Evolving autonomous learning in cognitive networks.

PubMed

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

The Effect of Conceptual and Contextual Familiarity on Transfer Performance

ERIC Educational Resources Information Center

Kulasegaram, Kulamakan; Min, Cynthia; Ames, Kimberly; Howey, Elizabeth; Neville, Alan; Norman, Geoffrey

2012-01-01

Applying a previously learned concept to a novel problem is an important but difficult process called transfer. It is suggested that a commonsense analogy aids in transfer by linking novel concepts to familiar ones. How the context of practice affects transfer when learning using analogies is still unclear. This study investigated the effect of a…

Developing a Learning Progression for Number Sense Based on the Rule Space Model in China

ERIC Educational Resources Information Center

Chen, Fu; Yan, Yue; Xin, Tao

2017-01-01

The current study focuses on developing the learning progression of number sense for primary school students, and it applies a cognitive diagnostic model, the rule space model, to data analysis. The rule space model analysis firstly extracted nine cognitive attributes and their hierarchy model from the analysis of previous research and the…

Successfully Integrating Novel Games into the Curriculum: Netball for All

ERIC Educational Resources Information Center

Clancy, Mary; Portman, Penelope; Bowersock, Amy

2007-01-01

Novel activities enrich a curriculum by allowing students to learn new skills and knowledge, to apply previously learned skills in new ways, and to be introduced to a new challenge that can increase student interest and motivation. This article first presents several considerations when adding a new game to the physical education curriculum. It…

Designing worked examples for learning tangent lines to circles

NASA Astrophysics Data System (ADS)

Retnowati, E.; Marissa

2018-03-01

Geometry is a branch of mathematics that deals with shape and space, including the circle. A difficult topic in the circle may be the tangent line to circle. This is considered a complex material since students have to simultaneously apply several principles to solve the problems, these are the property of circle, definition of the tangent, measurement and Pythagorean theorem. This paper discusses designs of worked examples for learning tangent line to circles and how to apply this design to an effective and efficient instructional activity. When students do not have sufficient prior knowledge, solving tangent problems might be clumsy, and as a consequence, the problem-solving activity hinders learning. According to a Cognitive Load Theory, learning occurs when students can construct new knowledge based on the relevant knowledge previously learned. When the relevant knowledge is unavailable, providing students with the worked example is suggested. Worked example may reduce unproductive process during learning that causes extraneous cognitive load. Nevertheless, worked examples must be created in such a way facilitate learning.

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks

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

Phillips, Lawrence A.; Hodas, Nathan O.

Increasingly, cognitive scientists have demonstrated interest in applying tools from deep learning. One use for deep learning is in language acquisition where it is useful to know if a linguistic phenomenon can be learned through domain-general means. To assess whether unsupervised deep learning is appropriate, we first pose a smaller question: Can unsupervised neural networks apply linguistic rules productively, using them in novel situations. We draw from the literature on determiner/noun productivity by training an unsupervised, autoencoder network measuring its ability to combine nouns with determiners. Our simple autoencoder creates combinations it has not previously encountered, displaying a degree ofmore » overlap similar to actual children. While this preliminary work does not provide conclusive evidence for productivity, it warrants further investigation with more complex models. Further, this work helps lay the foundations for future collaboration between the deep learning and cognitive science communities.« less

The Next Era: Deep Learning in Pharmaceutical Research.

PubMed

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Assessing Students' Attitudes In A College Physics Course In Mexico

NASA Astrophysics Data System (ADS)

de la Garza, Jorge; Alarcon, Hugo

2010-10-01

Considering the benefits of modeling instruction in improving conceptual learning while students work more like scientists, an implementation was made in an introductory Physics course in a Mexican University. Recently Brewe, Kramer and O'Brien have observed positive attitudinal shifts using modeling instruction in a course with a reduced number of students. These results are opposite to previous observations with methodologies that promote active learning. Inspired in those results, the Colorado Learning Attitudes about Science Survey (CLASS) was applied as pre and post tests in two Mechanics courses with modeling. In comparison to the different categories of the CLASS, significant positive shifts have been determined in Overall, Sophistication in Problem Solving, and Applied Conceptual Understanding in a sample of 44 students.

Multimedia and Audience: Implications for Executive Summaries

DTIC Science & Technology

1995-12-01

by the Institute for Defense Analysis ( Fetterman , 1993:123). As well, organizations such as Federal Express, IBM, and the U.S. Army have studied the...training compression, less delivery variance, better consistency of learning, and greater learning gains ( Fetterman , 1993:128-129). As defined previously... Fetterman , Roger L. and Satish K. Gupta. Mainstream Multimedia: Applying Multimedia in Business. New York: Van Nostrand Reinhold, 1993. Grice, Roger A

Changes in the Linguistic Confidence of Primary and Secondary Students in Catalonia: A Longitudinal Study

ERIC Educational Resources Information Center

Bretxa, Vanessa; Comajoan, Llorenç; Ubalde, Josep; Vila, F. Xavier

2016-01-01

Previous research in first (L1) and second language (L2) acquisition has provided evidence that linguistic confidence is a key construct that can explain linguistic behaviour. In this paper, we apply previous research in the socio-contextual model of L2 learning to data from Catalonia. More specifically, the paper investigates linguistic…

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex

PubMed Central

Tobler, Philippe N.

2015-01-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others’ rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. PMID:25326037

The Next Era: Deep Learning in Pharmaceutical Research

PubMed Central

Ekins, Sean

2016-01-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique. PMID:27599991

An Investigation into the Process of Transference, through the Integration of Art with Science and Math Curricula, in a California Community College: A Case Study

ERIC Educational Resources Information Center

Rachford, Maryann Kvietkauskas

2011-01-01

The transference of learning from one discipline to another creates new knowledge between subjects. Students can connect and apply what they learn in one subject to previously existing knowledge. Art expression is an integral part of human nature and has been a means of communication throughout history. Through the integration of art with science…

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.

Reverse case study: to think like a nurse.

PubMed

Beyer, Deborah A

2011-01-01

Reverse case study is a collaborative, innovative, active learning strategy that nurse educators can use in the classroom. Groups of students develop a case study and a care plan from a list of medications and a short two- to three-sentence scenario. The students apply the nursing process to thoroughly develop a complete case study written as a concept map. The strategy builds on previous learned information and applies the information to new content, thus promoting critical thinking and problem solving. Reverse case study has been used in both associate and baccalaureate nursing degree theory courses to generate discussion and assist students in thinking like a nurse. 2011, SLACK Incorporated.

Novelty and Inductive Generalization in Human Reinforcement Learning

PubMed Central

Gershman, Samuel J.; Niv, Yael

2015-01-01

In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: what is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: how can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of reinforcement learning in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional reinforcement learning algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. PMID:25808176

Cooperative inference: Features, objects, and collections.

PubMed

Searcy, Sophia Ray; Shafto, Patrick

2016-10-01

Cooperation plays a central role in theories of development, learning, cultural evolution, and education. We argue that existing models of learning from cooperative informants have fundamental limitations that prevent them from explaining how cooperation benefits learning. First, existing models are shown to be computationally intractable, suggesting that they cannot apply to realistic learning problems. Second, existing models assume a priori agreement about which concepts are favored in learning, which leads to a conundrum: Learning fails without precise agreement on bias yet there is no single rational choice. We introduce cooperative inference, a novel framework for cooperation in concept learning, which resolves these limitations. Cooperative inference generalizes the notion of cooperation used in previous models from omission of labeled objects to the omission values of features, labels for objects, and labels for collections of objects. The result is an approach that is computationally tractable, does not require a priori agreement about biases, applies to both Boolean and first-order concepts, and begins to approximate the richness of real-world concept learning problems. We conclude by discussing relations to and implications for existing theories of cognition, cognitive development, and cultural evolution. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

PubMed

Seid-Fatemi, Azade; Tobler, Philippe N

2015-05-01

When we are learning to associate novel cues with outcomes, learning is more efficient if we take advantage of previously learned associations and thereby avoid redundant learning. The blocking effect represents this sort of efficiency mechanism and refers to the phenomenon in which a novel stimulus is blocked from learning when it is associated with a fully predicted outcome. Although there is sufficient evidence that this effect manifests itself when individuals learn about their own rewards, it remains unclear whether it also does when they learn about others' rewards. We employed behavioral and neuroimaging methods to address this question. We demonstrate that blocking does indeed occur in the social domain and it does so to a similar degree as observed in the individual domain. On the neural level, activations in the medial prefrontal cortex (mPFC) show a specific contribution to blocking and learning-related prediction errors in the social domain. These findings suggest that the efficiency principle that applies to reward learning in the individual domain also applies to that in the social domain, with the mPFC playing a central role in implementing it. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Opportunistic Behavior in Motivated Learning Agents.

PubMed

Graham, James; Starzyk, Janusz A; Jachyra, Daniel

2015-08-01

This paper focuses on the novel motivated learning (ML) scheme and opportunistic behavior of an intelligent agent. It extends previously developed ML to opportunistic behavior in a multitask situation. Our paper describes the virtual world implementation of autonomous opportunistic agents learning in a dynamically changing environment, creating abstract goals, and taking advantage of arising opportunities to improve their performance. An opportunistic agent achieves better results than an agent based on ML only. It does so by minimizing the average value of all need signals rather than a dominating need. This paper applies to the design of autonomous embodied systems (robots) learning in real-time how to operate in a complex environment.

James Webb Space Telescope - Applying Lessons Learned to I&T

NASA Technical Reports Server (NTRS)

Johns, Alan; Seaton, Bonita; Gal-Edd, Jonathan; Jones, Ronald; Fatig, Curtis; Wasiak, Francis

2008-01-01

The James Webb Space Telescope (JWST) is part of a new generation of spacecraft acquiring large data volumes from remote regions in space. To support a mission such as the JWST, it is imperative that lessons learned from the development of previous missions such as the Hubble Space Telescope and the Earth Observing System mission set be applied throughout the development and operational lifecycles. One example of a key lesson that should be applied is that core components, such as the command and telemetry system and the project database, should be developed early, used throughout development and testing, and evolved into the operational system. The purpose of applying lessons learned is to reap benefits in programmatic or technical parameters such as risk reduction, end product quality, cost efficiency, and schedule optimization. In the cited example, the early development and use of the operational command and telemetry system as well as the establishment of the intended operational database will allow these components to be used by the developers of various spacecraft components such that development, testing, and operations will all use the same core components. This will reduce risk through the elimination of transitions between development and operational components and improve end product quality by extending the verification of those components through continual use. This paper will discuss key lessons learned that have been or are being applied to the JWST Ground Segment integration and test program.

Microstimulation of the Human Substantia Nigra Alters Reinforcement Learning

PubMed Central

Ramayya, Ashwin G.; Misra, Amrit

2014-01-01

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action–reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action–reward associations rather than stimulus–reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action–reward associations during reinforcement learning. PMID:24828643

Can active learning principles be applied to the bioscience assessments of nursing students? A review of the literature.

PubMed

Bakon, Shannon; Craft, Judy; Christensen, Martin; Wirihana, Lisa

2016-02-01

To explore if active learning principles be applied to nursing bioscience assessments and will this influence student perception of confidence in applying theory to practice? A review of the literature utilising searches of various databases including CINAHL, PUBMED, Google Scholar and Mosby's Journal Index. The literature search identified research from twenty-six original articles, two electronic books, one published book and one conference proceedings paper. Bioscience has been identified as an area that nurses struggle to learn in tertiary institutions and then apply to clinical practice. A number of problems have been identified and explored that may contribute to this poor understanding and retention. University academics need to be knowledgeable of innovative teaching and assessing modalities that focus on enhancing student learning and address the integration issues associated with the theory practice gap. Increased bioscience education is associated with improved patient outcomes therefore by addressing this "bioscience problem" and improving the integration of bioscience in clinical practice there will subsequently be an improvement in health care outcomes. From the literature several themes were identified. First there are many problems with teaching nursing students bioscience education. These include class sizes, motivation, concentration, delivery mode, lecturer perspectives, student's previous knowledge, anxiety, and a lack of confidence. Among these influences the type of assessment employed by the educator has not been explored or identified as a contributor to student learning specifically in nursing bioscience instruction. Second that educating could be achieved more effectively if active learning principles were applied and the needs and expectations of the student were met. Lastly, assessment influences student retention and the student experience and as such assessment should be congruent with the subject content, align with the learning objectives and be used as a stimulus tool for learning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Learning styles in otolaryngology fellowships.

PubMed

Varela, David A Diaz Voss; Malik, Mohammad U; Laeeq, Kulsoom; Pandian, Vinciya; Brown, David J; Weatherly, Robert A; Cummings, Charles W; Bhatti, Nasir I

2011-12-01

Previous studies have identified a predominant learning style in trainees from different specialties, more recently in otolaryngology residents. The purpose of our study was to determine a predominant learning style within otolaryngology fellowships and to identify any differences between otolaryngology fellows and residents. We conducted a survey of otolaryngology fellows at 25 otolaryngology fellowship programs accredited by the Accreditation Council for Graduate Medical Education. We emailed Kolb's Learning Style Index version 3.1 to 16 pediatric otolaryngology (PO) and 24 otology/neurotology (ON) fellows. This index is a widely used 12-item questionnaire. The participants answered each item in the questionnaire as it applied to their preferred learning style: accommodating, converging, diverging, or assimilating. Results were then analyzed and compared between each subspecialty and the previously reported preferred styles of otolaryngology residents. Ten PO and 20 ON fellows completed the survey, with an overall response rate of 75%. PO and ON fellows (60% of each group) preferred a learning style that was "balanced" across all four styles. For ON fellows, 35% preferred converging and 5% preferred accommodating styles. For PO fellows, converging and accommodating styles accounted for 20% each. It was previously reported that 74.4% of otolaryngology residents prefer either converging or accommodating styles. We believe that the fellowship training environment calls for fellows to use more than one learning style to become proficient physicians, hence the trend toward potentially developing a balanced style when at this level. Copyright © 2011 The American Laryngological, Rhinological, and Otological Society, Inc.

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

PubMed

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

2017-01-01

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

An Active Learning Activity to Reinforce the Design Components of the Corticosteroids

PubMed Central

Mandela, Prashant

2018-01-01

Despite the popularity of active learning applications over the past few decades, few activities have been reported for the field of medicinal chemistry. The purpose of this study is to report a new active learning activity, describe participant contributions, and examine participant performance on the assessment questions mapped to the objective covered by the activity. In this particular activity, students are asked to design two novel corticosteroids as a group (6–8 students per group) based on the design characteristics of marketed corticosteroids covered in lecture coupled with their pharmaceutics knowledge from the previous semester and then defend their design to the class through an interactive presentation model. Although class performance on the objective mapped to this material on the assessment did not reach statistical significance, use of this activity has allowed fruitful discussion of misunderstood concepts and facilitated multiple changes to the lecture presentation. As pharmacy schools continue to emphasize alternative learning pedagogies, publication of previously implemented activities demonstrating their use will help others apply similar methodologies. PMID:29401733

An Active Learning Activity to Reinforce the Design Components of the Corticosteroids.

PubMed

Slauson, Stephen R; Mandela, Prashant

2018-02-05

Despite the popularity of active learning applications over the past few decades, few activities have been reported for the field of medicinal chemistry. The purpose of this study is to report a new active learning activity, describe participant contributions, and examine participant performance on the assessment questions mapped to the objective covered by the activity. In this particular activity, students are asked to design two novel corticosteroids as a group (6-8 students per group) based on the design characteristics of marketed corticosteroids covered in lecture coupled with their pharmaceutics knowledge from the previous semester and then defend their design to the class through an interactive presentation model. Although class performance on the objective mapped to this material on the assessment did not reach statistical significance, use of this activity has allowed fruitful discussion of misunderstood concepts and facilitated multiple changes to the lecture presentation. As pharmacy schools continue to emphasize alternative learning pedagogies, publication of previously implemented activities demonstrating their use will help others apply similar methodologies.

Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

PubMed

Liu, Wenbo; Li, Ming; Yi, Li

2016-08-01

The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Tracking Decimal Misconceptions: Strategic Instructional Choices

ERIC Educational Resources Information Center

Griffin, Linda B.

2016-01-01

Understanding the decimal system is challenging, requiring coordination of place-value concepts with features of whole-number and fraction knowledge (Moloney and Stacey 1997). Moreover, the learner must discern if and how previously learned concepts and procedures apply. The process is complex, and misconceptions will naturally arise. In a…

Astrophysics: An Integrative Course

ERIC Educational Resources Information Center

Gutsche, Graham D.

1975-01-01

Describes a one semester course in introductory stellar astrophysics at the advanced undergraduate level. The course aims to integrate all previously learned physics by applying it to the study of stars. After a brief introductory section on basic astronomical measurements, the main topics covered are stellar atmospheres, stellar structure, and…

Biomechanics and Developmental Neuromotor Control.

ERIC Educational Resources Information Center

Zernicke, Ronald F.; Schneider, Klaus

1993-01-01

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

Effective classroom teaching methods: a critical incident technique from millennial nursing students' perspective.

PubMed

Robb, Meigan

2014-01-11

Engaging nursing students in the classroom environment positively influences their ability to learn and apply course content to clinical practice. Students are motivated to engage in learning if their learning preferences are being met. The methods nurse educators have used with previous students in the classroom may not address the educational needs of Millennials. This manuscript presents the findings of a pilot study that used the Critical Incident Technique. The purpose of this study was to gain insight into the teaching methods that help the Millennial generation of nursing students feel engaged in the learning process. Students' perceptions of effective instructional approaches are presented in three themes. Implications for nurse educators are discussed.

Microstimulation of the human substantia nigra alters reinforcement learning.

PubMed

Ramayya, Ashwin G; Misra, Amrit; Baltuch, Gordon H; Kahana, Michael J

2014-05-14

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action-reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action-reward associations rather than stimulus-reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action-reward associations during reinforcement learning. Copyright © 2014 the authors 0270-6474/14/346887-09$15.00/0.

Agent Supported Serious Game Environment

ERIC Educational Resources Information Center

Terzidou, Theodouli; Tsiatsos, Thrasyvoulos; Miliou, Christina; Sourvinou, Athanasia

2016-01-01

This study proposes and applies a novel concept for an AI enhanced serious game collaborative environment as a supplementary learning tool in tertiary education. It is based on previous research that investigated pedagogical agents for a serious game in the OpenSim environment. The proposed AI features to support the serious game are the…

The Golden Gate: Building Bridges Between Research and Operations

NASA Technical Reports Server (NTRS)

Schmidt, Lacey L.

2010-01-01

Previous research has discussed the ongoing dilemma of implementing research-based findings in an applied setting. This panel will discuss lessons learned from various examples where bridges have been forged between research and operations, and examine ways to promote and achieve similar collaborations in other areas in the future.

Assessing Key Competences across the Curriculum--And Europe

ERIC Educational Resources Information Center

Pepper, David

2011-01-01

The development of key competences for lifelong learning has been an important policy imperative for EU Member States. The European Reference Framework of key competences (2006) built on previous developments by the OECD, UNESCO and Member States themselves. It defined key competences as knowledge, skills and attitudes applied appropriately to…

Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition.

PubMed

Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi

2014-01-01

In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree (DT) are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm (GA) weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.

Toward instructional design principles: Inducing Faraday's law with contrasting cases

NASA Astrophysics Data System (ADS)

Kuo, Eric; Wieman, Carl E.

2016-06-01

Although physics education research (PER) has improved instructional practices, there are not agreed upon principles for designing effective instructional materials. Here, we illustrate how close comparison of instructional materials could support the development of such principles. Specifically, in discussion sections of a large, introductory physics course, a pair of studies compare two instructional strategies for teaching a physics concept: having students (i) explain a set of contrasting cases or (ii) apply and build on previously learned concepts. We compare these strategies for the teaching of Faraday's law, showing that explaining a set of related contrasting cases not only improves student performance on Faraday's law questions over building on a previously learned concept (i.e., Lorentz force), but also prepares students to better learn subsequent topics, such as Lenz's law. These differences persist to the final exam. We argue that early exposure to contrasting cases better focuses student attention on a key feature related to both concepts: change in magnetic flux. Importantly, the benefits of contrasting cases for both learning and enjoyment are enhanced for students who did not first attend a Faraday's law lecture, consistent with previous research suggesting that being told a solution can circumvent the benefits of its discovery. These studies illustrate an experimental approach for understanding how the structure of activities affects learning and performance outcomes, a first step toward design principles for effective instructional materials.

Thermodynamic efficiency of learning a rule in neural networks

NASA Astrophysics Data System (ADS)

Goldt, Sebastian; Seifert, Udo

2017-11-01

Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

A processing architecture for associative short-term memory in electronic noses

NASA Astrophysics Data System (ADS)

Pioggia, G.; Ferro, M.; Di Francesco, F.; DeRossi, D.

2006-11-01

Electronic nose (e-nose) architectures usually consist of several modules that process various tasks such as control, data acquisition, data filtering, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, and fuzzy rules used to implement such tasks may lead to issues concerning module interconnection and cooperation. Moreover, a new learning phase is mandatory once new measurements have been added to the dataset, thus causing changes in the previously derived model. Consequently, if a loss in the previous learning occurs (catastrophic interference), real-time applications of e-noses are limited. To overcome these problems this paper presents an architecture for dynamic and efficient management of multi-transducer data processing techniques and for saving an associative short-term memory of the previously learned model. The architecture implements an artificial model of a hippocampus-based working memory, enabling the system to be ready for real-time applications. Starting from the base models available in the architecture core, dedicated models for neurons, maps and connections were tailored to an artificial olfactory system devoted to analysing olive oil. In order to verify the ability of the processing architecture in associative and short-term memory, a paired-associate learning test was applied. The avoidance of catastrophic interference was observed.

The time course of explicit and implicit categorization.

PubMed

Smith, J David; Zakrzewski, Alexandria C; Herberger, Eric R; Boomer, Joseph; Roeder, Jessica L; Ashby, F Gregory; Church, Barbara A

2015-10-01

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Properties of the Bayesian Knowledge Tracing Model

ERIC Educational Resources Information Center

van de Sande, Brett

2013-01-01

Bayesian Knowledge Tracing is used very widely to model student learning. It comes in two different forms: The first form is the Bayesian Knowledge Tracing "hidden Markov model" which predicts the probability of correct application of a skill as a function of the number of previous opportunities to apply that skill and the model…

Measuring Disorientation Based on the Needleman-Wunsch Algorithm

ERIC Educational Resources Information Center

Güyer, Tolga; Atasoy, Bilal; Somyürek, Sibel

2015-01-01

This study offers a new method to measure navigation disorientation in web based systems which is powerful learning medium for distance and open education. The Needleman-Wunsch algorithm is used to measure disorientation in a more precise manner. The process combines theoretical and applied knowledge from two previously distinct research areas,…

The Likelihood of Use of Social Power Strategies by School Psychologists when Consulting with Teachers

ERIC Educational Resources Information Center

Wilson, Kristen E.; Erchul, William P.; Raven, Bertram H.

2008-01-01

The Interpersonal Power Inventory (IPI) has been applied previously to investigate school psychologists engaged in problem-solving consultation with teachers concerning students having various learning and adjustment problems. Relevant prior findings include (a) consultants and teachers both perceive soft power strategies as more effective than…

Following the Template: Transferring Modeling Skills to Nonstandard Problems

ERIC Educational Resources Information Center

Tyumeneva, Yu. A.; Goncharova, M. V.

2017-01-01

This study seeks to analyze how students apply a mathematical modeling skill that was previously learned by solving standard word problems to the solution of word problems with nonstandard contexts. During the course of an experiment involving 106 freshmen, we assessed how well they were able to transfer the mathematical modeling skill that is…

Influences on Visual Attentional Distribution in Multimedia Instruction

ERIC Educational Resources Information Center

Wiebe, Eric; Annetta, Leonard

2008-01-01

Previous work applying cognitive load theory has demonstrated the effect of various text/graphic/narration relations on learning using multimedia material. Other work has looked at how the degree of integration between the text and graphics influences their use. This study set out to look at how the degree of integration between text and graphics…

Novelty and Inductive Generalization in Human Reinforcement Learning.

PubMed

Gershman, Samuel J; Niv, Yael

2015-07-01

In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional RL algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. Copyright © 2015 Cognitive Science Society, Inc.

A Flexible Mechanism of Rule Selection Enables Rapid Feature-Based Reinforcement Learning

PubMed Central

Balcarras, Matthew; Womelsdorf, Thilo

2016-01-01

Learning in a new environment is influenced by prior learning and experience. Correctly applying a rule that maps a context to stimuli, actions, and outcomes enables faster learning and better outcomes compared to relying on strategies for learning that are ignorant of task structure. However, it is often difficult to know when and how to apply learned rules in new contexts. In our study we explored how subjects employ different strategies for learning the relationship between stimulus features and positive outcomes in a probabilistic task context. We test the hypothesis that task naive subjects will show enhanced learning of feature specific reward associations by switching to the use of an abstract rule that associates stimuli by feature type and restricts selections to that dimension. To test this hypothesis we designed a decision making task where subjects receive probabilistic feedback following choices between pairs of stimuli. In the task, trials are grouped in two contexts by blocks, where in one type of block there is no unique relationship between a specific feature dimension (stimulus shape or color) and positive outcomes, and following an un-cued transition, alternating blocks have outcomes that are linked to either stimulus shape or color. Two-thirds of subjects (n = 22/32) exhibited behavior that was best fit by a hierarchical feature-rule model. Supporting the prediction of the model mechanism these subjects showed significantly enhanced performance in feature-reward blocks, and rapidly switched their choice strategy to using abstract feature rules when reward contingencies changed. Choice behavior of other subjects (n = 10/32) was fit by a range of alternative reinforcement learning models representing strategies that do not benefit from applying previously learned rules. In summary, these results show that untrained subjects are capable of flexibly shifting between behavioral rules by leveraging simple model-free reinforcement learning and context-specific selections to drive responses. PMID:27064794

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

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

Facilitating the learning process in design-based learning practices: an investigation of teachers' actions in supervising students

NASA Astrophysics Data System (ADS)

Gómez Puente, S. M.; van Eijck, M.; Jochems, W.

