent both theoretical and practical examination. A follow-up test was performed 1 month later. Results The students in the video group performed better than the illustrated text-based group in the practical examination, both in the primary test (P<0.001) and in the follow-up test (P<0.01). Regarding theoretical knowledge, no differences were found between the groups on the primary test, though the video group performed better on the follow-up test (P=0.04). Conclusion Video-based e-learning is superior to illustrated text-based e-learning when teaching certain practical clinical skills. PMID:25152638

Cognitive Control Predicts Use of Model-Based Reinforcement-Learning

PubMed Central

Otto, A. Ross; Skatova, Anya; Madlon-Kay, Seth; Daw, Nathaniel D.

2015-01-01

Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning (RL), called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information—in the service of overcoming habitual, stimulus-driven responses—in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior. PMID:25170791

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

PubMed

Zheng, Shuai; Lu, James J; Ghasemzadeh, Nima; Hayek, Salim S; Quyyumi, Arshed A; Wang, Fusheng

2017-05-09

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. 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. 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. 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%. 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. ©Shuai Zheng, James J Lu, Nima Ghasemzadeh, Salim S Hayek, Arshed A Quyyumi, Fusheng Wang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.05.2017.

Effects of image-based and text-based active learning exercises on student examination performance in a musculoskeletal anatomy course.

PubMed

Gross, M Melissa; Wright, Mary C; Anderson, Olivia S

2017-09-01

Research on the benefits of visual learning has relied primarily on lecture-based pedagogy, but the potential benefits of combining active learning strategies with visual and verbal materials on learning anatomy has not yet been explored. In this study, the differential effects of text-based and image-based active learning exercises on examination performance were investigated in a functional anatomy course. Each class session was punctuated with an average of 12 text-based and image-based active learning exercises. Participation data from 231 students were compared with their examination performance on 262 questions associated with the in-class exercises. Students also rated the helpfulness and difficulty of the in-class exercises on a survey. Participation in the active learning exercises was positively correlated with examination performance (r = 0.63, P < 0.001). When controlling for other key demographics (gender, underrepresented minority status) and prior grade point average, participation in the image-based exercises was significantly correlated with performance on examination questions associated with image-based exercises (P < 0.001) and text-based exercises (P < 0.01), while participation in text-based exercises was not. Additionally, students reported that the active learning exercises were helpful for seeing images of key ideas (94%) and clarifying key course concepts (80%), and that the image-based exercises were significantly less demanding, less hard and required less effort than text-based exercises (P < 0.05). The findings confirm the positive effect of using images and active learning strategies on student learning, and suggest that integrating them may be especially beneficial for learning anatomy. Anat Sci Educ 10: 444-455. © 2017 American Association of Anatomists. © 2017 American Association of Anatomists.

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…

Implementing Reading Strategies Based on Collaborative Learning Approach in an English Class

ERIC Educational Resources Information Center

Suwantharathip, Ornprapat

2015-01-01

The present study investigated the effects of reading strategies based on collaborative learning approach on students' reading comprehension and reading strategy use. The quasi-experimental research study was performed with two groups of students. While the control group was taught in the traditional way, the experimental group received reading…

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,…

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…

Giving Learning a Helping Hand: Finger Tracing of Temperature Graphs on an iPad

ERIC Educational Resources Information Center

Agostinho, Shirley; Tindall-Ford, Sharon; Ginns, Paul; Howard, Steven J.; Leahy, Wayne; Paas, Fred

2015-01-01

Gesturally controlled information and communication technologies, such as tablet devices, are becoming increasingly popular tools for teaching and learning. Based on the theoretical frameworks of cognitive load and embodied cognition, this study investigated the impact of explicit instructions to trace out elements of tablet-based worked examples…

Team-Based Learning: Moderating Effects of Metacognitive Elaborative Rehearsal and Middle School History Content Recall

ERIC Educational Resources Information Center

Roberts, Greg; Scammacca, Nancy; Osman, David J.; Hall, Colby; Mohammed, Sarojani S.; Vaughn, Sharon

2014-01-01

Promoting Acceleration of Comprehension and Content through Text (PACT) and similar team-based models directly engage and support students in learning situations that require cognitive elaboration as part of the processing of new information. Elaboration is subject to metacognitive control, as well (Karpicke, "Journal of Experimental…

Effects of Computer-Based Instruction on Student Learning of Psychophysiological Detection of Deception Test Question Formulation.

ERIC Educational Resources Information Center

Janniro, Michael J.

1993-01-01

Describes a study conducted by the Department of Defense Polygraph Institute for their forensic science curriculum that investigated the effects of computer-based instruction on student learning of psychophysiological detection of deception test question formulation. Treatment of the experimental and control group is explained and posttest scores…

Problem-Based Learning in the English Language Classroom

ERIC Educational Resources Information Center

Othman, Normala; Shah, Mohamed Ismail Ahamad

2013-01-01

The purpose of this study was to investigate the effects of the problem-based learning approach (PBL) on students in language classes in two areas: course content and language development. The study was conducted on 128 students, grouped into the experimental and control groups, and employed an experimental research design. The syllabus, textbook,…

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…

Evaluating the Effectiveness of Physlet-Based Materials in Supporting Conceptual Learning About Electricity

NASA Astrophysics Data System (ADS)

Ülen, Simon; Gerlič, Ivan; Slavinec, Mitja; Repnik, Robert

2017-04-01

To provide a good understanding of many abstract concepts in the field of electricity above that of their students is often a major challenge for secondary school teachers. Many educational researchers promote conceptual learning as a teaching approach that can help teachers to achieve this goal. In this paper, we present Physlet-based materials for supporting conceptual learning about electricity. To conduct research into the effectiveness of these materials, we designed two different physics courses: one group of students, the experimental group, was taught using Physlet-based materials and the second group of students, the control group, was taught using expository instruction without using Physlets. After completion of the teaching, we assessed students' thinking skills and analysed the materials with an independent t test, multiple regression analyses and one-way analysis of covariance. The test scores were significantly higher in the experimental group than in the control group ( p < 0.05). The results of this study confirmed the effectiveness of conceptual learning about electricity with the help of Physlet-based materials.

Project Clarion: Three Years of Science Instruction in Title I Schools among K-Third Grade Students

NASA Astrophysics Data System (ADS)

Kim, Kyung Hee; VanTassel-Baska, Joyce; Bracken, Bruce A.; Feng, Annie; Stambaugh, Tamra; Bland, Lori

2012-10-01

The purpose of the study was to measure the effects of higher level, inquiry-based science curricula on students at primary level in Title I schools. Approximately 3,300 K-3 students from six schools were assigned to experimental or control classes ( N = 115 total) on a random basis according to class. Experimental students were exposed to concept-based science curriculum that emphasized `deep learning' though concept mastery and investigation, whereas control classes learned science from traditional school-based curricula. Two ability measures, the Bracken Basic Concept Scale-Revised (BBCS-R, Bracken 1998) and the Naglieri Nonverbal Intelligence Test (NNAT, Naglieri 1991), were used for baseline information. Additionally, a standardized measure of student achievement in science (the MAT-8 science subtest), a standardized measure of critical thinking, and a measure for observing teachers' classroom behaviors were used to assess learning outcomes. Results indicated that all ability groups of students benefited from the science inquiry-based approach to learning that emphasized science concepts, and that there was a positive achievement effect for low socio-economic young children who were exposed to such a curriculum.

A theory-based approach to teaching young children about health: A recipe for understanding

PubMed Central

Nguyen, Simone P.; McCullough, Mary Beth; Noble, Ashley

2011-01-01

The theory-theory account of conceptual development posits that children’s concepts are integrated into theories. Concept learning studies have documented the central role that theories play in children’s learning of experimenter-defined categories, but have yet to extensively examine complex, real-world concepts such as health. The present study examined whether providing young children with coherent and causally-related information in a theory-based lesson would facilitate their learning about the concept of health. This study used a pre-test/lesson/post-test design, plus a five month follow-up. Children were randomly assigned to one of three conditions: theory (i.e., 20 children received a theory-based lesson); nontheory (i.e., 20 children received a nontheory-based lesson); and control (i.e., 20 children received no lesson). Overall, the results showed that children in the theory condition had a more accurate conception of health than children in the nontheory and control conditions, suggesting the importance of theories in children’s learning of complex, real-world concepts. PMID:21894237

Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier

NASA Astrophysics Data System (ADS)

Li, Qiang; Wang, Zhi; Le, Yansi; Sun, Chonghui; Song, Xiaojia; Wu, Chongqing

2016-10-01

Neuromorphic engineering has a wide range of applications in the fields of machine learning, pattern recognition, adaptive control, etc. Photonics, characterized by its high speed, wide bandwidth, low power consumption and massive parallelism, is an ideal way to realize ultrafast spiking neural networks (SNNs). Synaptic plasticity is believed to be critical for learning, memory and development in neural circuits. Experimental results have shown that changes of synapse are highly dependent on the relative timing of pre- and postsynaptic spikes. Synaptic plasticity in which presynaptic spikes preceding postsynaptic spikes results in strengthening, while the opposite timing results in weakening is called antisymmetric spike-timing-dependent plasticity (STDP) learning rule. And synaptic plasticity has the opposite effect under the same conditions is called antisymmetric anti-STDP learning rule. We proposed and experimentally demonstrated an optical implementation of neural learning algorithms, which can achieve both of antisymmetric STDP and anti-STDP learning rule, based on the cross-gain modulation (XGM) within a single semiconductor optical amplifier (SOA). The weight and height of the potentitation and depression window can be controlled by adjusting the injection current of the SOA, to mimic the biological antisymmetric STDP and anti-STDP learning rule more realistically. As the injection current increases, the width of depression and potentitation window decreases and height increases, due to the decreasing of recovery time and increasing of gain under a stronger injection current. Based on the demonstrated optical STDP circuit, ultrafast learning in optical SNNs can be realized.

Promoting Students' Problem Solving Skills and Knowledge of STEM Concepts in a Data-Rich Learning Environment: Using Online Data as a Tool for Teaching about Renewable Energy Technologies

NASA Astrophysics Data System (ADS)

Thurmond, Brandi

This study sought to compare a data-rich learning (DRL) environment that utilized online data as a tool for teaching about renewable energy technologies (RET) to a lecture-based learning environment to determine the impact of the learning environment on students' knowledge of Science, Technology, Engineering, and Math (STEM) concepts related to renewable energy technologies and students' problem solving skills. Two purposefully selected Advanced Placement (AP) Environmental Science teachers were included in the study. Each teacher taught one class about RET in a lecture-based environment (control) and another class in a DRL environment (treatment), for a total of four classes of students (n=128). This study utilized a quasi-experimental, pretest/posttest, control-group design. The initial hypothesis that the treatment group would have a significant gain in knowledge of STEM concepts related to RET and be better able to solve problems when compared to the control group was not supported by the data. Although students in the DRL environment had a significant gain in knowledge after instruction, posttest score comparisons of the control and treatment groups revealed no significant differences between the groups. Further, no significant differences were noted in students' problem solving abilities as measured by scores on a problem-based activity and self-reported abilities on a reflective questionnaire. This suggests that the DRL environment is at least as effective as the lecture-based learning environment in teaching AP Environmental Science students about RET and fostering the development of problem solving skills. As this was a small scale study, further research is needed to provide information about effectiveness of DRL environments in promoting students' knowledge of STEM concepts and problem-solving skills.

Improving Pediatric Basic Life Support Performance Through Blended Learning With Web-Based Virtual Patients: Randomized Controlled Trial.

