Errorless-based techniques can improve route finding in early Alzheimer's disease: a case study.

PubMed

Provencher, Véronique; Bier, Nathalie; Audet, Thérèse; Gagnon, Lise

2008-01-01

Topographical disorientation is a common and early manifestation of dementia of Alzheimer type, which threatens independence in activities of daily living. Errorless-based techniques appear to be effective in helping patients with amnesia to learn routes, but little is known about their effectiveness in early dementia of Alzheimer type. A 77-year-old woman with dementia of Alzheimer type had difficulty in finding her way around her seniors residence, which reduced her social activities. This study used an ABA design (A is the baseline and B is the intervention) with multiple baselines across routes for going to the rosary (target), laundry, and game rooms (controls). The errorless-based technique intervention was applied to 2 of the 3 routes. Analyses showed significant improvement only for the routes learned with errorless-based techniques. Following the study, the participant increased her topographical knowledge of her surroundings. Route learning interventions based on errorless-based techniques appear to be a promising approach for improving the independence in early dementia of Alzheimer type.

Experiments on Adaptive Techniques for Host-Based Intrusion Detection

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

DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.

2001-09-01

This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerablemore » preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.« less

Applying Brain-Based Learning Principles to Athletic Training Education

ERIC Educational Resources Information Center

Craig, Debbie I.

2007-01-01

Objective: To present different concepts and techniques related to the application of brain-based learning principles to Athletic Training clinical education. Background: The body of knowledge concerning how our brains physically learn continues to grow. Brain-based learning principles, developed by numerous authors, offer advice on how to…

Using Game Theory and Competition-Based Learning to Stimulate Student Motivation and Performance

ERIC Educational Resources Information Center

Burguillo, Juan C.

2010-01-01

This paper introduces a framework for using Game Theory tournaments as a base to implement Competition-based Learning (CnBL), together with other classical learning techniques, to motivate the students and increase their learning performance. The paper also presents a description of the learning activities performed along the past ten years of a…

Precision Learning Assessment: An Alternative to Traditional Assessment Techniques.

ERIC Educational Resources Information Center

Caltagirone, Paul J.; Glover, Christopher E.

1985-01-01

A continuous and curriculum-based assessment method, Precision Learning Assessment (PLA), which integrates precision teaching and norm-referenced techniques, was applied to a math computation curriculum for 214 third graders. The resulting districtwide learning curves defining average annual progress through the computation curriculum provided…

Employ Simulation Techniques. Second Edition. Module C-5 of Category C--Instructional Execution. Professional Teacher Education Module Series.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. National Center for Research in Vocational Education.

One of a series of performance-based teacher education learning packages focusing upon specific professional competencies of vocational teachers, this learning module deals with employing simulation techniques. It consists of an introduction and four learning experiences. Covered in the first learning experience are various types of simulation…

eLearning techniques supporting problem based learning in clinical simulation.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Green Map Exercises as an Avenue for Problem-Based Learning in a Data-Rich Environment

ERIC Educational Resources Information Center

Tulloch, David; Graff, Elizabeth

2007-01-01

This article describes a series of data-based Green Map learning exercises positioned within a problem-based framework and examines the appropriateness of projects like these as a form of geography education. Problem-based learning (PBL) is an educational technique that engages students in learning through activities that require creative problem…

Engaging Future Teachers in Problem-Based Learning with the Park City Mathematics Institute Problems

ERIC Educational Resources Information Center

Pilgrim, Mary E.

2014-01-01

Problem-based learning (PBL) is a pedagogical technique recommended for K-12 mathematics classrooms. However, the mathematics courses in future teachers' degree programs are often lecture based. Students typically learn about problem-based learning in theory, but rarely get to experience it first-hand in their mathematics courses. The premise…

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Problem-Based Learning in a General Psychology Course.

ERIC Educational Resources Information Center

Willis, Sandra A.

2002-01-01

Describes the adoption of problem-based learning (PBL) techniques in a general psychology course. States that the instructor used a combination of techniques, including think-pair-share, lecture/discussion, and PBL. Notes means and standard deviations for graded components of PBL format versus lecture/discussion format. (Contains 18 references.)…

Generalized query-based active learning to identify differentially methylated regions in DNA.

PubMed

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

Imaging and machine learning techniques for diagnosis of Alzheimer's disease.

PubMed

Mirzaei, Golrokh; Adeli, Anahita; Adeli, Hojjat

2016-12-01

Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

Simulation-based learning: Just like the real thing

PubMed Central

Lateef, Fatimah

2010-01-01

Simulation is a technique for practice and learning that can be applied to many different disciplines and trainees. It is a technique (not a technology) to replace and amplify real experiences with guided ones, often “immersive” in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion. Simulation-based learning can be the way to develop health professionals’ knowledge, skills, and attitudes, whilst protecting patients from unnecessary risks. Simulation-based medical education can be a platform which provides a valuable tool in learning to mitigate ethical tensions and resolve practical dilemmas. Simulation-based training techniques, tools, and strategies can be applied in designing structured learning experiences, as well as be used as a measurement tool linked to targeted teamwork competencies and learning objectives. It has been widely applied in fields such aviation and the military. In medicine, simulation offers good scope for training of interdisciplinary medical teams. The realistic scenarios and equipment allows for retraining and practice till one can master the procedure or skill. An increasing number of health care institutions and medical schools are now turning to simulation-based learning. Teamwork training conducted in the simulated environment may offer an additive benefit to the traditional didactic instruction, enhance performance, and possibly also help reduce errors. PMID:21063557

Simulation-based learning: Just like the real thing.

PubMed

Lateef, Fatimah

2010-10-01

Simulation is a technique for practice and learning that can be applied to many different disciplines and trainees. It is a technique (not a technology) to replace and amplify real experiences with guided ones, often "immersive" in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion. Simulation-based learning can be the way to develop health professionals' knowledge, skills, and attitudes, whilst protecting patients from unnecessary risks. Simulation-based medical education can be a platform which provides a valuable tool in learning to mitigate ethical tensions and resolve practical dilemmas. Simulation-based training techniques, tools, and strategies can be applied in designing structured learning experiences, as well as be used as a measurement tool linked to targeted teamwork competencies and learning objectives. It has been widely applied in fields such aviation and the military. In medicine, simulation offers good scope for training of interdisciplinary medical teams. The realistic scenarios and equipment allows for retraining and practice till one can master the procedure or skill. An increasing number of health care institutions and medical schools are now turning to simulation-based learning. Teamwork training conducted in the simulated environment may offer an additive benefit to the traditional didactic instruction, enhance performance, and possibly also help reduce errors.

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…

Collaborative Learning in the Dance Technique Class

ERIC Educational Resources Information Center

Raman, Tanja

2009-01-01

This research was designed to enhance dance technique learning by promoting critical thinking amongst students studying on a degree programme at the University of Wales Institute, Cardiff. Students were taught Cunningham-based dance technique using pair work together with the traditional demonstration/copying method. To evaluate the study,…

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

Problem-Based Learning Approaches in Meteorology

ERIC Educational Resources Information Center

Charlton-Perez, Andrew James

2013-01-01

Problem-Based Learning, despite recent controversies about its effectiveness, is used extensively as a teaching method throughout higher education. In meteorology, there has been little attempt to incorporate Problem-Based Learning techniques into the curriculum. Motivated by a desire to enhance the reflective engagement of students within a…

E-Learning System Using Segmentation-Based MR Technique for Learning Circuit Construction

ERIC Educational Resources Information Center

Takemura, Atsushi

2016-01-01

This paper proposes a novel e-Learning system using the mixed reality (MR) technique for technical experiments involving the construction of electronic circuits. The proposed system comprises experimenters' mobile computers and a remote analysis system. When constructing circuits, each learner uses a mobile computer to transmit image data from the…

Semantics of User Interface for Image Retrieval: Possibility Theory and Learning Techniques.

ERIC Educational Resources Information Center

Crehange, M.; And Others

1989-01-01

Discusses the need for a rich semantics for the user interface in interactive image retrieval and presents two methods for building such interfaces: possibility theory applied to fuzzy data retrieval, and a machine learning technique applied to learning the user's deep need. Prototypes developed using videodisks and knowledge-based software are…

Self-adaptive trust based ABR protocol for MANETs using Q-learning.

PubMed

Kumar, Anitha Vijaya; Jeyapal, Akilandeswari

2014-01-01

Mobile ad hoc networks (MANETs) are a collection of mobile nodes with a dynamic topology. MANETs work under scalable conditions for many applications and pose different security challenges. Due to the nomadic nature of nodes, detecting misbehaviour is a complex problem. Nodes also share routing information among the neighbours in order to find the route to the destination. This requires nodes to trust each other. Thus we can state that trust is a key concept in secure routing mechanisms. A number of cryptographic protection techniques based on trust have been proposed. Q-learning is a recently used technique, to achieve adaptive trust in MANETs. In comparison to other machine learning computational intelligence techniques, Q-learning achieves optimal results. Our work focuses on computing a score using Q-learning to weigh the trust of a particular node over associativity based routing (ABR) protocol. Thus secure and stable route is calculated as a weighted average of the trust value of the nodes in the route and associativity ticks ensure the stability of the route. Simulation results show that Q-learning based trust ABR protocol improves packet delivery ratio by 27% and reduces the route selection time by 40% over ABR protocol without trust calculation.

Self-Adaptive Trust Based ABR Protocol for MANETs Using Q-Learning

PubMed Central

Jeyapal, Akilandeswari

2014-01-01

Mobile ad hoc networks (MANETs) are a collection of mobile nodes with a dynamic topology. MANETs work under scalable conditions for many applications and pose different security challenges. Due to the nomadic nature of nodes, detecting misbehaviour is a complex problem. Nodes also share routing information among the neighbours in order to find the route to the destination. This requires nodes to trust each other. Thus we can state that trust is a key concept in secure routing mechanisms. A number of cryptographic protection techniques based on trust have been proposed. Q-learning is a recently used technique, to achieve adaptive trust in MANETs. In comparison to other machine learning computational intelligence techniques, Q-learning achieves optimal results. Our work focuses on computing a score using Q-learning to weigh the trust of a particular node over associativity based routing (ABR) protocol. Thus secure and stable route is calculated as a weighted average of the trust value of the nodes in the route and associativity ticks ensure the stability of the route. Simulation results show that Q-learning based trust ABR protocol improves packet delivery ratio by 27% and reduces the route selection time by 40% over ABR protocol without trust calculation. PMID:25254243

Contextualized Writing: Promoting Audience-Centered Writing through Scenario-Based Learning

ERIC Educational Resources Information Center

Golden, Paullett

2018-01-01

Scenario-based learning is an approach for student-centered learning used in the medical and legal fields, but is little used in liberal arts. In this study, I examine students' understanding and application of audience-centered writing techniques after a semester of formal scenario-based essays and problem-based activities. Comparing the grades…

The Global Aspects of Brain-Based Learning

ERIC Educational Resources Information Center

Connell, J. Diane

2009-01-01

Brain-Based Learning (BBL) can be viewed as techniques gleaned from research in neurology and cognitive science used to enhance teacher instruction. These strategies can also be used to enhance students' ability to learn using ways in which they feel most comfortable, neurologically speaking. Jensen (1995/2000) defines BBL as "learning in…

Analytical learning and term-rewriting systems

NASA Technical Reports Server (NTRS)

Laird, Philip; Gamble, Evan

1990-01-01

Analytical learning is a set of machine learning techniques for revising the representation of a theory based on a small set of examples of that theory. When the representation of the theory is correct and complete but perhaps inefficient, an important objective of such analysis is to improve the computational efficiency of the representation. Several algorithms with this purpose have been suggested, most of which are closely tied to a first order logical language and are variants of goal regression, such as the familiar explanation based generalization (EBG) procedure. But because predicate calculus is a poor representation for some domains, these learning algorithms are extended to apply to other computational models. It is shown that the goal regression technique applies to a large family of programming languages, all based on a kind of term rewriting system. Included in this family are three language families of importance to artificial intelligence: logic programming, such as Prolog; lambda calculus, such as LISP; and combinatorial based languages, such as FP. A new analytical learning algorithm, AL-2, is exhibited that learns from success but is otherwise quite different from EBG. These results suggest that term rewriting systems are a good framework for analytical learning research in general, and that further research should be directed toward developing new techniques.

Not another boring lecture: engaging learners with active learning techniques.

PubMed

Wolff, Margaret; Wagner, Mary Jo; Poznanski, Stacey; Schiller, Jocelyn; Santen, Sally

2015-01-01

Core content in Emergency Medicine Residency Programs is traditionally covered in didactic sessions, despite evidence suggesting that learners do not retain a significant portion of what is taught during lectures. We describe techniques that medical educators can use when leading teaching sessions to foster engagement and encourage self-directed learning, based on current literature and evidence about learning. When these techniques are incorporated, sessions can be effective in delivering core knowledge, contextualizing content, and explaining difficult concepts, leading to increased learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

2016-08-01

In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

Putting the Learning in Case Learning? The Effects of Case-Based Approaches on Student Knowledge, Attitudes, and Engagement

ERIC Educational Resources Information Center

Krain, Matthew

2016-01-01

This study revisits case learning's effects on student engagement and assesses student learning as a result of the use of case studies and problem-based learning. The author replicates a previous study that used indirect assessment techniques to get at case learning's impact, and then extends the analysis using a pre- and post-test experimental…

Optimal Sensor Management and Signal Processing for New EMI Systems

DTIC Science & Technology

2010-09-01

adaptive techniques that would improve the speed of data collection and increase the mobility of a TEMTADS system. Although an active learning technique...data, SIG has simulated the active selection based on the data already collected at Camp SLO. In this setup, the active learning approach was constrained...to work only on a 5x5 grid (corresponding to twenty five transmitters and co-located receivers). The first technique assumes that active learning will

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…

Adaptivity in Game-Based Learning: A New Perspective on Story

NASA Astrophysics Data System (ADS)

Berger, Florian; Müller, Wolfgang

Game-based learning as a novel form of e-learning still has issues in fundamental questions, the lack of a general model for adaptivity being one of them. Since adaptive techniques in traditional e-learning applications bear close similarity to certain interactive storytelling approaches, we propose a new notion of story as the joining element of arbitraty learning paths.

Developing International Managerial Skills through the Cross-Cultural Assignment: Experiential Learning by Matching U.S.-Based and International Students

ERIC Educational Resources Information Center

Neiva de Figueiredo, Joao; Mauri, Alfredo J.

2013-01-01

This article describes the "Cross-Cultural Assignment," an experiential learning technique for students of business that deepens self-awareness of their own attitudes toward different cultures and develops international managerial skills. The technique consists of pairing up small teams of U.S.-based business students with small teams of…

The Triangle Technique: a new evidence-based educational tool for pediatric medication calculations.

PubMed

Sredl, Darlene

2006-01-01

Many nursing student verbalize an aversion to mathematical concepts and experience math anxiety whenever a mathematical problem is confronted. Since nurses confront mathematical problems on a daily basis, they must learn to feel comfortable with their ability to perform these calculations correctly. The Triangle Technique, a new educational tool available to nurse educators, incorporates evidence-based concepts within a graphic model using visual, auditory, and kinesthetic learning styles to demonstrate pediatric medication calculations of normal therapeutic ranges. The theoretical framework for the technique is presented, as is a pilot study examining the efficacy of the educational tool. Statistically significant results obtained by Pearson's product-moment correlation indicate that students are better able to calculate accurate pediatric therapeutic dosage ranges after participation in the educational intervention of learning the Triangle Technique.

Towards a Compositional SPIN

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Giannakopoulou, Dimitra

2006-01-01

This paper discusses our initial experience with introducing automated assume-guarantee verification based on learning in the SPIN tool. We believe that compositional verification techniques such as assume-guarantee reasoning could complement the state-reduction techniques that SPIN already supports, thus increasing the size of systems that SPIN can handle. We present a "light-weight" approach to evaluating the benefits of learning-based assume-guarantee reasoning in the context of SPIN: we turn our previous implementation of learning for the LTSA tool into a main program that externally invokes SPIN to provide the model checking-related answers. Despite its performance overheads (which mandate a future implementation within SPIN itself), this approach provides accurate information about the savings in memory. We have experimented with several versions of learning-based assume guarantee reasoning, including a novel heuristic introduced here for generating component assumptions when their environment is unavailable. We illustrate the benefits of learning-based assume-guarantee reasoning in SPIN through the example of a resource arbiter for a spacecraft. Keywords: assume-guarantee reasoning, model checking, learning.

Navigating complex decision spaces: Problems and paradigms in sequential choice

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2015-01-01

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192

Theorists and Techniques: Connecting Education Theories to Lamaze Teaching Techniques

PubMed Central

Podgurski, Mary Jo

2016-01-01

ABSTRACT Should childbirth educators connect education theory to technique? Is there more to learning about theorists than memorizing facts for an assessment? Are childbirth educators uniquely poised to glean wisdom from theorists and enhance their classes with interactive techniques inspiring participant knowledge and empowerment? Yes, yes, and yes. This article will explore how an awareness of education theory can enhance retention of material through interactive learning techniques. Lamaze International childbirth classes already prepare participants for the childbearing year by using positive group dynamics; theory will empower childbirth educators to address education through well-studied avenues. Childbirth educators can provide evidence-based learning techniques in their classes and create true behavioral change. PMID:26848246

Evaluation of Keyphrase Extraction Algorithm and Tiling Process for a Document/Resource Recommender within E-Learning Environments

ERIC Educational Resources Information Center

Mangina, Eleni; Kilbride, John

2008-01-01

The research presented in this paper is an examination of the applicability of IUI techniques in an online e-learning environment. In particular we make use of user modeling techniques, information retrieval and extraction mechanisms and collaborative filtering methods. The domains of e-learning, web-based training and instruction and intelligent…

Dynamic Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Active Learning through Online Instruction

ERIC Educational Resources Information Center

Gulbahar, Yasemin; Kalelioglu, Filiz

2010-01-01

This article explores the use of proper instructional techniques in online discussions that lead to meaningful learning. The research study looks at the effective use of two instructional techniques within online environments, based on qualitative measures. "Brainstorming" and "Six Thinking Hats" were selected and implemented…

Improving the quality of learning in science through optimization of lesson study for learning community

NASA Astrophysics Data System (ADS)

Setyaningsih, S.

2018-03-01

Lesson Study for Learning Community is one of lecturer profession building system through collaborative and continuous learning study based on the principles of openness, collegiality, and mutual learning to build learning community in order to form professional learning community. To achieve the above, we need a strategy and learning method with specific subscription technique. This paper provides a description of how the quality of learning in the field of science can be improved by implementing strategies and methods accordingly, namely by applying lesson study for learning community optimally. Initially this research was focused on the study of instructional techniques. Learning method used is learning model Contextual teaching and Learning (CTL) and model of Problem Based Learning (PBL). The results showed that there was a significant increase in competence, attitudes, and psychomotor in the four study programs that were modelled. Therefore, it can be concluded that the implementation of learning strategies in Lesson study for Learning Community is needed to be used to improve the competence, attitude and psychomotor of science students.

Children's Negotiations of Visualization Skills during a Design-Based Learning Experience Using Nondigital and Digital Techniques

ERIC Educational Resources Information Center

Smith, Shaunna

2018-01-01

In the context of a 10-day summer camp makerspace experience that employed design-based learning (DBL) strategies, the purpose of this descriptive case study was to better understand the ways in which children use visualization skills to negotiate design as they move back and forth between the world of nondigital design techniques (i.e., drawing,…

Web-Based Interactive 3D Visualization as a Tool for Improved Anatomy Learning

ERIC Educational Resources Information Center

Petersson, Helge; Sinkvist, David; Wang, Chunliang; Smedby, Orjan

2009-01-01

Despite a long tradition, conventional anatomy education based on dissection is declining. This study tested a new virtual reality (VR) technique for anatomy learning based on virtual contrast injection. The aim was to assess whether students value this new three-dimensional (3D) visualization method as a learning tool and what value they gain…

Using Technology to Facilitate and Enhance Project-based Learning in Mathematical Physics

NASA Astrophysics Data System (ADS)

Duda, Gintaras

2011-04-01

Problem-based and project-based learning are two pedagogical techniques that have several clear advantages over traditional instructional methods: 1) both techniques are active and student centered, 2) students confront real-world and/or highly complex problems, and 3) such exercises model the way science and engineering are done professionally. This talk will present an experiment in project/problem-based learning in a mathematical physics course. The group project in the course involved modeling a zombie outbreak of the type seen in AMC's ``The Walking Dead.'' Students researched, devised, and solved their mathematical models for the spread of zombie-like infection. Students used technology in all stages; in fact, since analytical solutions to the models were often impossible, technology was a necessary and critical component of the challenge. This talk will explore the use of technology in general in problem and project-based learning and will detail some specific examples of how technology was used to enhance student learning in this course. A larger issue of how students use the Internet to learn will also be explored.

The implementation of portfolio assessment by the educators on the mathematics learning process in senior high school

NASA Astrophysics Data System (ADS)

Lestariani, Ida; Sujadi, Imam; Pramudya, Ikrar

2018-05-01

Portfolio assessment can shows the development of the ability of learners in a period through the work so that can be seen progress monitored learning of each learner. The purpose of research to describe and know the implementation of portfolio assessment on the mathematics learning process with the Senior High school math teacher class X as the subject because of the importance of applying the assessment for the progress of learning outcomes of learners. This research includes descriptive qualitative research type. Techniques of data collecting is done by observation method, interview and documentation. Data collection then validated using triangulation technique that is observation technique, interview and documentation. Data analysis technique is done by data reduction, data presentation and conclusion. The results showed that the steps taken by teachers in applying portfolio assessment obtained focused on learning outcomes. Student learning outcomes include homework and daily tests. Based on the results of research can be concluded that the implementation of portfolio assessment is the form of learning results are scored. Teachers have not yet implemented other portfolio assessment techniques such as student work.

Ontology-Based Learner Categorization through Case Based Reasoning and Fuzzy Logic

ERIC Educational Resources Information Center

Sarwar, Sohail; García-Castro, Raul; Qayyum, Zia Ul; Safyan, Muhammad; Munir, Rana Faisal

2017-01-01

Learner categorization has a pivotal role in making e-learning systems a success. However, learner characteristics exploited at abstract level of granularity by contemporary techniques cannot categorize the learners effectively. In this paper, an architecture of e-learning framework has been presented that exploits the machine learning based…

Variation in behavioral engagement during an active learning activity leads to differential knowledge gains in college students.

PubMed

LaDage, Lara D; Tornello, Samantha L; Vallejera, Jennilyn M; Baker, Emily E; Yan, Yue; Chowdhury, Anik

2018-03-01

There are many pedagogical techniques used by educators in higher education; however, some techniques and activities have been shown to be more beneficial to student learning than others. Research has demonstrated that active learning and learning in which students cognitively engage with the material in a multitude of ways result in better understanding and retention. The aim of the present study was to determine which of three pedagogical techniques led to improvement in learning and retention in undergraduate college students. Subjects partook in one of three different types of pedagogical engagement: hands-on learning with a model, observing someone else manipulate the model, and traditional lecture-based presentation. Students were then asked to take an online quiz that tested their knowledge of the new material, both immediately after learning the material and 2 wk later. Students who engaged in direct manipulation of the model scored higher on the assessment immediately after learning the material compared with the other two groups. However, there were no differences among the three groups when assessed after a 2-wk retention interval. Thus active learning techniques that involve direct interaction with the material can lead to learning benefits; however, how these techniques benefit long-term retention of the information is equivocal.

Use of Digital Game Based Learning and Gamification in Secondary School Science: The Effect on Student Engagement, Learning and Gender Difference

ERIC Educational Resources Information Center

Khan, Amna; Ahmad, Farzana Hayat; Malik, Muhammad Muddassir

2017-01-01

This study aimed to identify the impact of a game based learning (GBL) application using computer technologies on student engagement in secondary school science classrooms. The literature reveals that conventional Science teaching techniques (teacher-centered lecture and teaching), which foster rote learning among students, are one of the major…

SoS Navigator 2.0: A Context-Based Approach to System-of-Systems Challenges

DTIC Science & Technology

2008-06-01

in a Postindustrial Age. MIT Press, 1984. [ Kolb 1984] Kolb , David A. Experiential Learning : Experience as the Source of Learning and Develop- ment...terms of experiential learning , and the work of Rosen [Rosen 1991] in terms of the relational approach to understanding anticipa- tive systems. Our...Supporting Techniques and Tools 17  3.2  The Learning /Transformation Cycle 19  3.3  Summary of SoS Navigator Processes and Techniques 20  4  Case Summaries 22

Using deep learning for content-based medical image retrieval

NASA Astrophysics Data System (ADS)

Sun, Qinpei; Yang, Yuanyuan; Sun, Jianyong; Yang, Zhiming; Zhang, Jianguo

2017-03-01

Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as "deep learning". Unlike conventional machine learning methods that are often using "shallow" architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.

GLOBAL SOLUTIONS TO FOLDED CONCAVE PENALIZED NONCONVEX LEARNING

PubMed Central

Liu, Hongcheng; Yao, Tao; Li, Runze

2015-01-01

This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, there lack optimization techniques that guarantee global optimality in a general setting. In this paper, we show that a class of nonconvex learning problems are equivalent to general quadratic programs. This equivalence facilitates us in developing mixed integer linear programming reformulations, which admit finite algorithms that find a provably global optimal solution. We refer to this reformulation-based technique as the mixed integer programming-based global optimization (MIPGO). To our knowledge, this is the first global optimization scheme with a theoretical guarantee for folded concave penalized nonconvex learning with the SCAD penalty (Fan and Li, 2001) and the MCP penalty (Zhang, 2010). Numerical results indicate a significant outperformance of MIPGO over the state-of-the-art solution scheme, local linear approximation, and other alternative solution techniques in literature in terms of solution quality. PMID:27141126

Analyzing Convergence in e-Learning Resource Filtering Based on ACO Techniques: A Case Study with Telecommunication Engineering Students

ERIC Educational Resources Information Center

Munoz-Organero, Mario; Ramirez, Gustavo A.; Merino, Pedro Munoz; Kloos, Carlos Delgado

2010-01-01

The use of swarm intelligence techniques in e-learning scenarios provides a way to combine simple interactions of individual students to solve a more complex problem. After getting some data from the interactions of the first students with a central system, the use of these techniques converges to a solution that the rest of the students can…

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

Investigation of Learners' Perceptions for Video Summarization and Recommendation

ERIC Educational Resources Information Center

Yang, Jie Chi; Chen, Sherry Y.

2012-01-01

Recently, multimedia-based learning is widespread in educational settings. A number of studies investigate how to develop effective techniques to manage a huge volume of video sources, such as summarization and recommendation. However, few studies examine how these techniques affect learners' perceptions in multimedia learning systems. This…

Evaluating Experiential Learning in the Business Context: Contributions to Group-Based and Cross-Functional Working

ERIC Educational Resources Information Center

Piercy, Niall

2013-01-01

The use of experiential learning techniques has become popular in business education. Experiential learning approaches offer major benefits for teaching contemporary management practices such as cross-functional and team-based working. However, there remains relatively little empirical data on the success of experiential pedagogies in supporting…

Cognition, Corpora, and Computing: Triangulating Research in Usage-Based Language Learning

ERIC Educational Resources Information Center

Ellis, Nick C.

2017-01-01

Usage-based approaches explore how we learn language from our experience of language. Related research thus involves the analysis of the usage from which learners learn and of learner usage as it develops. This program involves considerable data recording, transcription, and analysis, using a variety of corpus and computational techniques, many of…

Career Goal-Based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

ERIC Educational Resources Information Center

Ma, Xueying; Ye, Lu

2018-01-01

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in…

Problem-Based Learning and Creative Instructional Approaches for Laboratory Exercises in Introductory Crop Science

ERIC Educational Resources Information Center

Teplitski, Max; McMahon, Margaret J.

2006-01-01

The implementation of problem-based learning (PBL) and other inquiry-driven educational techniques is often resisted by both faculty and students, who may not be comfortable with this learning/instructional style. We present here a hybrid approach, which combines elements of expository education with inquiry-driven laboratory exercises and…

Is Peer Interaction Necessary for Optimal Active Learning?

ERIC Educational Resources Information Center

Linton, Debra L.; Farmer, Jan Keith; Peterson, Ernie

2014-01-01

Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of…

Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

PubMed

Agarwalla, Swapna; Sarma, Kandarpa Kumar

2016-06-01

Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time. It is found that the proposed ML based sentence extraction techniques and the composite feature set used with RNN as classifier outperform all other approaches. By using ANN in FF form as feature extractor, the performance of the system is evaluated and a comparison is made. Experimental results show that the application of big data samples has enhanced the learning of the ASR system. Further, the ANN based sample and feature extraction techniques are found to be efficient enough to enable application of ML techniques in big data aspects as part of ASR systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Problem-based learning in laboratory medicine resident education: a satisfaction survey.

PubMed

Lepiller, Quentin; Solis, Morgane; Velay, Aurélie; Gantner, Pierre; Sueur, Charlotte; Stoll-Keller, Françoise; Barth, Heidi; Fafi-Kremer, Samira

2017-04-01

Theoretical knowledge in biology and medicine plays a substantial role in laboratory medicine resident education. In this study, we assessed the contribution of problem-based learning (PBL) to improve the training of laboratory medicine residents during their internship in the department of virology, Strasbourg University Hospital, France. We compared the residents' satisfaction regarding an educational program based on PBL and a program based on lectures and presentations. PBL induced a high level of satisfaction (100%) among residents compared to lectures and presentations (53%). The main advantages of this technique were to create a situational interest regarding virological problems, to boost the residents' motivation and to help them identify the most relevant learning objectives in virology. However, it appears pertinent to educate the residents in appropriate bibliographic research techniques prior to PBL use and to monitor their learning by regular formative assessment sessions.

Effects of Using Teams Games Tournaments (TGT) Cooperative Technique for Learning Mathematics in Secondary Schools of Bangladesh

ERIC Educational Resources Information Center

Salam, Abdus; Hossain, Anwar; Rahman, Shahidur

2015-01-01

Games-based learning has captured the interest of educationists and industrialists who seek to reveal the characteristics of computer games as perceived by some to be a potentially effective approach for teaching and learning. Despite this interest in using games-based learning, there is a dearth of studies on the context of gaming and education…

Physics faculty beliefs and values about the teaching and learning of problem solving. II. Procedures for measurement and analysis

NASA Astrophysics Data System (ADS)

Henderson, Charles; Yerushalmi, Edit; Kuo, Vince H.; Heller, Kenneth; Heller, Patricia

2007-12-01

To identify and describe the basis upon which instructors make curricular and pedagogical decisions, we have developed an artifact-based interview and an analysis technique based on multilayered concept maps. The policy capturing technique used in the interview asks instructors to make judgments about concrete instructional artifacts similar to those they likely encounter in their teaching environment. The analysis procedure alternatively employs both an a priori systems view analysis and an emergent categorization to construct a multilayered concept map, which is a hierarchically arranged set of concept maps where child maps include more details than parent maps. Although our goal was to develop a model of physics faculty beliefs about the teaching and learning of problem solving in the context of an introductory calculus-based physics course, the techniques described here are applicable to a variety of situations in which instructors make decisions that influence teaching and learning.