2013-11-01

Background: In research on design-based learning (DBL), inadequate attention is paid to the role the teacher plays in supervising students in gathering and applying knowledge to design artifacts, systems, and innovative solutions in higher education. Purpose: In this study, we examine whether teacher actions we previously identified in the DBL literature as important in facilitating learning processes and student supervision are present in current DBL engineering practices. Sample: The sample (N=16) consisted of teachers and supervisors in two engineering study programs at a university of technology: mechanical and electrical engineering. We selected randomly teachers from freshman and second-year bachelor DBL projects responsible for student supervision and assessment. Design and method: Interviews with teachers, and interviews and observations of supervisors were used to examine how supervision and facilitation actions are applied according to the DBL framework. Results: Major findings indicate that formulating questions is the most common practice seen in facilitating learning in open-ended engineering design environments. Furthermore, other DBL actions we expected to see based upon the literature were seldom observed in the coaching practices within these two programs. Conclusions: Professionalization of teachers in supervising students need to include methods to scaffold learning by supporting students in reflecting and in providing formative feedback.

Multi-stage learning aids applied to hands-on software training.

PubMed

Rother, Kristian; Rother, Magdalena; Pleus, Alexandra; Upmeier zu Belzen, Annette

2010-11-01

Delivering hands-on tutorials on bioinformatics software and web applications is a challenging didactic scenario. The main reason is that trainees have heterogeneous backgrounds, different previous knowledge and vary in learning speed. In this article, we demonstrate how multi-stage learning aids can be used to allow all trainees to progress at a similar speed. In this technique, the trainees can utilize cards with hints and answers to guide themselves self-dependently through a complex task. We have successfully conducted a tutorial for the molecular viewer PyMOL using two sets of learning aid cards. The trainees responded positively, were able to complete the task, and the trainer had spare time to respond to individual questions. This encourages us to conclude that multi-stage learning aids overcome many disadvantages of established forms of hands-on software training.

[The discussion of the infiltrative model of chemical knowledge stepping into genetics teaching in agricultural institute or university].

PubMed

Zou, Ping; Luo, Pei-Gao

2010-05-01

Chemistry is an important group of basic courses, while genetics is one of the important major-basic courses in curriculum of many majors in agricultural institutes or universities. In order to establish the linkage between the major course and the basic course, the ability of application of the chemical knowledge previously learned in understanding genetic knowledge in genetics teaching is worthy of discussion for genetics teachers. In this paper, the authors advocate to apply some chemical knowledge previously learned to understand genetic knowledge in genetics teaching with infiltrative model, which could help students learn and understand genetic knowledge more deeply. Analysis of the intrinsic logistic relationship among the knowledge of different courses and construction of the integral knowledge network are useful for students to improve their analytic, comprehensive and logistic abilities. By this way, we could explore a new teaching model to develop the talents with new ideas and comprehensive competence in agricultural fields.

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.

Robot learning and error correction

NASA Technical Reports Server (NTRS)

Friedman, L.

1977-01-01

A model of robot learning is described that associates previously unknown perceptions with the sensed known consequences of robot actions. For these actions, both the categories of outcomes and the corresponding sensory patterns are incorporated in a knowledge base by the system designer. Thus the robot is able to predict the outcome of an action and compare the expectation with the experience. New knowledge about what to expect in the world may then be incorporated by the robot in a pre-existing structure whether it detects accordance or discrepancy between a predicted consequence and experience. Errors committed during plan execution are detected by the same type of comparison process and learning may be applied to avoiding the errors.

Physiotherapy students' perspectives of online e-learning for interdisciplinary management of chronic health conditions: a qualitative study.

PubMed

Gardner, Peter; Slater, Helen; Jordan, Joanne E; Fary, Robyn E; Chua, Jason; Briggs, Andrew M

2016-02-16

To qualitatively explore physiotherapy students' perceptions of online e-learning for chronic disease management using a previously developed, innovative and interactive, evidence-based, e-learning package: Rheumatoid Arthritis for Physiotherapists e-Learning (RAP-eL). Physiotherapy students participated in three focus groups in Perth, Western Australia. Purposive sampling was employed to ensure maximum heterogeneity across age, gender and educational background. To explore students' perspectives on the advantages and disadvantages of online e-learning, ways to enhance e-learning, and information/learning gaps in relation to interdisciplinary management of chronic health conditions, a semi-structured interview schedule was developed. Verbatim transcripts were analysed using inductive methods within a grounded theory approach to derive key themes. Twenty-three students (78 % female; 39 % with previous tertiary qualification) of mean (SD) age 23 (3.6) years participated. Students expressed a preference for a combination of both online e-learning and lecture-style learning formats for chronic disease management, citing flexibility to work at one's own pace and time, and access to comprehensive information as advantages of e-learning learning. Personal interaction and ability to clarify information immediately were considered advantages of lecture-style formats. Perceived knowledge gaps included practical application of interdisciplinary approaches to chronic disease management and developing and implementing physiotherapy management plans for people with chronic health conditions. Physiotherapy students preferred multi-modal and blended formats for learning about chronic disease management. This study highlights the need for further development of practically-oriented knowledge and skills related to interdisciplinary care for people with chronic conditions among physiotherapy students. While RAP-eL focuses on rheumatoid arthritis, the principles of learning apply to the broader context of chronic disease management.

The Time Course of Explicit and Implicit Categorization

PubMed Central

Zakrzewski, Alexandria C.; Herberger, Eric; Boomer, Joseph; Roeder, Jessica; Ashby, F. Gregory; Church, Barbara A.

2015-01-01

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization. PMID:26025556

Understanding the Role of Academic Language on Conceptual Understanding in an Introductory Materials Science and Engineering Course

ERIC Educational Resources Information Center

Kelly, Jacquelyn

2012-01-01

Students may use the technical engineering terms without knowing what these words mean. This creates a language barrier in engineering that influences student learning. Previous research has been conducted to characterize the difference between colloquial and scientific language. Since this research had not yet been applied explicitly to…

Reading Strategies in Hypertexts and Factors Influencing Hyperlink Selection

ERIC Educational Resources Information Center

Protopsaltis, Aristidis

2008-01-01

Previous work applying cognitive load theory has demonstrated the effect of various text/graphic/narration relations on learning using multimedia material. Other work has looked at how the degree of integration between the text and graphics influences their use. This study set out to look at how the degree of integration between text and graphics…

Tool for Experimenting with Concepts of Mobile Robotics as Applied to Children's Education

ERIC Educational Resources Information Center

Jimenez Jojoa, E. M.; Bravo, E. C.; Bacca Cortes, E. B.

2010-01-01

This paper describes the design and implementation of a tool for experimenting with mobile robotics concepts, primarily for use by children and teenagers, or by the general public, without previous experience in robotics. This tool helps children learn about science in an approachable and interactive way, using scientific research principles in…

Functional Connectivity between Brain Regions Involved in Learning Words of a New Language

ERIC Educational Resources Information Center

Veroude, Kim; Norris, David G.; Shumskaya, Elena; Gullberg, Marianne; Indefrey, Peter

2010-01-01

Previous studies have identified several brain regions that appear to be involved in the acquisition of novel word forms. Standard word-by-word presentation is often used although exposure to a new language normally occurs in a natural, real world situation. In the current experiment we investigated naturalistic language exposure and applied a…

The Mediating Effect of Context Variation in Mixed Practice for Transfer of Basic Science

ERIC Educational Resources Information Center

Kulasegaram, Kulamakan; Min, Cynthia; Howey, Elizabeth; Neville, Alan; Woods, Nicole; Dore, Kelly; Norman, Geoffrey

2015-01-01

Applying a previously learned concept to a novel problem is an important but difficult process called transfer. Practicing multiple concepts together (mixed practice mode) has been shown superior to practicing concepts separately (blocked practice mode) for transfer. This study examined the effect of single and multiple practice contexts for both…

Correlation of self-assessment with attendance in an evidence-based medicine course.

PubMed

Ramirez, Beatriz U

2015-12-01

In previous studies, correlations between attendance and grades in lectures have given variable results and, when statistically significant, the correlation has been weak. In some studies, a sex effect has been reported. Lectures are a teacher-centered learning activity. Therefore, it appeared interesting to evaluate if a stronger correlation between attendance and grades would occur in a face-to-face "evidence-based medicine" course with few lectures and more time dedicated to active learning methods. Small-group work and peer learning were used to foster deep learning and to engage students in their own learning process. Most of the time, students worked in small groups solving contextualized problems and critically analyzing the quality of published medical literature. Peer learning was also developed in collaborative evaluations, and constant feedback was provided. Therefore, it was hypothesized that high attenders would develop a higher self-perception of learning and obtain higher marks than low attenders. Student self-perceptions of their capacity to apply evidence-based medicine were measured by the application of an online self-assessment survey, and objective learning was measured as the grades obtained in a final accumulative individual test. It was found that male students obtained higher grades and were more confident in their achievements than their female peers, despite male and female student attendance being similar. In addition, attendance was correlated with the perceived capacity to apply evidence-based medicine only in male students and was not correlated with academic outcome. Copyright © 2015 The American Physiological Society.

Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory.

PubMed

Lu, Chao; Zheng, Yefeng; Birkbeck, Neil; Zhang, Jingdan; Kohlberger, Timo; Tietjen, Christian; Boettger, Thomas; Duncan, James S; Zhou, S Kevin

2012-01-01

In this paper, we present a novel method by incorporating information theory into the learning-based approach for automatic and accurate pelvic organ segmentation (including the prostate, bladder and rectum). We target 3D CT volumes that are generated using different scanning protocols (e.g., contrast and non-contrast, with and without implant in the prostate, various resolution and position), and the volumes come from largely diverse sources (e.g., diseased in different organs). Three key ingredients are combined to solve this challenging segmentation problem. First, marginal space learning (MSL) is applied to efficiently and effectively localize the multiple organs in the largely diverse CT volumes. Second, learning techniques, steerable features, are applied for robust boundary detection. This enables handling of highly heterogeneous texture pattern. Third, a novel information theoretic scheme is incorporated into the boundary inference process. The incorporation of the Jensen-Shannon divergence further drives the mesh to the best fit of the image, thus improves the segmentation performance. The proposed approach is tested on a challenging dataset containing 188 volumes from diverse sources. Our approach not only produces excellent segmentation accuracy, but also runs about eighty times faster than previous state-of-the-art solutions. The proposed method can be applied to CT images to provide visual guidance to physicians during the computer-aided diagnosis, treatment planning and image-guided radiotherapy to treat cancers in pelvic region.

Integrated Bayesian models of learning and decision making for saccadic eye movements.

PubMed

Brodersen, Kay H; Penny, Will D; Harrison, Lee M; Daunizeau, Jean; Ruff, Christian C; Duzel, Emrah; Friston, Karl J; Stephan, Klaas E

2008-11-01

The neurophysiology of eye movements has been studied extensively, and several computational models have been proposed for decision-making processes that underlie the generation of eye movements towards a visual stimulus in a situation of uncertainty. One class of models, known as linear rise-to-threshold models, provides an economical, yet broadly applicable, explanation for the observed variability in the latency between the onset of a peripheral visual target and the saccade towards it. So far, however, these models do not account for the dynamics of learning across a sequence of stimuli, and they do not apply to situations in which subjects are exposed to events with conditional probabilities. In this methodological paper, we extend the class of linear rise-to-threshold models to address these limitations. Specifically, we reformulate previous models in terms of a generative, hierarchical model, by combining two separate sub-models that account for the interplay between learning of target locations across trials and the decision-making process within trials. We derive a maximum-likelihood scheme for parameter estimation as well as model comparison on the basis of log likelihood ratios. The utility of the integrated model is demonstrated by applying it to empirical saccade data acquired from three healthy subjects. Model comparison is used (i) to show that eye movements do not only reflect marginal but also conditional probabilities of target locations, and (ii) to reveal subject-specific learning profiles over trials. These individual learning profiles are sufficiently distinct that test samples can be successfully mapped onto the correct subject by a naïve Bayes classifier. Altogether, our approach extends the class of linear rise-to-threshold models of saccadic decision making, overcomes some of their previous limitations, and enables statistical inference both about learning of target locations across trials and the decision-making process within trials.

The impact of group membership on collaborative learning with wikis.

PubMed

Matschke, Christina; Moskaliuk, Johannes; Kimmerle, Joachim

2013-02-01

The social web stimulates learning through collaboration. However, information in the social web is often associated with information about its author. Based on previous evidence that ingroup information is preferred to outgroup information, the current research investigates whether group memberships of wiki authors affect learning. In an experimental study, we manipulated the group memberships (ingroup vs. outgroup) of wiki authors by using nicknames. The designated group memberships (being fans of a soccer team or not) were completely irrelevant for the domain of the wiki (the medical disorder fibromyalgia). Nevertheless, wiki information from the ingroup led to more integration of information into prior knowledge as well as more increase of factual knowledge than information from the outgroup. The results demonstrate that individuals apply social selection strategies when considering information from wikis, which may foster, but also hinder, learning and collaboration. Practical implications for collaborative learning in the social web are discussed.

The Impact of Group Membership on Collaborative Learning with Wikis

PubMed Central

Matschke, Christina; Moskaliuk, Johannes

2013-01-01

Abstract The social web stimulates learning through collaboration. However, information in the social web is often associated with information about its author. Based on previous evidence that ingroup information is preferred to outgroup information, the current research investigates whether group memberships of wiki authors affect learning. In an experimental study, we manipulated the group memberships (ingroup vs. outgroup) of wiki authors by using nicknames. The designated group memberships (being fans of a soccer team or not) were completely irrelevant for the domain of the wiki (the medical disorder fibromyalgia). Nevertheless, wiki information from the ingroup led to more integration of information into prior knowledge as well as more increase of factual knowledge than information from the outgroup. The results demonstrate that individuals apply social selection strategies when considering information from wikis, which may foster, but also hinder, learning and collaboration. Practical implications for collaborative learning in the social web are discussed. PMID:23113690

Demonstration of a tool for automatic learning and re-use of knowledge in the activated sludge process.

PubMed

Comas, J; Rodríguez-Roda, I; Poch, M; Gernaey, K V; Rosen, C; Jeppsson, U

2006-01-01

Wastewater treatment plant operators encounter complex operational problems related to the activated sludge process and usually respond to these by applying their own intuition and by taking advantage of what they have learnt from past experiences of similar problems. However, previous process experiences are not easy to integrate in numerical control, and new tools must be developed to enable re-use of plant operating experience. The aim of this paper is to investigate the usefulness of a case-based reasoning (CBR) approach to apply learning and re-use of knowledge gained during past incidents to confront actual complex problems through the IWA/COST Benchmark protocol. A case study shows that the proposed CBR system achieves a significant improvement of the benchmark plant performance when facing a high-flow event disturbance.

Differentially Private Empirical Risk Minimization

PubMed Central

Chaudhuri, Kamalika; Monteleoni, Claire; Sarwate, Anand D.

2011-01-01

Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed. We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the ε-differential privacy definition due to Dwork et al. (2006). First we apply the output perturbation ideas of Dwork et al. (2006), to ERM classification. Then we propose a new method, objective perturbation, for privacy-preserving machine learning algorithm design. This method entails perturbing the objective function before optimizing over classifiers. If the loss and regularizer satisfy certain convexity and differentiability criteria, we prove theoretical results showing that our algorithms preserve privacy, and provide generalization bounds for linear and nonlinear kernels. We further present a privacy-preserving technique for tuning the parameters in general machine learning algorithms, thereby providing end-to-end privacy guarantees for the training process. We apply these results to produce privacy-preserving analogues of regularized logistic regression and support vector machines. We obtain encouraging results from evaluating their performance on real demographic and benchmark data sets. Our results show that both theoretically and empirically, objective perturbation is superior to the previous state-of-the-art, output perturbation, in managing the inherent tradeoff between privacy and learning performance. PMID:21892342

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

PubMed Central

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-01-01

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. PMID:28773653

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers.

PubMed

García-Gonzalo, Esperanza; Fernández-Muñiz, Zulima; García Nieto, Paulino José; Bernardo Sánchez, Antonio; Menéndez Fernández, Marta

2016-06-29

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos.

PubMed

André, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2011-01-01

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available groundtruth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

Design & control of a 3D stroke rehabilitation platform.

PubMed

Cai, Z; Tong, D; Meadmore, K L; Freeman, C T; Hughes, A M; Rogers, E; Burridge, J H

2011-01-01

An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients performing 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation. © 2011 IEEE

Action Research to Improve the Learning Space for Diagnostic Techniques.

PubMed

Ariel, Ellen; Owens, Leigh

2015-12-01

The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of "knowledge" and "understanding." The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001), it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed. Journal of Microbiology & Biology Education.

The Shape of Things: The Origin of Young Children's Knowledge of the Names and Properties of Geometric Forms

ERIC Educational Resources Information Center

Verdine, Brian N.; Lucca, Kelsey R.; Golinkoff, Roberta M.; Hirsh-Pasek, Kathryn; Newcombe, Nora S.

2016-01-01

How do toddlers learn the names of geometric forms? Previous work suggests that preschoolers have fragmentary knowledge and that defining properties are not understood until well into elementary school. The current study investigated when children first begin to understand shape names and how they apply those labels to unusual instances. We tested…

Talking with John Trim (Part I): A Career in Phonetics, Applied Linguistics and the Public Service

ERIC Educational Resources Information Center

Little, David; King, Lid

2013-01-01

As this issue was in preparation, the journal learned with great regret of the passing of John Trim. John was a long-serving member of the "Language Teaching" Board and his insight and advice proved invaluable for this and previous editors. An expert in the field of phonetics, linguistics, language didactics and policy, John worked…

Design and Control of Large Collections of Learning Agents

NASA Technical Reports Server (NTRS)

Agogino, Adrian

2001-01-01

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

Serial killers with military experience: applying learning theory to serial murder.

PubMed

Castle, Tammy; Hensley, Christopher

2002-08-01

Scholars have endeavored to study the motivation and causality behind serial murder by researching biological, psychological, and sociological variables. Some of these studies have provided support for the relationship between these variables and serial murder. However, the study of serial murder continues to be an exploratory rather than explanatory research topic. This article examines the possible link between serial killers and military service. Citing previous research using social learning theory for the study of murder, this article explores how potential serial killers learn to reinforce violence, aggression, and murder in military boot camps. As with other variables considered in serial killer research, military experience alone cannot account for all cases of serial murder. Future research should continue to examine this possible link.

Deep Learning for Population Genetic Inference.

PubMed

Sheehan, Sara; Song, Yun S

2016-03-01

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

Deep Learning for Population Genetic Inference

PubMed Central

Sheehan, Sara; Song, Yun S.

2016-01-01

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

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

Machine learning for epigenetics and future medical applications.

PubMed

Holder, Lawrence B; Haque, M Muksitul; Skinner, Michael K

2017-07-03

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

Evolution of learning and levels of selection: A lesson from avian parent-offspring communication.

PubMed

Lotem, Arnon; Biran-Yoeli, Inbar

2013-09-20

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

Evolution of learning and levels of selection: a lesson from avian parent-offspring communication.

PubMed

Lotem, Arnon; Biran-Yoeli, Inbar

2014-02-01

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

PubMed

Giraldo, Sergio I; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

A model of self-directed learning in internal medicine residency: a qualitative study using grounded theory.

PubMed

Sawatsky, Adam P; Ratelle, John T; Bonnes, Sara L; Egginton, Jason S; Beckman, Thomas J

2017-02-02

Existing theories of self-directed learning (SDL) have emphasized the importance of process, personal, and contextual factors. Previous medical education research has largely focused on the process of SDL. We explored the experience with and perception of SDL among internal medicine residents to gain understanding of the personal and contextual factors of SDL in graduate medical education. Using a constructivist grounded theory approach, we conducted 7 focus group interviews with 46 internal medicine residents at an academic medical center. We processed the data by using open coding and writing analytic memos. Team members organized open codes to create axial codes, which were applied to all transcripts. Guided by a previous model of SDL, we developed a theoretical model that was revised through constant comparison with new data as they were collected, and we refined the theory until it had adequate explanatory power and was appropriately grounded in the experiences of residents. We developed a theoretical model of SDL to explain the process, personal, and contextual factors affecting SDL during residency training. The process of SDL began with a trigger that uncovered a knowledge gap. Residents progressed to formulating learning objectives, using resources, applying knowledge, and evaluating learning. Personal factors included motivations, individual characteristics, and the change in approach to SDL over time. Contextual factors included the need for external guidance, the influence of residency program structure and culture, and the presence of contextual barriers. We developed a theoretical model of SDL in medical education that can be used to promote and assess resident SDL through understanding the process, person, and context of SDL.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

PubMed Central

Giraldo, Sergio I.; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules. PMID:28066290

Online Bayesian Learning with Natural Sequential Prior Distribution Used for Wind Speed Prediction

NASA Astrophysics Data System (ADS)

Cheggaga, Nawal

2017-11-01

Predicting wind speed is one of the most important and critic tasks in a wind farm. All approaches, which directly describe the stochastic dynamics of the meteorological data are facing problems related to the nature of its non-Gaussian statistics and the presence of seasonal effects .In this paper, Online Bayesian learning has been successfully applied to online learning for three-layer perceptron's used for wind speed prediction. First a conventional transition model based on the squared norm of the difference between the current parameter vector and the previous parameter vector has been used. We noticed that the transition model does not adequately consider the difference between the current and the previous wind speed measurement. To adequately consider this difference, we use a natural sequential prior. The proposed transition model uses a Fisher information matrix to consider the difference between the observation models more naturally. The obtained results showed a good agreement between both series, measured and predicted. The mean relative error over the whole data set is not exceeding 5 %.

Distributed and opposing effects of incidental learning in the human brain.

PubMed

Hall, Michelle G; Naughtin, Claire K; Mattingley, Jason B; Dux, Paul E

2018-06-01

Incidental learning affords a behavioural advantage when sensory information matches regularities that have previously been encountered. Previous studies have taken a focused approach by probing the involvement of specific candidate brain regions underlying incidentally acquired memory representations, as well as expectation effects on early sensory representations. Here, we investigated the broader extent of the brain's sensitivity to violations and fulfilments of expectations, using an incidental learning paradigm in which the contingencies between target locations and target identities were manipulated without participants' overt knowledge. Multivariate analysis of functional magnetic resonance imaging data was applied to compare the consistency of neural activity for visual events that the contingency manipulation rendered likely versus unlikely. We observed widespread sensitivity to expectations across frontal, temporal, occipital, and sub-cortical areas. These activation clusters showed distinct response profiles, such that some regions displayed more reliable activation patterns under fulfilled expectations, whereas others showed more reliable patterns when expectations were violated. These findings reveal that expectations affect multiple stages of information processing during visual decision making, rather than early sensory processing stages alone. Copyright © 2018 Elsevier Inc. All rights reserved.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

PubMed Central

Aznar, Fidel; Pujol, Francisco A.; Pujol, Mar; Rizo, Ramón; Pujol, María-José

2014-01-01

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N 2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used. PMID:25415377

Learning probabilistic features for robotic navigation using laser sensors.

PubMed

Aznar, Fidel; Pujol, Francisco A; Pujol, Mar; Rizo, Ramón; Pujol, María-José

2014-01-01

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N(2)), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

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

PubMed

Park, Esther O; Park, Ji Hyun

2018-04-01

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

A Three-Attribute Transfer Skills Framework--Part II: Applying and Assessing the Model in Science Education

ERIC Educational Resources Information Center

Sasson, Irit; Dori, Yehudit Judy

2015-01-01

In an era in which information is rapidly growing and changing, it is very important to teach with the goal of students' engagement in life-long learning in mind. This can partially be achieved by developing transferable thinking skills. In our previous paper--Part I, we conducted a review of the transfer literature and suggested a three-attribute…

Design Principles for Applied Learning: Bringing Theory and Practice Together in an Online VET Teacher-Education Degree

ERIC Educational Resources Information Center

Downing, Jillian J.

2017-01-01

This paper reports on a doctoral study that investigated an alternative pedagogical approach in an online VET teacher-education course offered at a mid-sized university in Australia. Students in the course were mature-aged and adding study to their role as in-service VET teachers. Building on previous research, a set of design principles was…

Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

NASA Astrophysics Data System (ADS)

Fusco, Terence; Bi, Yaxin; Wang, Haiying; Browne, Fiona

2016-08-01

The key issues pertaining to collection of epidemic disease data for our analysis purposes are that it is a labour intensive, time consuming and expensive process resulting in availability of sparse sample data which we use to develop prediction models. To address this sparse data issue, we present the novel Incremental Transductive methods to circumvent the data collection process by applying previously acquired data to provide consistent, confidence-based labelling alternatives to field survey research. We investigated various reasoning approaches for semi-supervised machine learning including Bayesian models for labelling data. The results show that using the proposed methods, we can label instances of data with a class of vector density at a high level of confidence. By applying the Liberal and Strict Training Approaches, we provide a labelling and classification alternative to standalone algorithms. The methods in this paper are components in the process of reducing the proliferation of the Schistosomiasis disease and its effects.