PubMed

Lehmann, Ronny; Thiessen, Christiane; Frick, Barbara; Bosse, Hans Martin; Nikendei, Christoph; Hoffmann, Georg Friedrich; Tönshoff, Burkhard; Huwendiek, Sören

2015-07-02

E-learning and blended learning approaches gain more and more popularity in emergency medicine curricula. So far, little data is available on the impact of such approaches on procedural learning and skill acquisition and their comparison with traditional approaches. This study investigated the impact of a blended learning approach, including Web-based virtual patients (VPs) and standard pediatric basic life support (PBLS) training, on procedural knowledge, objective performance, and self-assessment. A total of 57 medical students were randomly assigned to an intervention group (n=30) and a control group (n=27). Both groups received paper handouts in preparation of simulation-based PBLS training. The intervention group additionally completed two Web-based VPs with embedded video clips. Measurements were taken at randomization (t0), after the preparation period (t1), and after hands-on training (t2). Clinical decision-making skills and procedural knowledge were assessed at t0 and t1. PBLS performance was scored regarding adherence to the correct algorithm, conformance to temporal demands, and the quality of procedural steps at t1 and t2. Participants' self-assessments were recorded in all three measurements. Procedural knowledge of the intervention group was significantly superior to that of the control group at t1. At t2, the intervention group showed significantly better adherence to the algorithm and temporal demands, and better procedural quality of PBLS in objective measures than did the control group. These aspects differed between the groups even at t1 (after VPs, prior to practical training). Self-assessments differed significantly only at t1 in favor of the intervention group. Training with VPs combined with hands-on training improves PBLS performance as judged by objective measures.

Emergence of Virtual Reality as a Tool for Upper Limb Rehabilitation: Incorporation of Motor Control and Motor Learning Principles

PubMed Central

Weiss, Patrice L.; Keshner, Emily A.

2015-01-01

The primary focus of rehabilitation for individuals with loss of upper limb movement as a result of acquired brain injury is the relearning of specific motor skills and daily tasks. This relearning is essential because the loss of upper limb movement often results in a reduced quality of life. Although rehabilitation strives to take advantage of neuroplastic processes during recovery, results of traditional approaches to upper limb rehabilitation have not entirely met this goal. In contrast, enriched training tasks, simulated with a wide range of low- to high-end virtual reality–based simulations, can be used to provide meaningful, repetitive practice together with salient feedback, thereby maximizing neuroplastic processes via motor learning and motor recovery. Such enriched virtual environments have the potential to optimize motor learning by manipulating practice conditions that explicitly engage motivational, cognitive, motor control, and sensory feedback–based learning mechanisms. The objectives of this article are to review motor control and motor learning principles, to discuss how they can be exploited by virtual reality training environments, and to provide evidence concerning current applications for upper limb motor recovery. The limitations of the current technologies with respect to their effectiveness and transfer of learning to daily life tasks also are discussed. PMID:25212522

Autonomy in Science Education: A Practical Approach in Attitude Shifting Towards Science Learning

NASA Astrophysics Data System (ADS)

Jalil, Pasl A.; Abu Sbeih, M. Z.; Boujettif, M.; Barakat, R.

2009-12-01

This work describes a 2-year study in teaching school science, based on the stimulation of higher thinking levels in learning science using a highly student-centred and constructivist learning approach. We sought to shift and strengthen students' positive attitudes towards science learning, self-efficacy towards invention, and achievement. Focusing on an important aspect of student's positive attitude towards learning, their preference (like/dislike) towards independent study with minimal or no teacher interference, which leads to increased learning autonomy, was investigated. The main research was conducted on elementary school students; 271 grade level one (G1; 6 years old) to grade level four (G4; 10 years old) participated in this study. As a result of this study, it was found that: (1) 73% of the students preferred minimal or no explanation at all, favoring to be left with the challenge of finding out what to do, compared to 20% of the control group, indicating a positive attitude shift in their learning approaches. (2) The experimental group achieved slightly more (9.5% difference) than the control group in knowledge-comprehension-level based exam; however, the experimental group scored much higher (63% difference) in challenging exams which required higher thinking levels. (3) The same trend was also observed in self-efficacy toward invention, where 82% of the experimental group saw themselves as possible inventors compared to 37% of the control group.

A serious game for improving the decision making skills and knowledge levels of Turkish football referees according to the laws of the game.

PubMed

Gulec, Ulas; Yilmaz, Murat

2016-01-01

Digital game-based learning environments provide emerging opportunities to overcome learning barriers by combining newly developed technologies and traditional game design. This study proposes a quantitative research approach supported by expert validation interviews to designing a game-based learning framework. The goal is to improve the learning experience and decision-making skills of soccer referees in Turkey. A serious game was developed and tested on a group of referees (N = 54). The assessment results of these referees were compared with two sample t-test and the Wilcoxon signed-ranked test for both the experimental group and the control group. The findings of the current study confirmed that a game-based learning environment has greater merit over the paper-based alternatives.

Low Cognitive Impulsivity Is Associated with Better Gain and Loss Learning in a Probabilistic Decision-Making Task

PubMed Central

Cáceres, Pablo; San Martín, René

2017-01-01

Many advances have been made over the last decades in describing, on the one hand, the link between reward-based learning and decision-making, and on the other hand, the link between impulsivity and decision-making. However, the association between reward-based learning and impulsivity remains poorly understood. In this study, we evaluated the association between individual differences in loss-minimizing and gain-maximizing behavior in a learning-based probabilistic decision-making task and individual differences in cognitive impulsivity. We found that low cognitive impulsivity was associated both with a better performance minimizing losses and maximizing gains during the task. These associations remained significant after controlling for mathematical skills and gender as potential confounders. We discuss potential mechanisms through which cognitive impulsivity might interact with reward-based learning and decision-making. PMID:28261137

Low Cognitive Impulsivity Is Associated with Better Gain and Loss Learning in a Probabilistic Decision-Making Task.

PubMed

Cáceres, Pablo; San Martín, René

2017-01-01

Many advances have been made over the last decades in describing, on the one hand, the link between reward-based learning and decision-making, and on the other hand, the link between impulsivity and decision-making. However, the association between reward-based learning and impulsivity remains poorly understood. In this study, we evaluated the association between individual differences in loss-minimizing and gain-maximizing behavior in a learning-based probabilistic decision-making task and individual differences in cognitive impulsivity. We found that low cognitive impulsivity was associated both with a better performance minimizing losses and maximizing gains during the task. These associations remained significant after controlling for mathematical skills and gender as potential confounders. We discuss potential mechanisms through which cognitive impulsivity might interact with reward-based learning and decision-making.

Frontal Alpha Oscillations and Attentional Control: A Virtual Reality Neurofeedback Study.

PubMed

Berger, Anna M; Davelaar, Eddy J

2018-05-15

Two competing views about alpha oscillations suggest that cortical alpha reflect either cortical inactivity or cortical processing efficiency. We investigated the role of alpha oscillations in attentional control, as measured with a Stroop task. We used neurofeedback to train 22 participants to increase their level of alpha amplitude. Based on the conflict/control loop theory, we selected to train prefrontal alpha and focus on the Gratton effect as an index of deployment of attentional control. We expected an increase or a decrease in the Gratton effect with increase in neural learning depending on whether frontal alpha oscillations reflect cortical idling or enhanced processing efficiency, respectively. In order to induce variability in neural learning beyond natural occurring individual differences, we provided half of the participants with feedback on alpha amplitude in a 3-dimensional (3D) virtual reality environment and the other half received feedback in a 2D environment. Our results showed variable neural learning rates, with larger rates in the 3D compared to the 2D group, corroborating prior evidence of individual differences in EEG-based learning and the influence of a virtual environment. Regression analyses revealed a significant association between the learning rate and changes on deployment of attentional control, with larger learning rates being associated with larger decreases in the Gratton effect. This association was not modulated by feedback medium. The study supports the view of frontal alpha oscillations being associated with efficient neurocognitive processing and demonstrates the utility of neurofeedback training in addressing theoretical questions in the non-neurofeedback literature. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

A neural fuzzy controller learning by fuzzy error propagation

NASA Technical Reports Server (NTRS)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Development of Scientific Approach Based on Discovery Learning Module

NASA Astrophysics Data System (ADS)

Ellizar, E.; Hardeli, H.; Beltris, S.; Suharni, R.

2018-04-01

Scientific Approach is a learning process, designed to make the students actively construct their own knowledge through stages of scientific method. The scientific approach in learning process can be done by using learning modules. One of the learning model is discovery based learning. Discovery learning is a learning model for the valuable things in learning through various activities, such as observation, experience, and reasoning. In fact, the students’ activity to construct their own knowledge were not optimal. It’s because the available learning modules were not in line with the scientific approach. The purpose of this study was to develop a scientific approach discovery based learning module on Acid Based, also on electrolyte and non-electrolyte solution. The developing process of this chemistry modules use the Plomp Model with three main stages. The stages are preliminary research, prototyping stage, and the assessment stage. The subject of this research was the 10th and 11th Grade of Senior High School students (SMAN 2 Padang). Validation were tested by the experts of Chemistry lecturers and teachers. Practicality of these modules had been tested through questionnaire. The effectiveness had been tested through experimental procedure by comparing student achievement between experiment and control groups. Based on the findings, it can be concluded that the developed scientific approach discovery based learning module significantly improve the students’ learning in Acid-based and Electrolyte solution. The result of the data analysis indicated that the chemistry module was valid in content, construct, and presentation. Chemistry module also has a good practicality level and also accordance with the available time. This chemistry module was also effective, because it can help the students to understand the content of the learning material. That’s proved by the result of learning student. Based on the result can conclude that chemistry module based on discovery learning and scientific approach in electrolyte and non-electrolyte solution and Acid Based for the 10th and 11th grade of senior high school students were valid, practice, and effective.

Neural networks for continuous online learning and control.

PubMed

Choy, Min Chee; Srinivasan, Dipti; Cheu, Ruey Long

2006-11-01

This paper proposes a new hybrid neural network (NN) model that employs a multistage online learning process to solve the distributed control problem with an infinite horizon. Various techniques such as reinforcement learning and evolutionary algorithm are used to design the multistage online learning process. For this paper, the infinite horizon distributed control problem is implemented in the form of real-time distributed traffic signal control for intersections in a large-scale traffic network. The hybrid neural network model is used to design each of the local traffic signal controllers at the respective intersections. As the state of the traffic network changes due to random fluctuation of traffic volumes, the NN-based local controllers will need to adapt to the changing dynamics in order to provide effective traffic signal control and to prevent the traffic network from becoming overcongested. Such a problem is especially challenging if the local controllers are used for an infinite horizon problem where online learning has to take place continuously once the controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District (CBD) of Singapore has been developed using PARAMICS microscopic simulation program. As the complexity of the simulation increases, results show that the hybrid NN model provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as a new, continuously updated simultaneous perturbation stochastic approximation-based neural network (SPSA-NN). Using the hybrid NN model, the total mean delay of each vehicle has been reduced by 78% and the total mean stoppage time of each vehicle has been reduced by 84% compared to the existing traffic signal control algorithm. This shows the efficacy of the hybrid NN model in solving large-scale traffic signal control problem in a distributed manner. Also, it indicates the possibility of using the hybrid NN model for other applications that are similar in nature as the infinite horizon distributed control problem.

Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control

PubMed Central

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms. PMID:25389391

Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

PubMed

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

Medical Students' Satisfaction and Academic Performance with Problem-Based Learning in Practice-Based Exercises for Epidemiology and Health Demographics

ERIC Educational Resources Information Center

Jiménez-Mejías, E.; Amezcua-Prieto, C.; Martínez-Ruiz, V.; Olvera-Porcel, M. C.; Jiménez-Moleón, J. J.; Lardelli Claret, P.

2015-01-01

The aim of this study was to evaluate the effect of problem-based learning (PBL) on university students' satisfaction with and academic performance in a course on epidemiology and social and demographic health. The participants in this interventional study were 529 students (272 in the intervention group and 257 in the control group) enrolled in a…

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.

Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm.

PubMed

Sadeghieh, Ali; Sazgar, Hadi; Goodarzi, Kamyar; Lucas, Caro

2012-01-01

This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC's online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Mobile-Based Video Learning Outcomes in Clinical Nursing Skill Education

PubMed Central

Lee, Nam-Ju; Chae, Sun-Mi; Kim, Haejin; Lee, Ji-Hye; Min, Hyojin Jennifer; Park, Da-Eun

2016-01-01

Mobile devices are a regular part of daily life among the younger generations. Thus, now is the time to apply mobile device use to nursing education. The purpose of this study was to identify the effects of a mobile-based video clip on learning motivation, competence, and class satisfaction in nursing students using a randomized controlled trial with a pretest and posttest design. A total of 71 nursing students participated in this study: 36 in the intervention group and 35 in the control group. A video clip of how to perform a urinary catheterization was developed, and the intervention group was able to download it to their own mobile devices for unlimited viewing throughout 1 week. All of the students participated in a practice laboratory to learn urinary catheterization and were blindly tested for their performance skills after participation in the laboratory. The intervention group showed significantly higher levels of learning motivation and class satisfaction than did the control. Of the fundamental nursing competencies, the intervention group was more confident in practicing catheterization than their counterparts. Our findings suggest that video clips using mobile devices are useful tools that educate student nurses on relevant clinical skills and improve learning outcomes. PMID:26389858

Adaptive and predictive control of a simulated robot arm.

PubMed

Tolu, Silvia; Vanegas, Mauricio; Garrido, Jesús A; Luque, Niceto R; Ros, Eduardo

2013-06-01

In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs).

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.

Effectiveness of simulation-based learning on student nurses' self-efficacy and performance while learning fundamental nursing skills.

PubMed

Lin, Hsin-Hsin

2015-01-01

It was noted worldwide while learning fundamental skills and facing skills assessments, nursing students seemed to experience low confidence and high anxiety levels. Could simulation-based learning help to enhance students' self-efficacy and performance? Its effectiveness is mostly unidentified. This study was conducted to provide a shared experience to give nurse educators confidence and an insight into how simulation-based teaching can fit into nursing skills learning. A pilot study was completed with 50 second-year undergraduate nursing students, and the main study included 98 students where a pretest-posttest design was adopted. Data were gathered through four questionnaires and a performance assessment under scrutinized controls such as previous experiences, lecturers' teaching skills, duration of teaching, procedure of skills performance assessment and the inter-rater reliability. The results showed that simulation-based learning significantly improved students' self-efficacy regarding skills learning and the skills performance that nurse educators wish students to acquire. However, technology anxiety, examiners' critical attitudes towards students' performance and their unpredicted verbal and non-verbal expressions, have been found as possible confounding factors. The simulation-based learning proved to have a powerful positive effect on students' achievement outcomes. Nursing skills learning is one area that can benefit greatly from this kind of teaching and learning method.

KISS--a new approach to self-controlled e-learning of selected chapters in Medical Engineering and other fields at bachelor and master course level.

PubMed

Hutten, Helmut; Stiegmaier, Wolfgang; Rauchegger, Günter

2005-09-01

Modern life style requires new methods for individual lifelong learning, based on access at every time and from every place. This fundamental requirement is provided by the Internet. The Internet technology promises an increasing potential in the future for e-learning or tele-learning. Some special requirements are password-controlled access, applicability of most commercially available PCs and laptops equipped with standard software (Microsoft Internet Explorer 6.0), central evaluation of the students' performance, inclusion of an examination part, provision of a picture gallery and a comprehensive glossary accessible in the learning mode. The KISS-shell has been developed based on the Oracle 10g application server in combination with a relational data base (Oracle 8i) on the server side and a web browser based interface using JavaScript for user control of data input on the client side (Kontrolliertes Intelligentes Selbstgesteuertes Studium, KISS). The first tutorial application has been realized with a chapter about cardiac pacemakers. The weight of that chapter (or module) is about 2 ECTS (i.e. the equivalent of 30 working hours; European Credit Transfer System, ECTS). The internal structure of the chapter is organized in sequential mode. It consists of five main sections. Each of those five sections is subdivided into five subsections of comparable length. Progression from one subsection to the next is possible only after successfully passing through the respective examination. The whole learning programme with the pacemaker chapter has been evaluated by 10 students. The system will be presented together with first experiences including the evaluation results. Until now the program has not been used for training purposes.

Cognitive learning: a machine learning approach for automatic process characterization from design

NASA Astrophysics Data System (ADS)

Foucher, J.; Baderot, J.; Martinez, S.; Dervilllé, A.; Bernard, G.

2018-03-01

Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

Motor-response learning at a process control panel by an autonomous robot

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

Spelt, P.F.; de Saussure, G.; Lyness, E.

1988-01-01

The Center for Engineering Systems Advanced Research (CESAR) was founded at Oak Ridge National Laboratory (ORNL) by the Department of Energy's Office of Energy Research/Division of Engineering and Geoscience (DOE-OER/DEG) to conduct basic research in the area of intelligent machines. Therefore, researchers at the CESAR Laboratory are engaged in a variety of research activities in the field of machine learning. In this paper, we describe our approach to a class of machine learning which involves motor response acquisition using feedback from trial-and-error learning. Our formulation is being experimentally validated using an autonomous robot, learning tasks of control panel monitoring andmore » manipulation for effect process control. The CLIPS Expert System and the associated knowledge base used by the robot in the learning process, which reside in a hypercube computer aboard the robot, are described in detail. Benchmark testing of the learning process on a robot/control panel simulation system consisting of two intercommunicating computers is presented, along with results of sample problems used to train and test the expert system. These data illustrate machine learning and the resulting performance improvement in the robot for problems similar to, but not identical with, those on which the robot was trained. Conclusions are drawn concerning the learning problems, and implications for future work on machine learning for autonomous robots are discussed. 16 refs., 4 figs., 1 tab.« less

Procedural Learning in Children With Developmental Coordination, Reading, and Attention Disorders.

PubMed

Magallón, Sara; Crespo-Eguílaz, Nerea; Narbona, Juan

2015-10-01

The aim is to assess repetition-based learning of procedures in children with developmental coordination disorder (DCD), reading disorder (RD) and attention-deficit hyperactivity disorder (ADHD). Participants included 187 children, studied in 4 groups: (a) DCD comorbid with RD and ADHD (DCD+RD+ADHD) (n = 30); (b) RD comorbid with ADHD (RD+ADHD) (n = 48); (c) ADHD (n = 19); and typically developing children (control group) (n = 90). Two procedural learning tasks were used: Assembly learning and Mirror drawing. Children were tested on 4 occasions for each task: 3 trials were consecutive and the fourth trial was performed after an interference task. Task performance by DCD+RD+ADHD children improved with training (P < .05); however, the improvement was significantly lower than that achieved by the other groups (RD+ADHD, ADHD and controls) (P < .05). In conclusion, children with DCD+RD+ADHD improve in their use of cognitive-motor procedures over a short training period. Aims of intervention in DCD+RD+ADHD should be based on individual learning abilities. © The Author(s) 2015.

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

PubMed

Vallila-Rohter, Sofia; Kiran, Swathi

2015-08-01

Our purpose was to study strategy use during nonlinguistic category learning in aphasia. 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. 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. 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.

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…

Contributions of Language and Memory Demands to Verbal Memory Performance in Language-Learning Disabilities

ERIC Educational Resources Information Center

Isaki, Emi; Spaulding, Tammie J.; Plante, Elena

2008-01-01

The purpose of this study is to investigate the performance of adults with language-based learning disorders (L/LD) and normal language controls on verbal short-term and verbal working memory tasks. Eighteen adults with L/LD and 18 normal language controls were compared on verbal short-term memory and verbal working memory tasks under low,…

Feedback Error Learning Controller for Functional Electrical Stimulation Assistance in a Hybrid Robotic System for Reaching Rehabilitation

PubMed Central

Resquín, Francisco; Gonzalez-Vargas, Jose; Ibáñez, Jaime; Brunetti, Fernando; Pons, José Luis

2016-01-01

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model. PMID:27990245

Learning Behaviour and Learning Outcomes: The Roles for Social Influence and Field of Study

ERIC Educational Resources Information Center

Smyth, Lillian; Mavor, Kenneth I.; Platow, Michael J.

2017-01-01

Research has demonstrated a significant role of discipline social identification in predicting learning approaches, even controlling for individual differences. Smyth et al. ("Educ Psychol" 35(1):53-72, 2015. doi:10.1080/01443410.2013.822962) suggest that learners share discipline-based social identifications, and that this…

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…

Peer Assisted Learning in the Clinical Setting: An Activity Systems Analysis

ERIC Educational Resources Information Center

Bennett, Deirdre; O'Flynn, Siun; Kelly, Martina

2015-01-01

Peer assisted learning (PAL) is a common feature of medical education. Understanding of PAL has been based on processes and outcomes in controlled settings, such as clinical skills labs. PAL in the clinical setting, a complex learning environment, requires fresh evaluation. Socio-cultural theory is proposed as a means to understand educational…

Learning about Computer-Based Education in Adult Basic Education.

ERIC Educational Resources Information Center

Fahy, Patrick J.

In 1979 the adult basic education department at the Alberta Vocational Centre (AVC), Edmonton, began to use the Control Data PLATO system. Results of the first PLATO project showed students using PLATO learned at least as much as students in regular classes. Students learned faster and reported great satisfaction with PLATO experiences. Staff and…

Frontal Oscillatory Dynamics Predict Feedback Learning and Action Adjustment

ERIC Educational Resources Information Center

van de Vijver, Irene; Ridderinkhof, K. Richard; Cohen, Michael X.

2011-01-01

Frontal oscillatory dynamics in the theta (4-8 Hz) and beta (20-30 Hz) frequency bands have been implicated in cognitive control processes. Here we investigated the changes in coordinated activity within and between frontal brain areas during feedback-based response learning. In a time estimation task, participants learned to press a button after…

The Effect of Pacing on Learners' Perceptions of Pedagogical Agents

ERIC Educational Resources Information Center

Schroeder, Noah L.; Craig, Scotty D.

2017-01-01

This study examined the influence of three levels of learner control on learners' perceptions when learning with a pedagogical agent. Pedagogical agents have shown promise for improving learning and connections with learning materials within video-based instruction, and research has shown that agent design choices can influence how agents are…

The Reading Disc: Learning to Read Using Interactive CD.

ERIC Educational Resources Information Center

Shaw, Simon

1991-01-01

Describes the development of an interactive compact disc on CD-ROM XA that was designed to help adults learn to read. The application of technology to learning is discussed, differences in learner control in computer-based systems are considered, virtual writing is described, and assessment activities available on the disc are explained. (five…

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

NASA Technical Reports Server (NTRS)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training.

PubMed

Robineau, F; Rieger, S W; Mermoud, C; Pichon, S; Koush, Y; Van De Ville, D; Vuilleumier, P; Scharnowski, F

2014-10-15

Recent advances in neurofeedback based on real-time functional magnetic resonance imaging (fMRI) allow for learning to control spatially localized brain activity in the range of millimeters across the entire brain. Real-time fMRI neurofeedback studies have demonstrated the feasibility of self-regulating activation in specific areas that are involved in a variety of functions, such as perception, motor control, language, and emotional processing. In most of these previous studies, participants trained to control activity within one region of interest (ROI). In the present study, we extended the neurofeedback approach by now training healthy participants to control the interhemispheric balance between their left and right visual cortices. This was accomplished by providing feedback based on the difference in activity between a target visual ROI and the corresponding homologue region in the opposite hemisphere. Eight out of 14 participants learned to control the differential feedback signal over the course of 3 neurofeedback training sessions spread over 3 days, i.e., they produced consistent increases in the visual target ROI relative to the opposite visual cortex. Those who learned to control the differential feedback signal were subsequently also able to exert that control in the absence of neurofeedback. Such learning to voluntarily control the balance between cortical areas of the two hemispheres might offer promising rehabilitation approaches for neurological or psychiatric conditions associated with pathological asymmetries in brain activity patterns, such as hemispatial neglect, dyslexia, or mood disorders. Copyright © 2014 Elsevier Inc. All rights reserved.