Improvement in Generic Problem-Solving Abilities of Students by Use of Tutor-Less Problem-Based Learning in a Large Classroom Setting

ERIC Educational Resources Information Center

Klegeris, Andis; Bahniwal, Manpreet; Hurren, Heather

2013-01-01

Problem-based learning (PBL) was originally introduced in medical education programs as a form of small-group learning, but its use has now spread to large undergraduate classrooms in various other disciplines. Introduction of new teaching techniques, including PBL-based methods, needs to be justified by demonstrating the benefits of such…

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

PubMed

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Applying an AR Technique to Enhance Situated Heritage Learning in a Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chang, Yi Hsing; Liu, Jen-ch'iang

2013-01-01

Since AR can display 3D materials and learner motivation is enhanced in a situated learning environment, this study explores the learning effectiveness of learners when combining AR technology and the situation learning theory. Based on the concept of embedding the characteristics of augmented reality and situated learning into a real situation to…

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

PubMed

Chang, Chin-Chun; Lin, Po-Yi

2015-03-01

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distributed and Problem-based Learning Techniques for the Family Communication Course.

ERIC Educational Resources Information Center

LeBlanc, H. Paul, III

Current technological advances have made possible teaching techniques which were previously impossible. Distance and distributed learning technologies have made it possible to instruct outside of the classroom setting. An advantage to this advance includes that ability to reach students who are unable to relocate to the university. However, there…

Integrating Traditional Learning and Games on Large Displays: An Experimental Study

ERIC Educational Resources Information Center

Ardito, Carmelo; Lanzilotti, Rosa; Costabile, Maria F.; Desolda, Giuseppe

2013-01-01

Current information and communication technology (ICT) has the potential to bring further changes to education. New learning techniques must be identified to take advantage of recent technological tools, such as smartphones, multimodal interfaces, multi-touch displays, etc. Game-based techniques that capitalize on ICT have proven to be very…

Determining the Deacetylation Degree of Chitosan: Opportunities to Learn Instrumental Techniques

ERIC Educational Resources Information Center

Pérez-Álvarez, Leyre; Ruiz-Rubio, Leire; Vilas-Vilela, Jose Luis

2018-01-01

To enhance critical thinking and problem-solving skills, a project-based learning (PBL) approach for "Instrumental Techniques" courses in undergraduate physical chemistry was specifically developed for a pharmacy bachelor degree program. The starting point of this PBL was an open-ended question that is close to the student scientist's…

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

E-Learning Personalization Based on Hybrid Recommendation Strategy and Learning Style Identification

ERIC Educational Resources Information Center

Klasnja-Milicevic, Aleksandra; Vesin, Boban; Ivanovic, Mirjana; Budimac, Zoran

2011-01-01

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a…

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…

Database Design Learning: A Project-Based Approach Organized through a Course Management System

ERIC Educational Resources Information Center

Dominguez, Cesar; Jaime, Arturo

2010-01-01

This paper describes an active method for database design learning through practical tasks development by student teams in a face-to-face course. This method integrates project-based learning, and project management techniques and tools. Some scaffolding is provided at the beginning that forms a skeleton that adapts to a great variety of…

Dyslexia

MedlinePlus

... techniques to diagnose and treat dyslexia and other learning disabilities, increasing the understanding of the biological and possible genetic bases of learning disabilities, and exploring the relationship between neurophysiological processes and ...

Mapping and Managing Knowledge and Information in Resource-Based Learning

ERIC Educational Resources Information Center

Tergan, Sigmar-Olaf; Graber, Wolfgang; Neumann, Anja

2006-01-01

In resource-based learning scenarios, students are often overwhelmed by the complexity of task-relevant knowledge and information. Techniques for the external interactive representation of individual knowledge in graphical format may help them to cope with complex problem situations. Advanced computer-based concept-mapping tools have the potential…

Effective in-service training design and delivery: evidence from an integrative literature review.

PubMed

Bluestone, Julia; Johnson, Peter; Fullerton, Judith; Carr, Catherine; Alderman, Jessica; BonTempo, James

2013-10-01

In-service training represents a significant financial investment for supporting continued competence of the health care workforce. An integrative review of the education and training literature was conducted to identify effective training approaches for health worker continuing professional education (CPE) and what evidence exists of outcomes derived from CPE. A literature review was conducted from multiple databases including PubMed, the Cochrane Library and Cumulative Index to Nursing and Allied Health Literature (CINAHL) between May and June 2011. The initial review of titles and abstracts produced 244 results. Articles selected for analysis after two quality reviews consisted of systematic reviews, randomized controlled trials (RCTs) and programme evaluations published in peer-reviewed journals from 2000 to 2011 in the English language. The articles analysed included 37 systematic reviews and 32 RCTs. The research questions focused on the evidence supporting educational techniques, frequency, setting and media used to deliver instruction for continuing health professional education. The evidence suggests the use of multiple techniques that allow for interaction and enable learners to process and apply information. Case-based learning, clinical simulations, practice and feedback are identified as effective educational techniques. Didactic techniques that involve passive instruction, such as reading or lecture, have been found to have little or no impact on learning outcomes. Repetitive interventions, rather than single interventions, were shown to be superior for learning outcomes. Settings similar to the workplace improved skill acquisition and performance. Computer-based learning can be equally or more effective than live instruction and more cost efficient if effective techniques are used. Effective techniques can lead to improvements in knowledge and skill outcomes and clinical practice behaviours, but there is less evidence directly linking CPE to improved clinical outcomes. Very limited quality data are available from low- to middle-income countries. Educational techniques are critical to learning outcomes. Targeted, repetitive interventions can result in better learning outcomes. Setting should be selected to support relevant and realistic practice and increase efficiency. Media should be selected based on the potential to support effective educational techniques and efficiency of instruction. CPE can lead to improved learning outcomes if effective techniques are used. Limited data indicate that there may also be an effect on improving clinical practice behaviours. The research agenda calls for well-constructed evaluations of culturally appropriate combinations of technique, setting, frequency and media, developed for and tested among all levels of health workers in low- and middle-income countries.

Effective in-service training design and delivery: evidence from an integrative literature review

PubMed Central

2013-01-01

Background In-service training represents a significant financial investment for supporting continued competence of the health care workforce. An integrative review of the education and training literature was conducted to identify effective training approaches for health worker continuing professional education (CPE) and what evidence exists of outcomes derived from CPE. Methods A literature review was conducted from multiple databases including PubMed, the Cochrane Library and Cumulative Index to Nursing and Allied Health Literature (CINAHL) between May and June 2011. The initial review of titles and abstracts produced 244 results. Articles selected for analysis after two quality reviews consisted of systematic reviews, randomized controlled trials (RCTs) and programme evaluations published in peer-reviewed journals from 2000 to 2011 in the English language. The articles analysed included 37 systematic reviews and 32 RCTs. The research questions focused on the evidence supporting educational techniques, frequency, setting and media used to deliver instruction for continuing health professional education. Results The evidence suggests the use of multiple techniques that allow for interaction and enable learners to process and apply information. Case-based learning, clinical simulations, practice and feedback are identified as effective educational techniques. Didactic techniques that involve passive instruction, such as reading or lecture, have been found to have little or no impact on learning outcomes. Repetitive interventions, rather than single interventions, were shown to be superior for learning outcomes. Settings similar to the workplace improved skill acquisition and performance. Computer-based learning can be equally or more effective than live instruction and more cost efficient if effective techniques are used. Effective techniques can lead to improvements in knowledge and skill outcomes and clinical practice behaviours, but there is less evidence directly linking CPE to improved clinical outcomes. Very limited quality data are available from low- to middle-income countries. Conclusions Educational techniques are critical to learning outcomes. Targeted, repetitive interventions can result in better learning outcomes. Setting should be selected to support relevant and realistic practice and increase efficiency. Media should be selected based on the potential to support effective educational techniques and efficiency of instruction. CPE can lead to improved learning outcomes if effective techniques are used. Limited data indicate that there may also be an effect on improving clinical practice behaviours. The research agenda calls for well-constructed evaluations of culturally appropriate combinations of technique, setting, frequency and media, developed for and tested among all levels of health workers in low- and middle-income countries. PMID:24083659

Convergent Technologies in Distance Learning Delivery.

ERIC Educational Resources Information Center

Wheeler, Steve

1999-01-01

Describes developments in British education in distance learning technologies. Highlights include networking the rural areas; communication, community, and paradigm shifts; digital compression techniques and telematics; Web-based material delivered over the Internet; system flexibility; social support; learning support; videoconferencing; and…

Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets

NASA Astrophysics Data System (ADS)

Goel, Amit; Montgomery, Michele

2015-08-01

Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Efficacy of problem based learning in a high school science classroom

NASA Astrophysics Data System (ADS)

Rissi, James Ryan

At the high school level, the maturity of the students, as well as constraints of the traditional high school (both in terms of class time, and number of students), impedes the use of the Problem-based instruction. But with more coaching, guidance, and planning, Problem-based Learning may be an effective teaching technique with secondary students. In recent years, the State of Michigan High School Content Expectations have emphasized the importance of inquiry and problem solving in the high school science classroom. In order to help students gain inquiry and problem solving skills, a move towards a problem-based curriculum and away from the didactic approach may lead to favorable results. In this study, the problem-based-learning framework was implemented in a high school Anatomy and Physiology classroom. Using pre-tests and post-tests over the material presented using the Problem-based technique, student comprehension and long-term retention of the material was monitored. It was found that Problem-based Learning produced comparable test performance when compared to traditional lecture, note-taking, and enrichment activities. In addition, students showed evidence of gaining research and team-working skills.

Dynamic Learning Style Prediction Method Based on a Pattern Recognition Technique

ERIC Educational Resources Information Center

Yang, Juan; Huang, Zhi Xing; Gao, Yue Xiang; Liu, Hong Tao

2014-01-01

During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of "learning deviation" and…

Interaction Pattern Analysis in cMOOCs Based on the Connectivist Interaction and Engagement Framework

ERIC Educational Resources Information Center

Wang, Zhijun; Anderson, Terry; Chen, Li; Barbera, Elena

2017-01-01

Connectivist learning is interaction-centered learning. A framework describing interaction and cognitive engagement in connectivist learning was constructed using logical reasoning techniques. The framework and analysis was designed to help researchers and learning designers understand and adapt the characteristics and principles of interaction in…

Improving Adaptive Learning Technology through the Use of Response Times

ERIC Educational Resources Information Center

Mettler, Everett; Massey, Christine M.; Kellman, Philip J.

2011-01-01

Adaptive learning techniques have typically scheduled practice using learners' accuracy and item presentation history. We describe an adaptive learning system (Adaptive Response Time Based Sequencing--ARTS) that uses both accuracy and response time (RT) as direct inputs into sequencing. Response times are used to assess learning strength and…

A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

ERIC Educational Resources Information Center

Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam

2013-01-01

Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

Using deep learning in image hyper spectral segmentation, classification, and detection

NASA Astrophysics Data System (ADS)

Zhao, Xiuying; Su, Zhenyu

2018-02-01

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

Aligning Goals, Assessments, and Activities: An Approach to Teaching PCR and Gel Electrophoresis

PubMed Central

Robertson, Amber L.; Batzli, Janet; Harris, Michelle; Miller, Sarah

2008-01-01

Polymerase chain reaction (PCR) and gel electrophoresis have become common techniques used in undergraduate molecular and cell biology labs. Although students enjoy learning these techniques, they often cannot fully comprehend and analyze the outcomes of their experiments because of a disconnect between concepts taught in lecture and experiments done in lab. Here we report the development and implementation of novel exercises that integrate the biological concepts of DNA structure and replication with the techniques of PCR and gel electrophoresis. Learning goals were defined based on concepts taught throughout the cell biology lab course and learning objectives specific to the PCR and gel electrophoresis lab. Exercises developed to promote critical thinking and target the underlying concepts of PCR, primer design, gel analysis, and troubleshooting were incorporated into an existing lab unit based on the detection of genetically modified organisms. Evaluative assessments for each exercise were aligned with the learning goals and used to measure student learning achievements. Our analysis found that the exercises were effective in enhancing student understanding of these concepts as shown by student performance across all learning goals. The new materials were particularly helpful in acquiring relevant knowledge, fostering critical-thinking skills, and uncovering prevalent misconceptions. PMID:18316813

The development of learning material using learning cycle 5E model based stem to improve students’ learning outcomes in Thermochemistry

NASA Astrophysics Data System (ADS)

sugiarti, A. C.; suyatno, S.; Sanjaya, I. G. M.

2018-04-01

The objective of this study is describing the feasibility of Learning Cycle 5E STEM (Science, Technology, Engineering, and Mathematics) based learning material which is appropriate to improve students’ learning achievement in Thermochemistry. The study design used 4-D models and one group pretest-posttest design to obtain the information about the improvement of sudents’ learning outcomes. The subject was learning cycle 5E based STEM learning materials which the data were collected from 30 students of Science class at 11th Grade. The techniques used in this study were validation, observation, test, and questionnaire. Some result attain: (1) all the learning materials contents were valid, (2) the practicality and the effectiveness of all the learning materials contents were classified as good. The conclution of this study based on those three condition, the Learnig Cycle 5E based STEM learning materials is appropriate to improve students’ learning outcomes in studying Thermochemistry.

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

PubMed Central

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C.

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages. PMID:28883801

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle.

PubMed

Cerezo, Rebeca; Esteban, María; Sánchez-Santillán, Miguel; Núñez, José C

2017-01-01

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs) . Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

Constructive and problem-based learning using blended learning anchored instruction approaches

NASA Astrophysics Data System (ADS)

Mayer, M.

2012-04-01

Based on an anchored instruction approach, an enriched blended learning lecture course ("Introduction into GNSS positioning") was established in order to enable constructive and problem-based learning. The lecture course "Introduction into GNSS positioning" is a compulsory part of the Bachelor study course "Geodesy and Geoinformatics" and also a supplementary module of the Bachelor study course "Geophysics". Within the lecture course, basic knowledge and basic principles of Global Navigation Satellite Systems, like GPS, are imparted. The presented higher education technique "anchored instruction" uses a real and up-to-date and therefore authentic scientific paper dealing with a recent large-scale geodetic project (Fehmarn Belt Fixed Link) in order to introduce the topic of GNSS-based positioning to the students. In the beginning of the semester, the students have to read the paper individually and carefully. This enables them to realize a lot of not-known GNSS-related facts. Therefore, questions can be formulated focusing on new, unclear or not-understood aspects of the paper. The lecture course deals with these questions, in order to answer them throughout the semester. During the lecture course this paper is referred, e.g., in the middle of the semester, the paper has to be read again in order to check which questions have been answered; in addition, new question arise. At the end of the lecture course, the author of the scientific paper gave a concluding lecture. The framing anchor technique enables the students to anchor their GNSS knowledge. The presented case study uses a teaching resp. learning setting consisting of classroom lectures (given by teachers and learners), practical trainings (e.g., field exercises, students select topics individually), and online lectures (learning management system ILIAS is used as data, result, and asynchronous communication platform). The implementation and the elements of the anchoring technique, which enables student-centered, cooperative, and individual learning, are going to be discussed in detail. A special focus of the presentation is on work assignments, time schedule, and work load. The anchor technique is applied within a blended learning teaching concept, therefore the role of the learning management system ILIAS will be treated as well.

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Electronic Learning in Yugoslavia.

ERIC Educational Resources Information Center

Barker, Philip G.

1990-01-01

Describes a course taught at the University of Zagreb (Yugoslavia) on electronic learning methods based upon computer-assisted learning techniques. The course content is outlined, including lectures, workshops, videotapes, demonstration software, and courseware authoring; a multimedia teaching laboratory is described; and an evaluation of course…

Active Learning and Teaching: Improving Postsecondary Library Instruction.

ERIC Educational Resources Information Center

Allen, Eileen E.

1995-01-01

Discusses ways to improve postsecondary library instruction based on theories of active learning. Topics include a historical background of active learning; student achievement and attitudes; cognitive development; risks; active teaching; and instructional techniques, including modified lectures, brainstorming, small group work, cooperative…

Integrating Text-to-Speech Software into Pedagogically Sound Teaching and Learning Scenarios

ERIC Educational Resources Information Center

Rughooputh, S. D. D. V.; Santally, M. I.

2009-01-01

This paper presents a new technique of delivery of classes--an instructional technique which will no doubt revolutionize the teaching and learning, whether for on-campus, blended or online modules. This is based on the simple task of instructionally incorporating text-to-speech software embedded in the lecture slides that will simulate exactly the…

A Severe Weather Laboratory Exercise for an Introductory Weather and Climate Class Using Active Learning Techniques

ERIC Educational Resources Information Center

Grundstein, Andrew; Durkee, Joshua; Frye, John; Andersen, Theresa; Lieberman, Jordan

2011-01-01

This paper describes a new severe weather laboratory exercise for an Introductory Weather and Climate class, appropriate for first and second year college students (including nonscience majors), that incorporates inquiry-based learning techniques. In the lab, students play the role of meteorologists making forecasts for severe weather. The…

Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

ERIC Educational Resources Information Center

Wang, Jingying; Wen, Ming-Lee; Jou, Min

2016-01-01

Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

PubMed

Le, Long N; Jones, Douglas L

2018-03-01

Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

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.

Revitalizing pathology laboratories in a gastrointestinal pathophysiology course using multimedia and team-based learning techniques.

PubMed

Carbo, Alexander R; Blanco, Paola G; Graeme-Cooke, Fiona; Misdraji, Joseph; Kappler, Steven; Shaffer, Kitt; Goldsmith, Jeffrey D; Berzin, Tyler; Leffler, Daniel; Najarian, Robert; Sepe, Paul; Kaplan, Jennifer; Pitman, Martha; Goldman, Harvey; Pelletier, Stephen; Hayward, Jane N; Shields, Helen M

2012-05-15

In 2008, we changed the gastrointestinal pathology laboratories in a gastrointestinal pathophysiology course to a more interactive format using modified team-based learning techniques and multimedia presentations. The results were remarkably positive and can be used as a model for pathology laboratory improvement in any organ system. Over a two-year period, engaging and interactive pathology laboratories were designed. The initial restructuring of the laboratories included new case material, Digital Atlas of Video Education Project videos, animations and overlays. Subsequent changes included USMLE board-style quizzes at the beginning of each laboratory, with individual readiness assessment testing and group readiness assessment testing, incorporation of a clinician as a co-teacher and role playing for the student groups. Student responses for pathology laboratory contribution to learning improved significantly compared to baseline. Increased voluntary attendance at pathology laboratories was observed. Spontaneous student comments noted the positive impact of the laboratories on their learning. Pathology laboratory innovations, including modified team-based learning techniques with individual and group self-assessment quizzes, multimedia presentations, and paired teaching by a pathologist and clinical gastroenterologist led to improvement in student perceptions of pathology laboratory contributions to their learning and better pathology faculty evaluations. These changes can be universally applied to other pathology laboratories to improve student satisfaction. Copyright © 2012 Elsevier GmbH. All rights reserved.

Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

PubMed Central

Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

2016-01-01

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Transforming Classrooms through Game-Based Learning: A Feasibility Study in a Developing Country

ERIC Educational Resources Information Center

Vate-U-Lan, Poonsri

2015-01-01

This article reports an exploratory study which investigated attitudes towards the practice of game-based learning in teaching STEM (science, technology, engineering and mathematics) within a Thai educational context. This self-administered Internet-based survey yielded 169 responses from a snowball sampling technique. Three fifths of respondents…

Adaptive neuro-fuzzy and expert systems for power quality analysis and prediction of abnormal operation

NASA Astrophysics Data System (ADS)

Ibrahim, Wael Refaat Anis

The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an alarm is instigated predicting the advent of system abnormal operation. The incoming data could be compared to previous trends as well as matched to trends developed through computer simulations and stored using fuzzy learning.

Dyslexic: Special Education and Research

MedlinePlus

... techniques to diagnose and treat dyslexia and other learning disabilities, increasing the understanding of the biological and possible genetic bases of learning disabilities, and exploring treatments to improve outcomes for children ...

Applications of Deep Learning and Reinforcement Learning to Biological Data.

PubMed

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

A Mobile-Based E-Learning System

ERIC Educational Resources Information Center

Ojokoh, Bolanle Adefowoke; Doyeni, Olubimtan Ayo; Adewale, Olumide Sunday; Isinkaye, Folasade Olubusola

2013-01-01

E-learning is an innovative approach for delivering electronically mediated, well-designed, learner-centred interactive learning environments by utilizing internet and digital technologies with respect to instructional design principles. This paper presents the application of Software Development techniques in the development of a Mobile Based…

Learning Science Using AR Book: A Preliminary Study on Visual Needs of Deaf Learners

NASA Astrophysics Data System (ADS)

Megat Mohd. Zainuddin, Norziha; Badioze Zaman, Halimah; Ahmad, Azlina

Augmented Reality (AR) is a technology that is projected to have more significant role in teaching and learning, particularly in visualising abstract concepts in the learning process. AR is a technology is based on visually oriented technique. Thus, it is suitable for deaf learners since they are generally classified as visual learners. Realising the importance of visual learning style for deaf learners in learning Science, this paper reports on a preliminary study of on an ongoing research on problems faced by deaf learners in learning the topic on Microorganisms. Being visual learners, they have problems with current text books that are more text-based that graphic based. In this preliminary study, a qualitative approach using the ethnographic observational technique was used so that interaction with three deaf learners who are participants throughout this study (they are also involved actively in the design and development of the AR Book). An interview with their teacher and doctor were also conducted to identify their learning and medical problems respectively. Preliminary findings have confirmed the need to design and develop a special Augmented Reality Book called AR-Science for Deaf Learners (AR-SiD).

Machine learning models in breast cancer survival prediction.

PubMed

Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

2016-01-01

Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Changing Attitudes and Facilitating Understanding in the Undergraduate Statistics Classroom: A Collaborative Learning Approach

ERIC Educational Resources Information Center

Curran, Erin; Carlson, Kerri; Celotta, Dayius Turvold

2013-01-01

Collaborative and problem-based learning strategies are theorized to be effective methods for strengthening undergraduate science, technology, engineering, and mathematics education. Peer-Led Team Learning (PLTL) is a collaborative learning technique that engages students in problem solving and discussion under the guidance of a trained peer…

The Place of Game-Based Learning in an Age of Austerity

ERIC Educational Resources Information Center

Whitton, Nicola

2012-01-01

Digital games have the potential to create active and engaging environments for learning, supporting problem-solving, communication and group activities, as well as providing a forum for practice and learning through failure. The use of game techniques such as gradually increasing levels of difficulty and contextual feedback support learning, and…

Developing effective web-based regional anesthesia education: a randomized study evaluating case-based versus non-case-based module design.

PubMed

Kopp, Sandra L; Smith, Hugh M

2011-01-01

Little is known about the use of Web-based education in regional anesthesia training. Benefits of Web-based education include the ability to standardize learning material quality and content, build appropriate learning progressions, use interactive multimedia technologies, and individualize delivery of course materials. The goals of this investigation were (1) to determine whether module design influences regional anesthesia knowledge acquisition, (2) to characterize learner preference patterns among anesthesia residents, and (3) to determine whether learner preferences play a role in knowledge acquisition. Direct comparison of knowledge assessments, learning styles, and learner preferences will be made between an interactive case-based and a traditional textbook-style module design. Forty-three Mayo Clinic anesthesiology residents completed 2 online modules, a knowledge pretest, posttest, an Index of Learning Styles assessment, and a participant satisfaction survey. Interscalene and lumbar plexus regional techniques were selected as the learning content for 4 Web modules constructed using the Blackboard Vista coursework application. One traditional textbook-style module and 1 interactive case-based module were designed for each of the interscalene and lumbar plexus techniques. Participants scored higher on the postmodule knowledge assessment for both of the interscalene and lumbar plexus modules. Postmodule knowledge performance scores were independent of both module design (interactive case-based versus traditional textbook style) and learning style preferences. However, nearly all participants reported a preference for Web-based learning and believe that it should be used in anesthesia resident education. Participants did not feel that Web-base learning should replace the current lecture-based curriculum. All residents scored higher on the postmodule knowledge assessment, but this improvement was independent of the module design and individual learning styles. Although residents believe that online learning should be used in anesthesia training, the results of this study do not demonstrate improved learning or justify the time and expense of developing complex case-based training modules. While there may be practical benefits of Web-based education, educators in regional anesthesia should be cautious about developing curricula based on learner preference data.

Machine learning for autonomous crystal structure identification.

PubMed

Reinhart, Wesley F; Long, Andrew W; Howard, Michael P; Ferguson, Andrew L; Panagiotopoulos, Athanassios Z

2017-07-21

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

Using Mixed-Modality Learning Strategies via e-Learning for Second Language Vocabulary Acquisition

ERIC Educational Resources Information Center

Yang, Fang-Chuan Ou; Wu, Wen-Chi Vivian

2015-01-01

This study demonstrated an e-learning system, MyEVA, based on a mixed-modality vocabulary strategy in assisting learners of English as a second language (L2 learners) to improve their vocabulary. To explore the learning effectiveness of MyEVA, the study compared four vocabulary-learning techniques, MyEVA in preference mode, MyEVA in basic mode, an…

Negotiating the Rules of Engagement: Exploring Perceptions of Dance Technique Learning through Bourdieu's Concept of "Doxa"

ERIC Educational Resources Information Center

Rimmer, Rachel

2017-01-01

This article presents the findings from a focus group discussion conducted with first year undergraduate dance students in March 2015. The focus group concluded a cycle of action research during which the researcher explored the use of enquiry-based learning approaches to teaching dance technique in higher education. Grounded in transformative and…

Practical Team-Based Learning from Planning to Implementation

PubMed Central

Bell, Edward; Eng, Marty; Fuentes, David G.; Helms, Kristen L.; Maki, Erik D.; Vyas, Deepti

2015-01-01

Team-based learning (TBL) helps instructors develop an active teaching approach for the classroom through group work. The TBL infrastructure engages students in the learning process through the Readiness Assessment Process, problem-solving through team discussions, and peer feedback to ensure accountability. This manuscript describes the benefits and barriers of TBL, and the tools necessary for developing, implementing, and critically evaluating the technique within coursework in a user-friendly method. Specifically, the manuscript describes the processes underpinning effective TBL development, preparation, implementation, assessment, and evaluation, as well as practical techniques and advice from authors’ classroom experiences. The paper also highlights published articles in the area of TBL in education, with a focus on pharmacy education. PMID:26889061

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Issues to Consider in Designing WebQuests: A Literature Review

ERIC Educational Resources Information Center

Kurt, Serhat

2012-01-01

A WebQuest is an inquiry-based online learning technique. This technique has been widely adopted in K-16 education. Therefore, it is important that conditions of effective WebQuest design are defined. Through this article the author presents techniques for improving WebQuest design based on current research. More specifically, the author analyzes…

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

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

Chen, G; Pan, X; Stayman, J

2014-06-15

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within themore » reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.« less

Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

NASA Astrophysics Data System (ADS)

Hindriks, Koen V.; Tykhonov, Dmytro

In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.

Scaffolding in geometry based on self regulated learning

NASA Astrophysics Data System (ADS)

Bayuningsih, A. S.; Usodo, B.; Subanti, S.

2017-12-01

This research aim to know the influence of problem based learning model by scaffolding technique on junior high school student’s learning achievement. This research took location on the junior high school in Banyumas. The research data obtained through mathematic learning achievement test and self-regulated learning (SRL) questioner. Then, the data analysis used two ways ANOVA. The results showed that scaffolding has positive effect to the mathematic learning achievement. The mathematic learning achievement use PBL-Scaffolding model is better than use PBL. The high SRL category student has better mathematic learning achievement than middle and low SRL categories, and then the middle SRL category has better than low SRL category. So, there are interactions between learning model with self-regulated learning in increasing mathematic learning achievement.

Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

ERIC Educational Resources Information Center

Khribi, Mohamed Koutheair; Jemni, Mohamed; Nasraoui, Olfa

2009-01-01

In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring their explicit feedback. Recommended learning resources are computed based on the current learner's recent navigation history, as well as exploiting similarities and dissimilarities among…

Applying Cognitive Psychology Based Instructional Design Principles in Mathematics Teaching and Learning: Introduction

ERIC Educational Resources Information Center

Verschaffel, Lieven; Van Dooren, W.; Star, J.

2017-01-01

This special issue comprises contributions that address the breadth of current lines of recent research from cognitive psychology that appear promising for positively impacting students' learning of mathematics. More specifically, we included contributions (a) that refer to cognitive psychology based principles and techniques, such as explanatory…

Mastery Learning in Physical Education.

ERIC Educational Resources Information Center

Annarino, Anthony

This paper discusses the design of a physical education curriculum to be used in advanced secondary physical education programs and in university basic instructional programs; the design is based on the premise of mastery learning and employs programed instructional techniques. The effective implementation of a mastery learning model necessitates…

Addressing Information Literacy through Student-Centered Learning

ERIC Educational Resources Information Center

Bond, Paul

2016-01-01

This case study describes several courses that resulted from a teaching partnership between an instructional technologist/professor and a librarian that evolved over several semesters, and the information literacy implications of the course formats. In order to increase student engagement, active learning and inquiry-based learning techniques were…

Mining Interactions in Immersive Learning Environments for Real-Time Student Feedback

ERIC Educational Resources Information Center

Kennedy, Gregor; Ioannou, Ioanna; Zhou, Yun; Bailey, James; O'Leary, Stephen

2013-01-01

The analysis and use of data generated by students' interactions with learning systems or programs--learning analytics--has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in…

Learner-Centred Pedagogy for Swim Coaching: A Complex Learning Theory-Informed Approach

ERIC Educational Resources Information Center

Light, Richard

2014-01-01

While constructivist theories of learning have been widely drawn on to understand and explain learning in games when using game-based approaches their use to inform pedagogy beyond games is limited. In particular, there has been little interest in applying constructivist perspectives on learning to sports in which technique is of prime importance.…

The Effectiveness of Web-Based Learning Environment: A Case Study of Public Universities in Kenya

ERIC Educational Resources Information Center

Kirui, Paul A.; Mutai, Sheila J.

2010-01-01

Web mining is emerging in many aspects of e-learning, aiming at improving online learning and teaching processes and making them more transparent and effective. Researchers using Web mining tools and techniques are challenged to learn more about the online students' reshaping online courses and educational websites, and create tools for…

Teaching with Dogs: Learning about Learning through Hands-on Experience in Dog Training

ERIC Educational Resources Information Center

McConnell, Bridget L.