Classification of optical coherence tomography images for diagnosing different ocular diseases

NASA Astrophysics Data System (ADS)

Gholami, Peyman; Sheikh Hassani, Mohsen; Kuppuswamy Parthasarathy, Mohana; Zelek, John S.; Lakshminarayanan, Vasudevan

2018-03-01

Optical Coherence tomography (OCT) images provide several indicators, e.g., the shape and the thickness of different retinal layers, which can be used for various clinical and non-clinical purposes. We propose an automated classification method to identify different ocular diseases, based on the local binary pattern features. The database consists of normal and diseased human eye SD-OCT images. We use a multiphase approach for building our classifier, including preprocessing, Meta learning, and active learning. Pre-processing is applied to the data to handle missing features from images and replace them with the mean or median of the corresponding feature. All the features are run through a Correlation-based Feature Subset Selection algorithm to detect the most informative features and omit the less informative ones. A Meta learning approach is applied to the data, in which a SVM and random forest are combined to obtain a more robust classifier. Active learning is also applied to strengthen our classifier around the decision boundary. The primary experimental results indicate that our method is able to differentiate between the normal and non-normal retina with an area under the ROC curve (AUC) of 98.6% and also to diagnose the three common retina-related diseases, i.e., Age-related Macular Degeneration, Diabetic Retinopathy, and Macular Hole, with an AUC of 100%, 95% and 83.8% respectively. These results indicate a better performance of the proposed method compared to most of the previous works in the literature.

Family caregiver learning--how family caregivers learn to provide care at the end of life: a qualitative secondary analysis of four datasets.

PubMed

Stajduhar, Kelli I; Funk, Laura; Outcalt, Linda

2013-07-01

Family caregivers are assuming growing responsibilities in providing care to dying family members. Supporting them is fundamental to ensure quality end-of-life care and to buffer potentially negative outcomes, although family caregivers frequently acknowledge a deficiency of information, knowledge, and skills necessary to assume the tasks involved in this care. The aim of this inquiry was to explore how family caregivers describe learning to provide care to palliative patients. Secondary analysis of data from four qualitative studies (n = 156) with family caregivers of dying people. Data included qualitative interviews with 156 family caregivers of dying people. Family caregivers learn through the following processes: trial and error, actively seeking needed information and guidance, applying knowledge and skills from previous experience, and reflecting on their current experiences. Caregivers generally preferred and appreciated a supported or guided learning process that involved being shown or told by others, usually learning reactively after a crisis. Findings inform areas for future research to identify effective, individualized programs and interventions to support positive learning experiences for family caregivers of dying people.

Engineering Lessons Learned and Systems Engineering Applications

NASA Technical Reports Server (NTRS)

Gill, Paul S.; Garcia, Danny; Vaughan, William W.

2005-01-01

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned and technical standards. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. Systems Engineering has been defined (EINIS-632) as "an interdisciplinary approach encompassing the entire technical effort to evolve and verify an integrated and life-cycle balanced set of system people, product, and process solutions that satisfy customer needs". Designing reliable space-based systems has always been a goal for NASA, and many painful lessons have been learned along the way. One of the continuing functions of a system engineer is to compile development and operations "lessons learned" documents and ensure their integration into future systems development activities. They can produce insights and information for risk identification identification and characterization. on a new project. Lessons learned files from previous projects are especially valuable in risk

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

PubMed Central

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-01-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections. PMID:28615794

Action Research to Improve the Learning Space for Diagnostic Techniques†

PubMed Central

Ariel, Ellen; Owens, Leigh

2015-01-01

The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of “knowledge” and “understanding.” The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001), it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed. Journal of Microbiology & Biology Education PMID:26753024

A Preliminary Comparison of Motor Learning Across Different Non-invasive Brain Stimulation Paradigms Shows No Consistent Modulations.

PubMed

Lopez-Alonso, Virginia; Liew, Sook-Lei; Fernández Del Olmo, Miguel; Cheeran, Binith; Sandrini, Marco; Abe, Mitsunari; Cohen, Leonardo G

2018-01-01

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS 25 ), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants ( n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning.

A Preliminary Comparison of Motor Learning Across Different Non-invasive Brain Stimulation Paradigms Shows No Consistent Modulations

PubMed Central

Lopez-Alonso, Virginia; Liew, Sook-Lei; Fernández del Olmo, Miguel; Cheeran, Binith; Sandrini, Marco; Abe, Mitsunari; Cohen, Leonardo G.

2018-01-01

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS25), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants (n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning. PMID:29740271

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

PubMed

Luo, Ying; Chen, Yangquan; Pi, Youguo

2010-10-01

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

Using isomorphic problems to learn introductory physics

NASA Astrophysics Data System (ADS)

Lin, Shih-Yin; Singh, Chandralekha

2011-12-01

In this study, we examine introductory physics students’ ability to perform analogical reasoning between two isomorphic problems which employ the same underlying physics principles but have different surface features. Three hundred sixty-two students from a calculus-based and an algebra-based introductory physics course were given a quiz in the recitation in which they had to first learn from a solved problem provided and take advantage of what they learned from it to solve another problem (which we call the quiz problem) which was isomorphic. Previous research suggests that the multiple-concept quiz problem is challenging for introductory students. Students in different recitation classes received different interventions in order to help them discern and exploit the underlying similarities of the isomorphic solved and quiz problems. We also conducted think-aloud interviews with four introductory students in order to understand in depth the difficulties they had and explore strategies to provide better scaffolding. We found that most students were able to learn from the solved problem to some extent with the scaffolding provided and invoke the relevant principles in the quiz problem. However, they were not necessarily able to apply the principles correctly. Research suggests that more scaffolding is needed to help students in applying these principles appropriately. We outline a few possible strategies for future investigation.

Enabling Software Acquisition Improvement: Government and Industry Software Development Team Acquisition Model

DTIC Science & Technology

2010-04-30

estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources , gathering and maintaining...previous and current complex SW development efforts, the program offices will have a source of objective lessons learned and metrics that can be applied...the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this

Living Animals in the Classroom: A Meta-Analysis on Learning Outcome and a Treatment-Control Study Focusing on Knowledge and Motivation

ERIC Educational Resources Information Center

Hummel, Eberhard; Randler, Christoph

2012-01-01

Prior research states that the use of living animals in the classroom leads to a higher knowledge but those previous studies have methodological and statistical problems. We applied a meta-analysis and developed a treatment-control study in a middle school classroom. The treatments (film vs. living animal) differed only by the presence of the…

Human Splice-Site Prediction with Deep Neural Networks.

PubMed

Naito, Tatsuhiko

2018-04-18

Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

Using comprehension strategies with authentic text in a college chemistry course

NASA Astrophysics Data System (ADS)

Cain, Stephen Daniel

College science students learn important topics by reading textbooks, which contain dense technical prose. Comprehension strategies are known to increase learning from reading. One class of comprehension strategies, called elaboration strategies, is intended to link new information with prior knowledge. Elaboration strategies have an appeal in science courses where new information frequently depends on previously learned information. The purpose of this study was to determine the effectiveness of an elaboration strategy in an authentic college environment. General chemistry students read text about Lewis structures, figures drawn by chemists to depict molecules, while assigned to use either an elaboration strategy, namely elaborative interrogation, or another strategy, rereading, which served as a placebo control. Two texts of equal length were employed in this pretest-posttest experimental design. One was composed by the researcher. The other was an excerpt from a college textbook and contained a procedure for constructing Lewis structures. Students (N = 252) attending a large community college were randomly assigned to one of the two texts and assigned one of the two strategies. The elaborative interrogation strategy was implemented with instructions to answer why-questions posed throughout the reading. Answering why-questions has been hypothesized to activate prior knowledge of a topic, and thus to aid in cognitively connecting new material with prior knowledge. The rereading strategy was implemented with instructions to read text twice. The use of authentic text was one of only a few instances of applying elaborative interrogation with a textbook. In addition, previous studies have generally focused on the learning of facts contained in prose. The application of elaborative interrogation to procedural text has not been previously reported. Results indicated that the more effective strategy was undetermined when reading authentic text in this setting. However, prior knowledge level was identified as a statistically significant factor for learning from authentic text. That is, students with high prior knowledge learned more, regardless of assigned strategy. Another descriptive study was conducted with a separate student sample (N = 34). Previously reported Lewis structure research was replicated. The trend of difficulty for 50 structures in the earlier work was supported.

An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.

PubMed

Liu, Yuzhe; Gopalakrishnan, Vanathi

2017-03-01

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training

NASA Astrophysics Data System (ADS)

de La Cal, E. A.; Fernández, E. M.; Quiroga, R.; Villar, J. R.; Sedano, J.

In previous works a methodology was defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. The GAP algorithm implements the automatic search for crisp trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. This work probes that the proposed methodology is valid for different assets in a different market than previous work.

Baby FaceTime: can toddlers learn from online video chat?

PubMed

Myers, Lauren J; LeWitt, Rachel B; Gallo, Renee E; Maselli, Nicole M

2017-07-01

There is abundant evidence for the 'video deficit': children under 2 years old learn better in person than from video. We evaluated whether these findings applied to video chat by testing whether children aged 12-25 months could form relationships with and learn from on-screen partners. We manipulated social contingency: children experienced either real-time FaceTime conversations or pre-recorded Videos as the partner taught novel words, actions and patterns. Children were attentive and responsive in both conditions, but only children in the FaceTime group responded to the partner in a temporally synced manner. After one week, children in the FaceTime condition (but not the Video condition) preferred and recognized their Partner, learned more novel patterns, and the oldest children learned more novel words. Results extend previous studies to demonstrate that children under 2 years show social and cognitive learning from video chat because it retains social contingency. A video abstract of this article can be viewed at: https://youtu.be/rTXaAYd5adA. © 2016 John Wiley & Sons Ltd.

Machine learning for epigenetics and future medical applications

PubMed Central

Holder, Lawrence B.; Haque, M. Muksitul; Skinner, Michael K.

2017-01-01

ABSTRACT Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review. PMID:28524769

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

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

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)

PubMed Central

2016-01-01

The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a mouse in vivo efficacy model as a step to predicting clinical efficacy. We previously analyzed over 70 years of this mouse in vivo efficacy data, which we used to generate and validate machine learning models. Curation of 60 additional small molecules with in vivo data published in 2014 and 2015 was undertaken to further test these models. This represents a much larger test set than for the previous models. Several computational approaches have now been applied to analyze these molecules and compare their molecular properties beyond those attempted previously. Our previous machine learning models have been updated, and a novel aspect has been added in the form of mouse liver microsomal half-life (MLM t1/2) and in vitro-based Mtb models incorporating cytotoxicity data that were used to predict in vivo activity for comparison. Our best Mtbin vivo models possess fivefold ROC values > 0.7, sensitivity > 80%, and concordance > 60%, while the best specificity value is >40%. Use of an MLM t1/2 Bayesian model affords comparable results for scoring the 60 compounds tested. Combining MLM stability and in vitroMtb models in a novel consensus workflow in the best cases has a positive predicted value (hit rate) > 77%. Our results indicate that Bayesian models constructed with literature in vivoMtb data generated by different laboratories in various mouse models can have predictive value and may be used alongside MLM t1/2 and in vitro-based Mtb models to assist in selecting antitubercular compounds with desirable in vivo efficacy. We demonstrate for the first time that consensus models of any kind can be used to predict in vivo activity for Mtb. In addition, we describe a new clustering method for data visualization and apply this to the in vivo training and test data, ultimately making the method accessible in a mobile app. PMID:27335215

Influence of educational context in Astronomy teaching for ninth graders of elementary school under different teaching methodologies

NASA Astrophysics Data System (ADS)

Schwarz, D.

2015-03-01

This present work, carried out at E. E. Monsenhor Dr. Arthur Ricci, in the city of Itupeva-SP, aims to analyze different teaching strategies applied to Astronomy teaching, along with students from the last year of primary school. The 8th grade C was chosen as a research group and the 8th grade D as a control group. The D. P. Ausubel's Meaningful Learning Theory was the chosen theoretical referential, by being exclusively developed for the classroom environment. A questionnaire of previous knowledge about astronomy was applied to both classrooms, in which the research group obtained an index of 25.8% of right answers above 50.0%. In the control group, only 6.1% got more than 50.0%. After the questionnaire application, the interventions began. In the research group, astronomy workshops, telescopic observations and the Communication and Information Technologies. In the control group, interventions were made by conventional classes. Four months after the end of the interventions, the post-intervention questionnaire was applied, in which the research group obtained 22.5% of right answers above 50.0%, indicating a 3.3% drop in the class efficiency. The control group obtained 61.0% of right answers above 50.0%. It's concluded that, to reach Meaningful Learning it's imperative that the student is willing to learn.

Strategic memory in adults with anorexia nervosa: are there similarities to obsessive compulsive spectrum disorders?

PubMed

Sherman, Bonnie J; Savage, Cary R; Eddy, Kamryn T; Blais, Mark A; Deckersbach, Thilo; Jackson, Safia C; Franko, Debra L; Rauch, Scott L; Herzog, David B

2006-09-01

There is growing interest in the relationship between anorexia nervosa (AN) and obsessive-compulsive (OC) spectrum disorders (e.g., OCD, body dysmorphic disorder [BDD]). Previous neuropsychological investigations of OC spectrum disorders have identified problems with the efficient use of strategy on complex measures of learning and memory. This study evaluated nonverbal strategic memory in AN outpatients using an approach previously applied to OC spectrum disorders. Eighteen patients with AN and 19 healthy control participants completed the Rey-Osterrieth Complex Figure Test (RCFT), a widely used measure of nonverbal strategic planning, learning, and memory. Individuals with AN differed significantly from healthy controls in the organizational strategies used to copy the RCFT figure, and they recalled significantly less information on both immediate and delayed testing. Multiple regression analyses indicated that group differences in learning were mediated by copy organizational strategies. These results are identical to study findings in OCD and BDD, indicating important shared neuropsychological features among AN and these OC spectrum disorders. As in OCD and BDD, the essential cognitive deficit in AN was impaired use of organizational strategies, which may inform our understanding of the pathophysiology of AN and potentially offer treatment implications. Copyright (c) 2006 by Wiley Periodicals, Inc.

Supporting self and others: from staff nurse to nurse consultant. Part 4: mentoring.

PubMed

Fowler, John

This series of articles explores various ways of supporting staff who work in the fast-moving and ever-changing health service. In previous articles, John Fowler an experienced nursing lecturer, author and consultant, examined the importance of developing a supportive working culture, learning from experience and the role of preceptorship. This article examines how the principles of mentoring, as practiced in the business world, can be applied to nursing.

Novel jet observables from machine learning

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

Fast machine-learning online optimization of ultra-cold-atom experiments.

PubMed

Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

2016-05-16

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

Fast machine-learning online optimization of ultra-cold-atom experiments

PubMed Central

Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

2016-01-01

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. PMID:27180805

Neural Network Training by Integration of Adjoint Systems of Equations Forward in Time

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Barhen, Jacob (Inventor)

1999-01-01

A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically. it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved. but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. Tbc trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.

Neural network training by integration of adjoint systems of equations forward in time

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Barhen, Jacob (Inventor)

1992-01-01

A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically, it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved, but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. The trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.

New learning following reactivation in the human brain: targeting emotional memories through rapid serial visual presentation.

PubMed

Wirkner, Janine; Löw, Andreas; Hamm, Alfons O; Weymar, Mathias

2015-03-01

Once reactivated, previously consolidated memories destabilize and have to be reconsolidated to persist, a process that might be altered non-invasively by interfering learning immediately after reactivation. Here, we investigated the influence of interference on brain correlates of reactivated episodic memories for emotional and neutral scenes using event-related potentials (ERPs). To selectively target emotional memories we applied a new reactivation method: rapid serial visual presentation (RSVP). RSVP leads to enhanced implicit processing (pop out) of the most salient memories making them vulnerable to disruption. In line, interference after reactivation of previously encoded pictures disrupted recollection particularly for emotional events. Furthermore, memory impairments were reflected in a reduced centro-parietal ERP old/new difference during retrieval of emotional pictures. These results provide neural evidence that emotional episodic memories in humans can be selectively altered through behavioral interference after reactivation, a finding with further clinical implications for the treatment of anxiety disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Applying a Force and Motion Learning Progression over an Extended Time Span using the Force Concept Inventory

NASA Astrophysics Data System (ADS)

Fulmer, Gavin W.; Liang, Ling L.; Liu, Xiufeng

2014-11-01

This exploratory study applied a proposed force and motion learning progression (LP) to high-school and university students and to content involving both one- and two-dimensional force and motion situations. The Force Concept Inventory (FCI) was adapted, based on a previous content analysis and coding of the questions in the FCI in terms of the level descriptors of the LP. Using a Rasch measurement model and latent class analysis, students' responses were tested for fit with the proposed LP. Results indicated that the recoded FCI response options are generally consistent with a progression of difficulties as proposed in the LP, and that the students could be organized into different groups with progressively different levels of ability. However, reliability for the ability estimates was only moderate and response options at lower levels of the LP were not well differentiated. Implications for the assessments with LPs and revisions for both the FCI and the force and motion LP are also discussed.

Using Model-Based Reasoning for Autonomous Instrument Operation - Lessons Learned From IMAGE/LENA

NASA Technical Reports Server (NTRS)

Johnson, Michael A.; Rilee, Michael L.; Truszkowski, Walt; Bailin, Sidney C.

2001-01-01

Model-based reasoning has been applied as an autonomous control strategy on the Low Energy Neutral Atom (LENA) instrument currently flying on board the Imager for Magnetosphere-to-Aurora Global Exploration (IMAGE) spacecraft. Explicit models of instrument subsystem responses have been constructed and are used to dynamically adapt the instrument to the spacecraft's environment. These functions are cast as part of a Virtual Principal Investigator (VPI) that autonomously monitors and controls the instrument. In the VPI's current implementation, LENA's command uplink volume has been decreased significantly from its previous volume; typically, no uplinks are required for operations. This work demonstrates that a model-based approach can be used to enhance science instrument effectiveness. The components of LENA are common in space science instrumentation, and lessons learned by modeling this system may be applied to other instruments. Future work involves the extension of these methods to cover more aspects of LENA operation and the generalization to other space science instrumentation.

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Language learning without control: the role of the PFC.

PubMed

Friederici, Angela D; Mueller, Jutta L; Sehm, Bernhard; Ragert, Patrick

2013-05-01

Learning takes place throughout lifetime but differs in infants and adults because of the development of the PFC, a brain region responsible for cognitive control. To test this hypothesis, adults were investigated in a language learning paradigm under inhibitory, cathodal transcranial direct current stimulation over PFC. The experiment included a learning session interspersed with test phases and a test-only session. The stimulus material required the learning of grammatical dependencies between two elements in a novel language. In a parallel design, cathodal transcranial direct current stimulation over the left PFC, right PFC, or sham stimulation was applied during the learning session but not during the test-only session. Event-related brain potentials (ERPs) were recorded during both sessions. Whereas no ERP learning effects were observed during the learning session, different ERP learning effects as a function of prior stimulation type were found during the test-only session, although behavioral learning success was equal across conditions. With sham stimulation, the ERP learning effect was reflected in a centro-parietal N400-like negativity indicating lexical processes. Inhibitory stimulation over the left PFC, but not over the right PFC, led to a late positivity similar to that previously observed in prelinguistic infants indicating associative learning. The present data demonstrate that adults can learn with and without cognitive control using different learning mechanisms. In the presence of cognitive control, adult language learning is lexically guided, whereas it appears to be associative in nature when PFC control is downregulated.

Differential Processing for Actively Ignored Pictures and Words

PubMed Central

Ciraolo, Margeaux

2017-01-01

Previous work suggests that, when attended, pictures may be processed more readily than words. The current study extends this research to assess potential differences in processing between these stimulus types when they are actively ignored. In a dual-task paradigm, facilitated recognition for previously ignored words was found provided that they appeared frequently with an attended target. When adapting the same paradigm here, previously unattended pictures were recognized at high rates regardless of how they were paired with items during the primary task, whereas unattended words were later recognized at higher rates only if they had previously been aligned with primary task targets. Implicit learning effects obtained by aligning unattended items with attended task-targets may apply only to conceptually abstract stimulus types, such as words. Pictures, on the other hand, may maintain direct access to semantic information, and are therefore processed more readily than words, even when being actively ignored. PMID:28122022

Differential Processing for Actively Ignored Pictures and Words.

PubMed

Walker, Maegen; Ciraolo, Margeaux; Dewald, Andrew; Sinnett, Scott

2017-01-01

Previous work suggests that, when attended, pictures may be processed more readily than words. The current study extends this research to assess potential differences in processing between these stimulus types when they are actively ignored. In a dual-task paradigm, facilitated recognition for previously ignored words was found provided that they appeared frequently with an attended target. When adapting the same paradigm here, previously unattended pictures were recognized at high rates regardless of how they were paired with items during the primary task, whereas unattended words were later recognized at higher rates only if they had previously been aligned with primary task targets. Implicit learning effects obtained by aligning unattended items with attended task-targets may apply only to conceptually abstract stimulus types, such as words. Pictures, on the other hand, may maintain direct access to semantic information, and are therefore processed more readily than words, even when being actively ignored.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Indirect decentralized repetitive control

NASA Technical Reports Server (NTRS)

Lee, Soo Cheol; Longman, Richard W.

1993-01-01

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

How much information is in a jet?

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew

2017-06-01

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

Neural correlates of the age-related changes in motor sequence learning and motor adaptation in older adults

PubMed Central

King, Bradley R.; Fogel, Stuart M.; Albouy, Geneviève; Doyon, Julien

2013-01-01

As the world's population ages, a deeper understanding of the relationship between aging and motor learning will become increasingly relevant in basic research and applied settings. In this context, this review aims to address the effects of age on motor sequence learning (MSL) and motor adaptation (MA) with respect to behavioral, neurological, and neuroimaging findings. Previous behavioral research investigating the influence of aging on motor learning has consistently reported the following results. First, the initial acquisition of motor sequences is not altered, except under conditions of increased task complexity. Second, older adults demonstrate deficits in motor sequence memory consolidation. And, third, although older adults demonstrate deficits during the exposure phase of MA paradigms, the aftereffects following removal of the sensorimotor perturbation are similar to young adults, suggesting that the adaptive ability of older adults is relatively intact. This paper will review the potential neural underpinnings of these behavioral results, with a particular emphasis on the influence of age-related dysfunctions in the cortico-striatal system on motor learning. PMID:23616757

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

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.

Innovation and behavioral flexibility in wild redfronted lemurs (Eulemur rufifrons).

PubMed

Huebner, Franziska; Fichtel, Claudia

2015-05-01

Innovations and problem-solving abilities can provide animals with important ecological advantages as they allow individuals to deal with novel social and ecological challenges. Innovation is a solution to a novel problem or a novel solution to an old problem, with the latter being especially difficult. Finding a new solution to an old problem requires individuals to inhibit previously applied solutions to invent new strategies and to behave flexibly. We examined the role of experience on cognitive flexibility to innovate and to find new problem-solving solutions with an artificial feeding task in wild redfronted lemurs (Eulemur rufifrons). Four groups of lemurs were tested with feeding boxes, each offering three different techniques to extract food, with only one technique being available at a time. After the subjects learned a technique, this solution was no longer successful and subjects had to invent a new technique. For the first transition between task 1 and 2, subjects had to rely on their experience of the previous technique to solve task 2. For the second transition, subjects had to inhibit the previously learned technique to learn the new task 3. Tasks 1 and 2 were solved by most subjects, whereas task 3 was solved by only a few subjects. In this task, besides behavioral flexibility, especially persistence, i.e., constant trying, was important for individual success during innovation. Thus, wild strepsirrhine primates are able to innovate flexibly, suggesting a general ecological relevance of behavioral flexibility and persistence during innovation and problem solving across all primates.

Assessment of learning powered mobility use--applying grounded theory to occupational performance.

PubMed

Nilsson, Lisbeth; Durkin, Josephine

2014-01-01

Collaboration by two grounded theory researchers, who each had developed a learning continuum instrument, led to the emergence of a new tool for assessment of learning powered mobility use. We undertook a rigorous process of comparative reanalysis that included merging, modifying, and expanding our previous research findings. A new instrument together with its facilitating strategies emerged in the course of revisits to our existing rich account of data taken from real environment powered mobility practice over an extensive time period. Instrument descriptors, categories, phases, and stages allow a facilitator to assess actual phase and plot actual occupational performance and provide a learner with the just right challenge through the learning process. Facilitating strategies are described for each of the phases and provide directions for involvement during learner performance. The learning approach is led by a belief system that the intervention is user-led, working in partnership and empowering the learner. The new assessment tool is inclusive of every potential powered mobility user because it focuses on the whole continuum of the learning process of powered mobility use from novice to expert. The new tool was appraised by clinicians and has been used successfully in clinical practice in the United Kingdom and Sweden.

A recommendation module to help teachers build courses through the Moodle Learning Management System

NASA Astrophysics Data System (ADS)

Limongelli, Carla; Lombardi, Matteo; Marani, Alessandro; Sciarrone, Filippo; Temperini, Marco

2016-01-01

In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

Instructable autonomous agents. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Huffman, Scott Bradley

1994-01-01

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial instruction presented here has two parts. First, a computational model of an intelligent agent, the problem space computational model, indicates the types of knowledge that determine an agent's performance, and thus, that should be acquirable via instruction. Second, a learning technique, called situated explanation specifies how the agent learns general knowledge from instruction. The theory is embodied by an implemented agent, Instructo-Soar, built within the Soar architecture. Instructo-Soar is able to learn hierarchies of completely new tasks, to extend task knowledge to apply in new situations, and in fact to acquire every type of knowledge it uses during task performance - control knowledge, knowledge of operators' effects, state inferences, etc. - from interactive natural language instructions. This variety of learning occurs by applying the situated explanation technique to a variety of instructional interactions involving a variety of types of instructions (commands, statements, conditionals, etc.). By taking seriously the requirements of flexible tutorial instruction, Instructo-Soar demonstrates a breadth of interaction and learning capabilities that goes beyond previous instructable systems, such as learning apprentice systems. Instructo-Soar's techniques could form the basis for future 'instructable technologies' that come equipped with basic capabilities, and can be taught by novice users to perform any number of desired tasks.

Can Transcranial Direct Current Stimulation Augment Extinction of Conditioned Fear?

PubMed Central

van ’t Wout, Mascha; Mariano, Timothy Y.; Garnaat, Sarah L.; Reddy, Madhavi K.; Rasmussen, Steven A.; Greenberg, Benjamin D.