Biologically Inspired SNN for Robot Control.

PubMed

Nichols, Eric; McDaid, Liam J; Siddique, Nazmul

2013-02-01

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

Gemini Observatory base facility operations: systems engineering process and lessons learned

NASA Astrophysics Data System (ADS)

Serio, Andrew; Cordova, Martin; Arriagada, Gustavo; Adamson, Andy; Close, Madeline; Coulson, Dolores; Nitta, Atsuko; Nunez, Arturo

2016-08-01

Gemini North Observatory successfully began nighttime remote operations from the Hilo Base Facility control room in November 2015. The implementation of the Gemini North Base Facility Operations (BFO) products was a great learning experience for many of our employees, including the author of this paper, the BFO Systems Engineer. In this paper we focus on the tailored Systems Engineering processes used for the project, the various software tools used in project support, and finally discuss the lessons learned from the Gemini North implementation. This experience and the lessons learned will be used both to aid our implementation of the Gemini South BFO in 2016, and in future technical projects at Gemini Observatory.

Consecutive learning of opposing unimanual motor tasks using the right arm followed by the left arm causes intermanual interference

PubMed Central

Thürer, Benjamin; Stein, Thorsten

2017-01-01

Intermanual transfer (motor memory generalization across arms) and motor memory interference (impairment of retest performance in consecutive motor learning) are well-investigated motor learning phenomena. However, the interplay of these phenomena remains elusive, i.e., whether intermanual interference occurs when two unimanual tasks are consecutively learned using different arms. Here, we examine intermanual interference when subjects consecutively adapt their right and left arm movements to novel dynamics. We considered two force field tasks A and B which were of the same structure but mirrored orientation (B = -A). The first test group (ABA-group) consecutively learned task A using their right arm and task B using their left arm before being retested for task A with their right arm. Another test group (AAA-group) learned only task A in the same right-left-right arm schedule. Control subjects learned task A using their right arm without intermediate left arm learning. All groups were able to adapt their right arm movements to force field A and both test groups showed significant intermanual transfer of this initial learning to the contralateral left arm of 21.9% (ABA-group) and 27.6% (AAA-group). Consecutively, both test groups adapted their left arm movements to force field B (ABA-group) or force field A (AAA-group). For the ABA-group, left arm learning caused significant intermanual interference of the initially learned right arm task (68.3% performance decrease). The performance decrease of the AAA-group (10.2%) did not differ from controls (15.5%). These findings suggest that motor control and learning of right and left arm movements involve partly similar neural networks or underlie a vital interhemispheric connectivity. Moreover, our results suggest a preferred internal task representation in extrinsic Cartesian-based coordinates rather than in intrinsic joint-based coordinates because interference was absent when learning was performed in extrinsically equivalent fashion (AAA-group) but interference occurred when learning was performed in intrinsically equivalent fashion (ABA-group). PMID:28459833

Consecutive learning of opposing unimanual motor tasks using the right arm followed by the left arm causes intermanual interference.

PubMed

Stockinger, Christian; Thürer, Benjamin; Stein, Thorsten

2017-01-01

Intermanual transfer (motor memory generalization across arms) and motor memory interference (impairment of retest performance in consecutive motor learning) are well-investigated motor learning phenomena. However, the interplay of these phenomena remains elusive, i.e., whether intermanual interference occurs when two unimanual tasks are consecutively learned using different arms. Here, we examine intermanual interference when subjects consecutively adapt their right and left arm movements to novel dynamics. We considered two force field tasks A and B which were of the same structure but mirrored orientation (B = -A). The first test group (ABA-group) consecutively learned task A using their right arm and task B using their left arm before being retested for task A with their right arm. Another test group (AAA-group) learned only task A in the same right-left-right arm schedule. Control subjects learned task A using their right arm without intermediate left arm learning. All groups were able to adapt their right arm movements to force field A and both test groups showed significant intermanual transfer of this initial learning to the contralateral left arm of 21.9% (ABA-group) and 27.6% (AAA-group). Consecutively, both test groups adapted their left arm movements to force field B (ABA-group) or force field A (AAA-group). For the ABA-group, left arm learning caused significant intermanual interference of the initially learned right arm task (68.3% performance decrease). The performance decrease of the AAA-group (10.2%) did not differ from controls (15.5%). These findings suggest that motor control and learning of right and left arm movements involve partly similar neural networks or underlie a vital interhemispheric connectivity. Moreover, our results suggest a preferred internal task representation in extrinsic Cartesian-based coordinates rather than in intrinsic joint-based coordinates because interference was absent when learning was performed in extrinsically equivalent fashion (AAA-group) but interference occurred when learning was performed in intrinsically equivalent fashion (ABA-group).

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.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

NASA Astrophysics Data System (ADS)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2017-12-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

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.

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

Designing Interactive Learning Systems.

ERIC Educational Resources Information Center

Barker, Philip

1990-01-01

Describes multimedia, computer-based interactive learning systems that support various forms of individualized study. Highlights include design models; user interfaces; design guidelines; media utilization paradigms, including hypermedia and learner-controlled models; metaphors and myths; authoring tools; optical media; workstations; four case…

Design and Validation of a Web-Based System for Assigning Members to Teams Using Instructor-Specified Criteria

ERIC Educational Resources Information Center

Layton, Richard A.; Loughry, Misty L.; Ohland, Matthew W.; Ricco, George D.

2010-01-01

A significant body of research identifies a large number of team composition characteristics that affect the success of individuals and teams in cooperative learning and project-based team environments. Controlling these factors when assigning students to teams should result in improved learning experiences. However, it is very difficult for…

Effect of Problem-Based Learning on Senior Secondary School Students' Achievements in Further Mathematics

ERIC Educational Resources Information Center

Fatade, Alfred Olufemi; Mogari, David; Arigbabu, Abayomi Adelaja

2013-01-01

The study investigated the effect of Problem-based learning (PBL) on senior secondary school students' achievements in Further Mathematics (FM) in Nigeria within the blueprint of pretest-post-test non-equivalent control group quasi-experimental design. Intact classes were used and in all, 96 students participated in the study (42 in the…

The Impact of Problem-Based Learning Approach to Senior High School Students' Mathematics Critical Thinking Ability

ERIC Educational Resources Information Center

Widyatiningtyas, Reviandari; Kusumah, Yaya S.; Sumarmo, Utari; Sabandar, Jozua

2015-01-01

The study reported the findings of an only post-test control group research design and aims to analyze the influence of problem-based learning approach, school level, and students' prior mathematical ability to student's mathematics critical thinking ability. The research subjects were 140 grade ten senior high school students coming from…

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…

The Constructionism and Neurocognitive-Based Teaching Model for Promoting Science Learning Outcomes and Creative Thinking

ERIC Educational Resources Information Center

Sripongwiwat, Supathida; Bunterm, Tassanee; Srisawat, Niwat; Tang, Keow Ngang

2016-01-01

The aim of this study was to examine the effect, after intervention on both experimental and control groups, of constructionism and neurocognitive-based teaching model, and conventional teaching model, on the science learning outcomes and creative thinking of Grade 11 students. The researchers developed a constructionism and neurocognitive-based…

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…

The Role of Visualization in Learning from Computer-Based Images. Research Report

ERIC Educational Resources Information Center

Piburn, Michael D.; Reynolds, Stephen J.; McAuliffe, Carla; Leedy, Debra E.; Birk, James P.; Johnson, Julia K.

2005-01-01

Among the sciences, the practice of geology is especially visual. To assess the role of spatial ability in learning geology, we designed an experiment using: (1) web-based versions of spatial visualization tests, (2) a geospatial test, and (3) multimedia instructional modules built around QuickTime Virtual Reality movies. Students in control and…

Problem-Based Learning in an Eleventh Grade Chemistry Class: "Factors Affecting Cell Potential"

ERIC Educational Resources Information Center

Tarhan, Leman; Acar, Burcin

2007-01-01

The purpose of this research study was to examine the effectiveness of problem-based learning (PBL) on eleventh grade students' understanding of "The effects of temperature, concentration and pressure on cell potential" and also their social skills. Stratified randomly selected control and experimental groups with 20 students each were used in…

Multimedia Scenario Based Learning Programme for Enhancing the English Language Efficiency among Primary School Students

ERIC Educational Resources Information Center

Tupe, Navnath

2015-01-01

This research was undertaken with a view to assess the deficiencies in English language among Primary School Children and to develop Multimedia Scenario Based Learning Programme (MSBLP) for mastery of English language which required special attention and effective treatment. The experimental study with pre-test, post-test control group design was…

Virtual Task-Based Situated Language-Learning with "Second Life": Developing EFL Pragmatic Writing and Technological Self-Efficacy

ERIC Educational Resources Information Center

Abdallah, Mahmoud M. S.; Mansour, Marian M.

2015-01-01

This paper reports on an experimental research study that aimed at investigating the effectiveness of employing a virtual task-based situated language learning (TBSLL) environment mediated by Second Life (SL) in developing EFL student teachers' pragmatic writing skills and their technological self-efficacy. To reach this goal, a control-only…

Do Students' Topic Interest and Tutors' Instructional Style Matter in Problem-Based Learning?

ERIC Educational Resources Information Center

Wijnia, Lisette; Loyens, Sofie M. M.; Derous, Eva; Schmidt, Henk G.

2014-01-01

Two studies investigated the importance of initial topic interest (i.e., expectation of interest) and tutors' autonomy-supportive or controlling instructional styles for students' motivation and performance in problem-based learning (PBL). In Study 1 (N = 93, a lab experiment), each student participated in a simulated group discussion in…

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…

The Effects of Problem-Based Learning (PBL) on the Academic Achievement of Students Studying "Electrochemistry"

ERIC Educational Resources Information Center

Günter, Tugçe; Alpat, Sibel Kilinç

2017-01-01

This study investigates the effects of problem-based learning (PBL) on students' academic achievements in studying "Electrochemistry" within a course on Analytical Chemistry. The research was of a pretest-posttest control group quasi-experimental design and it was conducted with second year students in the Chemistry Teaching Program at…

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 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

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

Building adaptive connectionist-based controllers: review of experiments in human-robot interaction, collective robotics, and computational neuroscience

NASA Astrophysics Data System (ADS)

Billard, Aude

2000-10-01

This paper summarizes a number of experiments in biologically inspired robotics. The common feature to all experiments is the use of artificial neural networks as the building blocks for the controllers. The experiments speak in favor of using a connectionist approach for designing adaptive and flexible robot controllers, and for modeling neurological processes. I present 1) DRAMA, a novel connectionist architecture, which has general property for learning time series and extracting spatio-temporal regularities in multi-modal and highly noisy data; 2) Robota, a doll-shaped robot, which imitates and learns a proto-language; 3) an experiment in collective robotics, where a group of 4 to 15 Khepera robots learn dynamically the topography of an environment whose features change frequently; 4) an abstract, computational model of primate ability to learn by imitation; 5) a model for the control of locomotor gaits in a quadruped legged robot.

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.

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.

Case-based learning and simulation: useful tools to enhance nurses' education? Nonrandomized controlled trial.