2016-01-01

This paper summarizes a pilot study of an experiential learning technique that was designed to give undergraduate students a greater understanding of the principles and theories of learning and behavior, which is traditionally taught only in a lecture-based format. Students were assigned the role of a dog trainer, and they were responsible for…

Online Learning Behaviors for Radiology Interns Based on Association Rules and Clustering Technique

ERIC Educational Resources Information Center

Chen, Hsing-Shun; Liou, Chuen-He

2014-01-01

In a hospital, clinical teachers must also care for patients, so there is less time for the teaching of clinical courses, or for discussing clinical cases with interns. However, electronic learning (e-learning) can complement clinical skills education for interns in a blended-learning process. Students discuss and interact with classmates in an…

A Hybrid Method for Opinion Finding Task (KUNLP at TREC 2008 Blog Track)

DTIC Science & Technology

2008-11-01

retrieve relevant documents. For the Opinion Retrieval subtask, we propose a hybrid model of lexicon-based approach and machine learning approach for...estimating and ranking the opinionated documents. For the Polarized Opinion Retrieval subtask, we employ machine learning for predicting the polarity...and linear combination technique for ranking polar documents. The hybrid model which utilize both lexicon-based approach and machine learning approach

Problem based learning: the effect of real time data on the website to student independence

NASA Astrophysics Data System (ADS)

Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

2018-05-01

Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

Figure analysis: A teaching technique to promote visual literacy and active Learning.

PubMed

Wiles, Amy M

2016-07-08

Learning often improves when active learning techniques are used in place of traditional lectures. For many of these techniques, however, students are expected to apply concepts that they have already grasped. A challenge, therefore, is how to incorporate active learning into the classroom of courses with heavy content, such as molecular-based biology courses. An additional challenge is that visual literacy is often overlooked in undergraduate science education. To address both of these challenges, a technique called figure analysis was developed and implemented in three different levels of undergraduate biology courses. Here, students learn content while gaining practice in interpreting visual information by discussing figures with their peers. Student groups also make connections between new and previously learned concepts on their own while in class. The instructor summarizes the material for the class only after students grapple with it in small groups. Students reported a preference for learning by figure analysis over traditional lecture, and female students in particular reported increased confidence in their analytical abilities. There is not a technology requirement for this technique; therefore, it may be utilized both in classrooms and in nontraditional spaces. Additionally, the amount of preparation required is comparable to that of a traditional lecture. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):336-344, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

Effectiveness of students worksheet based on mastery learning in genetics subject

NASA Astrophysics Data System (ADS)

Megahati, R. R. P.; Yanti, F.; Susanti, D.

2018-05-01

Genetics is one of the subjects that must be followed by students in Biology education department. Generally, students do not like the genetics subject because of genetics concepts difficult to understand and the unavailability of a practical students worksheet. Consequently, the complete learning process (mastery learning) is not fulfilled and low students learning outcomes. The aim of this study develops student worksheet based on mastery learning that practical in genetics subject. This research is a research and development using 4-D models. The data analysis technique used is the descriptive analysis that describes the results of the practicalities of students worksheets based on mastery learning by students and lecturer of the genetic subject. The result is the student worksheet based on mastery learning on genetics subject are to the criteria of 80,33% and 80,14%, which means that the students worksheet practical used by lecturer and students. Student’s worksheet based on mastery learning effective because it can increase the activity and student learning outcomes.

The Effects of Practice-Based Training on Graduate Teaching Assistants’ Classroom Practices

PubMed Central

Becker, Erin A.; Easlon, Erin J.; Potter, Sarah C.; Guzman-Alvarez, Alberto; Spear, Jensen M.; Facciotti, Marc T.; Igo, Michele M.; Singer, Mitchell; Pagliarulo, Christopher

2017-01-01

Evidence-based teaching is a highly complex skill, requiring repeated cycles of deliberate practice and feedback to master. Despite existing well-characterized frameworks for practice-based training in K–12 teacher education, the major principles of these frameworks have not yet been transferred to instructor development in higher educational contexts, including training of graduate teaching assistants (GTAs). We sought to determine whether a practice-based training program could help GTAs learn and use evidence-based teaching methods in their classrooms. We implemented a weekly training program for introductory biology GTAs that included structured drills of techniques selected to enhance student practice, logic development, and accountability and reduce apprehension. These elements were selected based on their previous characterization as dimensions of active learning. GTAs received regular performance feedback based on classroom observations. To quantify use of target techniques and levels of student participation, we collected and coded 160 h of video footage. We investigated the relationship between frequency of GTA implementation of target techniques and student exam scores; however, we observed no significant relationship. Although GTAs adopted and used many of the target techniques with high frequency, techniques that enforced student participation were not stably adopted, and their use was unresponsive to formal feedback. We also found that techniques discussed in training, but not practiced, were not used at quantifiable frequencies, further supporting the importance of practice-based training for influencing instructional practices. PMID:29146664

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…

Adaptive Social Learning Based on Crowdsourcing

ERIC Educational Resources Information Center

Karataev, Evgeny; Zadorozhny, Vladimir

2017-01-01

Many techniques have been developed to enhance learning experience with computer technology. A particularly great influence of technology on learning came with the emergence of the web and adaptive educational hypermedia systems. While the web enables users to interact and collaborate with each other to create, organize, and share knowledge via…

Student Modeling and Ab Initio Language Learning.

ERIC Educational Resources Information Center

Heift, Trude; Schulze, Mathias

2003-01-01

Provides examples of student modeling techniques that have been employed in computer-assisted language learning over the past decade. Describes two systems for learning German: "German Tutor" and "Geroline." Shows how a student model can support computerized adaptive language testing for diagnostic purposes in a Web-based language learning…

Applying problem-based learning to otolaryngology teaching.

PubMed

Abou-Elhamd, K A; Rashad, U M; Al-Sultan, A I

2011-02-01

Undergraduate medical education requires ongoing improvement in order to keep pace with the changing demands of twenty-first century medical practice. Problem-based learning is increasingly being adopted in medical schools worldwide. We review its application in the specialty of ENT, and we present our experience of using this approach combined with more traditional methods. We introduced problem-based learning techniques into the ENT course taught to fifth-year medical students at Al-Ahsa College of Medicine, King Faisal University, Saudi Arabia. As a result, the teaching schedule included both clinical and theoretical activities. Six clinical teaching days were allowed for history-taking, examination techniques and clinical scenario discussion. Case scenarios were discussed in small group teaching sessions. Conventional methods were employed to teach audiology and ENT radiology (one three-hour session each); a three-hour simulation laboratory session and three-hour student presentation were also scheduled. In addition, students attended out-patient clinics for three days, and used multimedia facilities to learn about various otolaryngology diseases (in another three-hour session). This input was supplemented with didactic teaching in the form of 16 instructional lectures per semester (one hour per week). From our teaching experience, we believe that the application of problem-based learning to ENT teaching has resulted in a substantial increase in students' knowledge. Furthermore, students have given encouraging feedback on their experience of combined problem-based learning and conventional teaching methods.

Energy-free machine learning force field for aluminum.

PubMed

Kruglov, Ivan; Sergeev, Oleg; Yanilkin, Alexey; Oganov, Artem R

2017-08-17

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

PubMed

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The PEL technique is capable of addressing imbalanced sample learning associated with acute appendicitis diagnosis. Our evaluation results suggest PEL is less biased toward a positive or negative class than the investigated benchmark techniques. In addition, our results indicate the overall effectiveness of the proposed technique, compared with prevalent scoring systems or salient classification techniques that follow the resampling approach. Copyright © 2013 Elsevier B.V. All rights reserved.

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes.

PubMed

Das, Barnan; Cook, Diane J; Krishnan, Narayanan C; Schmitter-Edgecombe, Maureen

2016-08-01

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step towards automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

Analysis of critical thinking ability of VII grade students based on the mathematical anxiety level through learning cycle 7E model

NASA Astrophysics Data System (ADS)

Widyaningsih, E.; Waluya, S. B.; Kurniasih, A. W.

2018-03-01

This study aims to know mastery learning of students’ critical thinking ability with learning cycle 7E, determine whether the critical thinking ability of the students with learning cycle 7E is better than students’ critical thinking ability with expository model, and describe the students’ critical thinking phases based on the mathematical anxiety level. The method is mixed method with concurrent embedded. The population is VII grade students of SMP Negeri 3 Kebumen academic year 2016/2017. Subjects are determined by purposive sampling, selected two students from each level of mathematical anxiety. Data collection techniques include test, questionnaire, interview, and documentation. Quantitative data analysis techniques include mean test, proportion test, difference test of two means, difference test of two proportions and for qualitative data used Miles and Huberman model. The results show that: (1) students’ critical thinking ability with learning cycle 7E achieve mastery learning; (2) students’ critical thinking ability with learning cycle 7E is better than students’ critical thinking ability with expository model; (3) description of students’ critical thinking phases based on the mathematical anxiety level that is the lower the mathematical anxiety level, the subjects have been able to fulfil all of the indicators of clarification, assessment, inference, and strategies phases.

Jet-images — deep learning edition

DOE PAGES

de Oliveira, Luke; Kagan, Michael; Mackey, Lester; ...

2016-07-13

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is generalmore » and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.« less

Jet-images — deep learning edition

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

de Oliveira, Luke; Kagan, Michael; Mackey, Lester

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. Finally, this interplay between physically-motivated feature driven tools and supervised learning algorithms is generalmore » and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.« less

Tailoring Modified Moore Method Techniques to Liberal Arts Mathematics Courses

ERIC Educational Resources Information Center

Hitchman, Theron J.; Shaw, Douglas

2015-01-01

Inquiry-based learning (IBL) techniques can be used in mathematics courses for non-majors, such as courses required for liberal arts majors to fulfill graduation requirements. Unique challenges are discussed, followed by adaptations of IBL techniques to overcome those challenges.

Group investigation with scientific approach in mathematics learning

NASA Astrophysics Data System (ADS)

Indarti, D.; Mardiyana; Pramudya, I.

2018-03-01

The aim of this research is to find out the effect of learning model toward mathematics achievement. This research is quasi-experimental research. The population of research is all VII grade students of Karanganyar regency in the academic year of 2016/2017. The sample of this research was taken using stratified cluster random sampling technique. Data collection was done based on mathematics achievement test. The data analysis technique used one-way ANOVA following the normality test with liliefors method and homogeneity test with Bartlett method. The results of this research is the mathematics learning using Group Investigation learning model with scientific approach produces the better mathematics learning achievement than learning with conventional model on material of quadrilateral. Group Investigation learning model with scientific approach can be used by the teachers in mathematics learning, especially in the material of quadrilateral, which is can improve the mathematics achievement.

The Node Acquisition and Integration Technique: A Node-Link Based Teaching/Learning Strategy.

ERIC Educational Resources Information Center

Diekhoff, George M.

This paper presents the results of three experiments conducted in connection with development of a node-link based teaching/learning strategy. In experiment 1, subjects were instructed to either define concepts selected from a unit of introductory psychology or to describe the relationships existing between pairs of concepts. The cognitive…

Machine Learning Based Evaluation of Reading and Writing Difficulties.

PubMed

Iwabuchi, Mamoru; Hirabayashi, Rumi; Nakamura, Kenryu; Dim, Nem Khan

2017-01-01

The possibility of auto evaluation of reading and writing difficulties was investigated using non-parametric machine learning (ML) regression technique for URAWSS (Understanding Reading and Writing Skills of Schoolchildren) [1] test data of 168 children of grade 1 - 9. The result showed that the ML had better prediction than the ordinary rule-based decision.

Using Augmented Reality to Support a Software Editing Course for College Students

ERIC Educational Resources Information Center

Wang, Y.-H.

2017-01-01

This study aimed to explore whether integrating augmented reality (AR) techniques could support a software editing course and to examine the different learning effects for students using online-based and AR-based blended learning strategies. The researcher adopted a comparative research approach with a total of 103 college students participating…

An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience

ERIC Educational Resources Information Center

Claros, Iván; Cobos, Ruth; Collazos, César A.

2016-01-01

The Social Network Analysis (SNA) techniques allow modelling and analysing the interaction among individuals based on their attributes and relationships. This approach has been used by several researchers in order to measure the social processes in collaborative learning experiences. But oftentimes such measures were calculated at the final state…

Effect of Problem-Based Learning on Students' Achievement in Chemistry

ERIC Educational Resources Information Center

Aidoo, Benjamin; Boateng, Sampson Kwadwo; Kissi, Philip Siaw; Ofori, Isaac

2016-01-01

The study investigated the effect of problem-based learning (PBL) on students' achievement in chemistry. Learners' low achievement in Science in South Africa has been a concern to government, stakeholders, school principals and parents over the years as a result of poor teaching techniques, students' attitudes, lack of teaching and learning…

School Psychologists' Knowledge and Use of Evidence-Based, Social-Emotional Learning Interventions

ERIC Educational Resources Information Center

McKevitt, Brian C.

2012-01-01

This article describes the results of a national survey pertaining to school psychologists' knowledge and use of evidence-based, social-emotional learning (SEL) interventions. For the study, 331 school psychologists responded to a survey that listed (a) techniques for identifying SEL interventions, (b) 16 SEL programs that have been identified by…

An Ill-Structured PBL-Based Microprocessor Course without Formal Laboratory

ERIC Educational Resources Information Center

Kim, Jungkuk

2012-01-01

This paper introduces a problem-based learning (PBL) microprocessor application course designed according to the following strategies: 1) hands-on training without having a formal laboratory, and 2) intense student-centered cooperative learning through an ill-structured problem. PBL was adopted as the core educational technique of the course to…

Frontiers of Crystallography: A Project-Based Research-Led Learning Exercise

ERIC Educational Resources Information Center

Wilson, Chick C.; Parkin, Andrew; Thomas, Lynne H.

2012-01-01

A highly interactive research-led learning session for chemistry undergraduates is described, which aims to lead students to an awareness of the applications of crystallography technique through a mentored hands-on crystal structure solution and refinement session. The research-based environment is inherent throughout the 4.5 h program and is…

Game-Based Learning in Science Education: A Review of Relevant Research

ERIC Educational Resources Information Center

Li, Ming-Chaun; Tsai, Chin-Chung

2013-01-01

The purpose of this study is to review empirical research articles regarding game-based science learning (GBSL) published from 2000 to 2011. Thirty-one articles were identified through the Web of Science and SCOPUS databases. A qualitative content analysis technique was adopted to analyze the research purposes and designs, game design and…

An Inquiry-Based Exercise for Demonstrating Prey Preference in Snakes

ERIC Educational Resources Information Center

Place, Aaron J.; Abramson, Charles I.

2006-01-01

The recent promotion of inquiry-based learning techniques (Uno, 1990) is well suited to the use of animals in the classroom. Working with living organisms directly engages students and stimulates them to actively participate in the learning process. Students develop a greater appreciation for living things, the natural world, and their impact on…

Goals, Success Factors, and Barriers for Simulation-Based Learning: A Qualitative Interview Study in Health Care

ERIC Educational Resources Information Center

Dieckmann, Peter; Friis, Susanne Molin; Lippert, Anne; Ostergaard, Doris

2012-01-01

Introduction: This study describes (a) process goals, (b) success factors, and (c) barriers for optimizing simulation-based learning environments within the simulation setting model developed by Dieckmann. Methods: Seven simulation educators of different experience levels were interviewed using the Critical Incident Technique. Results: (a) The…

An underwater turbulence degraded image restoration algorithm

NASA Astrophysics Data System (ADS)

Furhad, Md. Hasan; Tahtali, Murat; Lambert, Andrew

2017-09-01

Underwater turbulence occurs due to random fluctuations of temperature and salinity in the water. These fluctuations are responsible for variations in water density, refractive index and attenuation. These impose random geometric distortions, spatio-temporal varying blur, limited range visibility and limited contrast on the acquired images. There are some restoration techniques developed to address this problem, such as image registration based, lucky region based and centroid-based image restoration algorithms. Although these methods demonstrate better results in terms of removing turbulence, they require computationally intensive image registration, higher CPU load and memory allocations. Thus, in this paper, a simple patch based dictionary learning algorithm is proposed to restore the image by alleviating the costly image registration step. Dictionary learning is a machine learning technique which builds a dictionary of non-zero atoms derived from the sparse representation of an image or signal. The image is divided into several patches and the sharp patches are detected from them. Next, dictionary learning is performed on these patches to estimate the restored image. Finally, an image deconvolution algorithm is employed on the estimated restored image to remove noise that still exists.

Concept Mapping as an Innovative Tool for the Assessment of Learning: An Experimental Experience among Business Management Degree Students

ERIC Educational Resources Information Center

Ruiz-Palomino, Pablo; Martinez-Canas, Ricardo

2013-01-01

In the search to improve the quality of education at the university level, the use of concept mapping is becoming an important instructional technique for enhancing the teaching-learning process. This educational tool is based on cognitive theories by making a distinction between learning by rote (memorizing) and learning by meaning, where…

The Heuristic Method, Precursor of Guided Inquiry: Henry Armstrong and British Girls' Schools, 1890-1920

ERIC Educational Resources Information Center

Rayner-Canham, Geoff; Rayner-Canham, Marelene

2015-01-01

Though guided-inquiry learning, discovery learning, student-centered learning, and problem-based learning are commonly believed to be recent new approaches to the teaching of chemistry, in fact, the concept dates back to the late 19th century. Here, we will show that it was the British chemist, Henry Armstrong, who pioneered this technique,…

Comparing Student Learning in the Team-Based Learning Classroom with Different Team Reporting Methods

ERIC Educational Resources Information Center

Johnson, Staci Neas

2017-01-01

The increase in classroom technology has resulted in the use of clickers and other audience response systems (ARS) for simultaneous reporting of choices in the teambased learning (TBL) classroom. A variety of techniques and practices using ARS technology in TBL courses has been noted. Learning gains in the TBL classroom with ARS reporting has not…

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.

Hand rim wheelchair propulsion training using biomechanical real-time visual feedback based on motor learning theory principles.

PubMed

Rice, Ian; Gagnon, Dany; Gallagher, Jere; Boninger, Michael

2010-01-01

As considerable progress has been made in laboratory-based assessment of manual wheelchair propulsion biomechanics, the necessity to translate this knowledge into new clinical tools and treatment programs becomes imperative. The objective of this study was to describe the development of a manual wheelchair propulsion training program aimed to promote the development of an efficient propulsion technique among long-term manual wheelchair users. Motor learning theory principles were applied to the design of biomechanical feedback-based learning software, which allows for random discontinuous real-time visual presentation of key spatiotemporal and kinetic parameters. This software was used to train a long-term wheelchair user on a dynamometer during 3 low-intensity wheelchair propulsion training sessions over a 3-week period. Biomechanical measures were recorded with a SmartWheel during over ground propulsion on a 50-m level tile surface at baseline and 3 months after baseline. Training software was refined and administered to a participant who was able to improve his propulsion technique by increasing contact angle while simultaneously reducing stroke cadence, mean resultant force, peak and mean moment out of plane, and peak rate of rise of force applied to the pushrim after training. The proposed propulsion training protocol may lead to favorable changes in manual wheelchair propulsion technique. These changes could limit or prevent upper limb injuries among manual wheelchair users. In addition, many of the motor learning theory-based techniques examined in this study could be applied to training individuals in various stages of rehabilitation to optimize propulsion early on.

Hand Rim Wheelchair Propulsion Training Using Biomechanical Real-Time Visual Feedback Based on Motor Learning Theory Principles

PubMed Central

Rice, Ian; Gagnon, Dany; Gallagher, Jere; Boninger, Michael

2010-01-01

Background/Objective: As considerable progress has been made in laboratory-based assessment of manual wheelchair propulsion biomechanics, the necessity to translate this knowledge into new clinical tools and treatment programs becomes imperative. The objective of this study was to describe the development of a manual wheelchair propulsion training program aimed to promote the development of an efficient propulsion technique among long-term manual wheelchair users. Methods: Motor learning theory principles were applied to the design of biomechanical feedback-based learning software, which allows for random discontinuous real-time visual presentation of key spatio-temporal and kinetic parameters. This software was used to train a long-term wheelchair user on a dynamometer during 3 low-intensity wheelchair propulsion training sessions over a 3-week period. Biomechanical measures were recorded with a SmartWheel during over ground propulsion on a 50-m level tile surface at baseline and 3 months after baseline. Results: Training software was refined and administered to a participant who was able to improve his propulsion technique by increasing contact angle while simultaneously reducing stroke cadence, mean resultant force, peak and mean moment out of plane, and peak rate of rise of force applied to the pushrim after training. Conclusions: The proposed propulsion training protocol may lead to favorable changes in manual wheelchair propulsion technique. These changes could limit or prevent upper limb injuries among manual wheelchair users. In addition, many of the motor learning theory–based techniques examined in this study could be applied to training individuals in various stages of rehabilitation to optimize propulsion early on. PMID:20397442

Training hydrologists to be ecohydrologists: A ';how-you-can-do-it' example leveraging an active learning environment

NASA Astrophysics Data System (ADS)

Lyon, S. W.; Walter, M. T.; Jantze, E. J.; Archibald, J. A.

2013-12-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a ';how-you-can-do-it' example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at Stockholm University's Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of ';activeness' across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more ';active' techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Training hydrologists to be ecohydrologists: A 'how-you-can-do-it' example leveraging an active learning environment

NASA Astrophysics Data System (ADS)

Lyon, Steve W.; Walter, M. Todd; Jantze, Elin J.; Archibald, Josephine A.

2015-04-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a 'how-you-can-do-it' example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at Stockholm University's Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of 'activeness' across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more 'active' techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Training hydrologists to be ecohydrologists: a "how-you-can-do-it" example leveraging an active learning environment for studying plant-water interaction

NASA Astrophysics Data System (ADS)

Lyon, S. W.; Walter, M. T.; Jantze, E. J.; Archibald, J. A.

2012-08-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a "how-you-can-do-it" example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at the Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of "activeness" across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more "active" techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

The Implementation of Research-based Learning on Biology Seminar Course in Biology Education Study Program of FKIP UMRAH

NASA Astrophysics Data System (ADS)

Amelia, T.

2018-04-01

Biology Seminar is a course in Biology Education Study Program of Faculty of Teacher Training and Education University of Maritim Raja Ali Haji (FKIP UMRAH) that requires students to have the ability to apply scientific attitudes, perform scientific writing and undertake scientific publications on a small scale. One of the learning strategies that can drive the achievement of learning outcomes in this course is Research-Based Learning. Research-Based Learning principles are considered in accordance with learning outcomes in Biology Seminar courses and generally in accordance with the purpose of higher education. On this basis, this article which is derived from a qualitative research aims at describing Research-based Learning on Biology Seminar course. Based on a case study research, it was known that Research-Based Learning on Biology Seminar courses is applied through: designing learning activities around contemporary research issues; teaching research methods, techniques and skills explicitly within program; drawing on personal research in designing and teaching courses; building small-scale research activities into undergraduate assignment; and infusing teaching with the values of researchers.

Teaching communication skills: using action methods to enhance role-play in problem-based learning.

PubMed

Baile, Walter F; Blatner, Adam

2014-08-01

Role-play is a method of simulation used commonly to teach communication skills. Role-play methods can be enhanced by techniques that are not widely used in medical teaching, including warm-ups, role-creation, doubling, and role reversal. The purposes of these techniques are to prepare learners to take on the role of others in a role-play; to develop an insight into unspoken attitudes, thoughts, and feelings, which often determine the behavior of others; and to enhance communication skills through the participation of learners in enactments of communication challenges generated by them. In this article, we describe a hypothetical teaching session in which an instructor applies each of these techniques in teaching medical students how to break bad news using a method called SPIKES [Setting, Perception, Invitation, Knowledge, Emotions, Strategy, and Summary]. We illustrate how these techniques track contemporary adult learning theory through a learner-centered, case-based, experiential approach to selecting challenging scenarios in giving bad news, by attending to underlying emotion and by using reflection to anchor new learning.

Geometry-based ensembles: toward a structural characterization of the classification boundary.

PubMed

Pujol, Oriol; Masip, David

2009-06-01

This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.

Experiential Education--Scandinavian Style

ERIC Educational Resources Information Center

Mortensen, Erik

1978-01-01

Learning a foreign language by the cultural immersion technique, used by the Scandinavian Seminar, an international experiential education organization, is described, based on American college students' experiences studying in Scandinavia. Also explored is the learning concept of experiential education. (JMD)

Relationships Between the External and Internal Training Load in Professional Soccer: What Can We Learn From Machine Learning?

PubMed

Jaspers, Arne; De Beéck, Tim Op; Brink, Michel S; Frencken, Wouter G P; Staes, Filip; Davis, Jesse J; Helsen, Werner F

2018-05-01

Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models' performance on predicting the reported RPE values for future training sessions was compared with the naive baseline's performance. Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.

The relevance of Newton's laws and selected principles of physics to dance techniques: Theory and application

NASA Astrophysics Data System (ADS)

Lei, Li

1999-07-01

In this study the researcher develops and presents a new model, founded on the laws of physics, for analyzing dance technique. Based on a pilot study of four advanced dance techniques, she creates a new model for diagnosing, analyzing and describing basic, intermediate and advanced dance techniques. The name for this model is ``PED,'' which stands for Physics of Expressive Dance. The research design consists of five phases: (1) Conduct a pilot study to analyze several advanced dance techniques chosen from Chinese dance, modem dance, and ballet; (2) Based on learning obtained from the pilot study, create the PED Model for analyzing dance technique; (3) Apply this model to eight categories of dance technique; (4) Select two advanced dance techniques from each category and analyze these sample techniques to demonstrate how the model works; (5) Develop an evaluation framework and use it to evaluate the effectiveness of the model, taking into account both scientific and artistic aspects of dance training. In this study the researcher presents new solutions to three problems highly relevant to dance education: (1) Dancers attempting to learn difficult movements often fail because they are unaware of physics laws; (2) Even those who do master difficult movements can suffer injury due to incorrect training methods; (3) Even the best dancers can waste time learning by trial and error, without scientific instruction. In addition, the researcher discusses how the application of the PED model can benefit dancers, allowing them to avoid inefficient and ineffective movements and freeing them to focus on the artistic expression of dance performance. This study is unique, presenting the first comprehensive system for analyzing dance techniques in terms of physics laws. The results of this study are useful, allowing a new level of awareness about dance techniques that dance professionals can utilize for more effective and efficient teaching and learning. The approach utilized in this study is universal, and can be applied to any dance movement and to any dance style.

[Organization development of the public health system].

PubMed

Pfaff, Holger; Klein, Jürgen

2002-05-15

Changes in the German health care system require changes in health care institutions. Organizational development (OD) techniques can help them to cope successfully with their changing environment. OD is defined as a collective process of learning aiming to induce intended organizational change. OD is based on social science methods and conducted by process-oriented consultants. In contrast to techniques of organizational design, OD is characterized by employee participation. One of the most important elements of OD is the so-called "survey-feedback-technique". Five examples illustrate how the survey-feedback-technique can be used to facilitate organisational learning. OD technique supports necessary change in health care organizations. It should be used more frequently.

A service based adaptive U-learning system using UX.

PubMed

Jeong, Hwa-Young; Yi, Gangman

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

A Service Based Adaptive U-Learning System Using UX

PubMed Central

Jeong, Hwa-Young

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

Community-Based Philosophy and Service-Learning: A Case Study at Barry University

ERIC Educational Resources Information Center

Schlaerth, Christian A.

2014-01-01

Service-learning, as a pedagogical technique, presents unique learning opportunities for students, where they get to use their skills and knowledge from courses to help improve communities that have particular needs. Barry University has recently begun to expand it program across the school, reaching into disciplines that are not often associated…

Review of Mobile Learning Trends 2010-2015: A Meta-Analysis

ERIC Educational Resources Information Center

Chee, Ken Nee; Yahaya, Noraffandy; Ibrahim, Nor Hasniza; Hasan, Mohamed Noor

2017-01-01

This study examined the longitudinal trends of mobile learning (M-Learning) research using text mining techniques in a more comprehensive manner. One hundred and forty four (144) refereed journal articles were retrieved and analyzed from the Social Science Citation Index database selected from top six major educational technology-based learning…

College Communicative Teaching and E-Learning: A Training Scheme

ERIC Educational Resources Information Center

Ong, Charito G.

2017-01-01

This study sought to design and try out a training scheme for college teachers on e-learning use as a classroom strategy in a communicative teaching mode. Based on needs analysis the teachers of English were reoriented so that they became equipped with the rationale, strategies and assessment techniques of e-learning alongside communicative…

Natural Resource Service Learning to Link Students, Communities, and the Land

ERIC Educational Resources Information Center

Barlow, Rebecca J.

2013-01-01

University-based Extension specialists often face the dilemma of scheduling time for both teaching and outreach activities. Service learning projects that give hands-on experience in the application of classroom activities while giving back to the community can bridge this gap. A demonstration forest and service learning techniques were used to…

Incorporating Students' Self-Designed, Research-Based Analytical Chemistry Projects into the Instrumentation Curriculum

ERIC Educational Resources Information Center

Gao, Ruomei

2015-01-01

In a typical chemistry instrumentation laboratory, students learn analytical techniques through a well-developed procedure. Such an approach, however, does not engage students in a creative endeavor. To foster the intrinsic motivation of students' desire to learn, improve their confidence in self-directed learning activities and enhance their…

Developing Global Leaders: Building Effective Global- Intercultural Collaborative Online Learning Environments

ERIC Educational Resources Information Center

Ivy, Karen Lynne-Daniels

2017-01-01

This paper shares the findings of a study conducted on a virtual inter-cultural global leadership development learning project. Mixed Methods analysis techniques were used to examine the interviews of U.S. and Uganda youth project participants. The study, based on cultural and social constructivist learning theories, investigated the effects of…

Advancing Higher Education with Mobile Learning Technologies: Cases, Trends, and Inquiry-Based Methods

ERIC Educational Resources Information Center

Keengwe, Jared, Ed.; Maxfield, Marian B., Ed.

2015-01-01

Rapid advancements in technology are creating new opportunities for educators to enhance their classroom techniques with digital learning resources. Once used solely outside of the classroom, smartphones, tablets, and e-readers are becoming common in many school settings. "Advancing Higher Education with Mobile Learning Technologies: Cases,…

Query-based learning for aerospace applications.

PubMed

Saad, E W; Choi, J J; Vian, J L; Wunsch, D C Ii

2003-01-01

Models of real-world applications often include a large number of parameters with a wide dynamic range, which contributes to the difficulties of neural network training. Creating the training data set for such applications becomes costly, if not impossible. In order to overcome the challenge, one can employ an active learning technique known as query-based learning (QBL) to add performance-critical data to the training set during the learning phase, thereby efficiently improving the overall learning/generalization. The performance-critical data can be obtained using an inverse mapping called network inversion (discrete network inversion and continuous network inversion) followed by oracle query. This paper investigates the use of both inversion techniques for QBL learning, and introduces an original heuristic to select the inversion target values for continuous network inversion method. Efficiency and generalization was further enhanced by employing node decoupled extended Kalman filter (NDEKF) training and a causality index (CI) as a means to reduce the input search dimensionality. The benefits of the overall QBL approach are experimentally demonstrated in two aerospace applications: a classification problem with large input space and a control distribution problem.