2016-01-01

Background Exposure-based therapy parallels extinction learning of conditioned fear. Prior research points to the ventromedial prefrontal cortex as a potential site for the consolidation of extinction learning and subsequent retention of extinction memory. Objective/hypothesis The present study aimed to evaluate whether the application of non-invasive transcranial direct current stimulation (tDCS) during extinction learning enhances late extinction and early recall in human participants. Methods Forty-four healthy volunteers completed a 2-day Pavlovian fear conditioning, extinction, and recall paradigm while skin conductance activity was continuously measured. Twenty-six participants received 2 mA anodal tDCS over EEG coordinate AF3 during extinction of a first conditioned stimulus. The remaining 18 participants received similar tDCS during extinction of a second conditioned stimulus. Sham stimulation was applied for the balance of extinction trials in both groups. Normalized skin conductance changes were analyzed using linear mixed models to evaluate effects of tDCS over late extinction and early recall trials. Results We observed a significant interaction between timing of tDCS during extinction blocks and changes in skin conductance reactivity over late extinction trials. These data indicate that tDCS was associated with accelerated late extinction learning of a second conditioned stimulus after tDCS was combined with extinction learning of a previous conditioned stimulus. No significant effects of tDCS timing were observed on early extinction recall. Conclusions Results could be explained by an anxiolytic aftereffect of tDCS and extend previous studies on tDCS-induced modulation of fear and threat related learning processes. These findings support further exploration of the clinical use of tDCS. PMID:27037186

Learning during Processing: Word Learning Doesn't Wait for Word Recognition to Finish

ERIC Educational Resources Information Center

Apfelbaum, Keith S.; McMurray, Bob

2017-01-01

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed…

The Degree of Applying E-Learning in English Departments at Al-Balqa Applied University from Instructors' Perspectives

ERIC Educational Resources Information Center

Alzu'bi, Mohammad Akram Mohammad

2018-01-01

The study aimed at identifying the degree of applying e-learning in Al-Balqa Applied University from instructors' perspectives so the researcher designed a questionnaire of 20 items which is applied on a sample of 48 lecturers. The study showed that the percentage of (64.0%) out of 48 participants apply e-learning in English departments at…

Stress, burnout and doctors' attitudes to work are determined by personality and learning style: a twelve year longitudinal study of UK medical graduates.

PubMed

McManus, I C; Keeling, A; Paice, E

2004-08-18

The study investigated the extent to which approaches to work, workplace climate, stress, burnout and satisfaction with medicine as a career in doctors aged about thirty are predicted by measures of learning style and personality measured five to twelve years earlier when the doctors were applicants to medical school or were medical students. Prospective study of a large cohort of doctors. The participants were first studied when they applied to any of five UK medical schools in 1990. Postal questionnaires were sent to all doctors with a traceable address on the current or a previous Medical Register. The current questionnaire included measures of Approaches to Work, Workplace Climate, stress (General Health Questionnaire), burnout (Maslach Burnout Inventory), and satisfaction with medicine as a career and personality (Big Five). Previous questionnaires had included measures of learning style (Study Process Questionnaire) and personality. Doctors' approaches to work were predicted by study habits and learning styles, both at application to medical school and in the final year. How doctors perceive their workplace climate and workload is predicted both by approaches to work and by measures of stress, burnout and satisfaction with medicine. These characteristics are partially predicted by trait measures of personality taken five years earlier. Stress, burnout and satisfaction also correlate with trait measures of personality taken five years earlier. Differences in approach to work and perceived workplace climate seem mainly to reflect stable, long-term individual differences in doctors themselves, reflected in measures of personality and learning style.

Stress, burnout and doctors' attitudes to work are determined by personality and learning style: A twelve year longitudinal study of UK medical graduates

PubMed Central

McManus, IC; Keeling, A; Paice, E

2004-01-01

Background The study investigated the extent to which approaches to work, workplace climate, stress, burnout and satisfaction with medicine as a career in doctors aged about thirty are predicted by measures of learning style and personality measured five to twelve years earlier when the doctors were applicants to medical school or were medical students. Methods Prospective study of a large cohort of doctors. The participants were first studied when they applied to any of five UK medical schools in 1990. Postal questionnaires were sent to all doctors with a traceable address on the current or a previous Medical Register. The current questionnaire included measures of Approaches to Work, Workplace Climate, stress (General Health Questionnaire), burnout (Maslach Burnout Inventory), and satisfaction with medicine as a career and personality (Big Five). Previous questionnaires had included measures of learning style (Study Process Questionnaire) and personality. Results Doctors' approaches to work were predicted by study habits and learning styles, both at application to medical school and in the final year. How doctors perceive their workplace climate and workload is predicted both by approaches to work and by measures of stress, burnout and satisfaction with medicine. These characteristics are partially predicted by trait measures of personality taken five years earlier. Stress, burnout and satisfaction also correlate with trait measures of personality taken five years earlier. Conclusions Differences in approach to work and perceived workplace climate seem mainly to reflect stable, long-term individual differences in doctors themselves, reflected in measures of personality and learning style. PMID:15317650

Learning mechanisms to limit medication administration errors.

PubMed

Drach-Zahavy, Anat; Pud, Dorit

2010-04-01

This paper is a report of a study conducted to identify and test the effectiveness of learning mechanisms applied by the nursing staff of hospital wards as a means of limiting medication administration errors. Since the influential report ;To Err Is Human', research has emphasized the role of team learning in reducing medication administration errors. Nevertheless, little is known about the mechanisms underlying team learning. Thirty-two hospital wards were randomly recruited. Data were collected during 2006 in Israel by a multi-method (observations, interviews and administrative data), multi-source (head nurses, bedside nurses) approach. Medication administration error was defined as any deviation from procedures, policies and/or best practices for medication administration, and was identified using semi-structured observations of nurses administering medication. Organizational learning was measured using semi-structured interviews with head nurses, and the previous year's reported medication administration errors were assessed using administrative data. The interview data revealed four learning mechanism patterns employed in an attempt to learn from medication administration errors: integrated, non-integrated, supervisory and patchy learning. Regression analysis results demonstrated that whereas the integrated pattern of learning mechanisms was associated with decreased errors, the non-integrated pattern was associated with increased errors. Supervisory and patchy learning mechanisms were not associated with errors. Superior learning mechanisms are those that represent the whole cycle of team learning, are enacted by nurses who administer medications to patients, and emphasize a system approach to data analysis instead of analysis of individual cases.

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbedAllah, Loai

2016-12-22

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data.

PubMed

Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai

2016-12-01

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Applying Learning Design to Work-Based Learning

ERIC Educational Resources Information Center

Miao, Yongwu; Hoppe, Heinz Ulrich

2011-01-01

Learning design is currently slanted to reflect a course-based approach to learning. This article explores whether the concept of learning design could be applied to support the informal aspects of work-based learning (WBL). It also discusses the characteristics of WBL and presents a WBL-specific learning design that highlights the key features…

Disorientation, confabulation, and extinction capacity: clues on how the brain creates reality.

PubMed

Nahum, Louis; Ptak, Radek; Leemann, Béatrice; Schnider, Armin

2009-06-01

Disorientation and confabulation often have a common course, independent of amnesia. Behaviorally spontaneous confabulation is the form in which patients act according to a false concept of reality; they fail to abandon action plans (anticipations) that do not pertain to the present situation. This continued enactment of previously valid but meanwhile invalidated anticipations can be conceived as deficient extinction capacity, that is, failure to integrate negative prediction errors into behavior. In this study, we explored whether disorientation and behaviorally spontaneous confabulation are associated with extinction failure. Twenty-five patients hospitalized for neurorehabilitation after first-ever brain injury who either had severe amnesia (n = 17), an orbitofrontal lesion (n = 14), or both (n = 6) were tested regarding disorientation (questionnaire) and performed an experimental task of association learning and extinction. Five patients were also classified as behaviorally spontaneous confabulators. Extinction capacity explained 66% of the variance of orientation in the whole group of patients (amnesics only, 56%; orbitofrontal group only, 90%), whereas association learning explained only 17% of the variance in the whole group (amnesics only, 7%; orbitofrontal group only, 16%). Also, extinction capacity, but not association learning, significantly distinguished between behaviorally spontaneous confabulators and all other subjects. Disorientation and behaviorally spontaneous confabulation are strongly and specifically associated with a failure of extinction, the ability to learn that previously appropriate anticipations no longer apply. Rather than invoking high-level monitoring processes, the human brain seems to make use of an ancient biological faculty-extinction-to keep thought and behavior in phase with reality.

Applying HOPSCOTCH as an Exer-Learning Game in English Lessons: Two Exploratory Studies

ERIC Educational Resources Information Center

Lucht, Martina; Heidig, Steffi

2013-01-01

This article describes HOPSCOTCH, a design concept for an "exer-learning game" to engage elementary school children in learning. Exer-learning is a new genre of digital learning games that combines playing and learning with physical activity (exercise). HOPSCOTCH is a first design concept for exer-learning games that can be applied to…

Exploring the Earth Using Deep Learning Techniques

NASA Astrophysics Data System (ADS)

Larraondo, P. R.; Evans, B. J. K.; Antony, J.

2016-12-01

Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from our different data sources. This opens the door to an exciting new way of generating products and extracting features that have previously been labour intensive. In this paper, we will explore some of these geospatial use cases and share some of the lessons learned from this experience.

Learned together, extinguished apart: reducing fear to complex stimuli

PubMed Central

Jones, Carolyn E.; Ringuet, Stephanie; Monfils, Marie-H.

2013-01-01

Pairing a previously neutral conditioned stimulus (CS; e.g., a tone) to an aversive unconditioned stimulus (US; e.g., a footshock) leads to associative learning such that the tone alone comes to elicit a conditioned response (e.g., freezing). We have previously shown that an extinction session that occurs within the reconsolidation window attenuates fear responding and prevents the return of fear in pure tone Pavlovian fear conditioning. Here we sought to examine whether this effect also applies to a more complex fear memory. First, we show that after fear conditioning to the simultaneous presentation of a tone and a light (T+L) coterminating with a shock, the compound memory that ensues is more resistant to fear extinction than simple tone-shock pairings. Next, we demonstrate that the compound memory can be disrupted by interrupting the reconsolidation of the two individual components using a sequential retrieval+extinction paradigm, provided the stronger compound component is retrieved first. These findings provide insight into how compound memories are encoded, and could have important implications for PTSD treatment. PMID:24241750

Advanced Learning Theories Applied to Leadership Development

DTIC Science & Technology

2006-11-01

Theory . We combined the cognitive , experiential and motivational components of advanced learning theories to develop a training application...Center for Army Leadership Technical Report 2006-2 Advanced Learning Theories Applied to Leadership Development Christina Curnow...2006 5a. CONTRACT NUMBER W91QF4-05-F-0026 5b. GRANT NUMBER 4. TITLE AND SUBTITLE Advanced Learning Theories Applied to Leadership Development 5c

The Role of Response Bias in Perceptual Learning

PubMed Central

2015-01-01

Sensory judgments improve with practice. Such perceptual learning is often thought to reflect an increase in perceptual sensitivity. However, it may also represent a decrease in response bias, with unpracticed observers acting in part on a priori hunches rather than sensory evidence. To examine whether this is the case, 55 observers practiced making a basic auditory judgment (yes/no amplitude-modulation detection or forced-choice frequency/amplitude discrimination) over multiple days. With all tasks, bias was present initially, but decreased with practice. Notably, this was the case even on supposedly “bias-free,” 2-alternative forced-choice, tasks. In those tasks, observers did not favor the same response throughout (stationary bias), but did favor whichever response had been correct on previous trials (nonstationary bias). Means of correcting for bias are described. When applied, these showed that at least 13% of perceptual learning on a forced-choice task was due to reduction in bias. In other situations, changes in bias were shown to obscure the true extent of learning, with changes in estimated sensitivity increasing once bias was corrected for. The possible causes of bias and the implications for our understanding of perceptual learning are discussed. PMID:25867609

An analysis of reported motivational orientation in students undertaking doctoral studies in the biomedical sciences

PubMed Central

2014-01-01

Background As the source of a sizeable percentage of research output and the future arbiters of science policy, practice and direction, doctoral (Ph.D.) students represent a key demographic in the biomedical research community. Despite this, doctoral learning in the biomedical sciences has, to date, received little research attention. Methods In the present study we aimed to qualitatively describe the motivational orientations present in semi-structured interview transcripts from a cohort of seventeen biomedical Ph.D. students drawn from two research intensive Australian Group of Eight universities. Results Applying elements of self-determination theory, external and introjected control loci (both strongly associated with alienation, disengagement and poor learning outcomes) were identified as common motivational determinants in this cohort. Conclusions The importance of these findings to doctoral learning is discussed in light of previous research undertaken in higher education settings in the United States and the European Union. With motivation accepted as a malleable, context-sensitive factor, these data provide for both a better understanding of doctoral learning and highlight a potential avenue for future research aimed at improving outcomes and promoting meaningful learning processes in the biomedical doctorate. PMID:24571918

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization.

PubMed

Rajaei, Karim; Khaligh-Razavi, Seyed-Mahdi; Ghodrati, Masoud; Ebrahimpour, Reza; Shiri Ahmad Abadi, Mohammad Ebrahim

2012-01-01

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.

Effects of sublethal doses of thiacloprid and its formulation Calypso® on the learning and memory performance of honey bees.

PubMed

Tison, Léa; Holtz, Sophie; Adeoye, Amy; Kalkan, Önder; Irmisch, Nina S; Lehmann, Nadja; Menzel, Randolf

2017-10-15

Learning and memory play a central role in the behavior and communication of foraging bees. We have previously shown that chronic uptake of the neonicotinoid thiacloprid affects the behavior of honey bees in the field. Foraging behavior, homing success, navigation performance and social communication were impaired. Thiacloprid collected at a feeding site at low doses accumulates in foragers over time. Here, we applied a laboratory standard procedure (the proboscis-extension response conditioning) in order to assess which processes, acquisition, memory consolidation and/or memory retrieval were compromised after bees were fed either with thiacloprid or the formulation of thiacloprid named Calypso ® at different sublethal doses. Extinction and generalization tests allowed us to investigate whether bees respond to a learned stimulus, and how selectively. We showed that thiacloprid, as active substance and as formulation, poses a substantial risk to honey bees by disrupting learning and memory functions. These data support and specify the data collected in the field. © 2017. Published by The Company of Biologists Ltd.

Active Learning for Directed Exploration of Complex Systems

NASA Technical Reports Server (NTRS)

Burl, Michael C.; Wang, Esther

2009-01-01

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

Model of Learning Using iLearning on Independent Study Classes at University

ERIC Educational Resources Information Center

Sudaryono; Padeli; Febriyanto, Erick

2017-01-01

Raharja College is one of the universities who apply a learning method that is quite different which does not only rely on the conventional learning system in which Teaching and Learning Activity is done by students and lecturers are required to come face to face directly, but also applying e-learning method learning or better known as iLearning…

Evaluating the Theoretic Adequacy and Applied Potential of Computational Models of the Spacing Effect.

PubMed

Walsh, Matthew M; Gluck, Kevin A; Gunzelmann, Glenn; Jastrzembski, Tiffany; Krusmark, Michael

2018-06-01

The spacing effect is among the most widely replicated empirical phenomena in the learning sciences, and its relevance to education and training is readily apparent. Yet successful applications of spacing effect research to education and training is rare. Computational modeling can provide the crucial link between a century of accumulated experimental data on the spacing effect and the emerging interest in using that research to enable adaptive instruction. In this paper, we review relevant literature and identify 10 criteria for rigorously evaluating computational models of the spacing effect. Five relate to evaluating the theoretic adequacy of a model, and five relate to evaluating its application potential. We use these criteria to evaluate a novel computational model of the spacing effect called the Predictive Performance Equation (PPE). Predictive Performance Equation combines elements of earlier models of learning and memory including the General Performance Equation, Adaptive Control of Thought-Rational, and the New Theory of Disuse, giving rise to a novel computational account of the spacing effect that performs favorably across the complete sets of theoretic and applied criteria. We implemented two other previously published computational models of the spacing effect and compare them to PPE using the theoretic and applied criteria as guides. Copyright © 2018 Cognitive Science Society, Inc.

Beyond Astro 101: A First Report on Applying Interactive Education Techniques to an Astronphysics Class for Majors

NASA Astrophysics Data System (ADS)

Perrin, Marshall D.; Ghez, A. M.

2009-05-01

Learner-centered interactive instruction methods now have a proven track record in improving learning in "Astro 101" courses for non-majors, but have rarely been applied to higher-level astronomy courses. Can we hope for similar gains in classes aimed at astrophysics majors, or is the subject matter too fundamentally different for those techniques to apply? We present here an initial report on an updated calculus-based Introduction to Astrophysics class at UCLA that suggests such techniques can indeed result in increased learning for major students. We augmented the traditional blackboard-derivation lectures and challenging weekly problem sets by adding online questions on pre-reading assignments (''just-in-time teaching'') and frequent multiple-choice questions in class ("Think-Pair-Share''). We describe our approach, and present examples of the new Think-Pair-Share questions developed for this more sophisticated material. Our informal observations after one term are that with this approach, students are more engaged and alert, and score higher on exams than typical in previous years. This is anecdotal evidence, not hard data yet, and there is clearly a vast amount of work to be done in this area. But our first impressions strongly encourage us that interactive methods should be able improve the astrophysics major just as they have improved Astro 101.

Detecting Inappropriate Access to Electronic Health Records Using Collaborative Filtering.

PubMed

Menon, Aditya Krishna; Jiang, Xiaoqian; Kim, Jihoon; Vaidya, Jaideep; Ohno-Machado, Lucila

2014-04-01

Many healthcare facilities enforce security on their electronic health records (EHRs) through a corrective mechanism: some staff nominally have almost unrestricted access to the records, but there is a strict ex post facto audit process for inappropriate accesses, i.e., accesses that violate the facility's security and privacy policies. This process is inefficient, as each suspicious access has to be reviewed by a security expert, and is purely retrospective, as it occurs after damage may have been incurred. This motivates automated approaches based on machine learning using historical data. Previous attempts at such a system have successfully applied supervised learning models to this end, such as SVMs and logistic regression. While providing benefits over manual auditing, these approaches ignore the identity of the users and patients involved in a record access. Therefore, they cannot exploit the fact that a patient whose record was previously involved in a violation has an increased risk of being involved in a future violation. Motivated by this, in this paper, we propose a collaborative filtering inspired approach to predicting inappropriate accesses. Our solution integrates both explicit and latent features for staff and patients, the latter acting as a personalized "finger-print" based on historical access patterns. The proposed method, when applied to real EHR access data from two tertiary hospitals and a file-access dataset from Amazon, shows not only significantly improved performance compared to existing methods, but also provides insights as to what indicates an inappropriate access.

Detecting Inappropriate Access to Electronic Health Records Using Collaborative Filtering

PubMed Central

Menon, Aditya Krishna; Jiang, Xiaoqian; Kim, Jihoon; Vaidya, Jaideep; Ohno-Machado, Lucila

2013-01-01

Many healthcare facilities enforce security on their electronic health records (EHRs) through a corrective mechanism: some staff nominally have almost unrestricted access to the records, but there is a strict ex post facto audit process for inappropriate accesses, i.e., accesses that violate the facility’s security and privacy policies. This process is inefficient, as each suspicious access has to be reviewed by a security expert, and is purely retrospective, as it occurs after damage may have been incurred. This motivates automated approaches based on machine learning using historical data. Previous attempts at such a system have successfully applied supervised learning models to this end, such as SVMs and logistic regression. While providing benefits over manual auditing, these approaches ignore the identity of the users and patients involved in a record access. Therefore, they cannot exploit the fact that a patient whose record was previously involved in a violation has an increased risk of being involved in a future violation. Motivated by this, in this paper, we propose a collaborative filtering inspired approach to predicting inappropriate accesses. Our solution integrates both explicit and latent features for staff and patients, the latter acting as a personalized “finger-print” based on historical access patterns. The proposed method, when applied to real EHR access data from two tertiary hospitals and a file-access dataset from Amazon, shows not only significantly improved performance compared to existing methods, but also provides insights as to what indicates an inappropriate access. PMID:24683293

Validation of an instrument to measure students' motivation and self-regulation towards technology learning

NASA Astrophysics Data System (ADS)

Liou, Pey-Yan; Kuo, Pei-Jung

2014-05-01

Background:Few studies have examined students' attitudinal perceptions of technology. There is no appropriate instrument to measure senior high school students' motivation and self-regulation toward technology learning among the current existing instruments in the field of technology education. Purpose:The present study is to validate an instrument for assessing senior high school students' motivation and self-regulation towards technology learning. Sample:A total of 1822 Taiwanese senior high school students (1020 males and 802 females) responded to the newly developed instrument. Design and method:The Motivation and Self-regulation towards Technology Learning (MSRTL) instrument was developed based on the previous instruments measuring students' motivation and self-regulation towards science learning. Exploratory and confirmatory factor analyses were utilized to investigate the structure of the items. Cronbach's alpha was applied for measuring the internal consistency of each scale. Furthermore, multivariate analysis of variance was used to examine gender differences. Results:Seven scales, including 'Technology learning self-efficacy,' 'Technology learning value,' 'Technology active learning strategies,' 'Technology learning environment stimulation,' 'Technology learning goal-orientation,' 'Technology learning self-regulation-triggering,' and 'Technology learning self-regulation-implementing' were confirmed for the MSRTL instrument. Moreover, the results also showed that male and female students did not present the same degree of preference in all of the scales. Conclusions:The MSRTL instrument composed of seven scales corresponding to 39 items was shown to be valid based on validity and reliability analyses. While male students tended to express more positive and active performance in the motivation scales, no gender differences were found in the self-regulation scales.

Emulation, imitation, over-imitation and the scope of culture for child and chimpanzee

PubMed Central

Whiten, Andrew; McGuigan, Nicola; Marshall-Pescini, Sarah; Hopper, Lydia M.

2009-01-01

We describe our recent studies of imitation and cultural transmission in chimpanzees and children, which question late twentieth-century characterizations of children as imitators, but chimpanzees as emulators. As emulation entails learning only about the results of others' actions, it has been thought to curtail any capacity to sustain cultures. Recent chimpanzee diffusion experiments have by contrast documented a significant capacity for copying local behavioural traditions. Additionally, in recent ‘ghost’ experiments with no model visible, chimpanzees failed to replicate the object movements on which emulation is supposed to focus. We conclude that chimpanzees rely more on imitation and have greater cultural capacities than previously acknowledged. However, we also find that they selectively apply a range of social learning processes that include emulation. Recent studies demonstrating surprisingly unselective ‘over-imitation’ in children suggest that children's propensity to imitate has been underestimated too. We discuss the implications of these developments for the nature of social learning and culture in the two species. Finally, our new experiments directly address cumulative cultural learning. Initial results demonstrate a relative conservatism and conformity in chimpanzees' learning, contrasting with cumulative cultural learning in young children. This difference may contribute much to the contrast in these species' capacities for cultural evolution. PMID:19620112

Application of scl - pbl method to increase quality learning of industrial statistics course in department of industrial engineering pancasila university

NASA Astrophysics Data System (ADS)

Darmawan, M.; Hidayah, N. Y.

2017-12-01

Currently, there has been a change of new paradigm in the learning model in college, ie from Teacher Centered Learning (TCL) model to Student Centered Learing (SCL). It is generally assumed that the SCL model is better than the TCL model. The Courses of 2nd Industrial Statistics in the Department Industrial Engineering Pancasila University is the subject that belongs to the Basic Engineering group. So far, the applied learning model refers more to the TCL model, and field facts show that the learning outcomes are less satisfactory. Of the three consecutive semesters, ie even semester 2013/2014, 2014/2015, and 2015/2016 obtained grade average is equal to 56.0; 61.1, and 60.5. In the even semester of 2016/2017, Classroom Action Research (CAR) is conducted for this course through the implementation of SCL model with Problem Based Learning (PBL) methods. The hypothesis proposed is that the SCL-PBL model will be able to improve the final grade of the course. The results shows that the average grade of the course can be increased to 73.27. This value was then tested using the ANOVA and the test results concluded that the average grade was significantly different from the average grade value in the previous three semesters.

Machine Learning Techniques for Stellar Light Curve Classification

NASA Astrophysics Data System (ADS)

Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

2018-07-01

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

Development and assessment of a chemistry-based computer video game as a learning tool

NASA Astrophysics Data System (ADS)

Martinez-Hernandez, Kermin Joel

The chemistry-based computer video game is a multidisciplinary collaboration between chemistry and computer graphics and technology fields developed to explore the use of video games as a possible learning tool. This innovative approach aims to integrate elements of commercial video game and authentic chemistry context environments into a learning experience through gameplay. The project consists of three areas: development, assessment, and implementation. However, the foci of this study were the development and assessment of the computer video game including possible learning outcomes and game design elements. A chemistry-based game using a mixed genre of a single player first-person game embedded with action-adventure and puzzle components was developed to determine if students' level of understanding of chemistry concepts change after gameplay intervention. Three phases have been completed to assess students' understanding of chemistry concepts prior and after gameplay intervention. Two main assessment instruments (pre/post open-ended content survey and individual semi-structured interviews) were used to assess student understanding of concepts. In addition, game design elements were evaluated for future development phases. Preliminary analyses of the interview data suggest that students were able to understand most of the chemistry challenges presented in the game and the game served as a review for previously learned concepts as well as a way to apply such previous knowledge. To guarantee a better understanding of the chemistry concepts, additions such as debriefing and feedback about the content presented in the game seem to be needed. The use of visuals in the game to represent chemical processes, game genre, and game idea appear to be the game design elements that students like the most about the current computer video game.

[Digital learning object for diagnostic reasoning in nursing applied to the integumentary system].