PubMed

Raurell-Torredà, Marta; Olivet-Pujol, Josep; Romero-Collado, Àngel; Malagon-Aguilera, Maria Carmen; Patiño-Masó, Josefina; Baltasar-Bagué, Alícia

2015-01-01

To compare skills acquired by undergraduate nursing students enrolled in a medical-surgical course. To compare skills demonstrated by students with no previous clinical practice (undergraduates) and nurses with clinical experience enrolled in continuing professional education (CPE). In a nonrandomized clinical trial, 101 undergraduates enrolled in the "Adult Patients 1" course were assigned to the traditional lecture and discussion (n = 66) or lecture and discussion plus case-based learning (n = 35) arm of the study; 59 CPE nurses constituted a comparison group to assess the effects of previous clinical experience on learning outcomes. Scores on an objective structured clinical examination (OSCE), using a human patient simulator and cases validated by the National League for Nursing, were compared for the undergraduate control and intervention groups, and for CPE nurses (Student's t test). Controls scored lower than the intervention group on patient assessment (6.3 ± 2.3 vs 7.5 ± 1.4, p = .04, mean difference, -1.2 [95% confidence interval (CI) -2.4 to -0.03]) but the intervention group did not differ from CPE nurses (7.5 ± 1.4 vs 8.8 ± 1.5, p = .06, mean difference, -1.3 [95% CI -2.6 to 0.04]). The CPE nurses committed more "rules-based errors" than did undergraduates, specifically patient identifications (77.2% vs 55%, p = .7) and checking allergies before administering medication (68.2% vs 60%, p = .1). The intervention group developed better patient assessment skills than the control group. Case-based learning helps to standardize the process, which can contribute to quality and consistency in practice: It is essential to correctly identify a problem in order to treat it. Clinical experience of CPE nurses was not associated with better adherence to safety protocols. Case-based learning improves the patient assessment skills of undergraduate nursing students, thereby preparing them for clinical practice. © 2014 Sigma Theta Tau International.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

Building Knowledge Structures by Testing Helps Children With Mathematical Learning Difficulty.

PubMed

Zhang, Yiyun; Zhou, Xinlin

2016-01-01

Mathematical learning difficulty (MLD) is prevalent in the development of mathematical abilities. Previous interventions for children with MLD have focused on number sense or basic mathematical skills. This study investigated whether mathematical performance of fifth grade children with MLD could be improved by developing knowledge structures by testing using a web-based curriculum learning system. A total of 142 children with MLD were recruited; half of the children were in the experimental group (using the system), and the other half were in the control group (not using the system). The children were encouraged to use the web-based learning system at home for at least a 15-min session, at least once a week, for one and a half months. The mean accumulated time of testing on the system for children in the experimental group was 56.2 min. Children in the experimental group had significantly higher scores on their final mathematical examination compared to the control group. The results suggest that web-based curriculum learning through testing that promotes the building of knowledge structures for a mathematical course was helpful for children with MLD. © Hammill Institute on Disabilities 2014.

Goal selection versus process control while learning to use a brain-computer interface

NASA Astrophysics Data System (ADS)

Royer, Audrey S.; Rose, Minn L.; He, Bin

2011-06-01

A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.

Theories and control models and motor learning: clinical applications in neuro-rehabilitation.

PubMed

Cano-de-la-Cuerda, R; Molero-Sánchez, A; Carratalá-Tejada, M; Alguacil-Diego, I M; Molina-Rueda, F; Miangolarra-Page, J C; Torricelli, D

2015-01-01

In recent decades there has been a special interest in theories that could explain the regulation of motor control, and their applications. These theories are often based on models of brain function, philosophically reflecting different criteria on how movement is controlled by the brain, each being emphasised in different neural components of the movement. The concept of motor learning, regarded as the set of internal processes associated with practice and experience that produce relatively permanent changes in the ability to produce motor activities through a specific skill, is also relevant in the context of neuroscience. Thus, both motor control and learning are seen as key fields of study for health professionals in the field of neuro-rehabilitation. The major theories of motor control are described, which include, motor programming theory, systems theory, the theory of dynamic action, and the theory of parallel distributed processing, as well as the factors that influence motor learning and its applications in neuro-rehabilitation. At present there is no consensus on which theory or model defines the regulations to explain motor control. Theories of motor learning should be the basis for motor rehabilitation. The new research should apply the knowledge generated in the fields of control and motor learning in neuro-rehabilitation. Copyright © 2011 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.

Choline acetyltransferase in the hippocampus is associated with learning strategy preference in adult male rats.

PubMed

Hawley, Wayne R; Witty, Christine F; Daniel, Jill M; Dohanich, Gary P

2015-08-01

One principle of the multiple memory systems hypothesis posits that the hippocampus-based and striatum-based memory systems compete for control over learning. Consistent with this notion, previous research indicates that the cholinergic system of the hippocampus plays a role in modulating the preference for a hippocampus-based place learning strategy over a striatum-based stimulus--response learning strategy. Interestingly, in the hippocampus, greater activity and higher protein levels of choline acetyltransferase (ChAT), the enzyme that synthesizes acetylcholine, are associated with better performance on hippocampus-based learning and memory tasks. With this in mind, the primary aim of the current study was to determine if higher levels of ChAT and the high-affinity choline uptake transporter (CHT) in the hippocampus were associated with a preference for a hippocampus-based place learning strategy on a task that also could be solved by relying on a striatum-based stimulus--response learning strategy. Results confirmed that levels of ChAT in the dorsal region of the hippocampus were associated with a preference for a place learning strategy on a water maze task that could also be solved by adopting a stimulus-response learning strategy. Consistent with previous studies, the current results support the hypothesis that the cholinergic system of the hippocampus plays a role in balancing competition between memory systems that modulate learning strategy preference. Copyright © 2015 Elsevier B.V. All rights reserved.

Density control in ITER: an iterative learning control and robust control approach

NASA Astrophysics Data System (ADS)

Ravensbergen, T.; de Vries, P. C.; Felici, F.; Blanken, T. C.; Nouailletas, R.; Zabeo, L.

2018-01-01

Plasma density control for next generation tokamaks, such as ITER, is challenging because of multiple reasons. The response of the usual gas valve actuators in future, larger fusion devices, might be too slow for feedback control. Both pellet fuelling and the use of feedforward-based control may help to solve this problem. Also, tight density limits arise during ramp-up, due to operational limits related to divertor detachment and radiative collapses. As the number of shots available for controller tuning will be limited in ITER, in this paper, iterative learning control (ILC) is proposed to determine optimal feedforward actuator inputs based on tracking errors, obtained in previous shots. This control method can take the actuator and density limits into account and can deal with large actuator delays. However, a purely feedforward-based density control may not be sufficient due to the presence of disturbances and shot-to-shot differences. Therefore, robust control synthesis is used to construct a robustly stabilizing feedback controller. In simulations, it is shown that this combined controller strategy is able to achieve good tracking performance in the presence of shot-to-shot differences, tight constraints, and model mismatches.

A randomized, controlled trial of team-based competition to increase learner participation in quality-improvement education.

PubMed

Scales, Charles D; Moin, Tannaz; Fink, Arlene; Berry, Sandra H; Afsar-Manesh, Nasim; Mangione, Carol M; Kerfoot, B Price

2016-04-01

Several barriers challenge resident engagement in learning quality improvement (QI). We investigated whether the incorporation of team-based game mechanics into an evidence-based online learning platform could increase resident participation in a QI curriculum. Randomized, controlled trial. Tertiary-care medical center residency training programs. Resident physicians (n = 422) from nine training programs (anesthesia, emergency medicine, family medicine, internal medicine, ophthalmology, orthopedics, pediatrics, psychiatry and general surgery) randomly allocated to a team competition environment (n = 200) or the control group (n = 222). Specialty-based team assignment with leaderboards to foster competition, and alias assignment to de-identify individual participants. Participation in online learning, as measured by percentage of questions attempted (primary outcome) and additional secondary measures of engagement (i.e. response time). Changes in participation measures over time between groups were assessed with a repeated measures ANOVA framework. Residents in the intervention arm demonstrated greater participation than the control group. The percentage of questions attempted at least once was greater in the competition group (79% [SD ± 32] versus control, 68% [SD ± 37], P= 0.03). Median response time was faster in the competition group (P= 0.006). Differences in participation continued to increase over the duration of the intervention, as measured by average response time and cumulative percent of questions attempted (each P< 0.001). Team competition increases resident participation in an online course delivering QI content. Medical educators should consider game mechanics to optimize participation when designing learning experiences. Published by Oxford University Press in association with the International Society for Quality in Health Care 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Effects of Higher-Order Cognitive Strategy Training on Gist-Reasoning and Fact-Learning in Adolescents

PubMed Central

Gamino, Jacquelyn F.; Chapman, Sandra B.; Hull, Elizabeth L.; Lyon, G. Reid

2010-01-01

Improving the reasoning skills of adolescents across the United States has become a major concern for educators and scientists who are dedicated to identifying evidence-based protocols to improve student outcome. This small sample randomized, control pilot study sought to determine the efficacy of higher-order cognitive training on gist-reasoning and fact-learning in an inner-city public middle school. The study compared gist-reasoning and fact-learning performances after training in a smaller sample when tested in Spanish, many of the students’ native language, versus English. The 54 eighth grade students who participated in this pilot study were enroled in an urban middle school, predominantly from lower socio-economic status families, and were primarily of minority descent. The students were randomized into one of three groups, one that learned cognitive strategies promoting abstraction of meaning, a group that learned rote memory strategies, or a control group to ascertain the impact of each program on gist-reasoning and fact-learning from text-based information. We found that the students who had cognitive strategy instruction that entailed abstraction of meaning significantly improved their gist-reasoning and fact-learning ability. The students who learned rote memory strategies significantly improved their fact-learning scores from a text but not gist-reasoning ability. The control group showed no significant change in either gist-reasoning or fact-learning ability. A trend toward significant improvement in overall reading scores for the group that learned to abstract meaning as well as a significant correlation between gist-reasoning ability and the critical thinking on a state-mandated standardized reading test was also found. There were no significant differences between English and Spanish performance of gist-reasoning and fact-learning. Our findings suggest that teaching higher-order cognitive strategies facilitates gist-reasoning ability and student learning. PMID:21833248

A computer simulation approach to measurement of human control strategy

NASA Technical Reports Server (NTRS)

Green, J.; Davenport, E. L.; Engler, H. F.; Sears, W. E., III

1982-01-01

Human control strategy is measured through use of a psychologically-based computer simulation which reflects a broader theory of control behavior. The simulation is called the human operator performance emulator, or HOPE. HOPE was designed to emulate control learning in a one-dimensional preview tracking task and to measure control strategy in that setting. When given a numerical representation of a track and information about current position in relation to that track, HOPE generates positions for a stick controlling the cursor to be moved along the track. In other words, HOPE generates control stick behavior corresponding to that which might be used by a person learning preview tracking.

An integrated utility-based model of conflict evaluation and resolution in the Stroop task.

PubMed

Chuderski, Adam; Smolen, Tomasz

2016-04-01

Cognitive control allows humans to direct and coordinate their thoughts and actions in a flexible way, in order to reach internal goals regardless of interference and distraction. The hallmark test used to examine cognitive control is the Stroop task, which elicits both the weakly learned but goal-relevant and the strongly learned but goal-irrelevant response tendencies, and requires people to follow the former while ignoring the latter. After reviewing the existing computational models of cognitive control in the Stroop task, its novel, integrated utility-based model is proposed. The model uses 3 crucial control mechanisms: response utility reinforcement learning, utility-based conflict evaluation using the Festinger formula for assessing the conflict level, and top-down adaptation of response utility in service of conflict resolution. Their complex, dynamic interaction led to replication of 18 experimental effects, being the largest data set explained to date by 1 Stroop model. The simulations cover the basic congruency effects (including the response latency distributions), performance dynamics and adaptation (including EEG indices of conflict), as well as the effects resulting from manipulations applied to stimulation and responding, which are yielded by the extant Stroop literature. (c) 2016 APA, all rights reserved).