From Cure to Care: Assessing the Ethical and Professional Learning Needs of Medical Learners in a Care-Based Facility

ERIC Educational Resources Information Center

Hall, Pippa; O'Reilly, Jane; Dojeiji, Sue; Blair, Richard; Harley, Anne

2009-01-01

The purpose of this study was to assess the ethical and professional learning needs of medical trainees on clinical placements at a care-based facility, as they shifted from acute care to care-based philosophy. Using qualitative data analysis and grounded theory techniques, 12 medical learners and five clinical supervisors were interviewed. Five…

Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic

ERIC Educational Resources Information Center

Jain, G. Panka; Gurupur, Varadraj P.; Schroeder, Jennifer L.; Faulkenberry, Eileen D.

2014-01-01

In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of…

Effects of visual feedback-induced variability on motor learning of handrim wheelchair propulsion.

PubMed

Leving, Marika T; Vegter, Riemer J K; Hartog, Johanneke; Lamoth, Claudine J C; de Groot, Sonja; van der Woude, Lucas H V

2015-01-01

It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear simultaneously during the motor learning process. Their relationship is most likely modified by other factors such as the amount of the intra-individual variability.

Effects of Visual Feedback-Induced Variability on Motor Learning of Handrim Wheelchair Propulsion

PubMed Central

Leving, Marika T.; Vegter, Riemer J. K.; Hartog, Johanneke; Lamoth, Claudine J. C.; de Groot, Sonja; van der Woude, Lucas H. V.

2015-01-01

Background It has been suggested that a higher intra-individual variability benefits the motor learning of wheelchair propulsion. The present study evaluated whether feedback-induced variability on wheelchair propulsion technique variables would also enhance the motor learning process. Learning was operationalized as an improvement in mechanical efficiency and propulsion technique, which are thought to be closely related during the learning process. Methods 17 Participants received visual feedback-based practice (feedback group) and 15 participants received regular practice (natural learning group). Both groups received equal practice dose of 80 min, over 3 weeks, at 0.24 W/kg at a treadmill speed of 1.11 m/s. To compare both groups the pre- and post-test were performed without feedback. The feedback group received real-time visual feedback on seven propulsion variables with instruction to manipulate the presented variable to achieve the highest possible variability (1st 4-min block) and optimize it in the prescribed direction (2nd 4-min block). To increase motor exploration the participants were unaware of the exact variable they received feedback on. Energy consumption and the propulsion technique variables with their respective coefficient of variation were calculated to evaluate the amount of intra-individual variability. Results The feedback group, which practiced with higher intra-individual variability, improved the propulsion technique between pre- and post-test to the same extent as the natural learning group. Mechanical efficiency improved between pre- and post-test in the natural learning group but remained unchanged in the feedback group. Conclusion These results suggest that feedback-induced variability inhibited the improvement in mechanical efficiency. Moreover, since both groups improved propulsion technique but only the natural learning group improved mechanical efficiency, it can be concluded that the improvement in mechanical efficiency and propulsion technique do not always appear simultaneously during the motor learning process. Their relationship is most likely modified by other factors such as the amount of the intra-individual variability. PMID:25992626

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.

Examining Residents' Strategic Mindfulness During Self-Regulated Learning of a Simulated Procedural Skill.

PubMed

Brydges, Ryan; Hatala, Rose; Mylopoulos, Maria

2016-07-01

Simulation-based training is currently embedded in most health professions education curricula. Without evidence for how trainees think about their simulation-based learning, some training techniques may not support trainees' learning strategies. This study explored how residents think about and self-regulate learning during a lumbar puncture (LP) training session using a simulator. In 2010, 20 of 45 postgraduate year 1 internal medicine residents attended a mandatory procedural skills training boot camp. Independently, residents practiced the entire LP skill on a part-task trainer using a clinical LP tray and proper sterile technique. We interviewed participants regarding how they thought about and monitored their learning processes, and then we conducted a thematic analysis of the interview data. The analysis suggested that participants considered what they could and could not learn from the simulator; they developed their self-confidence by familiarizing themselves with the LP equipment and repeating the LP algorithmic steps. Participants articulated an idiosyncratic model of learning they used to interpret the challenges and successes they experienced. Participants reported focusing on obtaining cerebrospinal fluid and memorizing the "routine" version of the LP procedure. They did not report much thinking about their learning strategies (eg, self-questioning). During simulation-based training, residents described assigning greater weight to achieving procedural outcomes and tended to think that the simulated task provided them with routine, generalizable skills. Over this typical 1-hour session, trainees did not appear to consider their strategic mindfulness (ie, awareness and use of learning strategies).

The training and learning process of transseptal puncture using a modified technique.

PubMed

Yao, Yan; Ding, Ligang; Chen, Wensheng; Guo, Jun; Bao, Jingru; Shi, Rui; Huang, Wen; Zhang, Shu; Wong, Tom

2013-12-01

As the transseptal (TS) puncture has become an integral part of many types of cardiac interventional procedures, its technique that was initial reported for measurement of left atrial pressure in 1950s, continue to evolve. Our laboratory adopted a modified technique which uses only coronary sinus catheter as the landmark to accomplishing TS punctures under fluoroscopy. The aim of this study is prospectively to evaluate the training and learning process for TS puncture guided by this modified technique. Guided by the training protocol, TS puncture was performed in 120 consecutive patients by three trainees without previous personal experience in TS catheterization and one experienced trainer as a controller. We analysed the following parameters: one puncture success rate, total procedure time, fluoroscopic time, and radiation dose. The learning curve was analysed using curve-fitting methodology. The first attempt at TS crossing was successful in 74 (82%), a second attempt was successful in 11 (12%), and 5 patients failed to puncture the interatrial septal finally. The average starting process time was 4.1 ± 0.8 min, and the estimated mean learning plateau was 1.2 ± 0.2 min. The estimated mean learning rate for process time was 25 ± 3 cases. Important aspects of learning curve can be estimated by fitting inverse curves for TS puncture. The study demonstrated that this technique was a simple, safe, economic, and effective approach for learning of TS puncture. Base on the statistical analysis, approximately 29 TS punctures will be needed for trainee to pass the steepest area of learning curve.

A Re-Examination of the Argument against Problem-Based Learning in the Classroom

ERIC Educational Resources Information Center

Bryant, Lauren H.

2011-01-01

The primary purpose of this study is to examine Kirschner, Sweller, and Clark's (2006) argument against problem-based learning (PBL) by analyzing research used to support their stance. The secondary purpose is to develop a definition of PBL that helps practitioners use this technique. Seven studies were analyzed to determine whether the PBL…

An International Collaboration to Promote Inquiry-Based Learning in Undergraduate Engineering Classrooms

ERIC Educational Resources Information Center

Randall, D'Arcy C.; Moore, Christy; Carvalho, Isabel S.

2012-01-01

Purpose: The purpose of this paper is to describe specific techniques of "inquiry-based learning" employed by three instructors in Engineering schools, one in Europe and two in the USA. Design/methodology/approach: Theorists such as Bransford et al. argue that twenty-first century educators need to teach students to do more than simply…

The Effects of Jigsaw Technique Based on Cooperative Learning on Prospective Science Teachers' Science Process Skill

ERIC Educational Resources Information Center

Karacop, Ataman; Diken, Emine Hatun

2017-01-01

The purpose of this study is to investigate the effects of laboratory approach based on jigsaw method with cooperative learning and confirmatory laboratory approach on university students' cognitive process development in Science teaching laboratory applications, and to determine the opinions of the students on applied laboratory methods. The…

Updating Assessment Styles: Website Development Rather than Report Writing for Project Based Learning Courses

ERIC Educational Resources Information Center

Brown, Nicola

2017-01-01

While teaching methods tend to be updated frequently, the implementation of new innovative assessment tools is much slower. For example project based learning has become popular as a teaching technique, however, the assessment tends to be via traditional reports. This paper reports on the implementation and evaluation of using website development…

Achievement Together: The Development of an Intervention Using Relationship-Based Strategies to Promote Positive Learning Habits

ERIC Educational Resources Information Center

Earhart, James; Zamora, Irina

2015-01-01

This pilot study describes the development and initial implementation of a treatment program that uses relationship-based techniques as a basis for promoting characteristics important in learning and emotional regulation. A case example has been included as an illustration of the theoretical framework of this intervention, along with preliminary…

Introducing IoT and Wearable Technologies into Task-Based Language Learning for Young Children

ERIC Educational Resources Information Center

de la Guia, Elena; Camacho, Vincent Lopez; Orozco-Barbosa, Luis; Brea Lujan, Victor M.; Penichet, Victor M. R.; Perez, Maria Lozano

2016-01-01

In the last few years, in an attempt to further motivate students to learn a foreign language, there has been an increasing interest in task-based teaching techniques, which emphasize communication and the practical use of language, thus moving away from the repetitive grammar-translation methods. Within this approach, the significance of…

Implementing Problem-Based Learning in an Undergraduate Psychology Course

ERIC Educational Resources Information Center

Searight, H. Russell; Searight, Barbara K.

2009-01-01

Problem-based learning (PBL) is a small-group pedagogical technique widely used in fields such as business, medicine, engineering, and architecture. In PBL, pre-written cases are used to teach core course content. PBL advocates state that course material is more likely to be retained and applied when presented as cases reflecting "real life"…

Identifying Core Mobile Learning Faculty Competencies Based Integrated Approach: A Delphi Study

ERIC Educational Resources Information Center

Elbarbary, Rafik Said

2015-01-01

This study is based on the integrated approach as a concept framework to identify, categorize, and rank a key component of mobile learning core competencies for Egyptian faculty members in higher education. The field investigation framework used four rounds Delphi technique to determine the importance rate of each component of core competencies…

Towards Project-Based Learning: An Autoethnographic Account of One Assistant Professor's Struggle to Be a Better Teacher

ERIC Educational Resources Information Center

Greer, Wil

2016-01-01

This paper outlines an approach to incorporating project-based learning (PBL) in a master's level educational administration diversity course. It draws on the qualitative methodology of autoethnography, and details the characteristics of this technique. In alignment with that method, the author discusses his positionality and engages in…

D[superscript 4]S[superscript 4]: A Four Dimensions Instructional Strategy for Web-Based and Blended Learning

ERIC Educational Resources Information Center

Abdelaziz, Hamdy A.

2012-01-01

Web-based education is facing a paradigm shift under the rapid development of information and communication technology. The new paradigm of learning requires special techniques of course design, special instructional models, and special methods of evaluation. This paper investigates the effectiveness of an adaptive instructional strategy for…

Beverage-Agarose Gel Electrophoresis: An Inquiry-Based Laboratory Exercise with Virtual Adaptation

ERIC Educational Resources Information Center

Cunningham, Steven C.; McNear, Brad; Pearlman, Rebecca S.; Kern, Scott E.

2006-01-01

A wide range of literature and experience has shown that teaching methods that promote active learning, such as inquiry-based approaches, are more effective than those that rely on passive learning. Gel electrophoresis, one of the most common laboratory techniques in molecular biology, has a wide range of applications in the life sciences. As…

Problem-based learning biotechnology courses in chemical engineering.

PubMed

Glatz, Charles E; Gonzalez, Ramon; Huba, Mary E; Mallapragada, Surya K; Narasimhan, Balaji; Reilly, Peter J; Saunders, Kevin P; Shanks, Jacqueline V

2006-01-01

We have developed a series of upper undergraduate/graduate lecture and laboratory courses on biotechnological topics to supplement existing biochemical engineering, bioseparations, and biomedical engineering lecture courses. The laboratory courses are based on problem-based learning techniques, featuring two- and three-person teams, journaling, and performance rubrics for guidance and assessment. Participants initially have found them to be difficult, since they had little experience with problem-based learning. To increase enrollment, we are combining the laboratory courses into 2-credit groupings and allowing students to substitute one of them for the second of our 2-credit chemical engineering unit operations laboratory courses.

An experimental result of estimating an application volume by machine learning techniques.

PubMed

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Multi-documents summarization based on clustering of learning object using hierarchical clustering

NASA Astrophysics Data System (ADS)

Mustamiin, M.; Budi, I.; Santoso, H. B.

2018-03-01

The Open Educational Resources (OER) is a portal of teaching, learning and research resources that is available in public domain and freely accessible. Learning contents or Learning Objects (LO) are granular and can be reused for constructing new learning materials. LO ontology-based searching techniques can be used to search for LO in the Indonesia OER. In this research, LO from search results are used as an ingredient to create new learning materials according to the topic searched by users. Summarizing-based grouping of LO use Hierarchical Agglomerative Clustering (HAC) with the dependency context to the user’s query which has an average value F-Measure of 0.487, while summarizing by K-Means F-Measure only has an average value of 0.336.

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.

[Problem based learning from the perspective of tutors].

PubMed

Navarro Hernández, Nancy; Illesca P, Mónica; Cabezas G, Mirtha

2009-02-01

Problem based learning is a student centered learning technique that develops deductive, constructive and reasoning capacities among the students. Teachers must adapt to this paradigm of constructing rather than transmitting knowledge. To interpret the importance of tutors in problem based learning during a module of Health research and management given to medical, nursing, physical therapy, midwifery, technology and nutrition students. Eight teachers that participated in a module using problem based learning accepted to participate in an in depth interview. The qualitative analysis of the textual information recorded, was performed using the ATLAS software. We identified 662 meaning units, grouped in 29 descriptive categories, with eight emerging meta categories. The sequential and cross-generated qualitative analysis generated four domains: competence among students, competence of teachers, student-centered learning and evaluation process. Multiprofessional problem based learning contributes to the development of generic competences among future health professionals, such as multidisciplinary work, critical capacity and social skills. Teachers must shelter the students in the context of their problems and social situation.

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

NASA Astrophysics Data System (ADS)

Hannel, Mark D.; Abdulali, Aidan; O'Brien, Michael; Grier, David G.

2018-06-01

Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

DOE PAGES

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

2016-08-09

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

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

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2011-11-24

Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.

ROENTGEN: case-based reasoning and radiation therapy planning.

PubMed Central

Berger, J.

1992-01-01

ROENTGEN is a design assistant for radiation therapy planning which uses case-based reasoning, an artificial intelligence technique. It learns both from specific problem-solving experiences and from direct instruction from the user. The first sort of learning is the normal case-based method of storing problem solutions so that they can be reused. The second sort is necessary because ROENTGEN does not, initially, have an internal model of the physics of its problem domain. This dependence on explicit user instruction brings to the forefront representational questions regarding indexing, failure definition, failure explanation and repair. This paper presents the techniques used by ROENTGEN in its knowledge acquisition and design activities. PMID:1482869

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.

Dictionary learning and time sparsity in dynamic MRI.

PubMed

Caballero, Jose; Rueckert, Daniel; Hajnal, Joseph V

2012-01-01

Sparse representation methods have been shown to tackle adequately the inherent speed limits of magnetic resonance imaging (MRI) acquisition. Recently, learning-based techniques have been used to further accelerate the acquisition of 2D MRI. The extension of such algorithms to dynamic MRI (dMRI) requires careful examination of the signal sparsity distribution among the different dimensions of the data. Notably, the potential of temporal gradient (TG) sparsity in dMRI has not yet been explored. In this paper, a novel method for the acceleration of cardiac dMRI is presented which investigates the potential benefits of enforcing sparsity constraints on patch-based learned dictionaries and TG at the same time. We show that an algorithm exploiting sparsity on these two domains can outperform previous sparse reconstruction techniques.

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy.

PubMed

Kawata, Yasuo; Arimura, Hidetaka; Ikushima, Koujirou; Jin, Ze; Morita, Kento; Tokunaga, Chiaki; Yabu-Uchi, Hidetake; Shioyama, Yoshiyuki; Sasaki, Tomonari; Honda, Hiroshi; Sasaki, Masayuki

2017-10-01

The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for GTV regions, which were determined based on the knowledge of radiation oncologists, were fed on a pixel-by-pixel basis into the respective FCM, ANN, and SVM ML techniques. Then, the ML techniques were incorporated into the automated delineation framework of GTVs followed by an optimum contour selection (OCS) method, which we proposed in a previous study. The three-ML-based frameworks were evaluated for 16 lung cancer cases (six solid, four ground glass opacity (GGO), six part-solid GGO) with the datasets of planning computed tomography (CT) and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT images using the three-dimensional Dice similarity coefficient (DSC). DSC denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those estimated using the automated framework. The FCM-based framework achieved the highest DSCs of 0.79±0.06, whereas DSCs of the ANN-based and SVM-based frameworks were 0.76±0.14 and 0.73±0.14, respectively. The FCM-based framework provided the highest segmentation accuracy and precision without a learning process (lowest calculation cost). Therefore, the FCM-based framework can be useful for delineation of tumor regions in practical treatment planning. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Evaluation of an advanced physical diagnosis course using consumer preferences methods: the nominal group technique.

PubMed

Coker, Joshua; Castiglioni, Analia; Kraemer, Ryan R; Massie, F Stanford; Morris, Jason L; Rodriguez, Martin; Russell, Stephen W; Shaneyfelt, Terrance; Willett, Lisa L; Estrada, Carlos A

2014-03-01

Current evaluation tools of medical school courses are limited by the scope of questions asked and may not fully engage the student to think on areas to improve. The authors sought to explore whether a technique to study consumer preferences would elicit specific and prioritized information for course evaluation from medical students. Using the nominal group technique (4 sessions), 12 senior medical students prioritized and weighed expectations and topics learned in a 100-hour advanced physical diagnosis course (4-week course; February 2012). Students weighted their top 3 responses (top = 3, middle = 2 and bottom = 1). Before the course, 12 students identified 23 topics they expected to learn; the top 3 were review sensitivity/specificity and high-yield techniques (percentage of total weight, 18.5%), improving diagnosis (13.8%) and reinforce usual and less well-known techniques (13.8%). After the course, students generated 22 topics learned; the top 3 were practice and reinforce advanced maneuvers (25.4%), gaining confidence (22.5%) and learn the evidence (16.9%). The authors observed no differences in the priority of responses before and after the course (P = 0.07). In a physical diagnosis course, medical students elicited specific and prioritized information using the nominal group technique. The course met student expectations regarding education of the evidence-based physical examination, building skills and confidence on the proper techniques and maneuvers and experiential learning. The novel use for curriculum evaluation may be used to evaluate other courses-especially comprehensive and multicomponent courses.

The effect of team accelerated instruction on students’ mathematics achievement and learning motivation

NASA Astrophysics Data System (ADS)

Sri Purnami, Agustina; Adi Widodo, Sri; Charitas Indra Prahmana, Rully

2018-01-01

This study aimed to know the improvement of achievement and motivation of learning mathematics by using Team Accelerated Instruction. The research method used was the experiment with descriptive pre-test post-test experiment. The population in this study was all students of class VIII junior high school in Jogjakarta. The sample was taken using cluster random sampling technique. The instrument used in this research was questionnaire and test. Data analysis technique used was Wilcoxon test. It concluded that there was an increase in motivation and student achievement of class VII on linear equation system material by using the learning model of Team Accelerated Instruction. Based on the results of the learning model Team Accelerated Instruction can be used as a variation model in learning mathematics.

Critical Response Protocol

ERIC Educational Resources Information Center

Ellingson, Charlene; Roehrig, Gillian; Bakkum, Kris; Dubinsky, Janet M.

2016-01-01

This article introduces the Critical Response Protocol (CRP), an arts-based technique that engages students in equitable critical discourse and aligns with the "Next Generation Science Standards" vision for providing students opportunities for language learning while advancing science learning (NGSS Lead States 2013). CRP helps teachers…

Problem Based Learning (PBL) - An Effective Approach to Improve Learning Outcomes in Medical Teaching.

PubMed

Preeti, Bajaj; Ashish, Ahuja; Shriram, Gosavi

2013-12-01

As the "Science of Medicine" is getting advanced day-by-day, need for better pedagogies & learning techniques are imperative. Problem Based Learning (PBL) is an effective way of delivering medical education in a coherent, integrated & focused manner. It has several advantages over conventional and age-old teaching methods of routine. It is based on principles of adult learning theory, including student's motivation, encouragement to set goals, think critically about decision making in day-to-day operations. Above all these, it stimulates challenge acceptance and learning curiosity among students and creates pragmatic educational program. To measure the effectiveness of the "Problem Based Learning" as compared to conventional theory/didactic lectures based learning. The study was conducted on 72 medical students from Dayanand Medical College & Hospital, Ludhiana. Two modules of problem based sessions designed and delivered. Pre & Post-test score's scientific statistical analysis was done. Student feed-back received based on questionnaire in the five-point Likert scale format. Significant improvement in overall performance observed. Feedback revealed majority agreement that "Problem-based learning" helped them create interest (88.8 %), better understanding (86%) & promotes self-directed subject learning (91.6 %). Substantial improvement in the post-test scores clearly reveals acceptance of PBL over conventional learning. PBL ensures better practical learning, ability to create interest, subject understanding. It is a modern-day educational strategy, an effective tool to objectively improve the knowledge acquisition in Medical Teaching.

Attitudes toward Game Adoption: Preservice Teachers Consider Game-Based Teaching and Learning

ERIC Educational Resources Information Center

Sardone, Nancy B.

2018-01-01

Gaming has become a core activity with children and more teachers are using games for learning than five years ago. Yet, teachers report that they learn about game titles, impact studies, and facilitation techniques through their own initiatives or from other teachers rather than from their teacher education program. This article reports on a…

Student Perceptions of Academic Service Learning: Using Mixed Content Analysis to Examine the Effectiveness of the International Baccalaureate Creativity, Action, Service Programme

ERIC Educational Resources Information Center

Hatziconstantis, Christos; Kolympari, Tania

2016-01-01

The International Baccalaureate Diploma Programme for secondary education students requires the successful completion of the Creativity, Action, Service (CAS) component (more recently renamed Creativity, Activity, Service) which is based on the philosophy of experiential learning and Academic Service Learning. In this article, the technique of…

Suggestive, Accelerative Learning and Teaching: A Manual of Classroom Procedures Based on the Lozanov Method.

ERIC Educational Resources Information Center

Schuster, Donald H.; And Others

The Suggestive Accelerative Learning and Teaching Method uses aspects of suggestion and unusual styles of presenting material to accelerate classroom learning. The essence of this technique is the use of a combination of physical relaxation exercises, mental concentration and suggestive principles to strengthen a person's ego and expand his memory…

Participant Comfort with and Application of Inquiry-Based Learning: Results from 4-H Volunteer Training

ERIC Educational Resources Information Center

Haugen, Heidi; Stevenson, Anne; Meyer, Rebecca L.

2016-01-01

This article explores how a one-time training designed to support learning transfer affected 4-H volunteers' comfort levels with the training content and how comfort levels, in turn, affected the volunteers' application of tools and techniques learned during the training. Results of a follow-up survey suggest that the training participants…

Is Active Learning Like Broccoli? Student Perceptions of Active Learning in Large Lecture Classes

ERIC Educational Resources Information Center

Smith, C. Veronica; Cardaciotto, LeeAnn

2011-01-01

Although research suggests that active learning is associated with positive outcomes (e.g., memory, test performance), use of such techniques can be difficult to implement in large lecture-based classes. In the current study, 1,091 students completed out-of-class group exercises to complement course material in an Introductory Psychology class.…

A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

ERIC Educational Resources Information Center

Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

2011-01-01

This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

Context-Based Intent Understanding for Autonomous Systems in Naval and Collaborative Robot Applications

DTIC Science & Technology

2013-10-29

COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d...based on contextual information, 3) develop vision-based techniques for learning of contextual information, and detection and identification of...that takes into account many possible contexts. The probability distributions of these contexts will be learned from existing databases on common sense

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

PubMed

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

PubMed Central

Tian, Yuling; Zhang, Hongxian

2016-01-01

For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242

m-Learning and holography: Compatible techniques?

NASA Astrophysics Data System (ADS)

Calvo, Maria L.

2014-07-01

Since the last decades, cell phones have become increasingly popular and are nowadays ubiquitous. New generations of cell phones are now equipped with text messaging, internet, and camera features. They are now making their way into the classroom. This is creating a new teaching and learning technique, the so called m-Learning (or mobile-Learning). Because of the many benefits that cell phones offer, teachers could easily use them as a teaching and learning tool. However, an additional work from the teachers for introducing their students into the m-Learning in the classroom needs to be defined and developed. As an example, optical techniques, based upon interference and diffraction phenomena, such as holography, appear to be convenient topics for m-Learning. They can be approached with simple examples and experiments within the cell phones performances and classroom accessibility. We will present some results carried out at the Faculty of Physical Sciences in UCM to obtain very simple holographic recordings via cell phones. The activities were carried out inside the course on Optical Coherence and Laser, offered to students in the fourth course of the Grade in Physical Sciences. Some open conclusions and proposals will be presented.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

Facilitating interpersonal interaction and learning online: linking theory and practice.

PubMed

Sargeant, Joan; Curran, Vernon; Allen, Michael; Jarvis-Selinger, Sandra; Ho, Kendall

2006-01-01

An earlier study of physicians' perceptions of interactive online learning showed that these were shaped both by program design and quality and the quality and quantity of interpersonal interaction. We explore instructor roles in enhancing online learning through interpersonal interaction and the learning theories that inform these. This was a qualitative study using focus groups and interviews. Using purposive sampling, 50 physicians were recruited based on their experience with interactive online CME and face-to-face CME. Qualitative thematic and interpretive analysis was used. Two facilitation roles appeared key: creating a comfortable learning environment and enhancing the educational value of electronic discussions. Comfort developed gradually, and specific interventions like facilitating introductions and sharing experiences in a friendly, informative manner were helpful. As in facilitating effective small-group learning, instructors' thoughtful use of techniques that facilitated constructive interaction based on learner's needs and practice demands contributed to the educational value of interpersonal interactions. Facilitators require enhanced skills to engage learners in meaningful interaction and to overcome the transactional distance of online learning. The use of learning theories, including behavioral, cognitive, social, humanistic, and constructivist, can strengthen the educational design and facilitation of online programs. Preparation for online facilitation should include instruction in the roles and techniques required and the theories that inform them.

Evolutionary neural networks for anomaly detection based on the behavior of a program.

PubMed

Han, Sang-Jun; Cho, Sung-Bae

2006-06-01

The process of learning the behavior of a given program by using machine-learning techniques (based on system-call audit data) is effective to detect intrusions. Rule learning, neural networks, statistics, and hidden Markov models (HMMs) are some of the kinds of representative methods for intrusion detection. Among them, neural networks are known for good performance in learning system-call sequences. In order to apply this knowledge to real-world problems successfully, it is important to determine the structures and weights of these call sequences. However, finding the appropriate structures requires very long time periods because there are no suitable analytical solutions. In this paper, a novel intrusion-detection technique based on evolutionary neural networks (ENNs) is proposed. One advantage of using ENNs is that it takes less time to obtain superior neural networks than when using conventional approaches. This is because they discover the structures and weights of the neural networks simultaneously. Experimental results with the 1999 Defense Advanced Research Projects Agency (DARPA) Intrusion Detection Evaluation (IDEVAL) data confirm that ENNs are promising tools for intrusion detection.

VEG: An intelligent workbench for analysing spectral reflectance data

NASA Technical Reports Server (NTRS)

Harrison, P. Ann; Harrison, Patrick R.; Kimes, Daniel S.

1994-01-01

An Intelligent Workbench (VEG) was developed for the systematic study of remotely sensed optical data from vegetation. A goal of the remote sensing community is to infer the physical and biological properties of vegetation cover (e.g. cover type, hemispherical reflectance, ground cover, leaf area index, biomass, and photosynthetic capacity) using directional spectral data. VEG collects together, in a common format, techniques previously available from many different sources in a variety of formats. The decision as to when a particular technique should be applied is nonalgorithmic and requires expert knowledge. VEG has codified this expert knowledge into a rule-based decision component for determining which technique to use. VEG provides a comprehensive interface that makes applying the techniques simple and aids a researcher in developing and testing new techniques. VEG also provides a classification algorithm that can learn new classes of surface features. The learning system uses the database of historical cover types to learn class descriptions of one or more classes of cover types.

Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data

PubMed Central

Kate, Rohit J.; Swartz, Ann M.; Welch, Whitney A.; Strath, Scott J.

2016-01-01

Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities. PMID:26862679

Student Conceptions about the DNA Structure within a Hierarchical Organizational Level: Improvement by Experiment- and Computer-Based Outreach Learning

ERIC Educational Resources Information Center

Langheinrich, Jessica; Bogner, Franz X.

2015-01-01

As non-scientific conceptions interfere with learning processes, teachers need both, to know about them and to address them in their classrooms. For our study, based on 182 eleventh graders, we analyzed the level of conceptual understanding by implementing the "draw and write" technique during a computer-supported gene technology module.…

SBL-Online: Implementing Studio-Based Learning Techniques in an Online Introductory Programming Course to Address Common Programming Errors and Misconceptions

ERIC Educational Resources Information Center

Polo, Blanca J.

2013-01-01

Much research has been done in regards to student programming errors, online education and studio-based learning (SBL) in computer science education. This study furthers this area by bringing together this knowledge and applying it to proactively help students overcome impasses caused by common student programming errors. This project proposes a…

Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

NASA Astrophysics Data System (ADS)

Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

2017-11-01

This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

Emerging Approach of Natural Language Processing in Opinion Mining: A Review

NASA Astrophysics Data System (ADS)

Kim, Tai-Hoon

Natural language processing (NLP) is a subfield of artificial intelligence and computational linguistics. It studies the problems of automated generation and understanding of natural human languages. This paper outlines a framework to use computer and natural language techniques for various levels of learners to learn foreign languages in Computer-based Learning environment. We propose some ideas for using the computer as a practical tool for learning foreign language where the most of courseware is generated automatically. We then describe how to build Computer Based Learning tools, discuss its effectiveness, and conclude with some possibilities using on-line resources.

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

Artificial neural networks and approximate reasoning for intelligent control in space

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

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

New Trends in Computer Assisted Language Learning and Teaching.

ERIC Educational Resources Information Center

Perez-Paredes, Pascual, Ed.; Cantos-Gomez, Pascual, Ed.