PubMed

da Costa, Cecília Passos Vaz; Luz, Maria Helena Barros Araújo

2015-12-01

To describe the creation of a digital learning object for diagnostic reasoning in nursing applied to the integumentary system at a public university of Piaui. A methodological study applied to technological production based on the pedagogical framework of problem-based learning. The methodology for creating the learning object observed the stages of analysis, design, development, implementation and evaluation recommended for contextualized instructional design. The revised taxonomy of Bloom was used to list the educational goals. The four modules of the developed learning object were inserted into the educational platform Moodle. The theoretical assumptions allowed the design of an important online resource that promotes effective learning in the scope of nursing education. This study should add value to nursing teaching practices through the use of digital learning objects for teaching diagnostic reasoning applied to skin and skin appendages.

[The court physician, the clergyman, a learned society and smallpox].

PubMed

Hillen, H F P

2017-01-01

Variolation was introduced in England in the first half of the 18th century. The positive effects of this new method for preventing smallpox were already known in the Netherlands around 1720, one of whom was the Dutch physician Boerhaave. In spite of this, it took another 30 years before variolation was used in the Netherlands. Despite receiving positive advice and information from his learned English friends Sloane and Sherard, Boerhaave did not apply nor advise the use of variolation. There were various arguments for this restrained approach. In 1754 Thomas Schwencke found that conditions were favourable for the introduction of variolation in The Hague. There was support from the House of Orange-Nassau (the current royal family in the Netherlands) and from a learned society; a highly motivated clergyman acted as ambassador for the new technique and the court physician Schwencke was willing to take the lead. A similar combination had previously been effective in England, though the ambassador there was not a clergyman but an influential noble lady.

Bootstrapping language acquisition.

PubMed

Abend, Omri; Kwiatkowski, Tom; Smith, Nathaniel J; Goldwater, Sharon; Steedman, Mark

2017-07-01

The semantic bootstrapping hypothesis proposes that children acquire their native language through exposure to sentences of the language paired with structured representations of their meaning, whose component substructures can be associated with words and syntactic structures used to express these concepts. The child's task is then to learn a language-specific grammar and lexicon based on (probably contextually ambiguous, possibly somewhat noisy) pairs of sentences and their meaning representations (logical forms). Starting from these assumptions, we develop a Bayesian probabilistic account of semantically bootstrapped first-language acquisition in the child, based on techniques from computational parsing and interpretation of unrestricted text. Our learner jointly models (a) word learning: the mapping between components of the given sentential meaning and lexical words (or phrases) of the language, and (b) syntax learning: the projection of lexical elements onto sentences by universal construction-free syntactic rules. Using an incremental learning algorithm, we apply the model to a dataset of real syntactically complex child-directed utterances and (pseudo) logical forms, the latter including contextually plausible but irrelevant distractors. Taking the Eve section of the CHILDES corpus as input, the model simulates several well-documented phenomena from the developmental literature. In particular, the model exhibits syntactic bootstrapping effects (in which previously learned constructions facilitate the learning of novel words), sudden jumps in learning without explicit parameter setting, acceleration of word-learning (the "vocabulary spurt"), an initial bias favoring the learning of nouns over verbs, and one-shot learning of words and their meanings. The learner thus demonstrates how statistical learning over structured representations can provide a unified account for these seemingly disparate phenomena. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Predicting Failure Under Laboratory Conditions: Learning the Physics of Slow Frictional Slip and Dynamic Failure

NASA Astrophysics Data System (ADS)

Rouet-Leduc, B.; Hulbert, C.; Riviere, J.; Lubbers, N.; Barros, K.; Marone, C.; Johnson, P. A.

2016-12-01

Forecasting failure is a primary goal in diverse domains that include earthquake physics, materials science, nondestructive evaluation of materials and other engineering applications. Due to the highly complex physics of material failure and limitations on gathering data in the failure nucleation zone, this goal has often appeared out of reach; however, recent advances in instrumentation sensitivity, instrument density and data analysis show promise toward forecasting failure times. Here, we show that we can predict frictional failure times of both slow and fast stick slip failure events in the laboratory. This advance is made possible by applying a machine learning approach known as Random Forests1(RF) to the continuous acoustic emission (AE) time series recorded by detectors located on the fault blocks. The RF is trained using a large number of statistical features derived from the AE time series signal. The model is then applied to data not previously analyzed. Remarkably, we find that the RF method predicts upcoming failure time far in advance of a stick slip event, based only on a short time window of data. Further, the algorithm accurately predicts the time of the beginning and end of the next slip event. The predicted time improves as failure is approached, as other data features add to prediction. Our results show robust predictions of slow and dynamic failure based on acoustic emissions from the fault zone throughout the laboratory seismic cycle. The predictions are based on previously unidentified tremor-like acoustic signals that occur during stress build up and the onset of macroscopic frictional weakening. We suggest that the tremor-like signals carry information about fault zone processes and allow precise predictions of failure at any time in the slow slip or stick slip cycle2. If the laboratory experiments represent Earth frictional conditions, it could well be that signals are being missed that contain highly useful predictive information. 1Breiman, L. Random forests. Machine Learning 45, 5-32 (2001). 2Rouet-Leduc, B. C. Hulbert, N. Lubbers, K. Barros and P. A. Johnson, Learning the physics of failure, in review (2016).

Bold endeavors: behavioral lessons from polar and space exploration

NASA Technical Reports Server (NTRS)

Stuster, J. W.

2000-01-01

Anecdotal comparisons frequently are made between expeditions of the past and space missions of the future. Spacecraft are far more complex than sailing ships, but from a psychological perspective, the differences are few between confinement in a small wooden ship locked in the polar ice cap and confinement in a small high-technology ship hurtling through interplanetary space. This paper discusses some of the behavioral lessons that can be learned from previous expeditions and applied to facilitate human adjustment and performance during future space expeditions of long duration.

Concurrent engineering: Spacecraft and mission operations system design

NASA Technical Reports Server (NTRS)

Landshof, J. A.; Harvey, R. J.; Marshall, M. H.

1994-01-01

Despite our awareness of the mission design process, spacecraft historically have been designed and developed by one team and then turned over as a system to the Mission Operations organization to operate on-orbit. By applying concurrent engineering techniques and envisioning operability as an essential characteristic of spacecraft design, tradeoffs can be made in the overall mission design to minimize mission lifetime cost. Lessons learned from previous spacecraft missions will be described, as well as the implementation of concurrent mission operations and spacecraft engineering for the Near Earth Asteroid Rendezvous (NEAR) program.

Learning to Apply Models of Materials While Explaining Their Properties

ERIC Educational Resources Information Center

Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

2014-01-01

Background: Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose: This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials.…

Content Classification and Context-Based Retrieval System for E-Learning

ERIC Educational Resources Information Center

Mittal, Ankush; Krishnan, Pagalthivarthi V.; Altman, Edward

2006-01-01

A recent focus in web based learning systems has been the development of reusable learning materials that can be delivered as personalized courses depending of a number of factors such as the user's background, his/her learning preferences, current knowledge based on previous assessments, or previous browsing patterns. The student is often…

Rational and Mechanistic Perspectives on Reinforcement Learning

ERIC Educational Resources Information Center

Chater, Nick

2009-01-01

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: "mechanistic" and "rational." Reinforcement learning is often viewed in mechanistic terms--as…

A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes.

PubMed

Charalambous, Charalambos C; Alcantara, Carolina C; French, Margaret A; Li, Xin; Matt, Kathleen S; Kim, Hyosub E; Morton, Susanne M; Reisman, Darcy S

2018-05-15

Previous work demonstrated an effect of a single high-intensity exercise bout coupled with motor practice on the retention of a newly acquired skilled arm movement, in both neurologically intact and impaired adults. In the present study, using behavioural and computational analyses we demonstrated that a single exercise bout, regardless of its intensity and timing, did not increase the retention of a novel locomotor task after stroke. Considering both present and previous work, we postulate that the benefits of exercise effect may depend on the type of motor learning (e.g. skill learning, sensorimotor adaptation) and/or task (e.g. arm accuracy-tracking task, walking). Acute high-intensity exercise coupled with motor practice improves the retention of motor learning in neurologically intact adults. However, whether exercise could improve the retention of locomotor learning after stroke is still unknown. Here, we investigated the effect of exercise intensity and timing on the retention of a novel locomotor learning task (i.e. split-belt treadmill walking) after stroke. Thirty-seven people post stroke participated in two sessions, 24 h apart, and were allocated to active control (CON), treadmill walking (TMW), or total body exercise on a cycle ergometer (TBE). In session 1, all groups exercised for a short bout (∼5 min) at low (CON) or high (TMW and TBE) intensity and before (CON and TMW) or after (TBE) the locomotor learning task. In both sessions, the locomotor learning task was to walk on a split-belt treadmill in a 2:1 speed ratio (100% and 50% fast-comfortable walking speed) for 15 min. To test the effect of exercise on 24 h retention, we applied behavioural and computational analyses. Behavioural data showed that neither high-intensity group showed greater 24 h retention compared to CON, and computational data showed that 24 h retention was attributable to a slow learning process for sensorimotor adaptation. Our findings demonstrated that acute exercise coupled with a locomotor adaptation task, regardless of its intensity and timing, does not improve retention of the novel locomotor task after stroke. We postulate that exercise effects on motor learning may be context specific (e.g. type of motor learning and/or task) and interact with the presence of genetic variant (BDNF Val66Met). © 2018 The Authors. The Journal of Physiology © 2018 The Physiological Society.

Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

PubMed

Han, Xu; Kim, Jung-jae; Kwoh, Chee Keong

2016-01-01

Biomedical text mining may target various kinds of valuable information embedded in the literature, but a critical obstacle to the extension of the mining targets is the cost of manual construction of labeled data, which are required for state-of-the-art supervised learning systems. Active learning is to choose the most informative documents for the supervised learning in order to reduce the amount of required manual annotations. Previous works of active learning, however, focused on the tasks of entity recognition and protein-protein interactions, but not on event extraction tasks for multiple event types. They also did not consider the evidence of event participants, which might be a clue for the presence of events in unlabeled documents. Moreover, the confidence scores of events produced by event extraction systems are not reliable for ranking documents in terms of informativity for supervised learning. We here propose a novel committee-based active learning method that supports multi-event extraction tasks and employs a new statistical method for informativity estimation instead of using the confidence scores from event extraction systems. Our method is based on a committee of two systems as follows: We first employ an event extraction system to filter potential false negatives among unlabeled documents, from which the system does not extract any event. We then develop a statistical method to rank the potential false negatives of unlabeled documents 1) by using a language model that measures the probabilities of the expression of multiple events in documents and 2) by using a named entity recognition system that locates the named entities that can be event arguments (e.g. proteins). The proposed method further deals with unknown words in test data by using word similarity measures. We also apply our active learning method for the task of named entity recognition. We evaluate the proposed method against the BioNLP Shared Tasks datasets, and show that our method can achieve better performance than such previous methods as entropy and Gibbs error based methods and a conventional committee-based method. We also show that the incorporation of named entity recognition into the active learning for event extraction and the unknown word handling further improve the active learning method. In addition, the adaptation of the active learning method into named entity recognition tasks also improves the document selection for manual annotation of named entities.

Operant conditioning of enhanced pain sensitivity by heat-pain titration.

PubMed

Becker, Susanne; Kleinböhl, Dieter; Klossika, Iris; Hölzl, Rupert

2008-11-15

Operant conditioning mechanisms have been demonstrated to be important in the development of chronic pain. Most experimental studies have investigated the operant modulation of verbal pain reports with extrinsic reinforcement, such as verbal reinforcement. Whether this reflects actual changes in the subjective experience of the nociceptive stimulus remained unclear. This study replicates and extends our previous demonstration that enhanced pain sensitivity to prolonged heat-pain stimulation could be learned in healthy participants through intrinsic reinforcement (contingent changes in nociceptive input) independent of verbal pain reports. In addition, we examine whether different magnitudes of reinforcement differentially enhance pain sensitivity using an operant heat-pain titration paradigm. It is based on the previously developed non-verbal behavioral discrimination task for the assessment of sensitization, which uses discriminative down- or up-regulation of stimulus temperatures in response to changes in subjective intensity. In operant heat-pain titration, this discriminative behavior and not verbal pain report was contingently reinforced or punished by acute decreases or increases in heat-pain intensity. The magnitude of reinforcement was varied between three groups: low (N1=13), medium (N2=11) and high reinforcement (N3=12). Continuous reinforcement was applied to acquire and train the operant behavior, followed by partial reinforcement to analyze the underlying learning mechanisms. Results demonstrated that sensitization to prolonged heat-pain stimulation was enhanced by operant learning within 1h. The extent of sensitization was directly dependent on the received magnitude of reinforcement. Thus, operant learning mechanisms based on intrinsic reinforcement may provide an explanation for the gradual development of sustained hypersensitivity during pain that is becoming chronic.

Effectiveness of early cardiology undergraduate learning using simulation on retention, application of learning and level of confidence during clinical clerkships

PubMed Central

Lin, Weiqin; Lee, Glenn K; Loh, Joshua P; Tay, Edgar L; Sia, Winnie; Lau, Tang-Ching; Hooi, Shing-Chuan; Poh, Kian-Keong

2015-01-01

INTRODUCTION This study aimed to assess the effectiveness of the use of a cardiopulmonary patient simulator in the teaching of second-year medical students. Effectiveness was measured in terms of the extent of knowledge retention and students’ ability to apply the skills learned in subsequent real-life patient contact. METHODS In this study, ten third-year medical students who had previously undergone simulator training as part of their second-year curriculum underwent an objective structured clinical examination (OSCE) and a multiple-choice question (MCQ) test to assess their ability to apply the knowledge gained during the simulator training when dealing with real patients. The performance of this group of students was compared with that of a group of ten fourth-year medical students who did not undergo simulation training. RESULTS Although the third-year medical students performed well in the OSCE, they were outperformed by the group of fourth-year medical students, who had an extra year of clinical exposure. The MCQ scores of the two groups of students were similar. Post-simulation training survey revealed that students were generally in favour of incorporating cardiopulmonary simulator training in the preclinical curriculum. CONCLUSION Cardiopulmonary simulator training is a useful tool for the education of preclinical medical students. It aids the translation of preclinical knowledge into real-life clinical skills. PMID:25715855

An automatic taxonomy of galaxy morphology using unsupervised machine learning

NASA Astrophysics Data System (ADS)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

Joyful Learning in Kindergarten. Revised Edition.

ERIC Educational Resources Information Center

Fisher, Bobbi

Applying the conditions of natural learning to create caring kindergarten classroom environments may support students as lifelong learners. This book presents a natural learning classroom model for implementing a whole-language approach in kindergarten. The chapters are as follows: (1) "My Beliefs about How Children Learn"; (2) "Applying Whole…

How do verbal short-term memory and working memory relate to the acquisition of vocabulary and grammar? A comparison between first and second language learners.

PubMed

Verhagen, Josje; Leseman, Paul

2016-01-01

Previous studies show that verbal short-term memory (VSTM) is related to vocabulary learning, whereas verbal working memory (VWM) is related to grammar learning in children learning a second language (L2) in the classroom. In this study, we investigated whether the same relationships apply to children learning an L2 in a naturalistic setting and to monolingual children. We also investigated whether relationships with verbal memory differ depending on the type of grammar skill investigated (i.e., morphology vs. syntax). Participants were 63 Turkish children who learned Dutch as an L2 and 45 Dutch monolingual children (mean age = 5 years). Children completed a series of VSTM and VWM tasks, a Dutch vocabulary task, and a Dutch grammar task. A confirmatory factor analysis showed that VSTM and VWM represented two separate latent factors in both groups. Structural equation modeling showed that VSTM, treated as a latent factor, significantly predicted vocabulary and grammar. VWM, treated as a latent factor, predicted only grammar. Both memory factors were significantly related to the acquisition of morphology and syntax. There were no differences between the two groups. These results show that (a) VSTM and VWM are differentially associated with language learning and (b) the same memory mechanisms are employed for learning vocabulary and grammar in L1 children and in L2 children who learn their L2 naturalistically. Copyright © 2015 Elsevier Inc. All rights reserved.

Aversive learning of odor-heat associations in ants.

PubMed

Desmedt, Lucie; Baracchi, David; Devaud, Jean-Marc; Giurfa, Martin; d'Ettorre, Patrizia

2017-12-15

Ants have recently emerged as useful models for the study of olfactory learning. In this framework, the development of a protocol for the appetitive conditioning of the maxilla-labium extension response (MaLER) provided the possibility of studying Pavlovian odor-food learning in a controlled environment. Here we extend these studies by introducing the first Pavlovian aversive learning protocol for harnessed ants in the laboratory. We worked with carpenter ants Camponotus aethiops and first determined the capacity of different temperatures applied to the body surface to elicit the typical aversive mandible opening response (MOR). We determined that 75°C is the optimal temperature to induce MOR and chose the hind legs as the stimulated body region because of their high sensitivity. We then studied the ability of ants to learn and remember odor-heat associations using 75°C as the unconditioned stimulus. We studied learning and short-term retention after absolute (one odor paired with heat) and differential conditioning (a punished odor versus an unpunished odor). Our results show that ants successfully learn the odor-heat association under a differential-conditioning regime and thus exhibit a conditioned MOR to the punished odor. Yet, their performance under an absolute-conditioning regime is poor. These results demonstrate that ants are capable of aversive learning and confirm previous findings about the different attentional resources solicited by differential and absolute conditioning in general. © 2017. Published by The Company of Biologists Ltd.

Alternative Constraint Handling Technique for Four-Bar Linkage Path Generation

NASA Astrophysics Data System (ADS)

Sleesongsom, S.; Bureerat, S.

2018-03-01

This paper proposes an extension of a new concept for path generation from our previous work by adding a new constraint handling technique. The propose technique was initially designed for problems without prescribed timing by avoiding the timing constraint, while remain constraints are solving with a new constraint handling technique. The technique is one kind of penalty technique. The comparative study is optimisation of path generation problems are solved using self-adaptive population size teaching-learning based optimization (SAP-TLBO) and original TLBO. In this study, two traditional path generation test problem are used to test the proposed technique. The results show that the new technique can be applied with the path generation problem without prescribed timing and gives better results than the previous technique. Furthermore, SAP-TLBO outperforms the original one.

Learning impairment in honey bees caused by agricultural spray adjuvants.

PubMed

Ciarlo, Timothy J; Mullin, Christopher A; Frazier, James L; Schmehl, Daniel R

2012-01-01

Spray adjuvants are often applied to crops in conjunction with agricultural pesticides in order to boost the efficacy of the active ingredient(s). The adjuvants themselves are largely assumed to be biologically inert and are therefore subject to minimal scrutiny and toxicological testing by regulatory agencies. Honey bees are exposed to a wide array of pesticides as they conduct normal foraging operations, meaning that they are likely exposed to spray adjuvants as well. It was previously unknown whether these agrochemicals have any deleterious effects on honey bee behavior. An improved, automated version of the proboscis extension reflex (PER) assay with a high degree of trial-to-trial reproducibility was used to measure the olfactory learning ability of honey bees treated orally with sublethal doses of the most widely used spray adjuvants on almonds in the Central Valley of California. Three different adjuvant classes (nonionic surfactants, crop oil concentrates, and organosilicone surfactants) were investigated in this study. Learning was impaired after ingestion of 20 µg organosilicone surfactant, indicating harmful effects on honey bees caused by agrochemicals previously believed to be innocuous. Organosilicones were more active than the nonionic adjuvants, while the crop oil concentrates were inactive. Ingestion was required for the tested adjuvant to have an effect on learning, as exposure via antennal contact only induced no level of impairment. A decrease in percent conditioned response after ingestion of organosilicone surfactants has been demonstrated here for the first time. Olfactory learning is important for foraging honey bees because it allows them to exploit the most productive floral resources in an area at any given time. Impairment of this learning ability may have serious implications for foraging efficiency at the colony level, as well as potentially many social interactions. Organosilicone spray adjuvants may therefore contribute to the ongoing global decline in honey bee health.

Learning Impairment in Honey Bees Caused by Agricultural Spray Adjuvants

PubMed Central

Ciarlo, Timothy J.; Mullin, Christopher A.; Frazier, James L.; Schmehl, Daniel R.

2012-01-01

Background Spray adjuvants are often applied to crops in conjunction with agricultural pesticides in order to boost the efficacy of the active ingredient(s). The adjuvants themselves are largely assumed to be biologically inert and are therefore subject to minimal scrutiny and toxicological testing by regulatory agencies. Honey bees are exposed to a wide array of pesticides as they conduct normal foraging operations, meaning that they are likely exposed to spray adjuvants as well. It was previously unknown whether these agrochemicals have any deleterious effects on honey bee behavior. Methodology/Principal Findings An improved, automated version of the proboscis extension reflex (PER) assay with a high degree of trial-to-trial reproducibility was used to measure the olfactory learning ability of honey bees treated orally with sublethal doses of the most widely used spray adjuvants on almonds in the Central Valley of California. Three different adjuvant classes (nonionic surfactants, crop oil concentrates, and organosilicone surfactants) were investigated in this study. Learning was impaired after ingestion of 20 µg organosilicone surfactant, indicating harmful effects on honey bees caused by agrochemicals previously believed to be innocuous. Organosilicones were more active than the nonionic adjuvants, while the crop oil concentrates were inactive. Ingestion was required for the tested adjuvant to have an effect on learning, as exposure via antennal contact only induced no level of impairment. Conclusions/Significance A decrease in percent conditioned response after ingestion of organosilicone surfactants has been demonstrated here for the first time. Olfactory learning is important for foraging honey bees because it allows them to exploit the most productive floral resources in an area at any given time. Impairment of this learning ability may have serious implications for foraging efficiency at the colony level, as well as potentially many social interactions. Organosilicone spray adjuvants may therefore contribute to the ongoing global decline in honey bee health. PMID:22815841

Finding the Right Fit: Helping Students Apply Theory to Service-Learning Contexts

ERIC Educational Resources Information Center

Ricke, Audrey

2018-01-01

Background: Although past studies of service-learning focus on assessing student growth, few studies address how to support students in applying theory to their service-learning experiences. Yet, the task of applying theory is a central component of critical reflections within the social sciences in higher education and often causes anxiety among…

Emotional Intelligence Instruction in a Pharmacy Communications Course

PubMed Central

Lust, Elaine; Moore, Frances C.

2006-01-01

Objectives To determine the benefits of incorporating emotional intelligence instruction into a required pharmacy communications course. Design Specific learning objectives were developed based upon the emotional intelligence framework and how it can be applied to pharmacy practice. Qualitative data on student perceptions were collected and analyzed using theme analysis. Assessment Students found instruction on emotional intelligence to be a positive experience. Students reported learning the taxonomy of emotional intelligence – a concept that previously was difficult for them to articulate or describe, and could use this knowledge in future pharmacy management situations. Students also recognized that their new knowledge of emotional intelligence would lead to better patient outcomes. Conclusion Students had positive perceptions of the importance of emotional intelligence. They valued its inclusion in the pharmacy curriculum and saw practical applications of emotional intelligence to the practice of pharmacy. PMID:17136149

Implementation literacy strategies on health technology theme Learning to enhance Indonesian Junior High School Student's Physics Literacy

NASA Astrophysics Data System (ADS)

Feranie, Selly; Efendi, Ridwan; Karim, Saeful; Sasmita, Dedi

2016-08-01

The PISA results for Indonesian Students are lowest among Asian countries in the past two successive results. Therefore various Innovations in science learning process and its effectiveness enhancing student's science literacy is needed to enrich middle school science teachers. Literacy strategies have been implemented on health technologies theme learning to enhance Indonesian Junior high school Student's Physics literacy in three different health technologies e.g. Lasik surgery that associated with application of Light and Optics concepts, Ultra Sonographer (USG) associated with application of Sound wave concepts and Work out with stationary bike and walking associated with application of motion concepts. Science learning process involves at least teacher instruction, student learning and a science curriculum. We design two main part of literacy strategies in each theme based learning. First part is Integrated Reading Writing Task (IRWT) is given to the students before learning process, the second part is scientific investigation learning process design packed in Problem Based Learning. The first part is to enhance student's science knowledge and reading comprehension and the second part is to enhance student's science competencies. We design a transformation from complexity of physics language to Middle school physics language and from an expensive and complex science investigation to a local material and simply hands on activities. In this paper, we provide briefly how literacy strategies proposed by previous works is redesigned and applied in classroom science learning. Data were analysed using t- test. The increasing value of mean scores in each learning design (with a significance level of p = 0.01) shows that the implementation of this literacy strategy revealed a significant increase in students’ physics literacy achievement. Addition analysis of Avarage normalized gain show that each learning design is in medium-g courses effectiveness category according to Hake's classification.

Cross-Cultural Service Learning: American and Russian Students Learn Applied Organizational Communication.

ERIC Educational Resources Information Center

Stevens, Betsy

2001-01-01

Describes how American and Russian students engaged in service learning in their own communities as part of an organizational communication class in which they learned communication principles and applied their skills to assist non-profit organizations. Describes both projects, stumbling blocks, and course outcomes. (SR)

Acquiring Knowledge and Using It.

ERIC Educational Resources Information Center

Smilkstein, Rita

1993-01-01

Understanding why students are not naturally and easily able to generalize or apply what they have learned in other situations involves understanding what teachers want their students to learn; what learning is; what teaching is; and what is involved in generalizing or applying what has been learned. Research in educational psychology identifies…

Consider the category: The effect of spacing depends on individual learning histories.