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.

The Effect of Teaching Model ‘Learning Cycles 5E’ toward Students’ Achievement in Learning Mathematic at X Years Class SMA Negeri 1 Banuhampu 2013/2014 Academic Year

NASA Astrophysics Data System (ADS)

Yeni, N.; Suryabayu, E. P.; Handayani, T.

2017-02-01

Based on the survey showed that mathematics teacher still dominated in teaching and learning process. The process of learning is centered on the teacher while the students only work based on instructions provided by the teacher without any creativity and activities that stimulate students to explore their potential. Realized the problem above the writer interested in finding the solution by applying teaching model ‘Learning Cycles 5E’. The purpose of his research is to know whether teaching model ‘Learning Cycles 5E’ is better than conventional teaching in teaching mathematic. The type of the research is quasi experiment by Randomized Control test Group Only Design. The population in this research were all X years class students. The sample is chosen randomly after doing normality, homogeneity test and average level of students’ achievement. As the sample of this research was X.7’s class as experiment class used teaching model learning cycles 5E and X.8’s class as control class used conventional teaching. The result showed us that the students achievement in the class that used teaching model ‘Learning Cycles 5E’ is better than the class which did not use the model.

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.

Effects of team-based learning on problem-solving, knowledge and clinical performance of Korean nursing students.

PubMed

Kim, Hae-Ran; Song, Yeoungsuk; Lindquist, Ruth; Kang, Hee-Young

2016-03-01

Team-based learning (TBL) has been used as a learner-centered teaching strategy in efforts to improve students' problem-solving, knowledge and practice performance. Although TBL has been used in nursing education in Korea for a decade, few studies have studied its effects on Korean nursing students' learning outcomes. To examine the effects of TBL on problem-solving ability and learning outcomes (knowledge and clinical performance) of Korean nursing students. Randomized controlled trial. 63 third-year undergraduate nursing students attending a single university were randomly assigned to the TBL group (n=32), or a control group (n=31). The TBL and control groups attended 2h of class weekly for 3weeks. Three scenarios with pulmonary disease content were employed in both groups. However, the control group received lectures and traditional case study teaching/learning strategies instead of TBL. A questionnaire of problem-solving ability was administered at baseline, prior to students' exposure to the teaching strategies. Students' problem-solving ability, knowledge of pulmonary nursing care, and clinical performance were assessed following completion of the three-week pulmonary unit. After the three-week educational interventions, the scores on problem-solving ability in the TBL group were significantly improved relative to that of the control group (t=10.89, p<.001). In addition, there were significant differences in knowledge, and in clinical performance with standardized patients between the two groups (t=2.48, p=.016, t=12.22, p<.001). This study demonstrated that TBL is an effective teaching strategy to enhance problem-solving ability, knowledge and clinical performance. More research on other specific learning outcomes of TBL for nursing students is recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

Perspectives on Distance Education and Social Media

ERIC Educational Resources Information Center

Powers, Lisa; Alhussain, Ruqaya; Averbeck, Clemens; Warner, Andre

2012-01-01

There is a dramatic shift in the tools that are used in today's technology-based distance education. While distance education is not new, there are new types of socially rich, mobile technologies that empower learners to be more in control of what they learn, when they learn it, and how they learn it. Students are taking more responsibility for…

Identifying College Students at Risk for Learning Disabilities: Evidence for Use of the Learning Difficulties Assessment in Postsecondary Settings

ERIC Educational Resources Information Center

Kane, Steven T.; Roy, Soma; Medina, Steffanie

2013-01-01

This article describes research supporting the use of the Learning Difficulties Assessment (LDA), a normed and no-cost, web-based survey that assesses difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. Previous research has supported…

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.…

Wiki and Digital Video Use in Strategic Interaction-Based Experiential EFL Learning

ERIC Educational Resources Information Center

Dehaan, Jonathan; Johnson, Neil H.; Yoshimura, Noriko; Kondo, Takako

2012-01-01

This paper details the use of a free and access-controlled wiki as the learning management system for a four-week teaching module designed to improve the oral communication skills of Japanese university EFL students. Students engaged in repeated experiential learning cycles of planning, doing, observing, and evaluating their performance of a role…

Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

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

Ondrej Linda; Todd Vollmer; Jason Wright

Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrainedmore » computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.« less

Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems.

PubMed

Kool, Wouter; Gershman, Samuel J; Cushman, Fiery A

2017-09-01

Human behavior is sometimes determined by habit and other times by goal-directed planning. Modern reinforcement-learning theories formalize this distinction as a competition between a computationally cheap but inaccurate model-free system that gives rise to habits and a computationally expensive but accurate model-based system that implements planning. It is unclear, however, how people choose to allocate control between these systems. Here, we propose that arbitration occurs by comparing each system's task-specific costs and benefits. To investigate this proposal, we conducted two experiments showing that people increase model-based control when it achieves greater accuracy than model-free control, and especially when the rewards of accurate performance are amplified. In contrast, they are insensitive to reward amplification when model-based and model-free control yield equivalent accuracy. This suggests that humans adaptively balance habitual and planned action through on-line cost-benefit analysis.

Computer-mediated instructional video: a randomised controlled trial comparing a sequential and a segmented instructional video in surgical hand wash.

PubMed

Schittek Janda, M; Tani Botticelli, A; Mattheos, N; Nebel, D; Wagner, A; Nattestad, A; Attström, R

2005-05-01

Video-based instructions for clinical procedures have been used frequently during the preceding decades. To investigate in a randomised controlled trial the learning effectiveness of fragmented videos vs. the complete sequential video and to analyse the attitudes of the user towards video as a learning aid. An instructional video on surgical hand wash was produced. The video was available in two different forms in two separate web pages: one as a sequential video and one fragmented into eight short clips. Twenty-eight dental students in the second semester were randomised into an experimental (n = 15) and a control group (n = 13). The experimental group used the fragmented form of the video and the control group watched the complete one. The use of the videos was logged and the students were video taped whilst undertaking a test hand wash. The videos were analysed systematically and blindly by two independent clinicians. The students also performed a written test concerning learning outcome from the videos as well as they answered an attitude questionnaire. The students in the experimental group watched the video significantly longer than the control group. There were no significant differences between the groups with regard to the ratings and scores when performing the hand wash. The experimental group had significantly better results in the written test compared with those of the control group. There was no significant difference between the groups with regard to attitudes towards the use of video for learning, as measured by the Visual Analogue Scales. Most students in both groups expressed satisfaction with the use of video for learning. The students demonstrated positive attitudes and acceptable learning outcome from viewing CAL videos as a part of their pre-clinical training. Videos that are part of computer-based learning settings would ideally be presented to the students both as a segmented and as a whole video to give the students the option to choose the form of video which suits the individual student's learning style.

An insula-frontostriatal network mediates flexible cognitive control by adaptively predicting changing control demands

PubMed Central

Jiang, Jiefeng; Beck, Jeffrey; Heller, Katherine; Egner, Tobias

2015-01-01

The anterior cingulate and lateral prefrontal cortices have been implicated in implementing context-appropriate attentional control, but the learning mechanisms underlying our ability to flexibly adapt the control settings to changing environments remain poorly understood. Here we show that human adjustments to varying control demands are captured by a reinforcement learner with a flexible, volatility-driven learning rate. Using model-based functional magnetic resonance imaging, we demonstrate that volatility of control demand is estimated by the anterior insula, which in turn optimizes the prediction of forthcoming demand in the caudate nucleus. The caudate's prediction of control demand subsequently guides the implementation of proactive and reactive attentional control in dorsal anterior cingulate and dorsolateral prefrontal cortices. These data enhance our understanding of the neuro-computational mechanisms of adaptive behaviour by connecting the classic cingulate-prefrontal cognitive control network to a subcortical control-learning mechanism that infers future demands by flexibly integrating remote and recent past experiences. PMID:26391305

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

NASA Technical Reports Server (NTRS)

Jani, Yashvant

1993-01-01

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

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.

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.

A blended-learning programme regarding professional ethics in physiotherapy students.

PubMed

Aguilar-Rodríguez, Marta; Marques-Sule, Elena; Serra-Añó, Pilar; Espí-López, Gemma Victoria; Dueñas-Moscardó, Lirios; Pérez-Alenda, Sofía

2018-01-01

In the university context, assessing students' attitude, knowledge and opinions when applying an innovative methodological approach to teach professional ethics becomes fundamental to know if the used approach is enough motivating for students. To assess the effect of a blended-learning model, based on professional ethics and related to clinical practices, on physiotherapy students' attitude, knowledge and opinions towards learning professional ethics. Research design and participants: A simple-blind clinical trial was performed (NLM identifier NCT03241693) (control group, n = 64; experimental group, n = 65). Both groups followed clinical practices for 8 months. Control group performed a public exposition of a clinical case about professional ethics. By contrast, an 8-month blended-learning programme regarding professional ethics was worked out for experimental group. An online syllabus and online activities were elaborated, while face-to-face active participation techniques were performed to discuss ethical issues. Students' attitudes, knowledge and opinions towards learning professional ethics were assessed. Ethical considerations: The study was approved by the University Ethic Committee of Human Research and followed the ethical principles according to the Declaration of Helsinki. After the programme, attitudes and knowledge towards learning professional ethics of experimental group students significantly improved, while no differences were observed in control group. Moreover, opinions reported an adequate extension of themes and temporization, importance of clinical practices and interest of topics. Case study method and role playing were considered as the most helpful techniques. The blended-learning programme proposed, based on professional ethics and related to clinical practices, improves physiotherapy students' attitudes, knowledge and opinions towards learning professional ethics.

Rule-based and information-integration perceptual category learning in children with attention-deficit/hyperactivity disorder.

PubMed

Huang-Pollock, Cynthia L; Maddox, W Todd; Tam, Helen

2014-07-01

Suboptimal functioning of the basal ganglia is implicated in attention-deficit/hyperactivity disorder (ADHD). These structures are important to the acquisition of associative knowledge, leading some to theorize that associative learning deficits might be expected, despite the fact that most extant research in ADHD has focused on effortful control. We present 2 studies that examined the acquisition of explicit rule-based (RB) and associative information integration (II) category learning among school-age children with ADHD. In Study 1, we found deficits in both RB and II category learning tasks among children with ADHD (n = 81) versus controls (n = 42). Children with ADHD tended to sort by the more salient but irrelevant dimension (in the RB paradigm) and were unable to acquire a consistent sorting strategy (in the II paradigm). To disentangle whether the deficit was localized to II category learning versus a generalized inability to consider more than 1 stimulus dimension, in Study 2 children completed a conjunctive RB paradigm that required consideration of 2 stimulus dimensions. Children with ADHD (n = 50) continued to underperform controls (n = 33). Results provide partial support for neurocognitive developmental theories of ADHD that suggest that associative learning deficits should be found, and highlight the importance of using analytic approaches that go beyond asking whether an ADHD-related deficit exists to why such deficits exist.