2002-01-01

Articles in this special issue include the following: "ICT and Modern Foreign Languages: Learning Opportunities and Training Needs" (Graham Davies); "Authoring, Pedagogy and the Web: Expectations Versus Reality" (Paul Bangs); "Web-based Instructional Environments: Tools and Techniques for Effective Second Language…

Electrical test prediction using hybrid metrology and machine learning

NASA Astrophysics Data System (ADS)

Breton, Mary; Chao, Robin; Muthinti, Gangadhara Raja; de la Peña, Abraham A.; Simon, Jacques; Cepler, Aron J.; Sendelbach, Matthew; Gaudiello, John; Emans, Susan; Shifrin, Michael; Etzioni, Yoav; Urenski, Ronen; Lee, Wei Ti

2017-03-01

Electrical test measurement in the back-end of line (BEOL) is crucial for wafer and die sorting as well as comparing intended process splits. Any in-line, nondestructive technique in the process flow to accurately predict these measurements can significantly improve mean-time-to-detect (MTTD) of defects and improve cycle times for yield and process learning. Measuring after BEOL metallization is commonly done for process control and learning, particularly with scatterometry (also called OCD (Optical Critical Dimension)), which can solve for multiple profile parameters such as metal line height or sidewall angle and does so within patterned regions. This gives scatterometry an advantage over inline microscopy-based techniques, which provide top-down information, since such techniques can be insensitive to sidewall variations hidden under the metal fill of the trench. But when faced with correlation to electrical test measurements that are specific to the BEOL processing, both techniques face the additional challenge of sampling. Microscopy-based techniques are sampling-limited by their small probe size, while scatterometry is traditionally limited (for microprocessors) to scribe targets that mimic device ground rules but are not necessarily designed to be electrically testable. A solution to this sampling challenge lies in a fast reference-based machine learning capability that allows for OCD measurement directly of the electrically-testable structures, even when they are not OCD-compatible. By incorporating such direct OCD measurements, correlation to, and therefore prediction of, resistance of BEOL electrical test structures is significantly improved. Improvements in prediction capability for multiple types of in-die electrically-testable device structures is demonstrated. To further improve the quality of the prediction of the electrical resistance measurements, hybrid metrology using the OCD measurements as well as X-ray metrology (XRF) is used. Hybrid metrology is the practice of combining information from multiple sources in order to enable or improve the measurement of one or more critical parameters. Here, the XRF measurements are used to detect subtle changes in barrier layer composition and thickness that can have second-order effects on the electrical resistance of the test structures. By accounting for such effects with the aid of the X-ray-based measurements, further improvement in the OCD correlation to electrical test measurements is achieved. Using both types of solution incorporation of fast reference-based machine learning on nonOCD-compatible test structures, and hybrid metrology combining OCD with XRF technology improvement in BEOL cycle time learning could be accomplished through improved prediction capability.

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.

PubMed

Gehrmann, Sebastian; Dernoncourt, Franck; Li, Yeran; Carlson, Eric T; Wu, Joy T; Welt, Jonathan; Foote, John; Moseley, Edward T; Grant, David W; Tyler, Patrick D; Celi, Leo A

2018-01-01

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

An Indoor Positioning Technique Based on a Feed-Forward Artificial Neural Network Using Levenberg-Marquardt Learning Method

NASA Astrophysics Data System (ADS)

Pahlavani, P.; Gholami, A.; Azimi, S.

2017-09-01

This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg-Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.

Endoscopic skull base training using 3D printed models with pre-existing pathology.

PubMed

Narayanan, Vairavan; Narayanan, Prepageran; Rajagopalan, Raman; Karuppiah, Ravindran; Rahman, Zainal Ariff Abdul; Wormald, Peter-John; Van Hasselt, Charles Andrew; Waran, Vicknes

2015-03-01

Endoscopic base of skull surgery has been growing in acceptance in the recent past due to improvements in visualisation and micro instrumentation as well as the surgical maturing of early endoscopic skull base practitioners. Unfortunately, these demanding procedures have a steep learning curve. A physical simulation that is able to reproduce the complex anatomy of the anterior skull base provides very useful means of learning the necessary skills in a safe and effective environment. This paper aims to assess the ease of learning endoscopic skull base exposure and drilling techniques using an anatomically accurate physical model with a pre-existing pathology (i.e., basilar invagination) created from actual patient data. Five models of a patient with platy-basia and basilar invagination were created from the original MRI and CT imaging data of a patient. The models were used as part of a training workshop for ENT surgeons with varying degrees of experience in endoscopic base of skull surgery, from trainees to experienced consultants. The surgeons were given a list of key steps to achieve in exposing and drilling the skull base using the simulation model. They were then asked to list the level of difficulty of learning these steps using the model. The participants found the models suitable for learning registration, navigation and skull base drilling techniques. All participants also found the deep structures to be accurately represented spatially as confirmed by the navigation system. These models allow structured simulation to be conducted in a workshop environment where surgeons and trainees can practice to perform complex procedures in a controlled fashion under the supervision of experts.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

Machine Learning for Education: Learning to Teach

DTIC Science & Technology

2016-12-01

such as commercial aviation, healthcare, and military operations. In the context of military applications, serious gaming – the training warfighters...problems. Playing these games not only allowed the warfighter to discover and learn new tactics, techniques, and procedures, but also allowed the...collecting information across relevant sample sizes have motivated a data-driven, game - based simulation approach. For example, industry and academia alike

Analysis of Selected Aspects of Students' Performance and Satisfaction in a Moodle-Based E-Learning System Environment

ERIC Educational Resources Information Center

Umek, Lan; Aristovnik, Aleksander; Tomaževic, Nina; Keržic, Damijana

2015-01-01

The use of e-learning techniques in higher education is becoming ever more frequent. In some institutions, e-learning has completely replaced the traditional teaching methods, while in others it supplements classical courses. The paper presents a study conducted in a member institution of the University of Ljubljana that provides public…

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

ERIC Educational Resources Information Center

Mathews, Anne

2017-01-01

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

The Delphi Technique in Identifying Learning Objectives for the Development of Science, Technology and Society Modules for Palestinian Ninth Grade Science Curriculum

NASA Astrophysics Data System (ADS)

Abualrob, Marwan M. A.; Gnanamalar Sarojini Daniel, Esther

2013-10-01

This article outlines how learning objectives based upon science, technology and society (STS) elements for Palestinian ninth grade science textbooks were identified, which was part of a bigger study to establish an STS foundation in the ninth grade science curriculum in Palestine. First, an initial list of STS elements was determined. Second, using this list, ninth grade science textbooks and curriculum document contents were analyzed. Third, based on this content analysis, a possible list of 71 learning objectives for the integration of STS elements was prepared. This list of learning objectives was refined by using a two-round Delphi technique. The Delphi study was used to rate and to determine the consensus regarding which items (i.e. learning objectives for STS in the ninth grade science textbooks in Palestine) are to be accepted for inclusion. The results revealed that of the initial 71 objectives in round one, 59 objectives within round two had a mean score of 5.683 or higher, which indicated that the learning objectives could be included in the development of STS modules for ninth grade science in Palestine.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

The Analysis of Physics Learning in Senior High School of Semarang Based on The Scientific Approach and Assessment

NASA Astrophysics Data System (ADS)

Hardyanti, R. C.; Hartono; Fianti

2018-03-01

Physics Learning in Curriculum of 2013 is closely related to the implementation of scientific approach and authentic assessment in learning. This study aims to analyze the implementation of scientific approaches and authentic assessment in physics learning, as well as to analyze the constraints of scientific approach and authentic assessment in physics learning. The data collection techniques used in this study are questionnaires, observations, interviews, and documentation. The calculation results used are percentage techniques and analyzed by using qualitative descriptive approach. Based on the results of research and discussion, the implementation of physics learning based on the scientific approach goes well with the percentage of 84.60%. Physical learning activity based on authentic assessment also goes well with the percentage of 88%. The results of the percentage of scientific approaches and authentic assessment approaches are less than 100%. It shows that there are obstacles to the implementation of the scientific approach and the constraints of authentic assessment. The obstacles to the implementation of scientific approach include time, heavy load of material, input or ability of learners, the willingness of learners in asking questions, laboratory support, and the ability of students to process data. While the obstacles to the implementation of authentic assessment include the limited time for carrying out of authentic assessment, the components of the criteria in carrying out the authentic assessment, the lack of discipline in administering the administration, the difficulty of changing habits in carrying out the assessment from traditional assessment to the authentic assessment, the obstacle to process the score in accordance with the format Curriculum of 2013.

The development of learning materials based on core model to improve students’ learning outcomes in topic of Chemical Bonding

NASA Astrophysics Data System (ADS)

Avianti, R.; Suyatno; Sugiarto, B.

2018-04-01

This study aims to create an appropriate learning material based on CORE (Connecting, Organizing, Reflecting, Extending) model to improve students’ learning achievement in Chemical Bonding Topic. This study used 4-D models as research design and one group pretest-posttest as design of the material treatment. The subject of the study was teaching materials based on CORE model, conducted on 30 students of Science class grade 10. The collecting data process involved some techniques such as validation, observation, test, and questionnaire. The findings were that: (1) all the contents were valid, (2) the practicality and the effectiveness of all the contents were good. The conclusion of this research was that the CORE model is appropriate to improve students’ learning outcomes for studying Chemical Bonding.

Nontrivial, Nonintelligent, Computer-Based Learning.

ERIC Educational Resources Information Center

Bork, Alfred

1987-01-01

This paper describes three interactive computer programs used with personal computers to present science learning modules for all ages. Developed by groups of teachers at the Educational Technology Center at the University of California, Irvine, these instructional materials do not use the techniques of contemporary artificial intelligence. (GDC)

An Exploration of Community Learning Disability Nurses' Therapeutic Role

ERIC Educational Resources Information Center

Marsham, Marian

2012-01-01

This literature review and primary qualitative research explores therapeutic role from the perspective of Community Learning Disability Nurses. Semi-structured interviews, based on Critical Incident Technique ("Psychol Bull", 51, 1954, 327), and descriptive phenomenological methodology were adopted to elicit data amenable to systematic…

Reflection on Cuboid Net with Mathematical Learning Quality

NASA Astrophysics Data System (ADS)

Sari, Atikah; Suryadi, Didi; Syaodih, Ernawulan

2017-09-01

This research aims to formulate an alternative to the reflection in mathematics learning activities related to the activities of the professionalism of teachers motivated by a desire to improve the quality of learning. This study is a qualitative study using the Didactical Design research. This study was conducted in one of the elementary schools. The data collection techniques are triangulation with the research subject is teacher 5th grade. The results of this study indicate that through deep reflection, teachers can design learning design in accordance with the conditions of the class. Also revealed that teachers have difficulty in choosing methods of learning and contextual learning media. Based on the implementation of activities of reflection and make the learning design based on the results of reflection can be concluded that the quality of learning in the class will develop.

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.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

The Desired Learning Outcomes of School-Based Nutrition/Physical Activity Health Education: A Health Literacy Constructed Delphi Survey of Finnish Experts

ERIC Educational Resources Information Center

Ormshaw, Michael James; Kokko, Sami Petteri; Villberg, Jari; Kannas, Lasse

2016-01-01

Purpose: The purpose of this paper is to utilise the collective opinion of a group of Finnish experts to identify the most important learning outcomes of secondary-level school-based health education, in the specific domains of physical activity and nutrition. Design/ Methodology/ Approach: The study uses a Delphi survey technique to collect the…

Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

NASA Astrophysics Data System (ADS)

Calvin Frans Mariel, Wahyu; Mariyah, Siti; Pramana, Setia

2018-03-01

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.

Peer Learning in a MATLAB Programming Course

NASA Astrophysics Data System (ADS)

Reckinger, Shanon

2016-11-01

Three forms of research-based peer learning were implemented in the design of a MATLAB programming course for mechanical engineering undergraduate students. First, a peer learning program was initiated. These undergraduate peer learning leaders played two roles in the course, (I) they were in the classroom helping students' with their work, and, (II) they led optional two hour helps sessions outside of the class time. The second form of peer learning was implemented through the inclusion of a peer discussion period following in class clicker quizzes. The third form of peer learning had the students creating video project assignments and posting them on YouTube to explain course topics to their peers. Several other more informal techniques were used to encourage peer learning. Student feedback in the form of both instructor-designed survey responses and formal course evaluations (quantitative and narrative) will be presented. Finally, effectiveness will be measured by formal assessment, direct and indirect to these peer learning methods. This will include both academic data/grades and pre/post test scores. Overall, the course design and its inclusion of these peer learning techniques demonstrate effectiveness.

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

PubMed Central

Kudisthalert, Wasu

2018-01-01

Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6. PMID:29652912

Navajo Area Health and Physical Education Curriculum Guidelines.

ERIC Educational Resources Information Center

Tomah, Kent; And Others

Based on health education needs of Navajo children as established by the Navajo Area health and physical education committees, this curriculum guideline for health and physical education is delineated into three phases reflecting emphasis of instructional techniques (introductory, exploration/extended learning, widened learning) and three levels…

Timely Diagnostic Feedback for Database Concept Learning

ERIC Educational Resources Information Center

Lin, Jian-Wei; Lai, Yuan-Cheng; Chuang, Yuh-Shy

2013-01-01

To efficiently learn database concepts, this work adopts association rules to provide diagnostic feedback for drawing an Entity-Relationship Diagram (ERD). Using association rules and Asynchronous JavaScript and XML (AJAX) techniques, this work implements a novel Web-based Timely Diagnosis System (WTDS), which provides timely diagnostic feedback…

Digital education and dynamic assessment of tongue diagnosis based on Mashup technique.

PubMed

Tsai, Chin-Chuan; Lo, Yen-Cheng; Chiang, John Y; Sainbuyan, Natsagdorj

2017-01-24

To assess the digital education and dynamic assessment of tongue diagnosis based on Mashup technique (DEDATD) according to specifific user's answering pattern, and provide pertinent information tailored to user's specifific needs supplemented by the teaching materials constantly updated through the Mashup technique. Fifty-four undergraduate students were tested with DEDATD developed. The effificacy of the DEDATD was evaluated based on the pre- and post-test performance, with interleaving training sessions targeting on the weakness of the student under test. The t-test demonstrated that signifificant difference was reached in scores gained during pre- and post-test sessions, and positive correlation between scores gained and length of time spent on learning, while no signifificant differences between the gender and post-test score, and the years of students in school and the progress in score gained. DEDATD, coupled with Mashup technique, could provide updated materials fifiltered through diverse sources located across the network. The dynamic assessment could tailor each individual learner's needs to offer custom-made learning materials. DEDATD poses as a great improvement over the traditional teaching methods.

Review of Fluorescence-Based Velocimetry Techniques to Study High-Speed Compressible Flows

NASA Technical Reports Server (NTRS)

Bathel, Brett F.; Johansen, Criag; Inman, Jennifer A.; Jones, Stephen B.; Danehy, Paul M.

2013-01-01

This paper reviews five laser-induced fluorescence-based velocimetry techniques that have been used to study high-speed compressible flows at NASA Langley Research Center. The techniques discussed in this paper include nitric oxide (NO) molecular tagging velocimetry (MTV), nitrogen dioxide photodissociation (NO2-to-NO) MTV, and NO and atomic oxygen (O-atom) Doppler-shift-based velocimetry. Measurements of both single-component and two-component velocity have been performed using these techniques. This paper details the specific application and experiment for which each technique has been used, the facility in which the experiment was performed, the experimental setup, sample results, and a discussion of the lessons learned from each experiment.

For the Love of the Game: Game- Versus Lecture-Based Learning With Generation Z Patients.

PubMed

Adamson, Mary A; Chen, Hengyi; Kackley, Russell; Micheal, Alicia

2018-02-01

The current study evaluated adolescent patients' enjoyment of and knowledge gained from game-based learning compared with an interactive lecture format on the topic of mood disorders. It was hypothesized that game-based learning would be statistically more effective than a lecture in knowledge acquisition and satisfaction scores. A pre-post design was implemented in which a convenience sample of 160 adolescent patients were randomized to either a lecture (n = 80) or game-based (n = 80) group. Both groups completed a pretest/posttest and satisfaction survey. Results showed that both groups had significant improvement in knowledge from pretest compared to posttest. Game-based learning was statistically more effective than the interactive lecture in knowledge achievement and satisfaction scores. This finding supports the contention that game-based learning is an active technique that may be used with patient education. [Journal of Psychosocial Nursing and Mental Health Services, 56(2), 29-36.]. Copyright 2018, SLACK Incorporated.

[Problem-based learning, a strategy to employ it].

PubMed

Guillamet Lloveras, Ana; Celma Vicente, Matilde; González Carrión, Pilar; Cano-Caballero Gálvez, Ma Dolores; Pérez Ramírez, Francisca

2009-02-01

The Virgen de las Nieves University School of Nursing has adopted the methodology of Problem-Based Learning (ABP in Spanish acronym) as a supplementary method to gain specific transversal competencies. In so doing, all basic required/obligatory subjects necessary for a degree have been partially affected. With the objective of identifying and administering all the structural and cultural barriers which could impede the success or effectiveness of its adoption, a strategic analysis at the School was carried out. This technique was based on a) knowing the strong and weak points the School has for adopting the Problem-Based Learning methodology; b) describing the structural problems and necessities to carry out this teaching innovation; c) to discover the needs professors have regarding knowledge and skills related to Problem-Based Learning; d) to prepare students by informing them about the characteristics of Problem-Based Learning; e) to evaluate the results obtained by means of professor and student opinions, f) to adopt the improvements identified. The stages followed were: strategic analysis, preparation, pilot program, adoption and evaluation.

The Effectiveness of Song Technique in Teaching Paper Based TOEFL (PBT)'s Listening Comprehension Section

ERIC Educational Resources Information Center

Kuswoyo, Heri

2013-01-01

Among three sections that follow the Paper-Based TOEFL (PBT), many test takers find listening comprehension section is the most difficult. Thus, in this research the researcher aims to explore how students learn PBT's listening comprehension section effectively through song technique. This sounds like a more interesting and engaging way to learn…

Applied learning-based color tone mapping for face recognition in video surveillance system

NASA Astrophysics Data System (ADS)

Yew, Chuu Tian; Suandi, Shahrel Azmin

2012-04-01

In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

Diminishing-cues retrieval practice: A memory-enhancing technique that works when regular testing doesn't.

PubMed

Fiechter, Joshua L; Benjamin, Aaron S

2017-08-28

Retrieval practice has been shown to be a highly effective tool for enhancing memory, a fact that has led to major changes to educational practice and technology. However, when initial learning is poor, initial retrieval practice is unlikely to be successful and long-term benefits of retrieval practice are compromised or nonexistent. Here, we investigate the benefit of a scaffolded retrieval technique called diminishing-cues retrieval practice (Finley, Benjamin, Hays, Bjork, & Kornell, Journal of Memory and Language, 64, 289-298, 2011). Under learning conditions that favored a strong testing effect, diminishing cues and standard retrieval practice both enhanced memory performance relative to restudy. Critically, under learning conditions where standard retrieval practice was not helpful, diminishing cues enhanced memory performance substantially. These experiments demonstrate that diminishing-cues retrieval practice can widen the range of conditions under which testing can benefit memory, and so can serve as a model for the broader application of testing-based techniques for enhancing learning.

Student Perceptions of Value Added in an Active Learning Experience: Producing, Reviewing and Evaluating a Sales Team Video Presentation

ERIC Educational Resources Information Center

Corbett, James J.; Kezim, Boualem; Stewart, James

2010-01-01

This study investigates the effectiveness of a video team-based activity as a learning experience in a sales management course. Students perceived this learning activity approach as a beneficial and effective instructional technique. The benefits of making a video in a marketing course reinforce the understanding and the use of the sales process…

A Modified Moore Approach to Teaching Mathematical Statistics: An Inquiry Based Learning Technique to Teaching Mathematical Statistics

ERIC Educational Resources Information Center

McLoughlin, M. Padraig M. M.

2008-01-01

The author of this paper submits the thesis that learning requires doing; only through inquiry is learning achieved, and hence this paper proposes a programme of use of a modified Moore method in a Probability and Mathematical Statistics (PAMS) course sequence to teach students PAMS. Furthermore, the author of this paper opines that set theory…

Joint fMRI analysis and subject clustering using sparse dictionary learning

NASA Astrophysics Data System (ADS)

Kim, Seung-Jun; Dontaraju, Krishna K.

2017-08-01

Multi-subject fMRI data analysis methods based on sparse dictionary learning are proposed. In addition to identifying the component spatial maps by exploiting the sparsity of the maps, clusters of the subjects are learned by postulating that the fMRI volumes admit a subspace clustering structure. Furthermore, in order to tune the associated hyper-parameters systematically, a cross-validation strategy is developed based on entry-wise sampling of the fMRI dataset. Efficient algorithms for solving the proposed constrained dictionary learning formulations are developed. Numerical tests performed on synthetic fMRI data show promising results and provides insights into the proposed technique.

Development of Control Teaching Material for Mechatronics Education Based on Experience

NASA Astrophysics Data System (ADS)

Tasaki, Takao; Watanabe, Shinichi; Shikanai, Yoshihito; Ozaki, Koichi

In this paper, we have developed a teaching material for technical high school students to understand the control technique. The material makes the students understanding the control technique through the sensibility obtained from the experience of riding the robot. We have considered the correspondence of the teaching material with the ARCS Model. Therefore, the material aims to improve the interest and the willingness to learn mechatronics and control technique by experiencing the difference of the response by the change in the control parameters. As the results of the questionnaire to the technical high school students in the class, we have verified educative effect of the teaching material which can be improved willingness of learning and interesting for mechatronics and control technique.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

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.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Using Deep Learning for Tropical Cyclone Intensity Estimation

NASA Astrophysics Data System (ADS)

Miller, J.; Maskey, M.; Berendes, T.

2017-12-01

Satellite-based techniques are the primary approach to estimating tropical cyclone (TC) intensity. Tropical cyclone warning centers worldwide still apply variants of the Dvorak technique for such estimations that include visual inspection of the satellite images. The National Hurricane Center (NHC) estimates about 10-20% uncertainty in its post analyses when only satellite-based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to TC intensity. With the ever-increasing quality and quantity of satellite observations of TCs, deep learning techniques designed to excel at pattern recognition have become more relevant in this area of study. In our current study, we aim to provide a fully objective approach to TC intensity estimation by utilizing deep learning in the form of a convolutional neural network trained to predict TC intensity (maximum sustained wind speed) using IR satellite imagery. Large amounts of training data are needed to train a convolutional neural network, so we use GOES IR images from historical tropical storms from the Atlantic and Pacific basins spanning years 2000 to 2015. Images are labeled using a special subset of the HURDAT2 dataset restricted to time periods with airborne reconnaissance data available in order to improve the quality of the HURDAT2 data. Results and the advantages of this technique are to be discussed.

Introduction to a New Approach to Experiential Learning.

ERIC Educational Resources Information Center

Jackson, Lewis; MacIsaac, Doug

1994-01-01

A process model for experiential learning (EL) in adult education begins with the characteristics and needs of adult learners and conceptual foundations of EL. It includes methods and techniques for in-class and field-based experiences, building a folio (point-in-time performance assessment), and portfolio construction (assessing transitional…

Suggestopedia: A New Way to Learn.

ERIC Educational Resources Information Center

Thomas, Elaine

Suggestopedia is a recent teaching technique which enables students to learn with impressive speed, little conscious effort, and a great deal of pleasure. Developed by Georgi Lozanov, suggestopedia is based on the assumption that a number of environmental, social, and psychological variables can be altered to make more effective use of students'…

Exploring Physics in the Classroom

ERIC Educational Resources Information Center

Amann, George

2005-01-01

The key to learning is student involvement! This American Association of Physics Teachers/Physics Teaching Resource Agents (AAPT/PTRA) manual presents examples of two techniques that are proven to increase student involvement in your classroom. Based on the "5E" model of learning, exploratories are designed to get your students excited about the…

Perceptions of Saudi Students towards Electronic and Traditional Writing Groups

ERIC Educational Resources Information Center

Alqurashi, Fahad

2008-01-01

This paper reports the findings of an experiment that investigated the reactions of Saudi college students to collaborative learning techniques introduced in two modalities: face-to-face and web-based learning. Quantitative data were collected with a questionnaire that examined the changes of three constructs: attitudes toward collaboration,…

The "Iron Inventor": Using Creative Problem Solving to Spur Student Creativity

ERIC Educational Resources Information Center

Lee, Seung Hwan; Hoffman, K. Douglas

2014-01-01

Based on the popular television show the "Iron Chef," an innovative marketing activity called the "Iron Inventor" is introduced. Using the creative problem-solving approach and active learning techniques, the Iron Inventor facilitates student learning pertaining to the step-by-step processes of creating a new product and…

Improving Transfer of Learning in a Computer Based Classroom.

ERIC Educational Resources Information Center

Davis, Jay Bee

This report describes a program for improving the transfer of the learning of different techniques used in computer applications. The targeted population consisted of sophomores and juniors in a suburban high school in a middle class community. The problem was documented through teacher surveys, student surveys, anecdotal records and behavioral…

How Small Is a Cell?

ERIC Educational Resources Information Center

Rau, Gerald

2004-01-01

In this article, the author talks about an inquiry-based activity involving yeast, wherein students learned about cell size. The activity allows students to employ math connections and to learn experimental techniques while practicing microscope skills. The activity can be adapted for students at all levels of biology. The author presents details…

ALES: An Innovative Argument-Learning Environment

ERIC Educational Resources Information Center

Abbas, Safia; Sawamura, Hajime

2010-01-01

This paper presents the development of an Argument-Learning System (ALES). The idea is based on the AIF (argumentation interchange format) ontology using "Walton theory". ALES uses different mining techniques to manage a highly structured arguments repository. This repository was designed, developed and implemented by the authors. The aim is to…

Building Shared Responsibility for Student Learning.

ERIC Educational Resources Information Center

Conzemius, Anne; O'Neill, Jan

Shared responsibility for student learning is neither a program nor a curriculum. It incorporates a set of principles and techniques that gives members of a school community the authority and responsibility to create what is needed, based on the data and culture of their particular school and school district. Sharing responsibility for student…

Linear- and Repetitive Feature Detection Within Remotely Sensed Imagery

DTIC Science & Technology

2017-04-01

applicable to Python or other pro- gramming languages with image- processing capabilities. 4.1 Classification machine learning The first methodology uses...remotely sensed images that are in panchromatic or true-color formats. Image- processing techniques, in- cluding Hough transforms, machine learning, and...data fusion .................................................................................................... 44 6.3 Context-based processing

How Does Professional Development Improve Teaching?

ERIC Educational Resources Information Center

Kennedy, Mary M.

2016-01-01

Professional development programs are based on different theories of how students learn and different theories of how teachers learn. Reviewers often sort programs according to design features such as program duration, intensity, or the use of specific techniques such as coaches or online lessons, but these categories do not illuminate the…

Smart Training, Smart Learning: The Role of Cooperative Learning in Training for Youth Services.

ERIC Educational Resources Information Center

Doll, Carol A.

1997-01-01

Examines cooperative learning in youth services and adult education. Discusses characteristics of cooperative learning techniques; specific cooperative learning techniques (brainstorming, mini-lecture, roundtable technique, send-a-problem problem solving, talking chips technique, and three-step interview); and the role of the trainer. (AEF)

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Development of X-33/X-34 Aerothermodynamic Data Bases: Lessons Learned and Future Enhancements

NASA Technical Reports Server (NTRS)

Miller, C. G.

1999-01-01

A synoptic of programmatic and technical lessons learned in the development of aerothermodynamic data bases for the X-33 and X-34 programs is presented in general terms and from the perspective of the NASA Langley Research Center Aerothermodynamics Branch. The format used is that of the aerothermodynamic chain, the links of which are personnel, facilities, models/test articles, instrumentation, test techniques, and computational fluid dynamics (CFD). Because the aerodynamic data bases upon which the X-33 and X-34 vehicles will fly are almost exclusively from wind tunnel testing, as opposed to CFD, the primary focus of the lessons learned is on ground-based testing.

Learning outcomes and student-perceived value of clay modeling and cat dissection in undergraduate human anatomy and physiology.

PubMed

DeHoff, Mary Ellen; Clark, Krista L; Meganathan, Karthikeyan

2011-03-01

Alternatives and/or supplements to animal dissection are being explored by educators of human anatomy at different academic levels. Clay modeling is one such alternative that provides a kinesthetic, three-dimensional, constructive, and sensory approach to learning human anatomy. The present study compared two laboratory techniques, clay modeling of human anatomy and dissection of preserved cat specimens, in the instruction of muscles, peripheral nerves, and blood vessels. Specifically, we examined the effect of each technique on student performance on low-order and high-order questions related to each body system as well as the student-perceived value of each technique. Students who modeled anatomic structures in clay scored significantly higher on low-order questions related to peripheral nerves; scores were comparable between groups for high-order questions on peripheral nerves and for questions on muscles and blood vessels. Likert-scale surveys were used to measure student responses to statements about each laboratory technique. A significantly greater percentage of students in the clay modeling group "agreed" or "strongly agreed" with positive statements about their respective technique. These results indicate that clay modeling and cat dissection are equally effective in achieving student learning outcomes for certain systems in undergraduate human anatomy. Furthermore, clay modeling appears to be the preferred technique based on students' subjective perceptions of value to their learning experience.

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

PubMed

Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

2018-02-12

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

A lightweight network anomaly detection technique

DOE PAGES

Kim, Jinoh; Yoo, Wucherl; Sim, Alex; ...

2017-03-13

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 andmore » NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.« less

A lightweight network anomaly detection technique

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

Kim, Jinoh; Yoo, Wucherl; Sim, Alex

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 andmore » NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.« less

Three Reading Comprehension Strategies: TELLS, Story Mapping, and QARs.

ERIC Educational Resources Information Center

Sorrell, Adrian L.

1990-01-01

Three reading comprehension strategies are presented to assist learning-disabled students: an advance organizer technique called "TELLS Fact or Fiction" used before reading a passage, a schema-based technique called "Story Mapping" used while reading, and a postreading method of categorizing questions called…

The Effects of Practice-Based Training on Graduate Teaching Assistants' Classroom Practices.

PubMed

Becker, Erin A; Easlon, Erin J; Potter, Sarah C; Guzman-Alvarez, Alberto; Spear, Jensen M; Facciotti, Marc T; Igo, Michele M; Singer, Mitchell; Pagliarulo, Christopher

2017-01-01

Evidence-based teaching is a highly complex skill, requiring repeated cycles of deliberate practice and feedback to master. Despite existing well-characterized frameworks for practice-based training in K-12 teacher education, the major principles of these frameworks have not yet been transferred to instructor development in higher educational contexts, including training of graduate teaching assistants (GTAs). We sought to determine whether a practice-based training program could help GTAs learn and use evidence-based teaching methods in their classrooms. We implemented a weekly training program for introductory biology GTAs that included structured drills of techniques selected to enhance student practice, logic development, and accountability and reduce apprehension. These elements were selected based on their previous characterization as dimensions of active learning. GTAs received regular performance feedback based on classroom observations. To quantify use of target techniques and levels of student participation, we collected and coded 160 h of video footage. We investigated the relationship between frequency of GTA implementation of target techniques and student exam scores; however, we observed no significant relationship. Although GTAs adopted and used many of the target techniques with high frequency, techniques that enforced student participation were not stably adopted, and their use was unresponsive to formal feedback. We also found that techniques discussed in training, but not practiced, were not used at quantifiable frequencies, further supporting the importance of practice-based training for influencing instructional practices. © 2017 E. A. Becker et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Lesion Detection in CT Images Using Deep Learning Semantic Segmentation Technique

NASA Astrophysics Data System (ADS)

Kalinovsky, A.; Liauchuk, V.; Tarasau, A.