PubMed

Slone, Lauren K; Sandhofer, Catherine M

2017-07-01

The spacing effect refers to increased retention following learning instances that are spaced out in time compared with massed together in time. By one account, the advantages of spaced learning should be independent of task particulars and previous learning experiences given that spacing effects have been demonstrated in a variety of tasks across the lifespan. However, by another account, spaced learning should be affected by previous learning because past learning affects the memory and attention processes that form the crux of the spacing effect. The current study investigated whether individuals' learning histories affect the role of spacing in category learning. We examined the effect of spacing on 24 2- to 3.5-year-old children's learning of categories organized by properties to which children's previous learning experiences have biased them to attend (i.e., shape) and properties to which children are less biased to attend (i.e., texture and color). Spaced presentations led to significantly better learning of shape categories, but not of texture or color categories, compared with massed presentations. In addition, generalized estimating equations analyses revealed positive relations between the size of children's "shape-side" productive vocabularies and their shape category learning and between the size of children's "against-the-system" productive vocabularies and their texture category learning. These results suggest that children's attention to and memory for novel object categories are strongly related to their individual word-learning histories. Moreover, children's learned attentional biases affected the types of categories for which spacing facilitated learning. These findings highlight the importance of considering how learners' previous experiences may influence future learning. Copyright © 2017 Elsevier Inc. All rights reserved.

The Impact of Applied Cognitive Learning Theory on Engagement with eLearning Courseware

ERIC Educational Resources Information Center

Swann, William

2013-01-01

Since the emergence of eLearning in the 1990s, the craft of designing and developing online courseware has evolved alongside theoretical advances in the field. A variety of media combinations have been applied to course pages by eLearning practitioners, making it possible to examine learning concepts emerging from the research in the light of…

Place learning prior to and after telencephalon ablation in bamboo and coral cat sharks (Chiloscyllium griseum and Atelomycterus marmoratus).

PubMed

Fuss, Theodora; Bleckmann, Horst; Schluessel, Vera

2014-01-01

This study assessed complex spatial learning and memory in two species of shark, the grey bamboo shark (Chiloscyllium griseum) and the coral cat shark (Atelomycterus marmoratus). It was hypothesized that sharks can learn and apply an allocentric orientation strategy. Eight out of ten sharks successfully completed the initial training phase (by locating a fixed goal position in a diamond maze from two possible start points) within 14.9 ± 7.6 sessions and proceeded to seven sets of transfer tests, in which sharks had to perform under altered environmental conditions. Transfer tests revealed that sharks had oriented and solved the tasks visually, using all of the provided environmental cues. Unintentional cueing did not occur. Results correspond to earlier studies on spatial memory and cognitive mapping in other vertebrates. Future experiments should investigate whether sharks possess a cognitive spatial mapping system as has already been found in several teleosts and stingrays. Following the completion of transfer tests, sharks were subjected to ablation of most of the pallium, which compromised their previously acquired place learning abilities. These results indicate that the telencephalon plays a crucial role in the processing of information on place learning and allocentric orientation strategies.

Query construction, entropy, and generalization in neural-network models

NASA Astrophysics Data System (ADS)

Sollich, Peter

1994-05-01

We study query construction algorithms, which aim at improving the generalization ability of systems that learn from examples by choosing optimal, nonredundant training sets. We set up a general probabilistic framework for deriving such algorithms from the requirement of optimizing a suitable objective function; specifically, we consider the objective functions entropy (or information gain) and generalization error. For two learning scenarios, the high-low game and the linear perceptron, we evaluate the generalization performance obtained by applying the corresponding query construction algorithms and compare it to training on random examples. We find qualitative differences between the two scenarios due to the different structure of the underlying rules (nonlinear and ``noninvertible'' versus linear); in particular, for the linear perceptron, random examples lead to the same generalization ability as a sequence of queries in the limit of an infinite number of examples. We also investigate learning algorithms which are ill matched to the learning environment and find that, in this case, minimum entropy queries can in fact yield a lower generalization ability than random examples. Finally, we study the efficiency of single queries and its dependence on the learning history, i.e., on whether the previous training examples were generated randomly or by querying, and the difference between globally and locally optimal query construction.

Preliminary evidence for performance enhancement following parietal lobe stimulation in Developmental Dyscalculia.

PubMed

Iuculano, Teresa; Cohen Kadosh, Roi

2014-01-01

Nearly 7% of the population exhibit difficulties in dealing with numbers and performing arithmetic, a condition named Developmental Dyscalculia (DD), which significantly affects the educational and professional outcomes of these individuals, as it often persists into adulthood. Research has mainly focused on behavioral rehabilitation, while little is known about performance changes and neuroplasticity induced by the concurrent application of brain-behavioral approaches. It has been shown that numerical proficiency can be enhanced by applying a small-yet constant-current through the brain, a non-invasive technique named transcranial electrical stimulation (tES). Here we combined a numerical learning paradigm with transcranial direct current stimulation (tDCS) in two adults with DD to assess the potential benefits of this methodology to remediate their numerical difficulties. Subjects learned to associate artificial symbols to numerical quantities within the context of a trial and error paradigm, while tDCS was applied to the posterior parietal cortex (PPC). The first subject (DD1) received anodal stimulation to the right PPC and cathodal stimulation to the left PPC, which has been associated with numerical performance's improvements in healthy subjects. The second subject (DD2) received anodal stimulation to the left PPC and cathodal stimulation to the right PPC, which has been shown to impair numerical performance in healthy subjects. We examined two indices of numerical proficiency: (i) automaticity of number processing; and (ii) mapping of numbers onto space. Our results are opposite to previous findings with non-dyscalculic subjects. Only anodal stimulation to the left PPC improved both indices of numerical proficiency. These initial results represent an important step to inform the rehabilitation of developmental learning disabilities, and have relevant applications for basic and applied research in cognitive neuroscience, rehabilitation, and education.

Preliminary evidence for performance enhancement following parietal lobe stimulation in Developmental Dyscalculia

PubMed Central

Iuculano, Teresa; Cohen Kadosh, Roi

2014-01-01

Nearly 7% of the population exhibit difficulties in dealing with numbers and performing arithmetic, a condition named Developmental Dyscalculia (DD), which significantly affects the educational and professional outcomes of these individuals, as it often persists into adulthood. Research has mainly focused on behavioral rehabilitation, while little is known about performance changes and neuroplasticity induced by the concurrent application of brain-behavioral approaches. It has been shown that numerical proficiency can be enhanced by applying a small—yet constant—current through the brain, a non-invasive technique named transcranial electrical stimulation (tES). Here we combined a numerical learning paradigm with transcranial direct current stimulation (tDCS) in two adults with DD to assess the potential benefits of this methodology to remediate their numerical difficulties. Subjects learned to associate artificial symbols to numerical quantities within the context of a trial and error paradigm, while tDCS was applied to the posterior parietal cortex (PPC). The first subject (DD1) received anodal stimulation to the right PPC and cathodal stimulation to the left PPC, which has been associated with numerical performance's improvements in healthy subjects. The second subject (DD2) received anodal stimulation to the left PPC and cathodal stimulation to the right PPC, which has been shown to impair numerical performance in healthy subjects. We examined two indices of numerical proficiency: (i) automaticity of number processing; and (ii) mapping of numbers onto space. Our results are opposite to previous findings with non-dyscalculic subjects. Only anodal stimulation to the left PPC improved both indices of numerical proficiency. These initial results represent an important step to inform the rehabilitation of developmental learning disabilities, and have relevant applications for basic and applied research in cognitive neuroscience, rehabilitation, and education. PMID:24570659

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

PubMed Central

2012-01-01

Background Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this. PMID:22676920

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke.

PubMed

Meadmore, Katie L; Hughes, Ann-Marie; Freeman, Chris T; Cai, Zhonglun; Tong, Daisy; Burridge, Jane H; Rogers, Eric

2012-06-07

Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients' voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants' arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

Statistical learning of multisensory regularities is enhanced in musicians: An MEG study.

PubMed

Paraskevopoulos, Evangelos; Chalas, Nikolas; Kartsidis, Panagiotis; Wollbrink, Andreas; Bamidis, Panagiotis

2018-07-15

The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni- and multi-modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non-musicians. The behavioral results indicated that the learning was successful for both musicians and non-musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschl's gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top-down processing. This neuroplastic effect showed a cross-modal stability between the auditory and audiovisual modalities. Copyright © 2018 Elsevier Inc. All rights reserved.

The stress hormone cortisol blocks perceptual learning in humans.

PubMed

Dinse, Hubert R; Kattenstroth, J C; Lenz, M; Tegenthoff, M; Wolf, O T

2017-03-01

Cortisol, the primary glucocorticoid (GC) in humans, influences neuronal excitability and plasticity by acting on mineralocorticoid and glucocorticoid receptors. Cellular studies demonstrated that elevated GC levels affect neuronal plasticity, for example through a reduction of hippocampal long-term potentiation (LTP). At the behavioural level, after treatment with GCs, numerous studies have reported impaired hippocampal function, such as impaired memory retrieval. In contrast, relatively little is known about the impact of GCs on cortical plasticity and perceptual learning in adult humans. Therefore, in this study, we explored the impact of elevated GC levels on human perceptual learning. To this aim, we used a training-independent learning approach, where lasting changes in human perception can be induced by applying passive repetitive sensory stimulation (rss), the timing of which was determined from cellular LTP studies. In our placebo-controlled double-blind study, we used tactile LTP-like stimulation to induce improvements in tactile acuity (spatial two-point discrimination). Our results show that a single administration of hydrocortisone (30mg) completely blocked rss-induced changes in two-point discrimination. In contrast, the placebo group showed the expected rss-induced increase in two-point discrimination of over 14%. Our data demonstrate that high GC levels inhibit rss-induced perceptual learning. We suggest that the suppression of LTP, as previously reported in cellular studies, may explain the perceptual learning impairments observed here. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Instant transformation of learned repulsion into motivational "wanting".

PubMed

Robinson, Mike J F; Berridge, Kent C

2013-02-18

Learned cues for pleasant reward often elicit desire, which, in addicts, may become compulsive. According to the dominant view in addiction neuroscience and reinforcement modeling, such desires are the simple products of learning, coming from a past association with reward outcome. We demonstrate that cravings are more than merely the products of accumulated pleasure memories-even a repulsive learned cue for unpleasantness can become suddenly desired via the activation of mesocorticolimbic circuitry. Rats learned repulsion toward a Pavlovian cue (a briefly-inserted metal lever) that always predicted an unpleasant Dead Sea saltiness sensation. Yet, upon first reencounter in a novel sodium-depletion state to promote mesocorticolimbic reactivity (reflected by elevated Fos activation in ventral tegmentum, nucleus accumbens, ventral pallidum, and the orbitofrontal prefrontal cortex), the learned cue was instantly transformed into an attractive and powerful motivational magnet. Rats jumped and gnawed on the suddenly attractive Pavlovian lever cue, despite never having tasted intense saltiness as anything other than disgusting. Instant desire transformation of a learned cue contradicts views that Pavlovian desires are essentially based on previously learned values (e.g., prediction error or temporal difference models). Instead desire is recomputed at reencounter by integrating Pavlovian information with the current brain/physiological state. This powerful brain transformation reverses strong learned revulsion into avid attraction. When applied to addiction, related mesocorticolimbic transformations (e.g., drugs or neural sensitization) of cues for already-pleasant drug experiences could create even more intense cravings. This cue/state transformation helps define what it means to say that addiction hijacks brain limbic circuits of natural reward. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cooperative Learning as a Democratic Learning Method

ERIC Educational Resources Information Center

Erbil, Deniz Gökçe; Kocabas, Ayfer

2018-01-01

In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

E-Learning in Science and Technology via a Common Learning Platform in a Lifelong Learning Project

ERIC Educational Resources Information Center

Priem, Freddy; De Craemer, Renaat; Calu, Johan; Pedreschi, Fran; Zimmer, Thomas; Saighi, Sylvain; Lilja, Jarmo

2011-01-01

This three-year Virtual Measurements Environment curriculum development project for higher education within the Lifelong Learning Programme of the European Union is the result of intense collaboration among four institutions, teaching applied sciences and technology. It aims to apply the principles and possibilities of evolved distance and…

Learning Problems Reported by College Students: Are They Using Learning Strategies?

ERIC Educational Resources Information Center

Rachal, K. Chris; Daigle, Sherri; Rachal, Windy S.

2007-01-01

As teachers of higher education, we expect students to enter college with some understanding of what it means to be an effective learner and the ability to apply effective learning strategies. Unfortunately, many students do not develop effective learning strategies unless they receive explicit instruction and the opportunity to apply these…

Think Pair Share Using Realistic Mathematics Education Approach in Geometry Learning

NASA Astrophysics Data System (ADS)

Afthina, H.; Mardiyana; Pramudya, I.

2017-09-01

This research aims to determine the impact of mathematics learning applying Think Pair Share (TPS) using Realistic Mathematics Education (RME) viewed from mathematical-logical intelligence in geometry learning. Method that used in this research is quasi experimental research The result of this research shows that (1) mathematics achievement applying TPS using RME approach gives a better result than those applying direct learning model; (2) students with high mathematical-logical intelligence can reach a better mathematics achievement than those with average and low one, whereas students with average mathematical-logical intelligence can reach a better achievement than those with low one; (3) there is no interaction between learning model and the level of students’ mathematical-logical intelligence in giving a mathematics achievement. The impact of this research is that TPS model using RME approach can be applied in mathematics learning so that students can learn more actively and understand the material more, and mathematics learning become more meaningful. On the other hand, internal factors of students must become a consideration toward the success of students’ mathematical achievement particularly in geometry material.

Flexible explicit but rigid implicit learning in a visuomotor adaptation task

PubMed Central

Bond, Krista M.

2015-01-01

There is mounting evidence for the idea that performance in a visuomotor rotation task can be supported by both implicit and explicit forms of learning. The implicit component of learning has been well characterized in previous experiments and is thought to arise from the adaptation of an internal model driven by sensorimotor prediction errors. However, the role of explicit learning is less clear, and previous investigations aimed at characterizing the explicit component have relied on indirect measures such as dual-task manipulations, posttests, and descriptive computational models. To address this problem, we developed a new method for directly assaying explicit learning by having participants verbally report their intended aiming direction on each trial. While our previous research employing this method has demonstrated the possibility of measuring explicit learning over the course of training, it was only tested over a limited scope of manipulations common to visuomotor rotation tasks. In the present study, we sought to better characterize explicit and implicit learning over a wider range of task conditions. We tested how explicit and implicit learning change as a function of the specific visual landmarks used to probe explicit learning, the number of training targets, and the size of the rotation. We found that explicit learning was remarkably flexible, responding appropriately to task demands. In contrast, implicit learning was strikingly rigid, with each task condition producing a similar degree of implicit learning. These results suggest that explicit learning is a fundamental component of motor learning and has been overlooked or conflated in previous visuomotor tasks. PMID:25855690

A similarity based learning framework for interim analysis of outcome prediction of acupuncture for neck pain.

PubMed

Zhang, Gang; Liang, Zhaohui; Yin, Jian; Fu, Wenbin; Li, Guo-Zheng

2013-01-01

Chronic neck pain is a common morbid disorder in modern society. Acupuncture has been administered for treating chronic pain as an alternative therapy for a long time, with its effectiveness supported by the latest clinical evidence. However, the potential effective difference in different syndrome types is questioned due to the limits of sample size and statistical methods. We applied machine learning methods in an attempt to solve this problem. Through a multi-objective sorting of subjective measurements, outstanding samples are selected to form the base of our kernel-oriented model. With calculation of similarities between the concerned sample and base samples, we are able to make full use of information contained in the known samples, which is especially effective in the case of a small sample set. To tackle the parameters selection problem in similarity learning, we propose an ensemble version of slightly different parameter setting to obtain stronger learning. The experimental result on a real data set shows that compared to some previous well-known methods, the proposed algorithm is capable of discovering the underlying difference among different syndrome types and is feasible for predicting the effective tendency in clinical trials of large samples.

Global view of the mechanisms of improved learning and memory capability in mice with music-exposure by microarray.

PubMed

Meng, Bo; Zhu, Shujia; Li, Shijia; Zeng, Qingwen; Mei, Bing

2009-08-28

Music has been proved beneficial to improve learning and memory in many species including human in previous research work. Although some genes have been identified to contribute to the mechanisms, it is believed that the effect of music is manifold, behind which must concern a complex regulation network. To further understand the mechanisms, we exposed the mice to classical music for one month. The subsequent behavioral experiments showed improvement of spatial learning capability and elevation of fear-motivated memory in the mice with music-exposure as compared to the naïve mice. Meanwhile, we applied the microarray to compare the gene expression profiles of the hippocampus and cortex between the mice with music-exposure and the naïve mice. The results showed approximately 454 genes in cortex (200 genes up-regulated and 254 genes down-regulated) and 437 genes in hippocampus (256 genes up-regulated and 181 genes down-regulated) were significantly affected in music-exposing mice, which mainly involved in ion channel activity and/or synaptic transmission, cytoskeleton, development, transcription, hormone activity. Our work may provide some hints for better understanding the effects of music on learning and memory.

Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.

PubMed

Nguyen, M N

2010-04-01

Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.

Bimanual Coordination Learning with Different Augmented Feedback Modalities and Information Types

PubMed Central

Chiou, Shiau-Chuen; Chang, Erik Chihhung

2016-01-01

Previous studies have shown that bimanual coordination learning is more resistant to the removal of augmented feedback when acquired with auditory than with visual channel. However, it is unclear whether this differential “guidance effect” between feedback modalities is due to enhanced sensorimotor integration via the non-dominant auditory channel or strengthened linkage to kinesthetic information under rhythmic input. The current study aimed to examine how modalities (visual vs. auditory) and information types (continuous visuospatial vs. discrete rhythmic) of concurrent augmented feedback influence bimanual coordination learning. Participants either learned a 90°-out-of-phase pattern for three consecutive days with Lissajous feedback indicating the integrated position of both arms, or with visual or auditory rhythmic feedback reflecting the relative timing of the movement. The results showed diverse performance change after practice when the feedback was removed between Lissajous and the other two rhythmic groups, indicating that the guidance effect may be modulated by the type of information provided during practice. Moreover, significant performance improvement in the dual-task condition where the irregular rhythm counting task was applied as a secondary task also suggested that lower involvement of conscious control may result in better performance in bimanual coordination. PMID:26895286

Bimanual Coordination Learning with Different Augmented Feedback Modalities and Information Types.

PubMed

Chiou, Shiau-Chuen; Chang, Erik Chihhung

2016-01-01

Previous studies have shown that bimanual coordination learning is more resistant to the removal of augmented feedback when acquired with auditory than with visual channel. However, it is unclear whether this differential "guidance effect" between feedback modalities is due to enhanced sensorimotor integration via the non-dominant auditory channel or strengthened linkage to kinesthetic information under rhythmic input. The current study aimed to examine how modalities (visual vs. auditory) and information types (continuous visuospatial vs. discrete rhythmic) of concurrent augmented feedback influence bimanual coordination learning. Participants either learned a 90°-out-of-phase pattern for three consecutive days with Lissajous feedback indicating the integrated position of both arms, or with visual or auditory rhythmic feedback reflecting the relative timing of the movement. The results showed diverse performance change after practice when the feedback was removed between Lissajous and the other two rhythmic groups, indicating that the guidance effect may be modulated by the type of information provided during practice. Moreover, significant performance improvement in the dual-task condition where the irregular rhythm counting task was applied as a secondary task also suggested that lower involvement of conscious control may result in better performance in bimanual coordination.

An Approach for Calculating Student-Centered Value in Education – A Link between Quality, Efficiency, and the Learning Experience in the Health Professions

PubMed Central

Ooi, Caryn; Reeves, Scott; Walsh, Kieran

2016-01-01

Health professional education is experiencing a cultural shift towards student-centered education. Although we are now challenging our traditional training methods, our methods for evaluating the impact of the training on the learner remains largely unchanged. What is not typically measured is student-centered value; whether it was ‘worth’ what the learner paid. The primary aim of this study was to apply a method of calculating student-centered value, applied to the context of a change in teaching methods within a health professional program. This study took place over the first semester of the third year of the Bachelor of Physiotherapy at Monash University, Victoria, Australia, in 2014. The entire third year cohort (n = 78) was invited to participate. Survey based design was used to collect the appropriate data. A blended learning model was implemented; subsequently students were only required to attend campus three days per week, with the remaining two days comprising online learning. This was compared to the previous year’s format, a campus-based face-to-face approach where students attended campus five days per week, with the primary outcome—Value to student. Value to student incorporates, user costs associated with transportation and equipment, the amount of time saved, the price paid and perceived gross benefit. Of the 78 students invited to participate, 76 completed the post-unit survey (non-participation rate 2.6%). Based on Value to student the blended learning approach provided a $1,314.93 net benefit to students. Another significant finding was that the perceived gross benefit for the blended learning approach was $4014.84 compared to the campus-based face-to-face approach of $3651.72, indicating that students would pay more for the blended learning approach. This paper successfully applied a novel method of calculating student-centered value. This is the first step in validating the value to student outcome. Measuring economic value to the student may be used as a way of evaluating effective change in a modern health professional curriculum. This could extend to calculate total value, which would incorporate the economic implications for the educational providers. Further research is required for validation of this outcome. PMID:27632427

Twenty cultural and learning principles to guide the development of pharmacy curriculum in Pacific Island countries.

PubMed

Brown, Andrew N; McCormack, Coralie

2014-01-01

A lack of education capacity to support the development of medical supply management competency is a major issue affecting Pacific Islands countries (PICs). Limited human resources and underdeveloped medicines supply management competency are two significant impediments to reaching the health-related Millennium Development Goals in many countries in this rural and remote region. Two recent review publications have provided relevant background documenting factors affecting learning and teaching. These articles have presented available information regarding competency and training requirements for health personnel involved in essential medicine supply management in the region. This background research has provided a platform from which tangible principles can be developed to aid educators and professionals in PICs in the development and delivery of appropriate pharmacy curriculum. Specifically the aim of the present article is to identify culturally meaningful learning and teaching principles to guide the development and delivery of pharmaceutical curriculum in PICs. Subsequently, this information will be applied to develop and trial new pedagogical approaches to the training of health personnel involved in essential medicines supply management, to improve medicine availability for patients in their own environment. This article forms part of a wider research project involving the United Nations Population Fund Suva subregional office, the University of Canberra, Ministry of Health officials and health personnel within identified PICs. Two previous reviews, investigating Pacific culture, learning approaches, and training requirements affecting pharmaceutical personnel, were synthesised into a set of principles that could be applied to the development of pharmaceutical curriculum. These principles were validated through focus groups of health personnel using action research methods. An initial set of 16 principles was developed from the synthesis of the two reviews. These principles were reviewed by two focus groups held in Fiji and the Solomon Islands to produce a set of 20 validated principles. These validated principles can be grouped under the headings of learning theory, structure and design, and learning and teaching methods. The 20 principles outlined in this article will be used to develop and trial culturally relevant training approaches for the development of medicine management competencies for various cadres of health personnel in PICs. These principles provide a practical framework for educators and health professionals to apply to health-based education and training in the Pacific, with potential application to other rural and remote environments.

An Approach for Calculating Student-Centered Value in Education - A Link between Quality, Efficiency, and the Learning Experience in the Health Professions.

PubMed

Nicklen, Peter; Rivers, George; Ooi, Caryn; Ilic, Dragan; Reeves, Scott; Walsh, Kieran; Maloney, Stephen

2016-01-01

Health professional education is experiencing a cultural shift towards student-centered education. Although we are now challenging our traditional training methods, our methods for evaluating the impact of the training on the learner remains largely unchanged. What is not typically measured is student-centered value; whether it was 'worth' what the learner paid. The primary aim of this study was to apply a method of calculating student-centered value, applied to the context of a change in teaching methods within a health professional program. This study took place over the first semester of the third year of the Bachelor of Physiotherapy at Monash University, Victoria, Australia, in 2014. The entire third year cohort (n = 78) was invited to participate. Survey based design was used to collect the appropriate data. A blended learning model was implemented; subsequently students were only required to attend campus three days per week, with the remaining two days comprising online learning. This was compared to the previous year's format, a campus-based face-to-face approach where students attended campus five days per week, with the primary outcome-Value to student. Value to student incorporates, user costs associated with transportation and equipment, the amount of time saved, the price paid and perceived gross benefit. Of the 78 students invited to participate, 76 completed the post-unit survey (non-participation rate 2.6%). Based on Value to student the blended learning approach provided a $1,314.93 net benefit to students. Another significant finding was that the perceived gross benefit for the blended learning approach was $4014.84 compared to the campus-based face-to-face approach of $3651.72, indicating that students would pay more for the blended learning approach. This paper successfully applied a novel method of calculating student-centered value. This is the first step in validating the value to student outcome. Measuring economic value to the student may be used as a way of evaluating effective change in a modern health professional curriculum. This could extend to calculate total value, which would incorporate the economic implications for the educational providers. Further research is required for validation of this outcome.

Does segmental overlap help or hurt? Evidence from blocked cyclic naming in spoken and written production.

PubMed

Breining, Bonnie; Nozari, Nazbanou; Rapp, Brenda

2016-04-01

Past research has demonstrated interference effects when words are named in the context of multiple items that share a meaning. This interference has been explained within various incremental learning accounts of word production, which propose that each attempt at mapping semantic features to lexical items induces slight but persistent changes that result in cumulative interference. We examined whether similar interference-generating mechanisms operate during the mapping of lexical items to segments by examining the production of words in the context of others that share segments. Previous research has shown that initial-segment overlap amongst a set of target words produces facilitation, not interference. However, this initial-segment facilitation is likely due to strategic preparation, an external factor that may mask underlying interference. In the present study, we applied a novel manipulation in which the segmental overlap across target items was distributed unpredictably across word positions, in order to reduce strategic response preparation. This manipulation led to interference in both spoken (Exp. 1) and written (Exp. 2) production. We suggest that these findings are consistent with a competitive learning mechanism that applies across stages and modalities of word production.

Outcomes of Fundamentals of Laparoscopic Surgery (FLS) mastery training standards applied to an ergonomically different, lower cost platform.