The algorithm for duration acceleration of repetitive projects considering the learning effect

NASA Astrophysics Data System (ADS)

Chen, Hongtao; Wang, Keke; Du, Yang; Wang, Liwan

2018-03-01

Repetitive project optimization problem is common in project scheduling. Repetitive Scheduling Method (RSM) has many irreplaceable advantages in the field of repetitive projects. As the same or similar work is repeated, the proficiency of workers will be correspondingly low to high, and workers will gain experience and improve the efficiency of operations. This is learning effect. Learning effect is one of the important factors affecting the optimization results in repetitive project scheduling. This paper analyzes the influence of the learning effect on the controlling path in RSM from two aspects: one is that the learning effect changes the controlling path, the other is that the learning effect doesn't change the controlling path. This paper proposes corresponding methods to accelerate duration for different types of critical activities and proposes the algorithm for duration acceleration based on the learning effect in RSM. And the paper chooses graphical method to identity activities' types and considers the impacts of the learning effect on duration. The method meets the requirement of duration while ensuring the lowest acceleration cost. A concrete bridge construction project is given to verify the effectiveness of the method. The results of this study will help project managers understand the impacts of the learning effect on repetitive projects, and use the learning effect to optimize project scheduling.

Web-based e-learning and virtual lab of human-artificial immune system.

PubMed

Gong, Tao; Ding, Yongsheng; Xiong, Qin

2014-05-01

Human immune system is as important in keeping the body healthy as the brain in supporting the intelligence. However, the traditional models of the human immune system are built on the mathematics equations, which are not easy for students to understand. To help the students to understand the immune systems, a web-based e-learning approach with virtual lab is designed for the intelligent system control course by using new intelligent educational technology. Comparing the traditional graduate educational model within the classroom, the web-based e-learning with the virtual lab shows the higher inspiration in guiding the graduate students to think independently and innovatively, as the students said. It has been found that this web-based immune e-learning system with the online virtual lab is useful for teaching the graduate students to understand the immune systems in an easier way and design their simulations more creatively and cooperatively. The teaching practice shows that the optimum web-based e-learning system can be used to increase the learning effectiveness of the students.

Understanding well-being and learning of Nigerian nurses: a job demand control support model approach.

PubMed

van Doorn, Yvonne; van Ruysseveldt, Joris; van Dam, Karen; Mistiaen, Wilhelm; Nikolova, Irina

2016-10-01

This study investigated whether Nigerian nurses' emotional exhaustion and active learning were predicted by job demands, control and social support. Limited research has been conducted concerning nurses' work stress in developing countries, such as Nigeria. Accordingly, it is not clear whether work interventions for improving nurses' well-being in these countries can be based on work stress models that are developed in Western countries, such as the job demand control support model, as well as on empirical findings of job demand control support research. Nurses from Nurses Across the Borders Nigeria were invited to complete an online questionnaire containing validated scales; 210 questionnaires were fully completed and analysed. Multiple regression analysis was used to test the hypotheses. Emotional exhaustion was higher for nurses who experienced high demands and low supervisor support. Active learning occurred when nurses worked under conditions of high control and high supervisor support. The findings suggest that the job demand control support model is applicable in a Nigerian nursing situation; the model indicates which occupational stressors contribute to poor well-being in Nigerian nurses and which work characteristics may boost nurses' active learning. Job (re)design interventions can enhance nurses' well-being and learning by guarding nurses' job demands, and stimulating job control and supervisor support. © 2016 John Wiley & Sons Ltd.

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%).

Problem-Based Learning in Professional Entry-Level Therapy Education: A Review of Controlled Evaluation Studies

ERIC Educational Resources Information Center

O'Donoghue, Grainne; McMahon, Sinead; Doody, Catherine; Smith, Kathyrn; Cusack, Tara

2011-01-01

Although there has been growing interest in problem-based learning (PBL) by professional entry-level therapy educators, its effectiveness is as yet unclear. Existing overviews of the field do not provide high-quality evidence in terms of the effectiveness or otherwise of PBL in professional therapy education. The purposes of this article is to…

The Comparative Effects of Prediction/Discussion-Based Learning Cycle, Conceptual Change Text, and Traditional Instructions on Student Understanding of Genetics

ERIC Educational Resources Information Center

Yilmaz, Diba; Tekkaya, Ceren; Sungur, Semra

2011-01-01

The present study examined the comparative effects of a prediction/discussion-based learning cycle, conceptual change text (CCT), and traditional instructions on students' understanding of genetics concepts. A quasi-experimental research design of the pre-test-post-test non-equivalent control group was adopted. The three intact classes, taught by…

The Effects of Learning-Style Based Activities on Students' Reading Comprehension Skills and Self-Efficacy Perceptions in English Foreign Language Classes

ERIC Educational Resources Information Center

Balci, Özgül

2017-01-01

This study investigated the effects of learning-style based activities on students' reading comprehension skills and self-efficacy perceptions in English foreign language classes. A quasi-experimental, matching-only pretest-posttest control group design was utilized. The study was conducted with freshmen university students majoring in Elementary…

Guiding Learners through Technology-Based Instruction: The Effects of Adaptive Guidance Design and Individual Differences on Learning over Time

ERIC Educational Resources Information Center

Kanar, Adam M.; Bell, Bradford S.

2013-01-01

Adaptive guidance is an instructional intervention that helps learners to make use of the control inherent in technology-based instruction. The present research investigated the interactive effects of guidance design (i.e., framing of guidance information) and individual differences (i.e., pretraining motivation and ability) on learning basic and…

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.…

Online Case-Based Learning Design for Facilitating Classroom Teachers' Development of Technological, Pedagogical, and Content Knowledge

ERIC Educational Resources Information Center

Saltan, Fatih

2017-01-01

The aim of this study is to investigate whether, and if so how, online case-based learning influence pre-service classroom teachers' self-confidence on technological pedagogical content knowledge (TPACK). To achieve the goal, a control group pretest-posttest quasi experimental design was used. Participants of the study consisted of 160 pre-service…

The Effects of Varied Visual Organizational Strategies within Computer-Based Instruction on Factual, Conceptual and Problem Solving Learning.

ERIC Educational Resources Information Center

Haag, Brenda Bannan; Grabowski, Barbara L.

The purpose of this exploratory study was to examine the effectiveness of learner manipulation of visuals with and without organizing cues in computer-based instruction on adults' factual, conceptual, and problem-solving learning. An instructional unit involving the physiology and the anatomy of the heart was used. A post-test only control group…

An Increasing of Primary School Teachers' Competency in Brain-Based Learning

ERIC Educational Resources Information Center

Waree, Chaiwat

2017-01-01

The purpose of the study was to develop a powerful and empowering guide (CBT) of elementary school teachers, to compare the ability of elementary school teachers. Management learning uses brain as a base. The experimental group with a control group the experimental group used in this research was a teacher at the grade level. 4-6 in province By…

Experimental Evidence of the Relative Effectiveness of Problem-Based Learning for Knowledge Acquisition and Retention

ERIC Educational Resources Information Center

Wijnen, Marit; Loyens, Sofie M. M.; Schaap, Lydia

2016-01-01

This study investigated the effects of problem-based learning (PBL) on knowledge acquisition and knowledge retention in a controlled experiment in a lab setting. Eighty-eight first-year psychology students were randomly assigned to either a PBL condition, a lecture condition, or a self-study condition. All participants had the opportunity to study…

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…

Valence-dependent influence of serotonin depletion on model-based choice strategy

PubMed Central

Worbe, Y; Palminteri, S; Savulich, G; Daw, N D; Fernandez-Egea, E; Robbins, T W; Voon, V

2016-01-01

Human decision-making arises from both reflective and reflexive mechanisms, which underpin goal-directed and habitual behavioural control. Computationally, these two systems of behavioural control have been described by different learning algorithms, model-based and model-free learning, respectively. Here, we investigated the effect of diminished serotonin (5-hydroxytryptamine) neurotransmission using dietary tryptophan depletion (TD) in healthy volunteers on the performance of a two-stage decision-making task, which allows discrimination between model-free and model-based behavioural strategies. A novel version of the task was used, which not only examined choice balance for monetary reward but also for punishment (monetary loss). TD impaired goal-directed (model-based) behaviour in the reward condition, but promoted it under punishment. This effect on appetitive and aversive goal-directed behaviour is likely mediated by alteration of the average reward representation produced by TD, which is consistent with previous studies. Overall, the major implication of this study is that serotonin differentially affects goal-directed learning as a function of affective valence. These findings are relevant for a further understanding of psychiatric disorders associated with breakdown of goal-directed behavioural control such as obsessive-compulsive disorders or addictions. PMID:25869808

An Actor-Critic based controller for glucose regulation in type 1 diabetes.

PubMed

Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G

2013-02-01

A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Robust reinforcement learning.

PubMed

Morimoto, Jun; Doya, Kenji

2005-02-01

This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both offline learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H(infinity) control, we consider a differential game in which a "disturbing" agent tries to make the worst possible disturbance while a "control" agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H(infinity) control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.

Investigating the impact of a LEGO(TM)-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines

NASA Astrophysics Data System (ADS)

Marulcu, Ismail

This mixed method study examined the impact of a LEGO-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. This study takes a social constructivist theoretical stance that science learning involves learning scientific concepts and their relations to each other. From this perspective, students are active participants, and they construct their conceptual understanding through the guidance of their teacher. With the goal of better understanding the use of engineering education materials in classrooms the National Academy of Engineering and National Research Council in the book "Engineering in K-12 Education" conducted an in-depth review of the potential benefits of including engineering in K--12 schools as (a) improved learning and achievement in science and mathematics, (b) increased awareness of engineering and the work of engineers, (c) understanding of and the ability to engage in engineering design, (d) interest in pursuing engineering as a career, and (e) increased technological literacy (Katehi, Pearson, & Feder, 2009). However, they also noted a lack of reliable data and rigorous research to support these assertions. Data sources included identical written tests and interviews, classroom observations and videos, teacher interviews, and classroom artifacts. To investigate the impact of the design-based simple machines curriculum compared to the scientific inquiry-based simple machines curriculum on student learning outcomes, I compared the control and the experimental groups' scores on the tests and interviews by using ANCOVA. To analyze and characterize the classroom observation videotapes, I used Jordan and Henderson's (1995) method and divide them into episodes. My analyses revealed that the design-based Design a People Mover: Simple Machines unit was, if not better, as successful as the inquiry-based FOSS Levers and Pulleys unit in terms of students' content learning. I also found that students in the engineering group outperformed students in the control group in regards to their ability to answer open-ended questions when interviewed. Implications for students' science content learning and teachers' professional development are discussed.

Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

PubMed

Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

2012-01-01

Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

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.

Comparison of lecture and team-based learning in medical ethics education.

PubMed

Ozgonul, Levent; Alimoglu, Mustafa Kemal

2017-01-01

Medical education literature suggests that ethics education should be learner-centered and problem-based rather than theory-based. Team-based learning is an appropriate method for this suggestion. However, its effectiveness was not investigated enough in medical ethics education. Is team-based learning effective in medical ethics education in terms of knowledge retention, in-class learner engagement, and learner reactions? This was a prospective controlled follow-up study. We changed lecture with team-based learning method to teach four topics in a 2-week medical ethics clerkship, while the remaining topics were taught by lectures. For comparison, we formed team-based learning and lecture groups, in which the students and instructor are the same, but the topics and teaching methodologies are different. We determined in-class learner engagement by direct observation and student satisfaction by feedback forms. Student success for team-based learning and lecture topics in the end-of-clerkship exam and two retention tests performed 1 year and 2 years later were compared. Ethical considerations: Ethical approval for the study was granted by Akdeniz University Board of Ethics on Noninvasive Clinical Human Studies Ethics committee. Short-term knowledge retention did not differ; however, team-based learning was found superior to lecture at long-term retention tests. Student satisfaction was high with team-based learning and in-class engagement was better in team-based learning sessions. Our results on learner engagement and satisfaction with team-based learning were similar to those of previous reports. However, knowledge retention results in our study were contrary to literature. The reason might be the fact that students prepared for the end-of-clerkship pass/fail exam (short term) regardless of the teaching method. But, at long-term retention tests, they did not prepare for the exam and answered the questions just using the knowledge retained in their memories. Our findings suggest that team-based learning is a better alternative to lecture to teach ethics in medical education.