2017-05-01

In this paper, the problem of automatic detection of tuberculosis lesion on 3D lung CT images is considered as a benchmark for testing out algorithms based on a modern concept of Deep Learning. For training and testing of the algorithms a domestic dataset of 338 3D CT scans of tuberculosis patients with manually labelled lesions was used. The algorithms which are based on using Deep Convolutional Networks were implemented and applied in three different ways including slice-wise lesion detection in 2D images using semantic segmentation, slice-wise lesion detection in 2D images using sliding window technique as well as straightforward detection of lesions via semantic segmentation in whole 3D CT scans. The algorithms demonstrate superior performance compared to algorithms based on conventional image analysis methods.

Clustering Single-Cell Expression Data Using Random Forest Graphs.

PubMed

Pouyan, Maziyar Baran; Nourani, Mehrdad

2017-07-01

Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.

Learning class descriptions from a data base of spectral reflectance with multiple view angles

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Harrison, Patrick R.; Harrison, P. A.

1992-01-01

A learning program has been developed which combines 'learning by example' with the generate-and-test paradigm to furnish a robust learning environment capable of handling error-prone data. The problem is shown to be capable of learning class descriptions from positive and negative training examples of spectral and directional reflectance data taken from soil and vegetation. The program, which used AI techniques to automate very tedious processes, found the sequence of relationships that contained the most important information which could distinguish the classes.

Machine learning for medical images analysis.

PubMed

Criminisi, A

2016-10-01

This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications

PubMed Central

Chang, Hang; Han, Ju; Zhong, Cheng; Snijders, Antoine M.; Mao, Jian-Hua

2017-01-01

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains. PMID:28129148

Training and certification in endobronchial ultrasound-guided transbronchial needle aspiration

PubMed Central

Konge, Lars; Nayahangan, Leizl Joy; Clementsen, Paul Frost

2017-01-01

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) plays a key role in the staging of lung cancer, which is crucial for allocation to surgical treatment. EBUS-TBNA is a complicated procedure and simulation-based training is helpful in the first part of the long learning curve prior to performing the procedure on actual patients. New trainees should follow a structured training programme consisting of training on simulators to proficiency as assessed with a validated test followed by supervised practice on patients. The simulation-based training is superior to the traditional apprenticeship model and is recommended in the newest guidelines. EBUS-TBNA and oesophageal ultrasound-guided fine needle aspiration (EUS-FNA or EUS-B-FNA) are complementary to each other and the combined techniques are superior to either technique alone. It is logical to learn and to perform the two techniques in combination, however, for lung cancer staging solely EBUS-TBNA simulators exist, but hopefully in the future simulation-based training in EUS will be possible. PMID:28840013

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

PubMed

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.

A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion

NASA Astrophysics Data System (ADS)

Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

2017-09-01

Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in exascale simulations.

Using Elearning techniques to support problem based learning within a clinical simulation laboratory.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2004-01-01

This paper details the results of the first phase of a project that used eLearning to support students' learning within a simulated environment. The locus was a purpose built Clinical Simulation Laboratory (CSL) where the School's newly adopted philosophy of Problem Based Learning (PBL) was challenged through lecturers reverting to traditional teaching methods. The solution, a student-centred, problem-based approach to the acquisition of clinical skills was developed using learning objects embedded within web pages that substituted for lecturers providing instruction and demonstration. This allowed lecturers to retain their facilitator role, and encouraged students to explore, analyse and make decisions within the safety of a clinical simulation. Learning was enhanced through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that an elearning approach can support PBL in delivering a student centred learning experience.

On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

NASA Astrophysics Data System (ADS)

Pini, Giovanni; Tuci, Elio

2008-06-01

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).

Conceptual and operational understanding of learning for sustainability: a case study of the beef industry in north-eastern Australia.

PubMed

Lankester, Ally J

2013-04-15

Extensive attention has been given to understanding learning processes that foster sustainability. Despite this focus there is still limited knowledge of learning processes that create changes in perspectives and practices. This paper aims to increase understanding of learning processes in the context of sustainability and refers to the beef industry in north-eastern Australia. A framework based on adult learning theories was developed and used to analyse the what, why and how of beef producers' learning to improve land condition. Twenty-eight producers were interviewed face-to-face and another 91 participated in a telephone survey. Most beef producers were motivated to learn due to perceived problems with existing practices and described mainly learning new skills and techniques to improve production. Beef producers main learning sources were their own experiences, observing others' practices and sharing experiences with peers and family members. Results showed that organised collective learning, adversity and active experimentation with natural resource management skills and techniques can facilitate critical reflection of practices, questioning of the self, others and cultural norms and an enhanced sense of environmental responsibility. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

Application of Metamorphic Testing to Supervised Classifiers

PubMed Central

Xie, Xiaoyuan; Ho, Joshua; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2010-01-01

Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no “test oracle” to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called “metamorphic testing”, which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well. PMID:21243103

Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

PubMed

Hussain, Lal; Ahmed, Adeel; Saeed, Sharjil; Rathore, Saima; Awan, Imtiaz Ahmed; Shah, Saeed Arif; Majid, Abdul; Idris, Adnan; Awan, Anees Ahmed

2018-02-06

Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

A Machine Learning Concept for DTN Routing

NASA Technical Reports Server (NTRS)

Dudukovich, Rachel; Hylton, Alan; Papachristou, Christos

2017-01-01

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

Using Machine Learning for Behavior-Based Access Control: Scalable Anomaly Detection on TCP Connections and HTTP Requests

DTIC Science & Technology

2013-11-01

machine learning techniques used in BBAC to make predictions about the intent of actors establishing TCP connections and issuing HTTP requests. We discuss pragmatic challenges and solutions we encountered in implementing and evaluating BBAC, discussing (a) the general concepts underlying BBAC, (b) challenges we have encountered in identifying suitable datasets, (c) mitigation strategies to cope...and describe current plans for transitioning BBAC capabilities into the Department of Defense together with lessons learned for the machine learning

E-Learning as a new tool in bioinformatics teaching

PubMed Central

Saravanan, Vijayakumar; Shanmughavel, Piramanayagam

2007-01-01

In recent years, virtual learning is growing rapidly. Universities, colleges, and secondary schools are now delivering training and education over the internet. Beside this, resources available over the WWW are huge and understanding the various techniques employed in the field of Bioinformatics is increasingly complex for students during implementation. Here, we discuss its importance in developing and delivering an educational system in Bioinformatics based on e-learning environment. PMID:18292800

Learning Design Implementation for Distance e-Learning: Blending Rapid e-Learning Techniques with Activity-Based Pedagogies to Design and Implement a Socio-Constructivist Environment

ERIC Educational Resources Information Center

Santally, Mohammad Issack; Rajabalee, Yousra; Cooshna-Naik, Dorothy

2012-01-01

This paper discusses how modern technologies are changing the teacher-student-content relationships from the conception to the delivery of so-called "distance" education courses. The concept of Distance Education has greatly evolved in the digital era of 21st Century. With the widespread use and access to the Internet, exponential growth…

Working Notes of the 1990 Spring Symposium on Automated Abduction

DTIC Science & Technology

1990-09-27

possibilities for abstracting the leaf nodes in using apprenticeship learning techniques. In LTCAI.E the proof tree. Morgan Kaufmann, 1987. A detailed...ibm.com Abstract planation process and compute particular operational A major limitation of explanation-based learn - descriptions of the target...for the learning that would be difficult or impos- 3n educated, somewhat abstract guess at why the pro- sible using abduction. I position is likely to

Summary of Progress on SIG Ft. Ord ESTCP DemVal

DTIC Science & Technology

2007-04-01

We report on progress under an ESTCP demonstration plan dedicated to demonstrating active learning - based UXO detection on an actual former UXO site...Ft. Ord), using EMI data. In addition to describing the details of the active - learning algorithm, we discuss techniques that were required when...terms of two dipole-moment magnitudes and two resonant frequencies. Information-theoretic active learning is then conducted on all anomalies to

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

PubMed

Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

2017-01-01

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Improving wave forecasting by integrating ensemble modelling and machine learning

NASA Astrophysics Data System (ADS)

O'Donncha, F.; Zhang, Y.; James, S. C.

2017-12-01

Modern smart-grid networks use technologies to instantly relay information on supply and demand to support effective decision making. Integration of renewable-energy resources with these systems demands accurate forecasting of energy production (and demand) capacities. For wave-energy converters, this requires wave-condition forecasting to enable estimates of energy production. Current operational wave forecasting systems exhibit substantial errors with wave-height RMSEs of 40 to 60 cm being typical, which limits the reliability of energy-generation predictions thereby impeding integration with the distribution grid. In this study, we integrate physics-based models with statistical learning aggregation techniques that combine forecasts from multiple, independent models into a single "best-estimate" prediction of the true state. The Simulating Waves Nearshore physics-based model is used to compute wind- and currents-augmented waves in the Monterey Bay area. Ensembles are developed based on multiple simulations perturbing input data (wave characteristics supplied at the model boundaries and winds) to the model. A learning-aggregation technique uses past observations and past model forecasts to calculate a weight for each model. The aggregated forecasts are compared to observation data to quantify the performance of the model ensemble and aggregation techniques. The appropriately weighted ensemble model outperforms an individual ensemble member with regard to forecasting wave conditions.

Analysis of creative mathematic thinking ability in problem based learning model based on self-regulation learning

NASA Astrophysics Data System (ADS)

Munahefi, D. N.; Waluya, S. B.; Rochmad

2018-03-01

The purpose of this research identified the effectiveness of Problem Based Learning (PBL) models based on Self Regulation Leaning (SRL) on the ability of mathematical creative thinking and analyzed the ability of mathematical creative thinking of high school students in solving mathematical problems. The population of this study was students of grade X SMA N 3 Klaten. The research method used in this research was sequential explanatory. Quantitative stages with simple random sampling technique, where two classes were selected randomly as experimental class was taught with the PBL model based on SRL and control class was taught with expository model. The selection of samples at the qualitative stage was non-probability sampling technique in which each selected 3 students were high, medium, and low academic levels. PBL model with SRL approach effectived to students’ mathematical creative thinking ability. The ability of mathematical creative thinking of low academic level students with PBL model approach of SRL were achieving the aspect of fluency and flexibility. Students of academic level were achieving fluency and flexibility aspects well. But the originality of students at the academic level was not yet well structured. Students of high academic level could reach the aspect of originality.

Radiant thinking and the use of the mind map in nurse practitioner education.

PubMed

Spencer, Julie R; Anderson, Kelley M; Ellis, Kathryn K

2013-05-01

The concept of radiant thinking, which led to the concept of mind mapping, promotes all aspects of the brain working in synergy, with thought beginning from a central point. The mind map, which is a graphical technique to improve creative thinking and knowledge attainment, utilizes colors, images, codes, and dimensions to amplify and enhance key ideas. This technique augments the visualization of relationships and links between concepts, which aids in information acquisition, data retention, and overall comprehension. Faculty can promote students' use of the technique for brainstorming, organizing ideas, taking notes, learning collaboratively, presenting, and studying. These applications can be used in problem-based learning, developing plans of care, health promotion activities, synthesizing disease processes, and forming differential diagnoses. Mind mapping is a creative way for students to engage in a unique method of learning that can expand memory recall and help create a new environment for processing information. Copyright 2013, SLACK Incorporated.

Incremental online learning in high dimensions.

PubMed

Vijayakumar, Sethu; D'Souza, Aaron; Schaal, Stefan

2005-12-01

Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of-possibly redundant-inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces.

Design of environmental education module towards the needs of aboriginal community learning

NASA Astrophysics Data System (ADS)

Dasman, Siti Mariam; Yasin, Ruhizan Mohammad

2017-05-01

Non-formal education (NFE) refers to a program that is designed for personal and social education for learners to improve the level of skills and competencies outside formal educational curriculum. Issues related to geography and environment of different Aboriginal communities with other communities play an important role in determining the types and methods that should be made available to the minority community groups. Thus, this concept paper is intended to cater for educational environment through the design and development of learning modules based on non-formal education to the learning of Aboriginal community. Methods and techniques in the design and construction of the modules is based on the Design and Development Research (DDR) that was based on instructional design model of Morrison, Kemp and Ross which is more flexible and prioritizes the needs and characteristics of learners who were involved in the learning modules of the future. The discussion is related to the module development which is suitable to the learning needs of the community and there are several recommendations which may be applied in the implementation of this approach. In conclusion, the community of Orang Asli should be offered the same education as other communities but it is important to distinguish acceptance of learning techniques or approaches used in the education system to meet their standards. The implications of this concept paper is to meet the educational needs of the environment which includes a few aspects of science and some learning activities using effective approaches such as playing and building their own knowledge of meaning.

The Effectiveness of Guided Inquiry-based Learning Material on Students’ Science Literacy Skills

NASA Astrophysics Data System (ADS)

Aulia, E. V.; Poedjiastoeti, S.; Agustini, R.

2018-01-01

The purpose of this research is to describe the effectiveness of guided inquiry-based learning material to improve students’ science literacy skills on solubility and solubility product concepts. This study used Research and Development (R&D) design and was implemented to the 11th graders of Muhammadiyah 4 Senior High School Surabaya in 2016/2017 academic year with one group pre-test and post-test design. The data collection techniques used were validation, observation, test, and questionnaire. The results of this research showed that the students’ science literacy skills are different after implementation of guided inquiry-based learning material. The guided inquiry-based learning material is effective to improve students’ science literacy skills on solubility and solubility product concepts by getting N-gain score with medium and high category. This improvement caused by the developed learning material such as lesson plan, student worksheet, and science literacy skill tests were categorized as valid and very valid. In addition, each of the learning phases in lesson plan has been well implemented. Therefore, it can be concluded that the guided inquiry-based learning material are effective to improve students’ science literacy skills on solubility and solubility product concepts in senior high school.

The Strategy Selection Matrix--A Guide for Individualizing Instruction.

ERIC Educational Resources Information Center

Bell, Steven

The Strategy Selection Matrix (SSM) is offered as a means for matching teaching technique to the individual special needs student. Three steps in the SSM are described: development of an intra-individual learning style profile based on 14 learning components; review of the individualizing teaching strategies (such as tutoring, continuous progress…

Using Low-Tech Interactions in the Chemistry Classroom to Engage Students in Active Learning

ERIC Educational Resources Information Center

Shaver, Michael P.

2010-01-01

Two complementary techniques to gauge student understanding and inspire interactive learning in the chemistry classroom are presented. Specifically, this article explores the use of student responses with their thumbs as an alternative to electronic-response systems and complementing these experiences with longer, task-based questions in an…

Balancing Self-Directed Learning with Expert Mentoring: The Science Writing Heuristic Approach

ERIC Educational Resources Information Center

Shelley, Mack; Fostvedt, Luke; Gonwa-Reeves, Christopher; Baenziger, Joan; McGill, Michael; Seefeld, Ashley; Hand, Brian; Therrien, William; Taylor, Jonte; Villanueva, Mary Grace

2012-01-01

This study focuses on the implementation of the Science Writing Heuristic (SWH) curriculum (Hand, 2007), which combines current understandings of learning as a cognitive and negotiated process with the techniques of argument-based inquiry, critical thinking skills, and writing to strengthen student outcomes. Success of SWH is dependent on the…

Identifying Strategy Use in Category Learning Tasks: A Case for More Diagnostic Data and Models

ERIC Educational Resources Information Center

Donkin, Chris; Newell, Ben R.; Kalish, Mike; Dunn, John C.; Nosofsky, Robert M.

2015-01-01

The strength of conclusions about the adoption of different categorization strategies--and their implications for theories about the cognitive and neural bases of category learning--depend heavily on the techniques for identifying strategy use. We examine performance in an often-used "information-integration" category structure and…

The Method of Anschauung: From Johann H. Pestalozzi to Herbert Spencer.

ERIC Educational Resources Information Center

Takaya, Keiichi

2003-01-01

One of the major inventions of modern education is the instructional use of "Anschauung," an experience-based learning technique that was influential both as a method of instruction (more effective than mere book-learning and rote memorization) and as a rejection of old social arrangements that inculcated traditional values through deductive and…

Has the Construct "Intelligence" Determined Our Perception of Cognitive Hierarchy?

ERIC Educational Resources Information Center

Fuller, Renee

The discovery that retarded children can learn to read with comprehension suggests a critique of current educational testing and teaching practices. IQ tests, consisting of segmental, out-of-context tasks, originally were based on turn-of-the-century educational techniques that emphasized rote and segmental learning. Currently, most IQ tests still…

A System for English Vocabulary Acquisition Based on Code-Switching

ERIC Educational Resources Information Center

Mazur, Michal; Karolczak, Krzysztof; Rzepka, Rafal; Araki, Kenji

2016-01-01

Vocabulary plays an important part in second language learning and there are many existing techniques to facilitate word acquisition. One of these methods is code-switching, or mixing the vocabulary of two languages in one sentence. In this paper the authors propose an experimental system for computer-assisted English vocabulary learning in…

Learning, Remembering, Believing. Enhancing Human Performance.

ERIC Educational Resources Information Center

Druckman, Daniel, Ed.; Bjork, Robert A., Ed.

This book is the third report of the Committee on Techniques for the Enhancement of Human Performance. Based on hundreds of research studies of learning and human performance as reported in the literature, the book consists of 11 chapters organized in five parts. The two chapters of the first part provide the background and summary of the…

Cotton Island: Students' Learning Motivation Using a Virtual World

ERIC Educational Resources Information Center

Wyss, Jamie; Lee, Seung-Eun; Domina, Tanya; MacGillivray, Maureen

2014-01-01

As technology advances, it is important for teachers to seamlessly integrate technology into their innovative teaching techniques. Using virtual worlds is one alternative to traditional teaching methods that can provide rich learning experiences. The purpose of this article is twofold: (a) to present Cotton Island, an avatar-based 3-D virtual…

Surveillance in Programming Plagiarism beyond Techniques: An Incentive-Based Fishbone Model

ERIC Educational Resources Information Center

Wang, Yanqing; Chen, Min; Liang, Yaowen; Jiang, Yu

2013-01-01

Lots of researches have showed that plagiarism becomes a severe problem in higher education around the world, especially in programming learning for its essence. Therefore, an effective strategy for plagiarism surveillance in program learning is much essential. Some literature focus on code similarity algorithm and the related tools can help to…

AI Based Personal Learning Environments: Directions for Long Term Research. AI Memo 384.

ERIC Educational Resources Information Center

Goldstein, Ira P.; Miller, Mark L.

The application of artificial intelligence (AI) techniques to the design of personal learning environments is an enterprise of both theoretical and practical interest. In the short term, the process of developing and testing intelligent tutoring programs serves as a new experimental vehicle for exploring alternative cognitive and pedagogical…

Cooperative Learning Technique through Internet Based Education: A Model Proposal

ERIC Educational Resources Information Center

Ozkan, Hasan Huseyin

2010-01-01

Internet is gradually becoming the most valuable learning environment for the people which form the information society. That the internet provides written, oral and visual communication between the participants who are at different places, that it enables the students' interaction with other students and teachers, and that it does these so fast…

System for Training Aviation Regulations (STAR): Using Multiple Vantage Points To Learn Complex Information through Scenario-Based Instruction and Multimedia Techniques.

ERIC Educational Resources Information Center

Chandler, Terrell N.

1996-01-01

The System for Training of Aviation Regulations (STAR) provides comprehensive training in understanding and applying Federal aviation regulations. STAR gives multiple vantage points with multimedia presentations and storytelling within four categories of learning environments: overviews, scenarios, challenges, and resources. Discusses the…

Ubiquitous and Ambient Intelligence Assisted Learning Environment Infrastructures Development--A Review

ERIC Educational Resources Information Center

Kanagarajan, Sujith; Ramakrishnan, Sivakumar

2018-01-01

Ubiquitous Learning Environment (ULE) has been becoming a mobile and sensor based technology equipped environment that suits the modern world education discipline requirements for the past few years. Ambient Intelligence (AmI) makes much smarter the ULE by the support of optimization and intelligent techniques. Various efforts have been so far…

Acquiring Software Design Schemas: A Machine Learning Perspective

NASA Technical Reports Server (NTRS)

Harandi, Mehdi T.; Lee, Hing-Yan

1991-01-01

In this paper, we describe an approach based on machine learning that acquires software design schemas from design cases of existing applications. An overview of the technique, design representation, and acquisition system are presented. the paper also addresses issues associated with generalizing common features such as biases. The generalization process is illustrated using an example.

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.

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Machine learning molecular dynamics for the simulation of infrared spectra.

PubMed

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

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.

Using enquiry in learning: from vision to reality in higher education.

PubMed

Horne, Maria; Woodhead, Kath; Morgan, Liz; Smithies, Lynda; Megson, Denise; Lyte, Geraldine

2007-02-01

This paper reports on the contribution of six nurse educators to embed enquiry-led learning in a pre-registration nursing programme. Their focus was to evaluate student and facilitator perspectives of a hybrid model of problem-based learning, a form of enquiry-based learning and to focus on facilitators' perceptions of its longer-term utility with large student groups. Problem-based learning is an established learning strategy in healthcare internationally; however, insufficient evidence of its effectiveness with large groups of pre-registration students exists. Fourth Generation Evaluation was used, applying the Nominal Group Technique and Focus Group interviews, for data collection. In total, four groups representing different branches of pre-registration students (n = 121) and 15 facilitators participated. Students identified seven strengths and six areas for development related to problem-based learning. Equally, analysis of facilitators' discussions revealed several themes related to strengths and challenges. The consensus was that using enquiry aided the development of independent learning and encouraged deeper exploration of nursing and allied subject material. However, problems and frustrations were identified in relation to large numbers of groups, group dynamics, room and library resources and personal development. The implications of these findings for longer-term utility with large student groups are discussed.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

A Robust Deep Model for Improved Classification of AD/MCI Patients

PubMed Central

Li, Feng; Tran, Loc; Thung, Kim-Han; Ji, Shuiwang; Shen, Dinggang; Li, Jiang

2015-01-01

Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods. PMID:25955998

The Effects of Case-Based Team Learning on Students’ Learning, Self Regulation and Self Direction

PubMed Central

Rezaee, Rita; Mosalanejad, Leili

2015-01-01

Introduction: The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. Material & Methods: This is aquasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliablequestionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students’with points of view on educational methods. Results: The Results showed an increase in the students’ self directed learning based on their performance on the post-test. The results showed that the students’ self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students’ learning (p=0.003). Conclusion: This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students’ ’knowledge, but also the advancement of long- life learning skills. PMID:25946918

Applying machine learning classification techniques to automate sky object cataloguing

NASA Astrophysics Data System (ADS)

Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

1993-08-01

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is consistency of classification. The classification rules which are the product of the inductive learning techniques will form an objective, examinable basis for classifying sky objects. A final, not to be underestimated benefit is that astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems based on automatically catalogued data.

Mastering the Techniques of Teaching. Second Edition.

ERIC Educational Resources Information Center

Lowman, Joseph

This book examines elements of good college teaching and ways to master effective teaching techniques, drawing on direct observation, research on teaching and learning, and student accounts of outstanding professors. Chapter 1 reviews research on exemplary teaching and proposes a two-dimensional model of effective college teaching based on…

Teaching, Learning and Evaluation Techniques in the Engineering Courses.

ERIC Educational Resources Information Center

Vermaas, Luiz Lenarth G.; Crepaldi, Paulo Cesar; Fowler, Fabio Roberto

This article presents some techniques of professional formation from the Petra Model that can be applied in Engineering Programs. It shows its philosophy, teaching methods for listening, making abstracts, studying, researching, team working and problem solving. Some questions regarding planning and evaluation, based in the model are, as well,…

A decision-based perspective for the design of methods for systems design

NASA Technical Reports Server (NTRS)

Mistree, Farrokh; Muster, Douglas; Shupe, Jon A.; Allen, Janet K.

1989-01-01

Organization of material, a definition of decision based design, a hierarchy of decision based design, the decision support problem technique, a conceptual model design that can be manufactured and maintained, meta-design, computer-based design, action learning, and the characteristics of decisions are among the topics covered.

Analyzing the Effects of Various Concept Mapping Techniques on Learning Achievement under Different Learning Styles

ERIC Educational Resources Information Center

Chiou, Chei-Chang; Lee, Li-Tze; Tien, Li-Chu; Wang, Yu-Min

2017-01-01

This study explored the effectiveness of different concept mapping techniques on the learning achievement of senior accounting students and whether achievements attained using various techniques are affected by different learning styles. The techniques are computer-assisted construct-by-self-concept mapping (CACSB), computer-assisted…

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.

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Acquisition of an instrumental activity of daily living in patients with Korsakoff's syndrome: a comparison of trial and error and errorless learning.

PubMed

Oudman, Erik; Nijboer, Tanja C W; Postma, Albert; Wijnia, Jan W; Kerklaan, Sandra; Lindsen, Karen; Van der Stigchel, Stefan

2013-01-01

Patients with Korsakoff's syndrome show devastating amnesia and executive deficits. Consequently, the ability to perform instrumental activities such as making coffee is frequently diminished. It is currently unknown whether patients with Korsakoff's syndrome are able to (re)learn instrumental activities. A good candidate for an effective teaching technique in Korsakoff's syndrome is errorless learning as it is based on intact implicit memory functioning. Therefore, the aim of the current study was two-fold: to investigate whether patients with Korsakoff's syndrome are able to (re)learn instrumental activities, and to compare the effectiveness of errorless learning with trial and error learning in the acquisition and maintenance of an instrumental activity, namely using a washing machine to do the laundry. Whereas initial learning performance in the errorless learning condition was superior, both intervention techniques resulted in similar improvement over eight learning sessions. Moreover, performance in a different spatial layout showed a comparable improvement. Notably, in follow-up sessions starting after four weeks without practice, performance was still elevated in the errorless learning condition, but not in the trial and error condition. The current study demonstrates that (re)learning and maintenance of an instrumental activity is possible in patients with Korsakoff's syndrome.

Mapping of Primary Instructional Methods and Teaching Techniques for Regularly Scheduled, Formal Teaching Sessions in an Anesthesia Residency Program.

PubMed

Vested Madsen, Matias; Macario, Alex; Yamamoto, Satoshi; Tanaka, Pedro

2016-06-01

In this study, we examined the regularly scheduled, formal teaching sessions in a single anesthesiology residency program to (1) map the most common primary instructional methods, (2) map the use of 10 known teaching techniques, and (3) assess if residents scored sessions that incorporated active learning as higher quality than sessions with little or no verbal interaction between teacher and learner. A modified Delphi process was used to identify useful teaching techniques. A representative sample of each of the formal teaching session types was mapped, and residents anonymously completed a 5-question written survey rating the session. The most common primary instructional methods were computer slides-based classroom lectures (66%), workshops (15%), simulations (5%), and journal club (5%). The number of teaching techniques used per formal teaching session averaged 5.31 (SD, 1.92; median, 5; range, 0-9). Clinical applicability (85%) and attention grabbers (85%) were the 2 most common teaching techniques. Thirty-eight percent of the sessions defined learning objectives, and one-third of sessions engaged in active learning. The overall survey response rate equaled 42%, and passive sessions had a mean score of 8.44 (range, 5-10; median, 9; SD, 1.2) compared with a mean score of 8.63 (range, 5-10; median, 9; SD, 1.1) for active sessions (P = 0.63). Slides-based classroom lectures were the most common instructional method, and faculty used an average of 5 known teaching techniques per formal teaching session. The overall education scores of the sessions as rated by the residents were high.

Neighborhood graph and learning discriminative distance functions for clinical decision support.

PubMed

Tsymbal, Alexey; Zhou, Shaohua Kevin; Huber, Martin

2009-01-01

There are two essential reasons for the slow progress in the acceptance of clinical case retrieval and similarity search-based decision support systems; the especial complexity of clinical data making it difficult to define a meaningful and effective distance function on them and the lack of transparency and explanation ability in many existing clinical case retrieval decision support systems. In this paper, we try to address these two problems by introducing a novel technique for visualizing inter-patient similarity based on a node-link representation with neighborhood graphs and by considering two techniques for learning discriminative distance function that help to combine the power of strong "black box" learners with the transparency of case retrieval and nearest neighbor classification.

Efficient Testing Combining Design of Experiment and Learn-to-Fly Strategies

NASA Technical Reports Server (NTRS)

Murphy, Patrick C.; Brandon, Jay M.

2017-01-01

Rapid modeling and efficient testing methods are important in a number of aerospace applications. In this study efficient testing strategies were evaluated in a wind tunnel test environment and combined to suggest a promising approach for both ground-based and flight-based experiments. Benefits of using Design of Experiment techniques, well established in scientific, military, and manufacturing applications are evaluated in combination with newly developing methods for global nonlinear modeling. The nonlinear modeling methods, referred to as Learn-to-Fly methods, utilize fuzzy logic and multivariate orthogonal function techniques that have been successfully demonstrated in flight test. The blended approach presented has a focus on experiment design and identifies a sequential testing process with clearly defined completion metrics that produce increased testing efficiency.

Teaching Vectors Through an Interactive Game Based Laboratory

NASA Astrophysics Data System (ADS)

O'Brien, James; Sirokman, Gergely

2014-03-01

In recent years, science and particularly physics education has been furthered by the use of project based interactive learning [1]. There is a tremendous amount of evidence [2] that use of these techniques in a college learning environment leads to a deeper appreciation and understanding of fundamental concepts. Since vectors are the basis for any advancement in physics and engineering courses the cornerstone of any physics regimen is a concrete and comprehensive introduction to vectors. Here, we introduce a new turn based vector game that we have developed to help supplement traditional vector learning practices, which allows students to be creative, work together as a team, and accomplish a goal through the understanding of basic vector concepts.

A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

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

Xu, Songhua; Tourassi, Georgia

2012-01-01

The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using themore » BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.« less

Internet messenger based smart virtual class learning using ubiquitous computing

NASA Astrophysics Data System (ADS)

Umam, K.; Mardi, S. N. S.; Hariadi, M.