PubMed

Placek, Sarah B; Franklin, Brenton R; Haviland, Sarah M; Wagner, Mercy D; O'Donnell, Mary T; Cryer, Chad T; Trinca, Kristen D; Silverman, Elliott; Matthew Ritter, E

2017-06-01

Using previously established mastery learning standards, this study compares outcomes of training on standard FLS (FLS) equipment with training on an ergonomically different (ED-FLS), but more portable, lower cost platform. Subjects completed a pre-training FLS skills test on the standard platform and were then randomized to train on the FLS training platform (n = 20) or the ED-FLS platform (n = 19). A post-training FLS skills test was administered to both groups on the standard FLS platform. Group performance on the pretest was similar. Fifty percent of FLS and 32 % of ED-FLS subjects completed the entire curriculum. 100 % of subjects completing the curriculum achieved passing scores on the post-training test. There was no statistically discernible difference in scores on the final FLS exam (FLS 93.4, ED-FLS 93.3, p = 0.98) or training sessions required to complete the curriculum (FLS 7.4, ED-FLS 9.8, p = 0.13). These results show that when applying mastery learning theory to an ergonomically different platform, skill transfer occurs at a high level and prepares subjects to pass the standard FLS skills test.

Educational and evaluation strategies in the training of physician specialists

PubMed

Gaona-Flores, Verónica Alejandra; Campos-Navarro, Luz Arcelia; Arenas-Osuna, Jesús; Alcalá-Martínez, Enrique

2017-01-01

Teaching strategies have been defined as procedures, means or resources that teachers used to promote meaningful learning. Identify teaching strategies and evaluation used by the professor with residents in tertiary hospitals health care. This is a cross-sectional study conducted with full, associate and assistant professors of various medical specialties. A questionnaire was applied to evaluate the strategies used by professors to teach and evaluate students. We included a sample of 90 professors in 35 medical specialties. The most frequent teaching activities were: organizing students to develop presentations on specific subjects, followed by asking questions on previously reviewed subjects, In terms of the strategies employed, the most frequent "always" option was applied to case analyses. The most frequent methods used for the evaluation of theoretical knowledge were: participation in class, topic presentation and exams. Teaching activities were primarily based on the presentation of specific topics by the residents. The most commonly used educational strategies were clinical case analyses followed by problem-based learning and the use of illustrations. Evaluation of the residents' performance in theory knowledge, hinged on class participation, presentation of assigned topics and exams. Copyright: © 2017 SecretarÍa de Salud

An Account of Women's Progress in Engineering: a Social Cognitive Perspective

NASA Astrophysics Data System (ADS)

Vogt, Christina

Traditionally, women were not welcome in higher education, especially in male-dominated fields. Undoubtedly, women have dramatically increased their enrollments in many once male-only fields, such as law, medicine, and several of the sciences; nevertheless, engineering remains a field where women continue to be underrepresented. This has often been attributed to social barriers in engineering classrooms. However, a new turn of events has been reported: Young women entering engineering may receive higher grades and have a greater tendency to remain than men. To examine what has recently changed, the author applied Bandura's triadic model of reciprocity between environment, self, and behavior. The measured variables included academic integration or discrimination, self-measures of academic self-confidence, engineering self-efficacy, and behaviors taken to self-regulate learning: critical thinking, effort, peer learning, and help seeking. The data revealed that women apply slightly more effort and have slightly less self-efficacy than men. Their academic confidence is nearly equal in almost all areas. Most significantly, many previous gender biases appear diminished, and those that do exist are slight. However, it is recommended that continued efforts be undertaken to attract and retain women in engineering programs.

Self-Learning Off-Lattice Kinetic Monte Carlo method as applied to growth on metal surfaces

NASA Astrophysics Data System (ADS)

Trushin, Oleg; Kara, Abdelkader; Rahman, Talat

2007-03-01

We propose a new development in the Self-Learning Kinetic Monte Carlo (SLKMC) method with the goal of improving the accuracy with which atomic mechanisms controlling diffusive processes on metal surfaces may be identified. This is important for diffusion of small clusters (2 - 20 atoms) in which atoms may occupy Off-Lattice positions. Such a procedure is also necessary for consideration of heteroepitaxial growth. The new technique combines an earlier version of SLKMC [1] with the inclusion of off-lattice occupancy. This allows us to include arbitrary positions of adatoms in the modeling and makes the simulations more realistic and reliable. We have tested this new approach for the case of the diffusion of small 2D Cu clusters diffusion on Cu(111) and found good performance and satisfactory agreement with results obtained from previous version of SLKMC. The new method also helped reveal a novel atomic mechanism contributing to cluster migration. We have also applied this method to study the diffusion of Cu clusters on Ag(111), and find that Cu atoms generally prefer to occupy off-lattice sites. [1] O. Trushin, A. Kara, A. Karim, T.S. Rahman Phys. Rev B 2005

Category transfer in sequential causal learning: the unbroken mechanism hypothesis.

PubMed

Hagmayer, York; Meder, Björn; von Sydow, Momme; Waldmann, Michael R

2011-07-01

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis. Copyright © 2011 Cognitive Science Society, Inc.

Do Student-Centred Learning Activities Improve Learning Outcomes on a BTEC Applied Science Course in FE?

ERIC Educational Resources Information Center

Dear, Denise V.

2017-01-01

This article provides quantitative evidence on the effect on learning outcomes of contrasting teaching styles applied to a class of Level 3 final-year students on a BTEC Applied Science course within a further education college in the UK. Two topics within a unit were taught using either a student-centred or teacher-centric (instructional)…

Designing the Electronic Classroom: Applying Learning Theory and Ergonomic Design Principles.

ERIC Educational Resources Information Center

Emmons, Mark; Wilkinson, Frances C.

2001-01-01

Applies learning theory and ergonomic principles to the design of effective learning environments for library instruction. Discusses features of electronic classroom ergonomics, including the ergonomics of physical space, environmental factors, and workstations; and includes classroom layouts. (Author/LRW)

An Examination of Digital Game-Based Situated Learning Applied to Chinese Language Poetry Education

ERIC Educational Resources Information Center

Chen, Hong-Ren; Lin, You-Shiuan

2016-01-01

By gradually placing more importance on game-based education and changing learning motivation by applying game-playing characteristics, students' learning experiences can be enhanced and a better learning effect can be achieved. When teaching the content of Chinese poetry in Taiwanese junior high schools, most teachers only explain the meaning of…

Learning Quanta: Barriers to Stimulating Transitions in Student Understanding of Orbital Ideas

ERIC Educational Resources Information Center

Taber, Keith S.

2005-01-01

This paper reports the results of applying a particular analytical perspective to data from an interview study: a typology of learning impediments informed by research into learning and students' ideas in science. This typology is a heuristic tool that may help diagnose the origins of students' learning difficulties. Here it is applied to data…

Cascade Error Projection: A Learning Algorithm for Hardware Implementation

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher

1996-01-01

In this paper, we workout a detailed mathematical analysis for a new learning algorithm termed Cascade Error Projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters. Furthermore, CEP learning algorithm is operated only on one layer, whereas the other set of weights can be calculated deterministically. In association with the dynamical stepsize change concept to convert the weight update from infinite space into a finite space, the relation between the current stepsize and the previous energy level is also given and the estimation procedure for optimal stepsize is used for validation of our proposed technique. The weight values of zero are used for starting the learning for every layer, and a single hidden unit is applied instead of using a pool of candidate hidden units similar to cascade correlation scheme. Therefore, simplicity in hardware implementation is also obtained. Furthermore, this analysis allows us to select from other methods (such as the conjugate gradient descent or the Newton's second order) one of which will be a good candidate for the learning technique. The choice of learning technique depends on the constraints of the problem (e.g., speed, performance, and hardware implementation); one technique may be more suitable than others. Moreover, for a discrete weight space, the theoretical analysis presents the capability of learning with limited weight quantization. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

50 CFR 224.101 - Enumeration of endangered marine and anadromous species.

Code of Federal Regulations, 2012 CFR

2012-10-01

... institutions) and which are identified as fish belonging to the NYB DPS based on genetics analyses, previously... genetics analyses, previously applied tags, previously applied marks, or documentation to verify that the... Carolina DPS based on genetics analyses, previously applied tags, previously applied marks, or...

50 CFR 224.101 - Enumeration of endangered marine and anadromous species.

Code of Federal Regulations, 2013 CFR

2013-10-01

... institutions) and which are identified as fish belonging to the NYB DPS based on genetics analyses, previously... genetics analyses, previously applied tags, previously applied marks, or documentation to verify that the... Carolina DPS based on genetics analyses, previously applied tags, previously applied marks, or...

Applying ergonomics to systems: some documented "lessons learned".

PubMed

Hendrick, Hal W

2008-07-01

Based on evidence accumulated during the author's 45 years of professional experience, the author presents 23 important "lessons learned" regarding applying ergonomics to systems. Documented results from reported cases or other evidence are presented to validate each of these practical learning points.

Factors Influencing Learning Satisfaction of Migrant Workers in Korea with E-learning-Based Occupational Safety and Health Education

PubMed Central

Lee, Young Joo; Lee, Dongjoo

2015-01-01

Background E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. Methods The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. Results There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, “learning content” (β = 0.344, p < 0.001) affected the degree of learning satisfaction most significantly, followed by “motivation to learn” (β = 0.293, p < 0.001), “interactions with learners and instructors” (β = 0.149, p < 0.01), and “previous experience related to e-learning” (β = 0.095, p < 0.05). “Learning environment” had no significant influence on the degree of learning satisfaction. Conclusion E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs. PMID:26929830

Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds

NASA Astrophysics Data System (ADS)

Polewski, Przemyslaw; Yao, Wei; Heurich, Marco; Krzystek, Peter; Stilla, Uwe

2018-06-01

In this study, we present a method for improving the quality of automatic single fallen tree stem segmentation in ALS data by applying a specialized constrained conditional random field (CRF). The entire processing pipeline is composed of two steps. First, short stem segments of equal length are detected and a subset of them is selected for further processing, while in the second step the chosen segments are merged to form entire trees. The first step is accomplished using the specialized CRF defined on the space of segment labelings, capable of finding segment candidates which are easier to merge subsequently. To achieve this, the CRF considers not only the features of every candidate individually, but incorporates pairwise spatial interactions between adjacent segments into the model. In particular, pairwise interactions include a collinearity/angular deviation probability which is learned from training data as well as the ratio of spatial overlap, whereas unary potentials encode a learned probabilistic model of the laser point distribution around each segment. Each of these components enters the CRF energy with its own balance factor. To process previously unseen data, we first calculate the subset of segments for merging on a grid of balance factors by minimizing the CRF energy. Then, we perform the merging and rank the balance configurations according to the quality of their resulting merged trees, obtained from a learned tree appearance model. The final result is derived from the top-ranked configuration. We tested our approach on 5 plots from the Bavarian Forest National Park using reference data acquired in a field inventory. Compared to our previous segment selection method without pairwise interactions, an increase in detection correctness and completeness of up to 7 and 9 percentage points, respectively, was observed.

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

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

Focusing on Concepts by Covering Them Simultaneously

NASA Astrophysics Data System (ADS)

Schwartz, Pete

2017-05-01

"Parallel" pedagogy covers the four mechanics concepts of momentum, energy, forces, and kinematics simultaneously instead of building each concept on an understanding of the previous one. Course content is delivered through interactive videos, allowing class time for group work and student-centered activities. We start with simple examples, building complexity throughout the course with the introduction of springs, two dimensions, vectors, energy diagrams, universal gravitation, and rotation. Success means that students ponder underlying physics concepts rather than hunt for formulas. Surveys indicate that students accept this learning model well and have considerable improvement in applied conceptual understanding.

Marketing your expertise.

PubMed

Czaplewski, L M

1999-01-01

Marketing an existing or new venture is a vital part of business. For the nurse entrepreneur, marketing involves applying previously learned skills to new situations. The methods used to market a service may mean the difference between success and failure. Unfortunately many entrepreneurs think that because they have a great idea, clients will beat a path to their door. Marketing requires planning, creativity, time, and money. It is an ongoing process that must be evaluated regularly. When marketing achieves results, clients commit to using the entrepreneur's services and profits are realized. Basic marketing concepts are considered, and strategies for developing a workable marketing plan are presented.

Algorithmic tools for interpreting vital signs.

PubMed

Rathbun, Melina C; Ruth-Sahd, Lisa A

2009-07-01

Today's complex world of nursing practice challenges nurse educators to develop teaching methods that promote critical thinking skills and foster quick problem solving in the novice nurse. Traditional pedagogies previously used in the classroom and clinical setting are no longer adequate to prepare nursing students for entry into practice. In addition, educators have expressed frustration when encouraging students to apply newly learned theoretical content to direct the care of assigned patients in the clinical setting. This article presents algorithms as an innovative teaching strategy to guide novice student nurses in the interpretation and decision making related to vital sign assessment in an acute care setting.

Accelerate Healthcare Data Analytics: An Agile Practice to Perform Collaborative and Reproducible Analyses.

PubMed

Hao, Bibo; Sun, Wen; Yu, Yiqin; Li, Jing; Hu, Gang; Xie, Guotong

2016-01-01

Recent advances in cloud computing and machine learning made it more convenient for researchers to gain insights from massive healthcare data, while performing analyses on healthcare data in current practice still lacks efficiency for researchers. What's more, collaborating among different researchers and sharing analysis results are challenging issues. In this paper, we developed a practice to make analytics process collaborative and analysis results reproducible by exploiting and extending Jupyter Notebook. After applying this practice in our use cases, we can perform analyses and deliver results with less efforts in shorter time comparing to our previous practice.

Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.

PubMed

Huang, Xuelin; Choi, Sangbum; Wang, Lu; Thall, Peter F

2015-11-20

In medical therapies involving multiple stages, a physician's choice of a subject's treatment at each stage depends on the subject's history of previous treatments and outcomes. The sequence of decisions is known as a dynamic treatment regime or treatment policy. We consider dynamic treatment regimes in settings where each subject's final outcome can be defined as the sum of longitudinally observed values, each corresponding to a stage of the regime. Q-learning, which is a backward induction method, is used to first optimize the last stage treatment then sequentially optimize each previous stage treatment until the first stage treatment is optimized. During this process, model-based expectations of outcomes of late stages are used in the optimization of earlier stages. When the outcome models are misspecified, bias can accumulate from stage to stage and become severe, especially when the number of treatment stages is large. We demonstrate that a modification of standard Q-learning can help reduce the accumulated bias. We provide a computational algorithm, estimators, and closed-form variance formulas. Simulation studies show that the modified Q-learning method has a higher probability of identifying the optimal treatment regime even in settings with misspecified models for outcomes. It is applied to identify optimal treatment regimes in a study for advanced prostate cancer and to estimate and compare the final mean rewards of all the possible discrete two-stage treatment sequences. Copyright © 2015 John Wiley & Sons, Ltd.

Reduction in pediatric identification band errors: a quality collaborative.

PubMed

Phillips, Shannon Connor; Saysana, Michele; Worley, Sarah; Hain, Paul D

2012-06-01

Accurate and consistent placement of a patient identification (ID) band is used in health care to reduce errors associated with patient misidentification. Multiple safety organizations have devoted time and energy to improving patient ID, but no multicenter improvement collaboratives have shown scalability of previously successful interventions. We hoped to reduce by half the pediatric patient ID band error rate, defined as absent, illegible, or inaccurate ID band, across a quality improvement learning collaborative of hospitals in 1 year. On the basis of a previously successful single-site intervention, we conducted a self-selected 6-site collaborative to reduce ID band errors in heterogeneous pediatric hospital settings. The collaborative had 3 phases: preparatory work and employee survey of current practice and barriers, data collection (ID band failure rate), and intervention driven by data and collaborative learning to accelerate change. The collaborative audited 11377 patients for ID band errors between September 2009 and September 2010. The ID band failure rate decreased from 17% to 4.1% (77% relative reduction). Interventions including education of frontline staff regarding correct ID bands as a safety strategy; a change to softer ID bands, including "luggage tag" type ID bands for some patients; and partnering with families and patients through education were applied at all institutions. Over 13 months, a collaborative of pediatric institutions significantly reduced the ID band failure rate. This quality improvement learning collaborative demonstrates that safety improvements tested in a single institution can be disseminated to improve quality of care across large populations of children.

Multimodal connectivity of motor learning-related dorsal premotor cortex.

PubMed

Hardwick, Robert M; Lesage, Elise; Eickhoff, Claudia R; Clos, Mareike; Fox, Peter; Eickhoff, Simon B

2015-12-01

The dorsal premotor cortex (dPMC) is a key region for motor learning and sensorimotor integration, yet we have limited understanding of its functional interactions with other regions. Previous work has started to examine functional connectivity in several brain areas using resting state functional connectivity (RSFC) and meta-analytical connectivity modelling (MACM). More recently, structural covariance (SC) has been proposed as a technique that may also allow delineation of functional connectivity. Here, we applied these three approaches to provide a comprehensive characterization of functional connectivity with a seed in the left dPMC that a previous meta-analysis of functional neuroimaging studies has identified as playing a key role in motor learning. Using data from two sources (the Rockland sample, containing resting state data and anatomical scans from 132 participants, and the BrainMap database, which contains peak activation foci from over 10,000 experiments), we conducted independent whole-brain functional connectivity mapping analyses of a dPMC seed. RSFC and MACM revealed similar connectivity maps spanning prefrontal, premotor, and parietal regions, while the SC map identified more widespread frontal regions. Analyses indicated a relatively consistent pattern of functional connectivity between RSFC and MACM that was distinct from that identified by SC. Notably, results indicate that the seed is functionally connected to areas involved in visuomotor control and executive functions, suggesting that the dPMC acts as an interface between motor control and cognition. Copyright © 2015 Elsevier Inc. All rights reserved.

How Long Does It Take to Learn a Second Language?: Applying the "10,000-Hour Rule" as a Model for Fluency

ERIC Educational Resources Information Center

Eaton, Sarah Elaine

2011-01-01

This study applies the model of expertise developed by Ericsson et al (2007) to second and foreign language learning. Ericsson et al posits that in order to achieve expertise (as they define it) requires 10,000 or longer of "intense training". Applying this model to language learning, equating an expert level of competence with fluency, various…

Improving the efficiency of a user-driven learning system with reconfigurable hardware. Application to DNA splicing.

PubMed

Lemoine, E; Merceron, D; Sallantin, J; Nguifo, E M

1999-01-01

This paper describes a new approach to problem solving by splitting up problem component parts between software and hardware. Our main idea arises from the combination of two previously published works. The first one proposed a conceptual environment of concept modelling in which the machine and the human expert interact. The second one reported an algorithm based on reconfigurable hardware system which outperforms any kind of previously published genetic data base scanning hardware or algorithms. Here we show how efficient the interaction between the machine and the expert is when the concept modelling is based on reconfigurable hardware system. Their cooperation is thus achieved with an real time interaction speed. The designed system has been partially applied to the recognition of primate splice junctions sites in genetic sequences.

Evidence for view-invariant face recognition units in unfamiliar face learning.

PubMed

Etchells, David B; Brooks, Joseph L; Johnston, Robert A

2017-05-01

Many models of face recognition incorporate the idea of a face recognition unit (FRU), an abstracted representation formed from each experience of a face which aids recognition under novel viewing conditions. Some previous studies have failed to find evidence of this FRU representation. Here, we report three experiments which investigated this theoretical construct by modifying the face learning procedure from that in previous work. During learning, one or two views of previously unfamiliar faces were shown to participants in a serial matching task. Later, participants attempted to recognize both seen and novel views of the learned faces (recognition phase). Experiment 1 tested participants' recognition of a novel view, a day after learning. Experiment 2 was identical, but tested participants on the same day as learning. Experiment 3 repeated Experiment 1, but tested participants on a novel view that was outside the rotation of those views learned. Results revealed a significant advantage, across all experiments, for recognizing a novel view when two views had been learned compared to single view learning. The observed view invariance supports the notion that an FRU representation is established during multi-view face learning under particular learning conditions.

Low fidelity, high quality: a model for e-learning.

PubMed

Gordon, Morris; Chandratilake, Madawa; Baker, Paul

2013-08-01

E-learning continues to proliferate as a method to deliver continuing medical education. The effectiveness of e-learning has been widely studied, showing that it is as effective as traditional forms of education. However, most reports focus on whether the e-learning is effective, rather than discussing innovations to allow clinical educators to ask 'how' and 'why' it is effective, and to facilitate local reproduction. Previous work has set out a number of barriers to the introduction of e-learning interventions. Cost, the time to produce interventions, and the training requirements for educators and trainees have all been identified as barriers. We set out to design an e-learning intervention on paediatric prescribing that could address these issues using a low-fidelity approach, and report our methods so as to allow interested readers to use a similar approach. Using low-cost, readily accessible tools and applying appropriate educational theory, the intervention was produced in a short period of time. As part of a randomised controlled trial, long-term retention of prescribing skills was demonstrated, with significantly higher prescribing skill scores in the e-learning group at 4 and 12 weeks (p < 0.0001). Feedback was universally positive, with Likert responses suggesting that it was useful, convenient and easy to use. A low-fidelity approach to designing can successfully overcome many of the barriers to the introduction of e-learning. The design model described is simple and can be used by clinical teachers to support local development. Further research could investigate the experiences of these clinicians using this method of instructional design. © 2013 John Wiley & Sons Ltd.

Posttraining transcranial magnetic stimulation of striate cortex disrupts consolidation early in visual skill learning.

PubMed

De Weerd, Peter; Reithler, Joel; van de Ven, Vincent; Been, Marin; Jacobs, Christianne; Sack, Alexander T

2012-02-08

Practice-induced improvements in skilled performance reflect "offline " consolidation processes extending beyond daily training sessions. According to visual learning theories, an early, fast learning phase driven by high-level areas is followed by a late, asymptotic learning phase driven by low-level, retinotopic areas when higher resolution is required. Thus, low-level areas would not contribute to learning and offline consolidation until late learning. Recent studies have challenged this notion, demonstrating modified responses to trained stimuli in primary visual cortex (V1) and offline activity after very limited training. However, the behavioral relevance of modified V1 activity for offline consolidation of visual skill memory in V1 after early training sessions remains unclear. Here, we used neuronavigated transcranial magnetic stimulation (TMS) directed to a trained retinotopic V1 location to test for behaviorally relevant consolidation in human low-level visual cortex. Applying TMS to the trained V1 location within 45 min of the first or second training session strongly interfered with learning, as measured by impaired performance the next day. The interference was conditional on task context and occurred only when training in the location targeted by TMS was followed by training in a second location before TMS. In this condition, high-level areas may become coupled to the second location and uncoupled from the previously trained low-level representation, thereby rendering consolidation vulnerable to interference. Our data show that, during the earliest phases of skill learning in the lowest-level visual areas, a behaviorally relevant form of consolidation exists of which the robustness is controlled by high-level, contextual factors.

Development of Graduate Course Education by Industry Collaboration in Center for Engineering Education Development, CEED

NASA Astrophysics Data System (ADS)

Noguchi, Toru; Yoshikawa, Kozo; Nakamura, Masato; Kaneko, Katsuhiko

New education programs for engineering graduate courses, and the achievements are described. Following the previous reports on overseas and domestic internship2) , 3) , this article states other common programs ; seminars on state of technologies in industries, practical English and internationalization programs, and a program to accept overseas internship students. E-learning system to assist off-campus students is also described. All these programs are developed and conducted by specialist professors invited from industries and national institutions, in collaboration with faculty professors. Students learn how the engineering science apply to the practical problems, acquire wider view and deeper understanding on industries, and gain abilities to act in global society including communication skill, those are not taught in classrooms and laboratories. Educational effects of these industry collaborated programs is significant to activate the graduate course education, although the comprehensive evaluation is the future subject.

Laboratory preparation questionnaires as a tool for the implementation of the Just in Time Teaching in the Physics I laboratories: Research training

NASA Astrophysics Data System (ADS)

Miranda, David A.; Sanchez, Melba J.; Forero, Oscar M.

2017-06-01

The implementation of the JiTT (Just in Time Teaching) strategy is presented to increase the previous preparation of students enrolled in the subject Physics Laboratory I offered at the Industrial University of Santander (UIS), Colombia. In this study, a laboratory preparation questionnaire (CPL) was applied as a tool for the implementation of JiTT combined with elements of mediated learning. It was found that the CPL allows to improve the students’ experience regarding the preparation of the laboratory and the development of the experimental session. These questionnaires were implemented in an academic manager (Moodle) and a web application (lab.ciencias.uis.edu.co) was used to publish the contents essential for the preparation of the student before each practical session. The most significant result was that the students performed the experimental session with the basic knowledge to improve their learning experience.

Postconditioning Effectively Prevents Trimethyltin Induced Neuronal Damage in the Rat Brain.

PubMed

Lalkovicova, Maria; Burda, Jozef; Nemethova, Miroslava; Burda, Rastislav; Danielisova, Viera

Trimethyltin (TMT) is a toxic substance formerly used as a catalyst in the production of organic substances, as well as in industry and agriculture. TMT poisoning has caused death or severe injury in many dozens of people. The toxicity of TMT is mediated by dose dependent selective damage to the limbic system in humans and other animals, specifically the degeneration of CA1 neurons in the hippocampus. The typical symptoms include memory loss and decreased learning ability. Using knowledge gained in previous studies of global ischaemia, we used delayed postconditioning after TMT intoxication (8 mg/kg i.p.), consisting of applying a stressor (BR, bradykinin 150 μg/kg i.p.) 24 or 48 hours after the injection of TMT. We found that BR had preventive effects on neurodegenerative changes as well as learning and memory deficits induced by TMT intoxication.

Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.

PubMed

Hathout, Rania M; Metwally, Abdelkader A

2016-11-01

This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors). Copyright © 2016 Elsevier B.V. All rights reserved.

Category priming with aliens: analysing the influence of targets' prototypicality on the centre surround inhibition mechanism.