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Web-Based Testing Tools for Electrical Engineering Courses

DTIC Science & Technology

2001-09-01

ideas of distance learning are based on forming “ virtual teams” [2]. Each team is equipped with the same software packages and share information via...using virtual laboratories where they can simulate a laboratory experience in a web-based environment. They can also control laboratory devices over...possible to create a set of virtual laboratories that allow students to interact with the learning material at the same time that the student is

Do job demands and job control affect problem-solving?

PubMed

Bergman, Peter N; Ahlberg, Gunnel; Johansson, Gun; Stoetzer, Ulrich; Aborg, Carl; Hallsten, Lennart; Lundberg, Ingvar

2012-01-01

The Job Demand Control model presents combinations of working conditions that may facilitate learning, the active learning hypothesis, or have detrimental effects on health, the strain hypothesis. To test the active learning hypothesis, this study analysed the effects of job demands and job control on general problem-solving strategies. A population-based sample of 4,636 individuals (55% women, 45% men) with the same job characteristics measured at two times with a three year time lag was used. Main effects of demands, skill discretion, task authority and control, and the combined effects of demands and control were analysed in logistic regressions, on four outcomes representing general problem-solving strategies. Those reporting high on skill discretion, task authority and control, as well as those reporting high demand/high control and low demand/high control job characteristics were more likely to state using problem solving strategies. Results suggest that working conditions including high levels of control may affect how individuals cope with problems and that workplace characteristics may affect behaviour in the non-work domain.

The effectiveness of problem-based learning on development of nursing students' critical thinking: a systematic review and meta-analysis.

PubMed

Kong, Ling-Na; Qin, Bo; Zhou, Ying-qing; Mou, Shao-yu; Gao, Hui-Ming

2014-03-01

The objective of this systematic review and meta-analysis was to estimate the effectiveness of problem-based learning in developing nursing students' critical thinking. Searches of PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Proquest, Cochrane Central Register of Controlled Trials (CENTRAL) and China National Knowledge Infrastructure (CNKI) were undertaken to identify randomized controlled trails from 1965 to December 2012, comparing problem-based learning with traditional lectures on the effectiveness of development of nursing students' critical thinking, with no language limitation. The mesh-terms or key words used in the search were problem-based learning, thinking, critical thinking, nursing, nursing education, nurse education, nurse students, nursing students and pupil nurse. Two reviewers independently assessed eligibility and extracted data. Quality assessment was conducted independently by two reviewers using the Cochrane Collaboration's Risk of Bias Tool. We analyzed critical thinking scores (continuous outcomes) using a standardized mean difference (SMD) or weighted mean difference (WMD) with a 95% confidence intervals (CIs). Heterogeneity was assessed using the Cochran's Q statistic and I(2) statistic. Publication bias was assessed by means of funnel plot and Egger's test of asymmetry. Nine articles representing eight randomized controlled trials were included in the meta-analysis. Most studies were of low risk of bias. The pooled effect size showed problem-based learning was able to improve nursing students' critical thinking (overall critical thinking scores SMD=0.33, 95%CI=0.13-0.52, P=0.0009), compared with traditional lectures. There was low heterogeneity (overall critical thinking scores I(2)=45%, P=0.07) in the meta-analysis. No significant publication bias was observed regarding overall critical thinking scores (P=0.536). Sensitivity analysis showed that the result of our meta-analysis was reliable. Most effect sizes for subscales of the California Critical Thinking Dispositions Inventory (CCTDI) and Bloom's Taxonomy favored problem-based learning, while effect sizes for all subscales of the California Critical Thinking Skills Test (CCTST) and most subscales of the Watson-Glaser Critical Thinking Appraisal (WCGTA) were inconclusive. The results of the current meta-analysis indicate that problem-based learning might help nursing students to improve their critical thinking. More research with larger sample size and high quality in different nursing educational contexts are required. Copyright © 2013 Elsevier Ltd. All rights reserved.

Coupling machine learning with mechanistic models to study runoff production and river flow at the hillslope scale

NASA Astrophysics Data System (ADS)

Marçais, J.; Gupta, H. V.; De Dreuzy, J. R.; Troch, P. A. A.

2016-12-01

Geomorphological structure and geological heterogeneity of hillslopes are major controls on runoff responses. The diversity of hillslopes (morphological shapes and geological structures) on one hand, and the highly non linear runoff mechanism response on the other hand, make it difficult to transpose what has been learnt at one specific hillslope to another. Therefore, making reliable predictions on runoff appearance or river flow for a given hillslope is a challenge. Applying a classic model calibration (based on inverse problems technique) requires doing it for each specific hillslope and having some data available for calibration. When applied to thousands of cases it cannot always be promoted. Here we propose a novel modeling framework based on coupling process based models with data based approach. First we develop a mechanistic model, based on hillslope storage Boussinesq equations (Troch et al. 2003), able to model non linear runoff responses to rainfall at the hillslope scale. Second we set up a model database, representing thousands of non calibrated simulations. These simulations investigate different hillslope shapes (real ones obtained by analyzing 5m digital elevation model of Brittany and synthetic ones), different hillslope geological structures (i.e. different parametrizations) and different hydrologic forcing terms (i.e. different infiltration chronicles). Then, we use this model library to train a machine learning model on this physically based database. Machine learning model performance is then assessed by a classic validating phase (testing it on new hillslopes and comparing machine learning with mechanistic outputs). Finally we use this machine learning model to learn what are the hillslope properties controlling runoffs. This methodology will be further tested combining synthetic datasets with real ones.

Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

PubMed

Cheron, G; Márquez-Ruiz, J; Dan, B

2016-04-01

The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

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.

Motor skill learning and offline-changes in TGA patients with acute hippocampal CA1 lesions.

PubMed

Döhring, Juliane; Stoldt, Anne; Witt, Karsten; Schönfeld, Robby; Deuschl, Günther; Born, Jan; Bartsch, Thorsten

2017-04-01

Learning and the formation of memory are reflected in various memory systems in the human brain such as the hippocampus based declarative memory system and the striatum-cortex based system involved in motor sequence learning. It is a matter of debate how both memory systems interact in humans during learning and consolidation and how this interaction is influenced by sleep. We studied the effect of an acute dysfunction of hippocampal CA1 neurons on the acquisition (on-line condition) and off-line changes of a motor skill in patients with a transient global amnesia (TGA). Sixteen patients (68 ± 4.4 yrs) were studied in the acute phase and during follow-up using a declarative and procedural test, and were compared to controls. Acute TGA patients displayed profound deficits in all declarative memory functions. During the acute amnestic phase, patients were able to acquire the motor skill task reflected by increasing finger tapping speed across the on-line condition, albeit to a lesser degree than during follow-up or compared to controls. Retrieval two days later indicated a greater off-line gain in motor speed in patients than controls. Moreover, this gain in motor skill performance was negatively correlated to the declarative learning deficit. Our results suggest a differential interaction between procedural and declarative memory systems during acquisition and consolidation of motor sequences in older humans. During acquisition, hippocampal dysfunction attenuates fast learning and thus unmasks the slow and rigid learning curve of striatum-based procedural learning. The stronger gains in the post-consolidation condition in motor skill in CA1 lesioned patients indicate a facilitated consolidation process probably occurring during sleep, and suggest a competitive interaction between the memory systems. These findings might be a reflection of network reorganization and plasticity in older humans and in the presence of CA1 hippocampal pathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

Importance Of Quality Control in Reducing System Risk, a Lesson Learned From The Shuttle and a Recommendation for Future Launch Vehicles

NASA Technical Reports Server (NTRS)

Safie, Fayssal M.; Messer, Bradley P.

2006-01-01

This paper presents lessons learned from the Space Shuttle return to flight experience and the importance of these lessons learned in the development of new the NASA Crew Launch Vehicle (CLV). Specifically, the paper discusses the relationship between process control and system risk, and the importance of process control in improving space vehicle flight safety. It uses the External Tank (ET) Thermal Protection System (TPS) experience and lessons learned from the redesign and process enhancement activities performed in preparation for Return to Flight after the Columbia accident. The paper also, discusses in some details, the Probabilistic engineering physics based risk assessment performed by the Shuttle program to evaluate the impact of TPS failure on system risk and the application of the methodology to the CLV.

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.

Neural substrates of visuomotor learning based on improved feedback control and prediction

PubMed Central

Grafton, Scott T.; Schmitt, Paul; Horn, John Van; Diedrichsen, Jörn

2008-01-01

Motor skills emerge from learning feedforward commands as well as improvements in feedback control. These two components of learning were investigated in a compensatory visuomotor tracking task on a trial-by-trial basis. Between trial learning was characterized with a state-space model to provide smoothed estimates of feedforward and feedback learning, separable from random fluctuations in motor performance and error. The resultant parameters were correlated with brain activity using magnetic resonance imaging. Learning related to the generation of a feedforward command correlated with activity in dorsal premotor cortex, inferior parietal lobule, supplementary motor area and cingulate motor area, supporting a role of these areas in retrieving and executing a predictive motor command. Modulation of feedback control was associated with activity in bilateral posterior superior parietal lobule as well as right ventral premotor cortex. Performance error correlated with activity in a widespread cortical and subcortical network including bilateral parietal, premotor and rostral anterior cingulate cortex as well as the cerebellar cortex. Finally, trial-by-trial changes of kinematics, as measured by mean absolute hand acceleration, correlated with activity in motor cortex and anterior cerebellum. The results demonstrate that incremental, learning dependent changes can be modeled on a trial-by-trial basis and neural substrates for feedforward control of novel motor programs are localized to secondary motor areas. PMID:18032069

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

Self-controlled learning benefits: exploring contributions of self-efficacy and intrinsic motivation via path analysis.

PubMed

Ste-Marie, Diane M; Carter, Michael J; Law, Barbi; Vertes, Kelly; Smith, Victoria

2016-09-01

Research has shown learning advantages for self-controlled practice contexts relative to yoked (i.e., experimenter-imposed) contexts; yet, explanations for this phenomenon remain relatively untested. We examined, via path analysis, whether self-efficacy and intrinsic motivation are important constructs for explaining self-controlled learning benefits. The path model was created using theory-based and empirically supported relationships to examine causal links between these psychological constructs and physical performance. We hypothesised that self-efficacy and intrinsic motivation would have greater predictive power for learning under self-controlled compared to yoked conditions. Participants learned double-mini trampoline progressions, and measures of physical performance, self-efficacy and intrinsic motivation were collected over two practice days and a delayed retention day. The self-controlled group (M = 2.04, SD = .98) completed significantly more skill progressions in retention than their yoked counterparts (M = 1.3, SD = .65). The path model displayed adequate fit, and similar significant path coefficients were found for both groups wherein each variable was predominantly predicted by its preceding time point (e.g., self-efficacy time 1 predicts self-efficacy time 2). Interestingly, the model was not moderated by group; thus, failing to support the hypothesis that self-efficacy and intrinsic motivation have greater predictive power for learning under self-controlled relative to yoked conditions.

Effects of an online problem-based learning program on sexual health care competencies among oncology nurses: a pilot study.

PubMed

Kim, Jung-Hee; Shin, Jwa-Seop

2014-09-01

The purpose of this study was to test the effectiveness of an online problem-based learning (e-PBL) program that offers multimedia scenarios to develop sexual health care competencies. A pretest–posttest control group design was used with two randomized groups in one Korean tertiary hospital. The sample included 32 RNs who cared for oncology patients. The intervention group completed an e-PBL cycle consisting of eight tutorials. Nurses in the intervention group scored significantly higher on knowledge than did those in the control group. The intervention group exhibited no significant differences in attitude and practices following the intervention. The results show the potential of e-PBL to enhance traditional PBL by offering multimedia scenarios in an interactive and flexible learning environment.