2017-06-01

Internet messenger (IM) has become an important educational technology component in college education, IM makes it possible for students to engage in learning and collaborating at smart virtual class learning (SVCL) using ubiquitous computing. However, the model of IM-based smart virtual class learning using ubiquitous computing and empirical evidence that would favor a broad application to improve engagement and behavior are still limited. In addition, the expectation that IM based SVCL using ubiquitous computing could improve engagement and behavior on smart class cannot be confirmed because the majority of the reviewed studies followed instructions paradigms. This article aims to present the model of IM-based SVCL using ubiquitous computing and showing learners’ experiences in improved engagement and behavior for learner-learner and learner-lecturer interactions. The method applied in this paper includes design process and quantitative analysis techniques, with the purpose of identifying scenarios of ubiquitous computing and realize the impressions of learners and lecturers about engagement and behavior aspect and its contribution to learning

Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

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

Cui, Yonggang

In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integratedmore » analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.« less

New techniques for test development for tactical auto-pilots using microprocessors

NASA Astrophysics Data System (ADS)

Shemeta, E. H.

1980-07-01

This paper reports on a demonstration of the application of the method to generate system level tests for a typical tactical missile autopilot. The test algorithms are based on the autopilot control law. When loaded on the tester with appropriate control information, the complete autopilot is tested to establish if the specified control law requirements are met. Thus, the test procedure not only checks to see if the hardware is functional, but also checks the operational software. The technique also uses a 'learning' mode to allow minor timing or functional deviations from the expected responses to be incorporated in the test procedures. A potential application of this test development technique is the extraction of production test data for the various subassemblies. The technique will 'learn' the input-output patterns forming the basis for developement and production tests. If successful, these new techniques should allow the test development process to keep pace with semiconductor progress.

Mitigation of time-varying distortions in Nyquist-WDM systems using machine learning

NASA Astrophysics Data System (ADS)

Granada Torres, Jhon J.; Varughese, Siddharth; Thomas, Varghese A.; Chiuchiarelli, Andrea; Ralph, Stephen E.; Cárdenas Soto, Ana M.; Guerrero González, Neil

2017-11-01

We propose a machine learning-based nonsymmetrical demodulation technique relying on clustering to mitigate time-varying distortions derived from several impairments such as IQ imbalance, bias drift, phase noise and interchannel interference. Experimental results show that those impairments cause centroid movements in the received constellations seen in time-windows of 10k symbols in controlled scenarios. In our demodulation technique, the k-means algorithm iteratively identifies the cluster centroids in the constellation of the received symbols in short time windows by means of the optimization of decision thresholds for a minimum BER. We experimentally verified the effectiveness of this computationally efficient technique in multicarrier 16QAM Nyquist-WDM systems over 270 km links. Our nonsymmetrical demodulation technique outperforms the conventional QAM demodulation technique, reducing the OSNR requirement up to ∼0.8 dB at a BER of 1 × 10-2 for signals affected by interchannel interference.

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology.

PubMed

Álvarez de Toledo, Santiago; Anguera, Aurea; Barreiro, José M; Lara, Juan A; Lizcano, David

2017-01-19

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency.

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology

PubMed Central

Álvarez de Toledo, Santiago; Anguera, Aurea; Barreiro, José M.; Lara, Juan A.; Lizcano, David

2017-01-01

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency. PMID:28106849

3D interactive augmented reality-enhanced digital learning systems for mobile devices

NASA Astrophysics Data System (ADS)

Feng, Kai-Ten; Tseng, Po-Hsuan; Chiu, Pei-Shuan; Yang, Jia-Lin; Chiu, Chun-Jie

2013-03-01

With enhanced processing capability of mobile platforms, augmented reality (AR) has been considered a promising technology for achieving enhanced user experiences (UX). Augmented reality is to impose virtual information, e.g., videos and images, onto a live-view digital display. UX on real-world environment via the display can be e ectively enhanced with the adoption of interactive AR technology. Enhancement on UX can be bene cial for digital learning systems. There are existing research works based on AR targeting for the design of e-learning systems. However, none of these work focuses on providing three-dimensional (3-D) object modeling for en- hanced UX based on interactive AR techniques. In this paper, the 3-D interactive augmented reality-enhanced learning (IARL) systems will be proposed to provide enhanced UX for digital learning. The proposed IARL systems consist of two major components, including the markerless pattern recognition (MPR) for 3-D models and velocity-based object tracking (VOT) algorithms. Realistic implementation of proposed IARL system is conducted on Android-based mobile platforms. UX on digital learning can be greatly improved with the adoption of proposed IARL systems.

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

NASA Astrophysics Data System (ADS)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

Overview of existing algorithms for emotion classification. Uncertainties in evaluations of accuracies.

NASA Astrophysics Data System (ADS)

Avetisyan, H.; Bruna, O.; Holub, J.

2016-11-01

A numerous techniques and algorithms are dedicated to extract emotions from input data. In our investigation it was stated that emotion-detection approaches can be classified into 3 following types: Keyword based / lexical-based, learning based, and hybrid. The most commonly used techniques, such as keyword-spotting method, Support Vector Machines, Naïve Bayes Classifier, Hidden Markov Model and hybrid algorithms, have impressive results in this sphere and can reach more than 90% determining accuracy.

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review.

PubMed

Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana

2017-03-01

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

PubMed

Feng, Jingwen; Feng, Tong; Yang, Chengwen; Wang, Wei; Sa, Yu; Feng, Yuanming

2018-06-01

This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume.

PubMed

Meng, Qier; Kitasaka, Takayuki; Nimura, Yukitaka; Oda, Masahiro; Ueno, Junji; Mori, Kensaku

2017-02-01

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree. This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree. A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate. A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

The Development of Teaching and Learning in Bright-Field Microscopy Technique

ERIC Educational Resources Information Center

Iskandar, Yulita Hanum P.; Mahmud, Nurul Ethika; Wahab, Wan Nor Amilah Wan Abdul; Jamil, Noor Izani Noor; Basir, Nurlida

2013-01-01

E-learning should be pedagogically-driven rather than technologically-driven. The objectives of this study are to develop an interactive learning system in bright-field microscopy technique in order to support students' achievement of their intended learning outcomes. An interactive learning system on bright-field microscopy technique was…

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.

DNorm: disease name normalization with pairwise learning to rank.

PubMed

Leaman, Robert; Islamaj Dogan, Rezarta; Lu, Zhiyong

2013-11-15

Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research. In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator .

Powerful Learning Conversations: Evaluation Report and Executive Summary

ERIC Educational Resources Information Center

Rienzo, Cinzia; Rolfe, Heather; Wilkinson, David

2016-01-01

Powerful Learning Conversations (PLC) sought to improve the feedback that teachers give to pupils in Year 9, by training them to apply techniques used in sports coaching. It is based on the idea that feedback in sports coaching is often provided immediately after a task is performed, and delivered in a way that children are more likely to respond…

A Learning Evaluation for an Immersive Virtual Laboratory for Technical Training Applied into a Welding Workshop

ERIC Educational Resources Information Center

Torres, Francisco; Neira Tovar, Leticia A.; del Rio, Marta Sylvia

2017-01-01

This study aims to explore the results of welding virtual training performance, designed using a learning model based on cognitive and usability techniques, applying an immersive concept focused on person attention. Moreover, it also intended to demonstrate that exits a moderating effect of performance improvement when the user experience is taken…

3-Dimensional and Interactive Istanbul University Virtual Laboratory Based on Active Learning Methods

ERIC Educational Resources Information Center

Ince, Elif; Kirbaslar, Fatma Gulay; Yolcu, Ergun; Aslan, Ayse Esra; Kayacan, Zeynep Cigdem; Alkan Olsson, Johanna; Akbasli, Ayse Ceylan; Aytekin, Mesut; Bauer, Thomas; Charalambis, Dimitris; Gunes, Zeliha Ozsoy; Kandemir, Ceyhan; Sari, Umit; Turkoglu, Suleyman; Yaman, Yavuz; Yolcu, Ozgu

2014-01-01

The purpose of this study is to develop a 3-dimensional interactive multi-user and multi-admin IUVIRLAB featuring active learning methods and techniques for university students and to introduce the Virtual Laboratory of Istanbul University and to show effects of IUVIRLAB on students' attitudes on communication skills and IUVIRLAB. Although there…

Evaluation of the Technical Adequacy of Three Methods for Identifying Specific Learning Disabilities Based on Cognitive Discrepancies

ERIC Educational Resources Information Center

Stuebing, Karla K.; Fletcher, Jack M.; Branum-Martin, Lee; Francis, David J.

2012-01-01

This study used simulation techniques to evaluate the technical adequacy of three methods for the identification of specific learning disabilities via patterns of strengths and weaknesses in cognitive processing. Latent and observed data were generated and the decision-making process of each method was applied to assess concordance in…

Introducing 12 Year-Olds to Elementary Particles

ERIC Educational Resources Information Center

Wiener, Gerfried J.; Schmeling, Sascha M.; Hopf, Martin

2017-01-01

We present a new learning unit, which introduces 12 year-olds to the subatomic structure of matter. The learning unit was iteratively developed as a design-based research project using the technique of probing acceptance. We give a brief overview of the unit's final version, discuss its key ideas and main concepts, and conclude by highlighting the…

Does Medical Students' Diagnostic Performance Improve by Observing Examples of Self-Explanation Provided by Peers or Experts?

ERIC Educational Resources Information Center

Chamberland, Martine; Mamede, Sílvia; St-Onge, Christina; Setrakian, Jean; Schmidt, Henk G.

2015-01-01

Educational strategies that promote the development of clinical reasoning in students remain scarce. Generating self-explanations (SE) engages students in active learning and has shown to be an effective technique to improve clinical reasoning in clerks. Example-based learning has been shown to support the development of accurate knowledge…

Implementation of Structured Inquiry Based Model Learning toward Students' Understanding of Geometry

ERIC Educational Resources Information Center

Salim, Kalbin; Tiawa, Dayang Hjh

2015-01-01

The purpose of this study is implementation of a structured inquiry learning model in instruction of geometry. The model used is a model with a quasi-experimental study amounted to two classes of samples selected from the population of the ten classes with cluster random sampling technique. Data collection tool consists of a test item…

Learning Financial Accounting in a Tertiary Institution of a Developing Country. An Investigation into Instructional Methods

ERIC Educational Resources Information Center

Abeysekera, Indra

2011-01-01

This study examines three instructional methods (traditional, interactive, and group case-based study), and student opinions on their preference for learning financial accounting in large classes at a metropolitan university in Sri Lanka. It analyses the results of a survey questionnaire of students, using quantitative techniques to determine the…

The Keyword Method of Vocabulary Acquisition: An Experimental Evaluation.

ERIC Educational Resources Information Center

Griffith, Douglas

The keyword method of vocabulary acquisition is a two-step mnemonic technique for learning vocabulary terms. The first step, the acoustic link, generates a keyword based on the sound of the foreign word. The second step, the imagery link, ties the keyword to the meaning of the item to be learned, via an interactive visual image or other…

Cognitive Analysis of Meaning and Acquired Mental Representations as an Alternative Measurement Method Technique to Innovate E-Assessment

ERIC Educational Resources Information Center

Morales-Martinez, Guadalupe Elizabeth; Lopez-Ramirez, Ernesto Octavio; Castro-Campos, Claudia; Villarreal-Treviño, Maria Guadalupe; Gonzales-Trujillo, Claudia Jaquelina

2017-01-01

Empirical directions to innovate e-assessments and to support the theoretical development of e-learning are discussed by presenting a new learning assessment system based on cognitive technology. Specifically, this system encompassing trained neural nets that can discriminate between students who successfully integrated new knowledge course…

Personal Reflection: Reflections on a Family Health History Assignment for Undergraduate Public Health and Nursing Students

ERIC Educational Resources Information Center

Rooks, Ronica N.; Ford, Cassandra

2013-01-01

This personal reflection describes our experiences with incorporating the scholarship of teaching and learning and problem-based techniques to facilitate undergraduate student learning and their professional development in the health sciences. We created a family health history assignment to discuss key concepts in our courses, such as health…

Ascending Bloom's Pyramid: Fostering Student Creativity and Innovation in Academic Library Spaces

ERIC Educational Resources Information Center

Bieraugel, Mark; Neill, Stern

2017-01-01

Our research examined the degree to which behaviors and learning associated with creativity and innovation were supported in five academic library spaces and three other spaces at a mid-sized university. Based on survey data from 226 students, we apply a number of statistical techniques to measure student perceptions of the types of learning and…

Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques

ERIC Educational Resources Information Center

Wang, Cheng-Hung; Lin, Hao-Chiang Koong

2018-01-01

In a traditional class, the role of the teacher is to teach and that of the students is to learn. However, the constant and rapid technological advancements have transformed education in numerous ways. For instance, in addition to traditional, face to face teaching, E-learning is now possible. Nevertheless, face to face teaching is unavailable in…

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.

Using cooperative learning for a drug information assignment.

PubMed

Earl, Grace L

2009-11-12

To implement a cooperative learning activity to engage students in analyzing tertiary drug information resources in a literature evaluation course. The class was divided into 4 sections to form expert groups and each group researched a different set of references using the jigsaw technique. Each member of each expert group was reassigned to a jigsaw group so that each new group was composed of 4 students from 4 different expert groups. The jigsaw groups met to discuss search strategies and rate the usefulness of the references. In addition to group-based learning, teaching methods included students' writing an independent research paper to enhance their abilities to search and analyze drug information resources. The assignment and final course grades improved after implementation of the activity. Students agreed that class discussions were a useful learning experience and 75% (77/102) said they would use the drug information references for other courses. The jigsaw technique was successful in engaging students in cooperative learning to improve critical thinking skills regarding drug information.

Virtual Labs in proteomics: new E-learning tools.

PubMed

Ray, Sandipan; Koshy, Nicole Rachel; Reddy, Panga Jaipal; Srivastava, Sanjeeva

2012-05-17

Web-based educational resources have gained enormous popularity recently and are increasingly becoming a part of modern educational systems. Virtual Labs are E-learning platforms where learners can gain the experience of practical experimentation without any direct physical involvement on real bench work. They use computerized simulations, models, videos, animations and other instructional technologies to create interactive content. Proteomics being one of the most rapidly growing fields of the biological sciences is now an important part of college and university curriculums. Consequently, many E-learning programs have started incorporating the theoretical and practical aspects of different proteomic techniques as an element of their course work in the form of Video Lectures and Virtual Labs. To this end, recently we have developed a Virtual Proteomics Lab at the Indian Institute of Technology Bombay, which demonstrates different proteomics techniques, including basic and advanced gel and MS-based protein separation and identification techniques, bioinformatics tools and molecular docking methods, and their applications in different biological samples. This Tutorial will discuss the prominent Virtual Labs featuring proteomics content, including the Virtual Proteomics Lab of IIT-Bombay, and E-resources available for proteomics study that are striving to make proteomic techniques and concepts available and accessible to the student and research community. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 14). Details can be found at: http://www.proteomicstutorials.org/. Copyright © 2012 Elsevier B.V. All rights reserved.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Classification-free threat detection based on material-science-informed clustering

NASA Astrophysics Data System (ADS)

Yuan, Siyang; Wolter, Scott D.; Greenberg, Joel A.

2017-05-01

X-ray diffraction (XRD) is well-known for yielding composition and structural information about a material. However, in some applications (such as threat detection in aviation security), the properties of a material are more relevant to the task than is a detailed material characterization. Furthermore, the requirement that one first identify a material before determining its class may be difficult or even impossible for a sufficiently large pool of potentially present materials. We therefore seek to learn relevant composition-structure-property relationships between materials to enable material-identification-free classification. We use an expert-informed, data-driven approach operating on a library of XRD spectra from a broad array of stream of commerce materials. We investigate unsupervised learning techniques in order to learn about naturally emergent groupings, and apply supervised learning techniques to determine how well XRD features can be used to separate user-specified classes in the presence of different types and degrees of signal degradation.

Quality in Web-Supported Learning.

ERIC Educational Resources Information Center

Fresen, Jill

2002-01-01

Discusses quality assurance for Web-based courses, based on experiences at the University of Pretoria. Topics include evaluation of courseware; the concept of quality, including quality control, quality assurance, and total quality management; implementing a quality management system; measurement techniques; and partnerships. (LRW)

A serious game for learning ultrasound-guided needle placement skills.

PubMed

Chan, Wing-Yin; Qin, Jing; Chui, Yim-Pan; Heng, Pheng-Ann

2012-11-01

Ultrasound-guided needle placement is a key step in a lot of radiological intervention procedures such as biopsy, local anesthesia and fluid drainage. To help training future intervention radiologists, we develop a serious game to teach the skills involved. We introduce novel techniques for realistic simulation and integrate game elements for active and effective learning. This game is designed in the context of needle placement training based on the some essential characteristics of serious games. Training scenarios are interactively generated via a block-based construction scheme. A novel example-based texture synthesis technique is proposed to simulate corresponding ultrasound images. Game levels are defined based on the difficulties of the generated scenarios. Interactive recommendation of desirable insertion paths is provided during the training as an adaptation mechanism. We also develop a fast physics-based approach to reproduce the shadowing effect of needles in ultrasound images. Game elements such as time-attack tasks, hints and performance evaluation tools are also integrated in our system. Extensive experiments are performed to validate its feasibility for training.

A Mixed-Methods Investigation of Clicker Implementation Styles in STEM.

PubMed

Solomon, Erin D; Repice, Michelle D; Mutambuki, Jacinta M; Leonard, Denise A; Cohen, Cheryl A; Luo, Jia; Frey, Regina F

2018-06-01

Active learning with clickers is a common approach in high-enrollment, lecture-based courses in science, technology, engineering, and mathematics. In this study, we describe the procedures that faculty at one institution used when implementing clicker-based active learning, and how they situated these activities in their class sessions. Using a mixed-methods approach, we categorized faculty into four implementation styles based on quantitative observation data and conducted qualitative interviews to further understand why faculty used these styles. We found that faculty tended to use similar procedures when implementing a clicker activity, but differed on how they situated the clicker-based active learning into their courses. These variations were attributed to different faculty goals for using clicker-based active learning, with some using it to engage students at specific time points throughout their class sessions and others who selected it as the best way to teach a concept from several possible teaching techniques. Future research should continue to investigate and describe how active-learning strategies from literature may differ from what is being implemented.

Learner-Adaptive Educational Technology for Simulation in Healthcare: Foundations and Opportunities.

PubMed

Lineberry, Matthew; Dev, Parvati; Lane, H Chad; Talbot, Thomas B

2018-06-01

Despite evidence that learners vary greatly in their learning needs, practical constraints tend to favor ''one-size-fits-all'' educational approaches, in simulation-based education as elsewhere. Adaptive educational technologies - devices and/or software applications that capture and analyze relevant data about learners to select and present individually tailored learning stimuli - are a promising aid in learners' and educators' efforts to provide learning experiences that meet individual needs. In this article, we summarize and build upon the 2017 Society for Simulation in Healthcare Research Summit panel discussion on adaptive learning. First, we consider the role of adaptivity in learning broadly. We then outline the basic functions that adaptive learning technologies must implement and the unique affordances and challenges of technology-based approaches for those functions, sharing an illustrative example from healthcare simulation. Finally, we consider future directions for accelerating research, development, and deployment of effective adaptive educational technology and techniques in healthcare simulation.

The colloquial approach: An active learning technique

NASA Astrophysics Data System (ADS)

Arce, Pedro

1994-09-01

This paper addresses the very important problem of the effectiveness of teaching methodologies in fundamental engineering courses such as transport phenomena. An active learning strategy, termed the colloquial approach, is proposed in order to increase student involvement in the learning process. This methodology is a considerable departure from traditional methods that use solo lecturing. It is based on guided discussions, and it promotes student understanding of new concepts by directing the student to construct new ideas by building upon the current knowledge and by focusing on key cases that capture the essential aspects of new concepts. The colloquial approach motivates the student to participate in discussions, to develop detailed notes, and to design (or construct) his or her own explanation for a given problem. This paper discusses the main features of the colloquial approach within the framework of other current and previous techniques. Problem-solving strategies and the need for new textbooks and for future investigations based on the colloquial approach are also outlined.

Student conceptions about the DNA structure within a hierarchical organizational level: Improvement by experiment- and computer-based outreach learning.

PubMed

Langheinrich, Jessica; Bogner, Franz X

2015-01-01

As non-scientific conceptions interfere with learning processes, teachers need both, to know about them and to address them in their classrooms. For our study, based on 182 eleventh graders, we analyzed the level of conceptual understanding by implementing the "draw and write" technique during a computer-supported gene technology module. To give participants the hierarchical organizational level which they have to draw, was a specific feature of our study. We introduced two objective category systems for analyzing drawings and inscriptions. Our results indicated a long- as well as a short-term increase in the level of conceptual understanding and in the number of drawn elements and their grades concerning the DNA structure. Consequently, we regard the "draw and write" technique as a tool for a teacher to get to know students' alternative conceptions. Furthermore, our study points the modification potential of hands-on and computer-supported learning modules. © 2015 The International Union of Biochemistry and Molecular Biology.

Tapping Their Patients' Problems Away? Characteristics of Psychotherapists Using Energy Meridian Techniques

ERIC Educational Resources Information Center

Gaudiano, Brandon A.; Brown, Lily A.; Miller, Ivan W.

2012-01-01

Objective: The objective was to learn about the characteristics of psychotherapists who use energy meridian techniques (EMTs). Methods: We conducted an Internet-based survey of the practices and attitudes of licensed psychotherapists. Results: Of 149 survey respondents (21.4% social workers), 42.3% reported that they frequently use or are inclined…

Accommodation Strategies of College Students with Disabilities

ERIC Educational Resources Information Center

Barnard-Brak, Lucy; Lechtenberger, DeAnn; Lan, William Y.

2010-01-01

College students with disabilities develop and utilize strategies to facilitate their learning experiences due to their unique academic needs. Using a semi-structured interview technique to collect data and a technique based in grounded theory to analyze this data, the purpose of this study was to discern the meaning of disclosure for college…

Focus Group Meets Nominal Group Technique: An Effective Combination for Student Evaluation?

ERIC Educational Resources Information Center

Varga-Atkins, Tünde; McIsaac, Jaye; Willis, Ian

2017-01-01

In Higher Education Focus Groups and Nominal Group Technique are two well-established methods for obtaining student feedback about their learning experience. These methods are regularly used for the enhancement and quality assurance. Based on small-scale research of educational developers' practice in curriculum development, this study presents…

Three Techniques to Help Students Teach Themselves Concepts in Environmental Geochemistry.

ERIC Educational Resources Information Center

Brown, I. Foster

1984-01-01

Describes techniques in which students learn to: (1) create elemental "fairy tales" based on the geochemical behavior of elements and on imagination to integrate concepts; (2) to visually eliminate problems of bias; and (3) to utilize multiple working hypotheses as a basis for testing concepts of classification and distinguishing…

Integration of Video-Based Demonstrations to Prepare Students for the Organic Chemistry Laboratory

ERIC Educational Resources Information Center

Nadelson, Louis S.; Scaggs, Jonathan; Sheffield, Colin; McDougal, Owen M.

2015-01-01

Consistent, high-quality introductions to organic chemistry laboratory techniques effectively and efficiently support student learning in the organic chemistry laboratory. In this work, we developed and deployed a series of instructional videos to communicate core laboratory techniques and concepts. Using a quasi-experimental design, we tested the…

Technologies of Student Testing for Learning Quality Evaluation in the System of Higher Education

ERIC Educational Resources Information Center

Bayukova, Nadezhda Olegovna; Kareva, Ludmila Alexandrovna; Rudometova, Liliya Tarasovna; Shlangman, Marina Konstantinovna; Yarantseva, Natalia Vladislavovna

2015-01-01

The paper deals with technology of students' achievement in the area of educational activities, methods, techniques, forms and conditions of monitoring knowledge quality in accordance with the requirements of Russian higher education system modernization. The authors propose methodic techniques of students' training for testing based on innovative…

Financial model calibration using consistency hints.

PubMed

Abu-Mostafa, Y S

2001-01-01

We introduce a technique for forcing the calibration of a financial model to produce valid parameters. The technique is based on learning from hints. It converts simple curve fitting into genuine calibration, where broad conclusions can be inferred from parameter values. The technique augments the error function of curve fitting with consistency hint error functions based on the Kullback-Leibler distance. We introduce an efficient EM-type optimization algorithm tailored to this technique. We also introduce other consistency hints, and balance their weights using canonical errors. We calibrate the correlated multifactor Vasicek model of interest rates, and apply it successfully to Japanese Yen swaps market and US dollar yield market.

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

PubMed

Gao, Chao; Sun, Hanbo; Wang, Tuo; Tang, Ming; Bohnen, Nicolaas I; Müller, Martijn L T M; Herman, Talia; Giladi, Nir; Kalinin, Alexandr; Spino, Cathie; Dauer, William; Hausdorff, Jeffrey M; Dinov, Ivo D

2018-05-08

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.

Adaptive Batch Mode Active Learning.

PubMed

Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

2015-08-01

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

Use of Team-Based Learning Pedagogy for Internal Medicine Ambulatory Resident Teaching.

PubMed

Balwan, Sandy; Fornari, Alice; DiMarzio, Paola; Verbsky, Jennifer; Pekmezaris, Renee; Stein, Joanna; Chaudhry, Saima

2015-12-01

Team-based learning (TBL) is used in undergraduate medical education to facilitate higher-order content learning, promote learner engagement and collaboration, and foster positive learner attitudes. There is a paucity of data on the use of TBL in graduate medical education. Our aim was to assess resident engagement, learning, and faculty/resident satisfaction with TBL in internal medicine residency ambulatory education. Survey and nominal group technique methodologies were used to assess learner engagement and faculty/resident satisfaction. We assessed medical learning using individual (IRAT) and group (GRAT) readiness assurance tests. Residents (N = 111) involved in TBL sessions reported contributing to group discussions and actively discussing the subject material with other residents. Faculty echoed similar responses, and residents and faculty reported a preference for future teaching sessions to be offered using the TBL pedagogy. The average GRAT score was significantly higher than the average IRAT score by 22%. Feedback from our nominal group technique rank ordered the following TBL strengths by both residents and faculty: (1) interactive format, (2) content of sessions, and (3) competitive nature of sessions. We successfully implemented TBL pedagogy in the internal medicine ambulatory residency curriculum, with learning focused on the care of patients in the ambulatory setting. TBL resulted in active resident engagement, facilitated group learning, and increased satisfaction by residents and faculty. To our knowledge this is the first study that implemented a TBL program in an internal medicine residency curriculum.

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach.

PubMed

McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

2018-01-01

Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction.

Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach

PubMed Central

McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

2018-01-01

Background: Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Methods: Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Results: Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. Conclusions: The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction. PMID:29707649

Deep learning with convolutional neural network in radiology.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Implementation of authentic assessment in the project based learning to improve student's concept mastering

NASA Astrophysics Data System (ADS)

Sambeka, Yana; Nahadi, Sriyati, Siti

2017-05-01

The study aimed to obtain the scientific information about increase of student's concept mastering in project based learning that used authentic assessment. The research was conducted in May 2016 at one of junior high school in Bandung in the academic year of 2015/2016. The research method was weak experiment with the one-group pretest-posttest design. The sample was taken by random cluster sampling technique and the sample was 24 students. Data collected through instruments, i.e. written test, observation sheet, and questionnaire sheet. Student's concept mastering test obtained N-Gain of 0.236 with the low category. Based on the result of paired sample t-test showed that implementation of authentic assessment in the project based learning increased student's concept mastering significantly, (sig<0.05).

Teaching renewable energy using online PBL in investigating its effect on behaviour towards energy conservation among Malaysian students: ANOVA repeated measures approach

NASA Astrophysics Data System (ADS)

Nordin, Norfarah; Samsudin, Mohd Ali; Hadi Harun, Abdul

2017-01-01

This research aimed to investigate whether online problem based learning (PBL) approach to teach renewable energy topic improves students’ behaviour towards energy conservation. A renewable energy online problem based learning (REePBaL) instruction package was developed based on the theory of constructivism and adaptation of the online learning model. This study employed a single group quasi-experimental design to ascertain the changed in students’ behaviour towards energy conservation after underwent the intervention. The study involved 48 secondary school students in a Malaysian public school. ANOVA Repeated Measure technique was employed in order to compare scores of students’ behaviour towards energy conservation before and after the intervention. Based on the finding, students’ behaviour towards energy conservation improved after the intervention.

Using Trained Pixel Classifiers to Select Images of Interest

NASA Technical Reports Server (NTRS)

Mazzoni, D.; Wagstaff, K.; Castano, R.

2004-01-01

We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.

Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

PubMed

Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

2011-07-01

In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

MO-FG-BRD-01: Real-Time Imaging and Tracking Techniques for Intrafractional Motion Management: Introduction and KV Tracking

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

Fahimian, B.

2015-06-15

Intrafraction target motion is a prominent complicating factor in the accurate targeting of radiation within the body. Methods compensating for target motion during treatment, such as gating and dynamic tumor tracking, depend on the delineation of target location as a function of time during delivery. A variety of techniques for target localization have been explored and are under active development; these include beam-level imaging of radio-opaque fiducials, fiducial-less tracking of anatomical landmarks, tracking of electromagnetic transponders, optical imaging of correlated surrogates, and volumetric imaging within treatment delivery. The Joint Imaging and Therapy Symposium will provide an overview of the techniquesmore » for real-time imaging and tracking, with special focus on emerging modes of implementation across different modalities. In particular, the symposium will explore developments in 1) Beam-level kilovoltage X-ray imaging techniques, 2) EPID-based megavoltage X-ray tracking, 3) Dynamic tracking using electromagnetic transponders, and 4) MRI-based soft-tissue tracking during radiation delivery. Learning Objectives: Understand the fundamentals of real-time imaging and tracking techniques Learn about emerging techniques in the field of real-time tracking Distinguish between the advantages and disadvantages of different tracking modalities Understand the role of real-time tracking techniques within the clinical delivery work-flow.« less

MO-FG-BRD-04: Real-Time Imaging and Tracking Techniques for Intrafractional Motion Management: MR Tracking

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

Low, D.

2015-06-15

Intrafraction target motion is a prominent complicating factor in the accurate targeting of radiation within the body. Methods compensating for target motion during treatment, such as gating and dynamic tumor tracking, depend on the delineation of target location as a function of time during delivery. A variety of techniques for target localization have been explored and are under active development; these include beam-level imaging of radio-opaque fiducials, fiducial-less tracking of anatomical landmarks, tracking of electromagnetic transponders, optical imaging of correlated surrogates, and volumetric imaging within treatment delivery. The Joint Imaging and Therapy Symposium will provide an overview of the techniquesmore » for real-time imaging and tracking, with special focus on emerging modes of implementation across different modalities. In particular, the symposium will explore developments in 1) Beam-level kilovoltage X-ray imaging techniques, 2) EPID-based megavoltage X-ray tracking, 3) Dynamic tracking using electromagnetic transponders, and 4) MRI-based soft-tissue tracking during radiation delivery. Learning Objectives: Understand the fundamentals of real-time imaging and tracking techniques Learn about emerging techniques in the field of real-time tracking Distinguish between the advantages and disadvantages of different tracking modalities Understand the role of real-time tracking techniques within the clinical delivery work-flow.« less

MO-FG-BRD-02: Real-Time Imaging and Tracking Techniques for Intrafractional Motion Management: MV Tracking

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

Berbeco, R.

2015-06-15

Intrafraction target motion is a prominent complicating factor in the accurate targeting of radiation within the body. Methods compensating for target motion during treatment, such as gating and dynamic tumor tracking, depend on the delineation of target location as a function of time during delivery. A variety of techniques for target localization have been explored and are under active development; these include beam-level imaging of radio-opaque fiducials, fiducial-less tracking of anatomical landmarks, tracking of electromagnetic transponders, optical imaging of correlated surrogates, and volumetric imaging within treatment delivery. The Joint Imaging and Therapy Symposium will provide an overview of the techniquesmore » for real-time imaging and tracking, with special focus on emerging modes of implementation across different modalities. In particular, the symposium will explore developments in 1) Beam-level kilovoltage X-ray imaging techniques, 2) EPID-based megavoltage X-ray tracking, 3) Dynamic tracking using electromagnetic transponders, and 4) MRI-based soft-tissue tracking during radiation delivery. Learning Objectives: Understand the fundamentals of real-time imaging and tracking techniques Learn about emerging techniques in the field of real-time tracking Distinguish between the advantages and disadvantages of different tracking modalities Understand the role of real-time tracking techniques within the clinical delivery work-flow.« less

MO-FG-BRD-03: Real-Time Imaging and Tracking Techniques for Intrafractional Motion Management: EM Tracking

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

Keall, P.

2015-06-15

Intrafraction target motion is a prominent complicating factor in the accurate targeting of radiation within the body. Methods compensating for target motion during treatment, such as gating and dynamic tumor tracking, depend on the delineation of target location as a function of time during delivery. A variety of techniques for target localization have been explored and are under active development; these include beam-level imaging of radio-opaque fiducials, fiducial-less tracking of anatomical landmarks, tracking of electromagnetic transponders, optical imaging of correlated surrogates, and volumetric imaging within treatment delivery. The Joint Imaging and Therapy Symposium will provide an overview of the techniquesmore » for real-time imaging and tracking, with special focus on emerging modes of implementation across different modalities. In particular, the symposium will explore developments in 1) Beam-level kilovoltage X-ray imaging techniques, 2) EPID-based megavoltage X-ray tracking, 3) Dynamic tracking using electromagnetic transponders, and 4) MRI-based soft-tissue tracking during radiation delivery. Learning Objectives: Understand the fundamentals of real-time imaging and tracking techniques Learn about emerging techniques in the field of real-time tracking Distinguish between the advantages and disadvantages of different tracking modalities Understand the role of real-time tracking techniques within the clinical delivery work-flow.« less

MO-FG-BRD-00: Real-Time Imaging and Tracking Techniques for Intrafractional Motion Management

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

NONE

2015-06-15

Intrafraction target motion is a prominent complicating factor in the accurate targeting of radiation within the body. Methods compensating for target motion during treatment, such as gating and dynamic tumor tracking, depend on the delineation of target location as a function of time during delivery. A variety of techniques for target localization have been explored and are under active development; these include beam-level imaging of radio-opaque fiducials, fiducial-less tracking of anatomical landmarks, tracking of electromagnetic transponders, optical imaging of correlated surrogates, and volumetric imaging within treatment delivery. The Joint Imaging and Therapy Symposium will provide an overview of the techniquesmore » for real-time imaging and tracking, with special focus on emerging modes of implementation across different modalities. In particular, the symposium will explore developments in 1) Beam-level kilovoltage X-ray imaging techniques, 2) EPID-based megavoltage X-ray tracking, 3) Dynamic tracking using electromagnetic transponders, and 4) MRI-based soft-tissue tracking during radiation delivery. Learning Objectives: Understand the fundamentals of real-time imaging and tracking techniques Learn about emerging techniques in the field of real-time tracking Distinguish between the advantages and disadvantages of different tracking modalities Understand the role of real-time tracking techniques within the clinical delivery work-flow.« less

Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

PubMed

Lai, Ying-Hui; Tsao, Yu; Lu, Xugang; Chen, Fei; Su, Yu-Ting; Chen, Kuang-Chao; Chen, Yu-Hsuan; Chen, Li-Ching; Po-Hung Li, Lieber; Lee, Chin-Hui

2018-01-20

We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients. The deep learning-based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach. Moreover, we evaluate the speech recognition performance of the NC + DDAE NR and classical single-microphone NR approaches for Mandarin-speaking CI recipients under different noisy conditions. The testing set contains Mandarin sentences corrupted by two types of maskers, two-talker babble noise, and a construction jackhammer noise, at 0 and 5 dB SNR levels. Two conventional NR techniques and the proposed deep learning-based approach are used to process the noisy utterances. We qualitatively compare the NR approaches by the amplitude envelope and spectrogram plots of the processed utterances. Quantitative objective measures include (1) normalized covariance measure to test the intelligibility of the utterances processed by each of the NR approaches; and (2) speech recognition tests conducted by nine Mandarin-speaking CI recipients. These nine CI recipients use their own clinical speech processors during testing. The experimental results of objective evaluation and listening test indicate that under challenging listening conditions, the proposed NC + DDAE NR approach yields higher intelligibility scores than the two compared classical NR techniques, under both matched and mismatched training-testing conditions. When compared to the two well-known conventional NR techniques under challenging listening condition, the proposed NC + DDAE NR approach has superior noise suppression capabilities and gives less distortion for the key speech envelope information, thus, improving speech recognition more effectively for Mandarin CI recipients. The results suggest that the proposed deep learning-based NR approach can potentially be integrated into existing CI signal processors to overcome the degradation of speech perception caused by noise.

A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

PubMed

Liarokapis, Minas V; Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J; Manolakos, Elias S

2013-09-01

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

A Physics-Based Deep Learning Approach to Shadow Invariant Representations of Hyperspectral Images.

PubMed

Windrim, Lloyd; Ramakrishnan, Rishi; Melkumyan, Arman; Murphy, Richard J

2018-02-01

This paper proposes the Relit Spectral Angle-Stacked Autoencoder, a novel unsupervised feature learning approach for mapping pixel reflectances to illumination invariant encodings. This work extends the Spectral Angle-Stacked Autoencoder so that it can learn a shadow-invariant mapping. The method is inspired by a deep learning technique, Denoising Autoencoders, with the incorporation of a physics-based model for illumination such that the algorithm learns a shadow invariant mapping without the need for any labelled training data, additional sensors, a priori knowledge of the scene or the assumption of Planckian illumination. The method is evaluated using datasets captured from several different cameras, with experiments to demonstrate the illumination invariance of the features and how they can be used practically to improve the performance of high-level perception algorithms that operate on images acquired outdoors.

Learning Negotiation Policies Using IB3 and Bayesian Networks

NASA Astrophysics Data System (ADS)

Nalepa, Gislaine M.; Ávila, Bráulio C.; Enembreck, Fabrício; Scalabrin, Edson E.

This paper presents an intelligent offer policy in a negotiation environment, in which each agent involved learns the preferences of its opponent in order to improve its own performance. Each agent must also be able to detect drifts in the opponent's preferences so as to quickly adjust itself to their new offer policy. For this purpose, two simple learning techniques were first evaluated: (i) based on instances (IB3) and (ii) based on Bayesian Networks. Additionally, as its known that in theory group learning produces better results than individual/single learning, the efficiency of IB3 and Bayesian classifier groups were also analyzed. Finally, each decision model was evaluated in moments of concept drift, being the drift gradual, moderate or abrupt. Results showed that both groups of classifiers were able to effectively detect drifts in the opponent's preferences.

Summary of vulnerability related technologies based on machine learning

NASA Astrophysics Data System (ADS)

Zhao, Lei; Chen, Zhihao; Jia, Qiong

2018-04-01

As the scale of information system increases by an order of magnitude, the complexity of system software is getting higher. The vulnerability interaction from design, development and deployment to implementation stages greatly increases the risk of the entire information system being attacked successfully. Considering the limitations and lags of the existing mainstream security vulnerability detection techniques, this paper summarizes the development and current status of related technologies based on the machine learning methods applied to deal with massive and irregular data, and handling security vulnerabilities.

Piaget and Organic Chemistry: Teaching Introductory Organic Chemistry through Learning Cycles

NASA Astrophysics Data System (ADS)

Libby, R. Daniel

1995-07-01

This paper describes the first application of the Piaget-based learning cycle technique (Atkin & Karplus, Sci. Teach. 1962, 29, 45-51) to an introductory organic chemistry course. It also presents the step-by-step process used to convert a lecture course into a discussion-based active learning course. The course is taught in a series of learning cycles. A learning cycle is a three phase process that provides opportunities for students to explore new material and work with an instructor to recognize logical patterns in data, and devise and test hypotheses. In this application, the first phase, exploration, involves out-of-class student evaluation of data in attempts to identify significant trends and develop hypotheses that might explain the trends in terms of fundamental scientific principles. In the second phase, concept invention, the students and instructor work together in-class to evaluate student hypotheses and find concepts that work best in explaining the data. The third phase, application, is an out-of-class application of the concept to new situations. The development of learning cycles from lecture notes is presented as an 8 step procedure. The process involves revaluation and restructuring of the course material to maintain a continuity of concept development according to the instructor's logic, dividing topics into individual concepts or techniques, and refocusing the presentation in terms of large numbers of examples that can serve as data for students in their exploration and application activities. A sample learning cycle and suggestions for ways of limited implementation of learning cycles into existing courses are also provided.

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.

Impact of problem-based learning in a large classroom setting: student perception and problem-solving skills.

PubMed

Klegeris, Andis; Hurren, Heather

2011-12-01

Problem-based learning (PBL) can be described as a learning environment where the problem drives the learning. This technique usually involves learning in small groups, which are supervised by tutors. It is becoming evident that PBL in a small-group setting has a robust positive effect on student learning and skills, including better problem-solving skills and an increase in overall motivation. However, very little research has been done on the educational benefits of PBL in a large classroom setting. Here, we describe a PBL approach (using tutorless groups) that was introduced as a supplement to standard didactic lectures in University of British Columbia Okanagan undergraduate biochemistry classes consisting of 45-85 students. PBL was chosen as an effective method to assist students in learning biochemical and physiological processes. By monitoring student attendance and using informal and formal surveys, we demonstrated that PBL has a significant positive impact on student motivation to attend and participate in the course work. Student responses indicated that PBL is superior to traditional lecture format with regard to the understanding of course content and retention of information. We also demonstrated that student problem-solving skills are significantly improved, but additional controlled studies are needed to determine how much PBL exercises contribute to this improvement. These preliminary data indicated several positive outcomes of using PBL in a large classroom setting, although further studies aimed at assessing student learning are needed to further justify implementation of this technique in courses delivered to large undergraduate classes.

Unpacking the Complexity of Patient Handoffs Through the Lens of Cognitive Load Theory.

PubMed

Young, John Q; Ten Cate, Olle; O'Sullivan, Patricia S; Irby, David M

2016-01-01

The transfer of a patient from one clinician to another is a high-risk event. Errors are common and lead to patient harm. More effective methods for learning how to give and receive sign-out is an important public health priority. Performing a handoff is a complex task. Trainees must simultaneously apply and integrate clinical, communication, and systems skills into one time-limited and highly constrained activity. The task demands can easily exceed the information-processing capacity of the trainee, resulting in impaired learning and performance. Appreciating the limits of working memory can help identify the challenges that instructional techniques and research must then address. Cognitive load theory (CLT) identifies three types of load that impact working memory: intrinsic (task-essential), extraneous (not essential to task), and germane (learning related). The authors generated a list of factors that affect a trainee's learning and performance of a handoff based on CLT. The list was revised based on feedback from experts in medical education and in handoffs. By consensus, the authors associated each factor with the type of cognitive load it primarily effects. The authors used this analysis to build a conceptual model of handoffs through the lens of CLT. The resulting conceptual model unpacks the complexity of handoffs and identifies testable hypotheses for educational research and instructional design. The model identifies features of a handoff that drive extraneous, intrinsic, and germane load for both the sender and the receiver. The model highlights the importance of reducing extraneous load, matching intrinsic load to the developmental stage of the learner and optimizing germane load. Specific CLT-informed instructional techniques for handoffs are explored. Intrinsic and germane load are especially important to address and include factors such as knowledge of the learner, number of patients, time constraints, clinical uncertainties, overall patient/panel complexity, interacting comorbidities or therapeutics, experience or specialty gradients between the sender and receiver, the maturity of the evidence base for the patient's disease, and the use of metacognitive techniques. Research that identifies which cognitive load factors most significantly affect the learning and performance of handoffs can lead to novel, contextually adapted instructional techniques and handoff protocols. The application of CLT to handoffs may also help with the further development of CLT as a learning theory.

Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

PubMed

Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

2015-01-01

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

Analyzing Activity Behavior and Movement in a Naturalistic Environment using Smart Home Techniques

PubMed Central

Cook, Diane J.; Schmitter-Edgecombe, Maureen; Dawadi, Prafulla

2015-01-01

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study we use smart home and wearable sensors to collect data while (n=84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an AUC value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant. PMID:26259225

Inductive System Health Monitoring

NASA Technical Reports Server (NTRS)

Iverson, David L.

2004-01-01

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques.

PubMed

Cook, Diane J; Schmitter-Edgecombe, Maureen; Dawadi, Prafulla

2015-11-01

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study, we use smart home and wearable sensors to collect data, while ( n = 84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an area under the ROC curve value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.

A review of approaches to identifying patient phenotype cohorts using electronic health records

PubMed Central

Shivade, Chaitanya; Raghavan, Preethi; Fosler-Lussier, Eric; Embi, Peter J; Elhadad, Noemie; Johnson, Stephen B; Lai, Albert M

2014-01-01

Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses. PMID:24201027

Visual Hybrid Development Learning System (VHDLS) framework for children with autism.

PubMed

Banire, Bilikis; Jomhari, Nazean; Ahmad, Rodina

2015-10-01

The effect of education on children with autism serves as a relative cure for their deficits. As a result of this, they require special techniques to gain their attention and interest in learning as compared to typical children. Several studies have shown that these children are visual learners. In this study, we proposed a Visual Hybrid Development Learning System (VHDLS) framework that is based on an instructional design model, multimedia cognitive learning theory, and learning style in order to guide software developers in developing learning systems for children with autism. The results from this study showed that the attention of children with autism increased more with the proposed VHDLS framework.

A Corpus-Based Approach for Automatic Thai Unknown Word Recognition Using Boosting Techniques

NASA Astrophysics Data System (ADS)

Techo, Jakkrit; Nattee, Cholwich; Theeramunkong, Thanaruk

While classification techniques can be applied for automatic unknown word recognition in a language without word boundary, it faces with the problem of unbalanced datasets where the number of positive unknown word candidates is dominantly smaller than that of negative candidates. To solve this problem, this paper presents a corpus-based approach that introduces a so-called group-based ranking evaluation technique into ensemble learning in order to generate a sequence of classification models that later collaborate to select the most probable unknown word from multiple candidates. Given a classification model, the group-based ranking evaluation (GRE) is applied to construct a training dataset for learning the succeeding model, by weighing each of its candidates according to their ranks and correctness when the candidates of an unknown word are considered as one group. A number of experiments have been conducted on a large Thai medical text to evaluate performance of the proposed group-based ranking evaluation approach, namely V-GRE, compared to the conventional naïve Bayes classifier and our vanilla version without ensemble learning. As the result, the proposed method achieves an accuracy of 90.93±0.50% when the first rank is selected while it gains 97.26±0.26% when the top-ten candidates are considered, that is 8.45% and 6.79% improvement over the conventional record-based naïve Bayes classifier and the vanilla version. Another result on applying only best features show 93.93±0.22% and up to 98.85±0.15% accuracy for top-1 and top-10, respectively. They are 3.97% and 9.78% improvement over naive Bayes and the vanilla version. Finally, an error analysis is given.

Identification of Effective Teaching Behaviors

DTIC Science & Technology

1993-07-01

of proximal development reflects his two part theory of learning. Specifically, Vygotsky believes that learning has social and developmental components...which--in theory -- utilize artificial intelligence (Al) techniques to provide highly individualized instruction, much like that of a human tutor. The...this approach can produce acceptable instruction, it is not optimal. * Theory driven. Tutoring principles are based on some type of theory . Usually

Investigating an Application of Speech-to-Text Recognition: A Study on Visual Attention and Learning Behaviour

ERIC Educational Resources Information Center

Huang, Y-M.; Liu, C-J.; Shadiev, Rustam; Shen, M-H.; Hwang, W-Y.

2015-01-01

One major drawback of previous research on speech-to-text recognition (STR) is that most findings showing the effectiveness of STR for learning were based upon subjective evidence. Very few studies have used eye-tracking techniques to investigate visual attention of students on STR-generated text. Furthermore, not much attention was paid to…

Teaching Students How to Study: A Workshop on Information Processing and Self-Testing Helps Students Learn

ERIC Educational Resources Information Center

Stanger-Hall, Kathrin F.; Shockley, Floyd W.; Wilson, Rachel E.

2011-01-01

We implemented a "how to study" workshop for small groups of students (6-12) for N = 93 consenting students, randomly assigned from a large introductory biology class. The goal of this workshop was to teach students self-regulating techniques with visualization-based exercises as a foundation for learning and critical thinking in two areas:…

Evaluation of the Stress Resilience Training System

DTIC Science & Technology

2014-10-30

enhanced by combining cognitive learning methodologies grounded in learning theory and biofeedback techniques based on heart rate variability ( HRV ) with...to reduce arousal. Biofeedback has been shown to reduce subjective stress, lower depression scores, decrease anxiety in athletes, and reduce...user’s biology (e.g., HRV -controlled games) provide a unique and highly immersive gaming experience (Prensky, 2001). These findings have been adopted

UK Health-Care Professionals' Experience of On-Line Learning Techniques: A Systematic Review of Qualitative Data

ERIC Educational Resources Information Center

Carroll, Christopher; Booth, Andrew; Papaioannou, Diana; Sutton, Anthea; Wong, Ruth

2009-01-01

Introduction: Continuing professional development and education is vital to the provision of better health services and outcomes. The aim of this study is to contribute to the evidence base by performing a systematic review of qualitative data from studies reporting health professionals' experience of e-learning. No such previous review has been…

Towards an Object-Oriented Model for the Design and Development of Learning Objects

ERIC Educational Resources Information Center

Chrysostomou, Chrysostomos; Papadopoulos, George

2008-01-01

This work introduces the concept of an Object-Oriented Learning Object (OOLO) that is developed in a manner similar to the one that software objects are developed through Object-Oriented Software Engineering (OO SWE) techniques. In order to make the application of the OOLO feasible and efficient, an OOLO model needs to be developed based on…

Enhanced Teaching and Student Learning through a Simulator-Based Course in Chemical Unit Operations Design

ERIC Educational Resources Information Center

Ghasem, Nayef

2016-01-01

This paper illustrates a teaching technique used in computer applications in chemical engineering employed for designing various unit operation processes, where the students learn about unit operations by designing them. The aim of the course is not to teach design, but rather to teach the fundamentals and the function of unit operation processes…

Role Playing in Online Education: A Teaching Tool to Enhance Student Engagement and Sustained Learning

ERIC Educational Resources Information Center

Bender, Tisha

2005-01-01

As online education escalates, it is important for instructors to explore teaching techniques that engage students and enhance learning at a profound level. To achieve this goal, instructors must look at the primarily text-based environment of the online class not as a limitation, but as an opportunity. Attentive and highly personal teaching that…

Active Learning to Improve Presentation Skills: The Use of Pecha Kucha in Undergraduate Sales Management Classes

ERIC Educational Resources Information Center

McDonald, Robert E.; Derby, Joseph M.

2015-01-01

Recruiters seek candidates with certain business skills that are not developed in the typical lecture-based classroom. Instead, active-learning techniques have been shown to be effective in honing these skills. One skill that is particularly important in sales careers is the ability to make a powerful and effective presentation. To help students…

ConfChem Conference on Select 2016 BCCE Presentations: Putting Your Own Personal Twist on a Flipped Organic Classroom and Selling the Idea to Students

ERIC Educational Resources Information Center

Thomas, Ashleigh L. P.

2017-01-01

This paper presents gradual implementation of active learning approaches in an organic chemistry classroom based on student feedback and strategies for getting students on-board with this new approach. Active learning techniques discussed include videos, online quizzes, reading assignments, and classroom activities. Preliminary findings indicate a…

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

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

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach

NASA Astrophysics Data System (ADS)

Lin, Y.; Harp, D. R.; Chen, B.; Pawar, R.

2017-12-01

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including pressure. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning technique based on support vector regression to effectively and efficiently predict the leakage locations and leakage rates based on limited number of pressure observations. Compared to the conventional data-driven approaches, which can be usually seem as a "black box" procedure, we develop a physics-guided machine learning method to incorporate the governing physics into the learning procedure. To validate the performance of our proposed leakage detection method, we employ our method to both 2D and 3D synthetic subsurface models. Our novel CO2 leakage detection method has shown high detection accuracy in the example problems.

Obstetric ultrasound education for the developing world: A learning partnership with the World Federation for Ultrasound in Medicine and Biology

PubMed Central

Dubbins, P; Evans, JA

2015-01-01

The ultrasound techniques in pregnancy e-learning project is an online resource commissioned and supported by the Education Committee of the World Federation for Ultrasound in Medicine and Biology (WFUMB). This currently consists of 10 e-learning sessions aimed at midwives and other health workers in developing countries where WFUMB has Educational Centres of Excellence, and in particular at those based mainly in rural communities at considerable distance from urban training centres. The project covers all of the basics of obstetric ultrasound such as fetal and maternal anatomy, ultrasound techniques, assessment in both early and late pregnancy, prediction of pregnancy complications and identification of common abnormalities that might interfere with delivery. The e-learning project complements a wider training programme which covers operator skills and machine controls, in order to minimise the time that the professional has to leave their rural, often poorly staffed, workplace to attend classroom-based courses in the city. Each session outlines often complex concepts using simple diagrams, interactive exercises and cine clips. Tips, tricks and best practice guidelines are provided in simple terms. PMID:27433236

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

A pilot study of team learning on in-patient rounds.

PubMed

Colbert, James; Pelletier, Stephen; Xavier-Depina, Francisca; Shields, Helen

2016-02-01

Medical trainees often do not receive structured teaching during in-patient rounds. To assess whether the addition of a collaborative team learning technique would improve the learning experience on a general medicine in-patient team. Eight learners participated in this pilot study. Learning teams consisted of internal medicine residents and third-year medical students on a general medicine in-patient rotation. The experimental curriculum covered four common topics: cardiac stress testing; syncope; pneumonia; and valvular heart disease. Sessions had the following format: (1) each learner answered five self-assessment questions using an immediate feedback technique; (2) learners were divided into groups of two or three to discuss their answers; (3) the teaching doctor led a discussion to clarify and summarise, and also distributed a handout delineating key learning points. Control sessions consisted of the usual teaching rounds. Learners were e-mailed a daily online survey asking them to rate the rounds and handouts on a Likert scale. Medical trainees often do not receive structured teaching during in-patient rounds All of the learners rated the collaborative team learning intervention as either 'excellent' or 'very good'. Learners also indicated that they found the take-away handout valuable, and positive responses were also noted in the survey comments. A novel collaborative team learning technique resulted in high ratings of teaching rounds by medical residents and medical students. Learners found the sessions engaging, high yield, and educationally valuable. This interactive discussion-based teaching method could be used to enhance the learning experience during teaching rounds on medical, surgical and subspecialty services. © 2015 John Wiley & Sons Ltd.

Machine Learning in Intrusion Detection

DTIC Science & Technology

2005-07-01

machine learning tasks. Anomaly detection provides the core technology for a broad spectrum of security-centric applications. In this dissertation, we examine various aspects of anomaly based intrusion detection in computer security. First, we present a new approach to learn program behavior for intrusion detection. Text categorization techniques are adopted to convert each process to a vector and calculate the similarity between two program activities. Then the k-nearest neighbor classifier is employed to classify program behavior as normal or intrusive. We demonstrate

A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

PubMed

Jiang, Wenhao; Chung, Fu-lai

2014-01-01

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology.

PubMed

Kumar, Neeraj; Verma, Ruchika; Sharma, Sanuj; Bhargava, Surabhi; Vahadane, Abhishek; Sethi, Amit

2017-07-01

Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.

Enhanced teaching and student learning through a simulator-based course in chemical unit operations design

NASA Astrophysics Data System (ADS)

Ghasem, Nayef

2016-07-01

This paper illustrates a teaching technique used in computer applications in chemical engineering employed for designing various unit operation processes, where the students learn about unit operations by designing them. The aim of the course is not to teach design, but rather to teach the fundamentals and the function of unit operation processes through simulators. A case study presenting the teaching method was evaluated using student surveys and faculty assessments, which were designed to measure the quality and effectiveness of the teaching method. The results of the questionnaire conclusively demonstrate that this method is an extremely efficient way of teaching a simulator-based course. In addition to that, this teaching method can easily be generalised and used in other courses. A student's final mark is determined by a combination of in-class assessments conducted based on cooperative and peer learning, progress tests and a final exam. Results revealed that peer learning can improve the overall quality of student learning and enhance student understanding.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

[Multifamily therapy in children with learning disabilities].

PubMed

Retzlaff, Rüdiger; Brazil, Susanne; Goll-Kopka, Andrea

2008-01-01

Multifamily therapy is an evidence-based method used in the treatment and prevention of severe psychiatric disorders, behavioral problems and physical illnesses in children, adolescents and adults. For preventive family-oriented work with children with learning disorders there is a lack of therapeutic models. This article presents results from an innovative pilot project--multiple family groups for families with a learning disabled child of primary school age (six to eleven years old). Based on a systemic approach, this resource-oriented program integrates creative, activity-based interventions and group therapy techniques and conveys a comprehensive understanding of the challenges associated with learning disorders. Because of the pilot character of the study and the small sample size, the results have to be interpreted with care. The results do however clearly support the wider implementation and evaluation of the program in child guidance clinics, social-pediatric centers, as well as child and adolescent clinics and schools.

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.

Improving mathematical problem solving ability through problem-based learning and authentic assessment for the students of Bali State Polytechnic

NASA Astrophysics Data System (ADS)

Darma, I. K.

2018-01-01

This research is aimed at determining: 1) the differences of mathematical problem solving ability between the students facilitated with problem-based learning model and conventional learning model, 2) the differences of mathematical problem solving ability between the students facilitated with authentic and conventional assessment model, and 3) interaction effect between learning and assessment model on mathematical problem solving. The research was conducted in Bali State Polytechnic, using the 2x2 experiment factorial design. The samples of this research were 110 students. The data were collected using a theoretically and empirically-validated test. Instruments were validated by using Aiken’s approach of technique content validity and item analysis, and then analyzed using anova stylistic. The result of the analysis shows that the students facilitated with problem-based learning and authentic assessment models get the highest score average compared to the other students, both in the concept understanding and mathematical problem solving. The result of hypothesis test shows that, significantly: 1) there is difference of mathematical problem solving ability between the students facilitated with problem-based learning model and conventional learning model, 2) there is difference of mathematical problem solving ability between the students facilitated with authentic assessment model and conventional assessment model, and 3) there is interaction effect between learning model and assessment model on mathematical problem solving. In order to improve the effectiveness of mathematics learning, collaboration between problem-based learning model and authentic assessment model can be considered as one of learning models in class.

A novel method for predicting kidney stone type using ensemble learning.

PubMed

Kazemi, Yassaman; Mirroshandel, Seyed Abolghasem

2018-01-01

The high morbidity rate associated with kidney stone disease, which is a silent killer, is one of the main concerns in healthcare systems all over the world. Advanced data mining techniques such as classification can help in the early prediction of this disease and reduce its incidence and associated costs. The objective of the present study is to derive a model for the early detection of the type of kidney stone and the most influential parameters with the aim of providing a decision-support system. Information was collected from 936 patients with nephrolithiasis at the kidney center of the Razi Hospital in Rasht from 2012 through 2016. The prepared dataset included 42 features. Data pre-processing was the first step toward extracting the relevant features. The collected data was analyzed with Weka software, and various data mining models were used to prepare a predictive model. Various data mining algorithms such as the Bayesian model, different types of Decision Trees, Artificial Neural Networks, and Rule-based classifiers were used in these models. We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm. In addition, a novel technique for combining individual classifiers in ensemble learning was proposed. In this technique, for each individual classifier, a weight is assigned based on our proposed genetic algorithm based method. The generated knowledge was evaluated using a 10-fold cross-validation technique based on standard measures. However, the assessment of each feature for building a predictive model was another significant challenge. The predictive strength of each feature for creating a reproducible outcome was also investigated. Regarding the applied models, parameters such as sex, acid uric condition, calcium level, hypertension, diabetes, nausea and vomiting, flank pain, and urinary tract infection (UTI) were the most vital parameters for predicting the chance of nephrolithiasis. The final ensemble-based model (with an accuracy of 97.1%) was a robust one and could be safely applied to future studies to predict the chances of developing nephrolithiasis. This model provides a novel way to study stone disease by deciphering the complex interaction among different biological variables, thus helping in an early identification and reduction in diagnosis time. Copyright © 2017 Elsevier B.V. All rights reserved.

An Approach to V&V of Embedded Adaptive Systems

NASA Technical Reports Server (NTRS)

Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth

2004-01-01

Rigorous Verification and Validation (V&V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V&V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online stability monitoring tools. The novelty detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology. ,

Use of Formative Classroom Assessment Techniques in a Project Management Course

ERIC Educational Resources Information Center

Purcell, Bernice M.

2014-01-01

Formative assessment is considered to be an evaluation technique that informs the instructor of the level of student learning, giving evidence when it may be necessary for the instructor to make a change in delivery based upon the results. Several theories of formative assessment exist, all which propound the importance of feedback to the student.…

Incorporating Biological Mass Spectrometry into Undergraduate Teaching Labs, Part 1: Identifying Proteins Based on Molecular Mass

ERIC Educational Resources Information Center

Arnquist, Isaac J.; Beussman, Douglas J.

2007-01-01

Biological mass spectrometry is an important analytical technique in drug discovery, proteomics, and research at the biology-chemistry interface. Currently, few hands-on opportunities exist for undergraduate students to learn about this technique. With the 2002 Nobel Prize being awarded, in part, for the development of biological mass…

Making Quality Sense: A Guide to Quality, Tools and Techniques, Awards and the Thinking Behind Them.

ERIC Educational Resources Information Center

Owen, Jane

This document is intended to guide further education colleges and work-based learning providers through some of the commonly used tools, techniques, and theories of quality management. The following are among the topics discussed: (1) various ways of defining quality; methods used by organizations to achieve quality (quality control, quality…