PubMed

Frings, Christian; Göbel, Ariane; Mast, Frank; Sutter, Julia; Bermeitinger, Christina; Wentura, Dirk

2011-08-01

Marginally perceptible prototypes as primes lead to slowed reactions to related category exemplars as compared to unrelated ones. This at first glance counterintuitive finding has been interpreted as evidence for a particular mechanism of lateral inhibition, namely the centre surround inhibition mechanism. We investigated the semantic surround of category labels by experimentally manipulating the prototypicality of stimuli. Participants first learned two new categories of fantasy creatures in a 5-day-long learning phase before they worked through a semantic priming task with the category prototypes as primes and category exemplars as targets. For high-prototypical targets we observed benefit effects from related primes, whereas for low-prototypical targets we observed cost effects. The results define when the centre surround inhibition mechanism is applied, and furthermore might explain why previous studies with word stimuli (i.e., material that prevents experimental manipulation of prototypicality) observed mixed results concerning the prototypicality of targets.

Postgenomic strategies in antibacterial drug discovery.

PubMed

Brötz-Oesterhelt, Heike; Sass, Peter

2010-10-01

During the last decade the field of antibacterial drug discovery has changed in many aspects including bacterial organisms of primary interest, discovery strategies applied and pharmaceutical companies involved. Target-based high-throughput screening had been disappointingly unsuccessful for antibiotic research. Understanding of this lack of success has increased substantially and the lessons learned refer to characteristics of targets, screening libraries and screening strategies. The 'genomics' approach was replaced by a diverse array of discovery strategies, for example, searching for new natural product leads among previously abandoned compounds or new microbial sources, screening for synthetic inhibitors by targeted approaches including structure-based design and analyses of focused libraries and designing resistance-breaking properties into antibiotics of established classes. Furthermore, alternative treatment options are being pursued including anti-virulence strategies and immunotherapeutic approaches. This article summarizes the lessons learned from the genomics era and describes discovery strategies resulting from that knowledge.

Transferring x-ray based automated threat detection between scanners with different energies and resolution

NASA Astrophysics Data System (ADS)

Caldwell, M.; Ransley, M.; Rogers, T. W.; Griffin, L. D.

2017-10-01

A significant obstacle to developing high performance Deep Learning algorithms for Automated Threat Detection (ATD) in security X-ray imagery, is the difficulty of obtaining large training datasets. In our previous work, we circumvented this problem for ATD in cargo containers, using Threat Image Projection and data augmentation. In this work, we investigate whether data scarcity for other modalities, such as parcels and baggage, can be ameliorated by transforming data from one domain so that it approximates the appearance of another. We present an ontology of ATD datasets to assess where transfer learning may be applied. We define frameworks for transfer at the training and testing stages, and compare the results for both methods against ATD where a common data source is used for training and testing. Our results show very poor transfer, which we attribute to the difficulty of accurately matching the blur and contrast characteristics of different scanners.

Acquisition of background and technical information and class trip planning

NASA Technical Reports Server (NTRS)

Mackinnon, R. M.; Wake, W. H.

1981-01-01

Instructors who are very familiar with a study area, as well as those who are not, find the field trip information acquisition and planning process speeded and made more effective by organizing it in stages. The stage follow a deductive progression: from the associated context region, to the study area, to the specific sample window sites, and from generalized background information on the study region to specific technical data on the environmental and human use systems to be interpreted at each site. On the class trip and in the follow up laboratory, the learning/interpretive process are at first deductive in applying previously learned information and skills to analysis of the study site, then inductive in reading and interpreting the landscape, imagery, and maps of the site, correlating them with information of other samples sites and building valid generalizations about the larger study area, its context region, and other (similar and/or contrasting) regions.

Applying the Science of Learning: Evidence-Based Principles for the Design of Multimedia Instruction

ERIC Educational Resources Information Center

Mayer, Richard E.

2008-01-01

During the last 100 years, a major accomplishment of psychology has been the development of a science of learning aimed at understanding how people learn. In attempting to apply the science of learning, a central challenge of psychology and education is the development of a science of instruction aimed at understanding how to present material in…

Associations between the Classroom Learning Environment and Student Engagement in Learning 1: A Rasch Model Approach

ERIC Educational Resources Information Center

Cavanagh, Rob

2012-01-01

This report is about one of two phases in an investigation into associations between student engagement in classroom learning and the classroom learning environment. Both phases applied the same instrumentation to the same sample. The difference between the phases was in the measurement approach applied. This report is about application of the…

Reconstructing spatial organizations of chromosomes through manifold learning

PubMed Central

Deng, Wenxuan; Hu, Hailin; Ma, Rui; Zhang, Sai; Yang, Jinglin; Peng, Jian; Kaplan, Tommy; Zeng, Jianyang

2018-01-01

Abstract Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data. PMID:29408992

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.

PubMed

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-02-11

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

The Design of Collectives of Agents to Control Non-Markovian Systems

NASA Technical Reports Server (NTRS)

Lawson, John W.; Wolpert, David H.

2004-01-01

The Collective Intelligence (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided "world" utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional "team games". We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents ability to learn. The implication is that learning is a property only of high-enough dimensional systems.

Management education within pharmacy curricula: A need for innovation.

PubMed

Mospan, Cortney M

To encourage the academy to pursue innovative management education strategies within pharmacy curricula and highlight these experiences in a scholarly dialogue. Management has often been a dreaded, dry, and often neglected aspect of pharmacy curricula. With the release of Center for Advancement of Pharmacy Education (CAPE) Educational Outcomes 2013 as well as Entry-Level Competencies Needed for Community Pharmacy Practice by National Association of Chain Drug Stores (NACDS) Foundation, National Community Pharmacists Association (NCPA), and Accreditation Council for Pharmacy Education (ACPE) in 2012, managerial skills have seen a new emphasis in pharmacy education. Further, management has greater emphasis within ACPE "Standards 2016" through adoption of CAPE Educational Outcomes 2013 into the standards. Previous literature has shown success of innovative learning strategies in management education such as active learning, use of popular television shows, and emotional intelligence. The academy must build a more extensive scholarly body of work highlighting successful educational strategies to engage pharmacy students in an often-dreaded subject through applying the Scholarship of Teaching and Learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Reconstructing spatial organizations of chromosomes through manifold learning.

PubMed

Zhu, Guangxiang; Deng, Wenxuan; Hu, Hailin; Ma, Rui; Zhang, Sai; Yang, Jinglin; Peng, Jian; Kaplan, Tommy; Zeng, Jianyang

2018-05-04

Decoding the spatial organizations of chromosomes has crucial implications for studying eukaryotic gene regulation. Recently, chromosomal conformation capture based technologies, such as Hi-C, have been widely used to uncover the interaction frequencies of genomic loci in a high-throughput and genome-wide manner and provide new insights into the folding of three-dimensional (3D) genome structure. In this paper, we develop a novel manifold learning based framework, called GEM (Genomic organization reconstructor based on conformational Energy and Manifold learning), to reconstruct the three-dimensional organizations of chromosomes by integrating Hi-C data with biophysical feasibility. Unlike previous methods, which explicitly assume specific relationships between Hi-C interaction frequencies and spatial distances, our model directly embeds the neighboring affinities from Hi-C space into 3D Euclidean space. Extensive validations demonstrated that GEM not only greatly outperformed other state-of-art modeling methods but also provided a physically and physiologically valid 3D representations of the organizations of chromosomes. Furthermore, we for the first time apply the modeled chromatin structures to recover long-range genomic interactions missing from original Hi-C data.

Evaluation of an instructional model to teach clinically relevant medicinal chemistry in a campus and a distance pathway.

PubMed

Alsharif, Naser Z; Galt, Kimberly A

2008-04-15

To evaluate an instructional model for teaching clinically relevant medicinal chemistry. An instructional model that uses Bloom's cognitive and Krathwohl's affective taxonomy, published and tested concepts in teaching medicinal chemistry, and active learning strategies, was introduced in the medicinal chemistry courses for second-professional year (P2) doctor of pharmacy (PharmD) students (campus and distance) in the 2005-2006 academic year. Student learning and the overall effectiveness of the instructional model were assessed. Student performance after introducing the instructional model was compared to that in prior years. Student performance on course examinations improved compared to previous years. Students expressed overall enthusiasm about the course and better understood the value of medicinal chemistry to clinical practice. The explicit integration of the cognitive and affective learning objectives improved student performance, student ability to apply medicinal chemistry to clinical practice, and student attitude towards the discipline. Testing this instructional model provided validation to this theoretical framework. The model is effective for both our campus and distance-students. This instructional model may also have broad-based applications to other science courses.

Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

PubMed Central

Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

2016-01-01

We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods. PMID:26864172

Designing Specification Languages for Process Control Systems: Lessons Learned and Steps to the Future

NASA Technical Reports Server (NTRS)

Leveson, Nancy G.; Heimdahl, Mats P. E.; Reese, Jon Damon

1999-01-01

Previously, we defined a blackbox formal system modeling language called RSML (Requirements State Machine Language). The language was developed over several years while specifying the system requirements for a collision avoidance system for commercial passenger aircraft. During the language development, we received continual feedback and evaluation by FAA employees and industry representatives, which helped us to produce a specification language that is easily learned and used by application experts. Since the completion of the PSML project, we have continued our research on specification languages. This research is part of a larger effort to investigate the more general problem of providing tools to assist in developing embedded systems. Our latest experimental toolset is called SpecTRM (Specification Tools and Requirements Methodology), and the formal specification language is SpecTRM-RL (SpecTRM Requirements Language). This paper describes what we have learned from our use of RSML and how those lessons were applied to the design of SpecTRM-RL. We discuss our goals for SpecTRM-RL and the design features that support each of these goals.

The Design of Collectives of Agents to Control Non-Markovian Systems

NASA Technical Reports Server (NTRS)

Lawson, John W.; Wolpert, David H.; Clancy, Daniel (Technical Monitor)

2002-01-01

The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided 'world' utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional-'team games'. We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents' ability to learn. The implication is that 'learning' is a property only of high-enough dimensional systems.

Semi-supervised tracking of extreme weather events in global spatio-temporal climate datasets

NASA Astrophysics Data System (ADS)

Kim, S. K.; Prabhat, M.; Williams, D. N.

2017-12-01

Deep neural networks have been successfully applied to solve problem to detect extreme weather events in large scale climate datasets and attend superior performance that overshadows all previous hand-crafted methods. Recent work has shown that multichannel spatiotemporal encoder-decoder CNN architecture is able to localize events in semi-supervised bounding box. Motivated by this work, we propose new learning metric based on Variational Auto-Encoders (VAE) and Long-Short-Term-Memory (LSTM) to track extreme weather events in spatio-temporal dataset. We consider spatio-temporal object tracking problems as learning probabilistic distribution of continuous latent features of auto-encoder using stochastic variational inference. For this, we assume that our datasets are i.i.d and latent features is able to be modeled by Gaussian distribution. In proposed metric, we first train VAE to generate approximate posterior given multichannel climate input with an extreme climate event at fixed time. Then, we predict bounding box, location and class of extreme climate events using convolutional layers given input concatenating three features including embedding, sampled mean and standard deviation. Lastly, we train LSTM with concatenated input to learn timely information of dataset by recurrently feeding output back to next time-step's input of VAE. Our contribution is two-fold. First, we show the first semi-supervised end-to-end architecture based on VAE to track extreme weather events which can apply to massive scaled unlabeled climate datasets. Second, the information of timely movement of events is considered for bounding box prediction using LSTM which can improve accuracy of localization. To our knowledge, this technique has not been explored neither in climate community or in Machine Learning community.

Dissociation between sensitization and learning-related neuromodulation in an aplysiid species.

PubMed

Erixon, N J; Demartini, L J; Wright, W G

1999-06-14

Previous phylogenetic analyses of learning and memory in an opisthobranch lineage uncovered a correlation between two learning-related neuromodulatory traits and their associated behavioral phenotypes. In particular, serotonin-induced increases in sensory neuron spike duration and excitability, which are thought to underlie several facilitatory forms of learning in Aplysia, appear to have been lost over the course of evolution in a distantly related aplysiid, Dolabrifera dolabrifera. This deficit is paralleled by a behavioral deficit: individuals of Dolabrifera do not express generalized sensitization (reflex enhancement of an unhabituated response after a noxious stimulus is applied outside of the reflex receptive field) or dishabituation (reflex enhancement of a habituated reflex). The goal of the present study was to confirm and extend this correlation by testing for the neuromodulatory traits and generalized sensitization in an additional species, Phyllaplysia taylori, which is closely related to Dolabrifera. Instead, our results indicated a lack of correlation between the neuromodulatory and behavioral phenotypes. In particular, sensory neuron homologues in Phyllaplysia showed the ancestral neuromodulatory phenotype typified by Aplysia. Bath-applied 10 microM serotonin significantly increased homologue spike duration and excitability. However, when trained with the identical apparatus and protocols that produced generalized sensitization in Aplysia, individuals of Phyllaplysia showed no evidence of sensitization. Thus, this species expresses the neuromodulatory phenotype of its ancestors while appearing to express the behavioral phenotype of its near relative. These results suggests that generalized sensitization can be lost during the course of evolution in the absence of a deficit in these two neuromodulatory traits, and raises the possibility that the two traits may support some other form of behavioral plasticity in Phyllaplysia. The results also raise the question of the mechanistic basis of the behavioral deficit in Phyllaplysia.

Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV).

PubMed

Piette, Elizabeth R; Moore, Jason H

2018-01-01

Machine learning methods and conventions are increasingly employed for the analysis of large, complex biomedical data sets, including genome-wide association studies (GWAS). Reproducibility of machine learning analyses of GWAS can be hampered by biological and statistical factors, particularly so for the investigation of non-additive genetic interactions. Application of traditional cross validation to a GWAS data set may result in poor consistency between the training and testing data set splits due to an imbalance of the interaction genotypes relative to the data as a whole. We propose a new cross validation method, proportional instance cross validation (PICV), that preserves the original distribution of an independent variable when splitting the data set into training and testing partitions. We apply PICV to simulated GWAS data with epistatic interactions of varying minor allele frequencies and prevalences and compare performance to that of a traditional cross validation procedure in which individuals are randomly allocated to training and testing partitions. Sensitivity and positive predictive value are significantly improved across all tested scenarios for PICV compared to traditional cross validation. We also apply PICV to GWAS data from a study of primary open-angle glaucoma to investigate a previously-reported interaction, which fails to significantly replicate; PICV however improves the consistency of testing and training results. Application of traditional machine learning procedures to biomedical data may require modifications to better suit intrinsic characteristics of the data, such as the potential for highly imbalanced genotype distributions in the case of epistasis detection. The reproducibility of genetic interaction findings can be improved by considering this variable imbalance in cross validation implementation, such as with PICV. This approach may be extended to problems in other domains in which imbalanced variable distributions are a concern.

Effect of portfolio assessment on student learning in prenatal training for midwives.

PubMed

Kariman, Nourossadat; Moafi, Farnoosh

2011-01-01

The tendency to use portfolios for evaluation has been developed with the aim of optimizing the culture of assessment. The present study was carried out to determine the effect of using portfolios as an evaluation method on midwifery students' learning and satisfaction in prenatal practical training. In this prospective cohort study, all midwifery students in semester four (n=40), were randomly allocated to portfolio and routine evaluation groups. Based on their educational goals, the portfolio groups prepared packages which consisted of a complete report of the history, physical examinations, and methods of patient management (as evaluated by a checklist) for women who visited a prenatal clinic. During the last day of their course, a posttest, clinical exam, and student satisfaction form were completed. The two groups' mean age, mean pretest scores, and their prerequisite course that they should have taken in the previous semester were similar. The mean difference in the pre and post test scores for the two groups' knowledge and comprehension levels did not differ significantly (P>0.05). The average scores on questions in Bloom's taxonomy 2 and 3 of the portfolio group were significantly greater than those of the routine evaluation group (P=0.002, P=0.03, respectively). The mean of the two groups' clinical exam scores was significantly different. The portfolio group's mean scores on generating diagnostic and therapeutic solutions and the ability to apply theory in practice were higher than those of the routine group. Overall, students' satisfaction scores in the two evaluation methods were relatively similar. Portfolio evaluation provides the opportunity for more learning by increasing the student's participation in the learning process and helping them to apply theory in practice.

Producing or reproducing reasoning? Socratic dialog is very effective, but only for a few.

PubMed

Goldin, Andrea Paula; Pedroncini, Olivia; Sigman, Mariano

2017-01-01

Successful communication between a teacher and a student is at the core of pedagogy. A well known example of a pedagogical dialog is 'Meno', a socratic lesson of geometry in which a student learns (or 'discovers') how to double the area of a given square 'in essence, a demonstration of Pythagoras' theorem. In previous studies we found that after engaging in the dialog participants can be divided in two kinds: those who can only apply a rule to solve the problem presented in the dialog and those who can go beyond and generalize that knowledge to solve any square problems. Here we study the effectiveness of this socratic dialog in an experimental and a control high-school classrooms, and we explore the boundaries of what is learnt by testing subjects with a set of 9 problems of varying degrees of difficulty. We found that half of the adolescents did not learn anything from the dialog. The other half not only learned to solve the problem, but could abstract something more: the geometric notion that the diagonal can be used to solve diverse area problems. Conceptual knowledge is critical for achievement in geometry, and it is not clear whether geometric concepts emerge spontaneously on the basis of universal experience with space, or reflect intrinsic properties of the human mind. We show that, for half of the learners, an exampled-based Socratic dialog in lecture form can give rise to formal geometric knowledge that can be applied to new, different problems.

Goal orientation, perceived task outcome and task demands in mathematics tasks: effects on students' attitude in actual task settings.

PubMed

Seegers, Gerard; van Putten, Cornelis M; de Brabander, Cornelis J

2002-09-01

In earlier studies, it has been found that students' domain-specific cognitions and personal learning goals (goal orientation) influence task-specific appraisals of actual learning tasks. The relations between domain-specific and task-specific variables have been specified in the model of adaptive learning. In this study, additional influences, i.e., perceived task outcome on a former occasion and variations in task demands, were investigated. The purpose of this study was to identify personality and situational variables that mediate students' attitude when confronted with a mathematics task. Students worked on a mathematics task in two subsequent sessions. Effects of perceived task outcome at the first session on students' attitude at the second session were investigated. In addition, we investigated how differences in task demands influenced students' attitude. Variations in task demands were provoked by different conditions in task-instruction. In one condition, students were told that the result on the test would add to their mark on mathematics. This outcome orienting condition was contrasted with a task-orienting condition where students were told that the results on the test would not be used to give individual grades. Participants were sixth grade students (N = 345; aged 11-12 years) from 14 primary schools. Multivariate and univariate analyses of (co)variance were applied to the data. Independent variables were goal orientation, task demands, and perceived task outcome, with task-specific variables (estimated competence for the task, task attraction, task relevance, and willingness to invest effort) as the dependent variables. The results showed that previous perceived task outcome had a substantial impact on students' attitude. Additional but smaller effects were found for variation in task demands. Furthermore, effects of previous perceived task outcome and task demands were related to goal orientation. The resulting pattern confirmed that, in general, performance-oriented learning goals emphasised the negative impact of failure experiences, whereas task-oriented learning goals had a strengthening effect on how success experiences influenced students' attitude.

Applied Drama and the Higher Education Learning Spaces: A Reflective Analysis

ERIC Educational Resources Information Center

Moyo, Cletus

2015-01-01

This paper explores Applied Drama as a teaching approach in Higher Education learning spaces. The exploration takes a reflective analysis approach by first examining the impact that Applied Drama has had on my career as a Lecturer/Educator/Teacher working in Higher Education environments. My engagement with Applied Drama practice and theory is…

Recall is not necessary for verbal sequence learning.

PubMed

Kalm, Kristjan; Norris, Dennis

2016-01-01

The question of whether overt recall of to-be-remembered material accelerates learning is important in a wide range of real-world learning settings. In the case of verbal sequence learning, previous research has proposed that recall either is necessary for verbal sequence learning (Cohen & Johansson Journal of Verbal Learning and Verbal Behavior, 6, 139-143, 1967; Cunningham, Healy, & Williams Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 575-597, 1984), or at least contributes significantly to it (Glass, Krejci, & Goldman Journal of Memory and Language, 28, 189-199, 1989; Oberauer & Meyer Memory, 17, 774-781, 2009). In contrast, here we show that the amount of previous spoken recall does not predict learning and is not necessary for it. We suggest that previous research may have underestimated participants' learning by using suboptimal performance measures, or by using manual or written recall. However, we show that the amount of spoken recall predicted how much interference from other to-be-remembered sequences would be observed. In fact, spoken recall mediated most of the error learning observed in the task. Our data support the view that the learning of overlapping auditory-verbal sequences is driven by learning the phonological representations and not the articulatory motor responses. However, spoken recall seems to reinforce already learned representations, whether they are correct or incorrect, thus contributing to a participant identifying a specific stimulus as either "learned" or "new" during the presentation phase.

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

The educational value of Disaster Victim Identification (DVI) missions-transfer of knowledge.

PubMed

Winskog, Calle; Tonkin, Anne; Byard, Roger W

2012-06-01

Transfer of knowledge is the cornerstone of any educational organisation, with senior staff expected to participate in the training of less experienced colleagues and students. Teaching in the field is, however, slightly different, and a less theoretical approach is usually recommended. In terms of Disaster Victim Identification (DVI) activities, practical work under supervision of a field team stimulates tactile memory. A more practical approach is also useful when multiple organizations from a variety of countries are involved, as language barriers make it easier to manually show someone how to solve a problem, instead of attempting to explain complex concepts verbally. "See one, do one, teach one" is an approach that can be used to ensure that teaching is undertaken with the teacher grasping the essentials of a situation before passing on the information to someone else. The key principles of adult learning that need to be applied to DVI situations include the following: participants need to know why they are learning and to be motivated to learn by the need to solve problems; previous experience must be respected and built upon and learning approaches should match participants' background and diversity; and finally participants need to be actively involved in the learning process. Active learning involves the active acquisition of knowledge and/or skills during the performance of a task and characterizes DVI activities. Learning about DVI structure, activities and responsibilities incorporates both the learning of facts ("declarative knowledge") and practical skills ("procedural knowledge"). A fundamental requirement of all DVI exercises should be succession planning with involvement of less experienced colleagues at every opportunity so that essential teaching and learning opportunities are maximized. DVI missions provide excellent teaching opportunities and international agencies have a responsibility to teach less experienced colleagues and local staff during deployment.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Prescribed fire: The fundamental solution

Treesearch

Jim Saveland

1998-01-01

The theory and practice that embodies "learning organizations" can be applied to developing and implementing effective natural resource policy and management. A learning organization is a group of people who are continually enhancing their capacity to create the results they want. At the heart of learning organizations is systems thinking. This paper applies...

Action Learning in Virtual Higher Education: Applying Leadership Theory

ERIC Educational Resources Information Center

Curtin, Joseph

2016-01-01

This paper reports the historical foundation of Northeastern University's course, LDR 6100: Developing Your Leadership Capability, a partial literature review of action learning (AL) and virtual action learning (VAL), a course methodology of LDR 6100 requiring students to apply leadership perspectives using VAL as instructed by the author,…

Using an isomorphic problem pair to learn introductory physics: Transferring from a two-step problem to a three-step problem

NASA Astrophysics Data System (ADS)

Lin, Shih-Yin; Singh, Chandralekha

2013-12-01

In this study, we examine introductory physics students’ ability to perform analogical reasoning between two isomorphic problems which employ the same underlying physics principles but have different surface features. 382 students from a calculus-based and an algebra-based introductory physics course were administered a quiz in the recitation in which they had to learn from a solved problem provided and take advantage of what they learned from it to solve another isomorphic problem (which we call the quiz problem). The solved problem provided has two subproblems while the quiz problem has three subproblems, which is known from previous research to be challenging for introductory students. In addition to the solved problem, students also received extra scaffolding supports that were intended to help them discern and exploit the underlying similarities of the isomorphic solved and quiz problems. The data analysis suggests that students had great difficulty in transferring what they learned from a two-step problem to a three-step problem. Although most students were able to learn from the solved problem to some extent with the scaffolding provided and invoke the relevant principles in the quiz problem, they were not necessarily able to apply the principles correctly. We also conducted think-aloud interviews with six introductory students in order to understand in depth the difficulties they had and explore strategies to provide better scaffolding. The interviews suggest that students often superficially mapped the principles employed in the solved problem to the quiz problem without necessarily understanding the governing conditions underlying each principle and examining the applicability of the principle in the new situation in an in-depth manner. Findings suggest that more scaffolding is needed to help students in transferring from a two-step problem to a three-step problem and applying the physics principles appropriately. We outline a few possible strategies for future investigation.

Comparing American and Chinese Students' Learning Progression on Carbon Cycling in Socio-Ecological Systems

ERIC Educational Resources Information Center

Chen, J.; Anderson, C. W.

2015-01-01

Previous studies identified a learning progression on the concept of carbon cycling that was typically followed by American students when they progress from elementary to high school. This study examines the validity of this previously identified learning progression for a different group of learners--Chinese students. The results indicate that…

Rapid Association Learning in the Primate Prefrontal Cortex in the Absence of Behavioral Reversals

ERIC Educational Resources Information Center

Cromer, Jason A.; Machon, Michelle; Miller, Earl K.

2011-01-01

The PFC plays a central role in our ability to learn arbitrary rules, such as "green means go." Previous experiments from our laboratory have used conditional association learning to show that slow, gradual changes in PFC neural activity mirror monkeys' slow acquisition of associations. These previous experiments required monkeys to repeatedly…

Caffeine prevents cognitive impairment induced by chronic psychosocial stress and/or high fat-high carbohydrate diet.

PubMed

Alzoubi, K H; Abdul-Razzak, K K; Khabour, O F; Al-Tuweiq, G M; Alzubi, M A; Alkadhi, K A

2013-01-15

Caffeine alleviates cognitive impairment associated with a variety of health conditions. In this study, we examined the effect of caffeine treatment on chronic stress- and/or high fat-high carbohydrate Western diet (WD)-induced impairment of learning and memory in rats. Chronic psychosocial stress, WD and caffeine (0.3 g/L in drinking water) were simultaneously administered for 3 months to adult male Wistar rats. At the conclusion of the 3 months, and while the previous treatments continued, rats were tested in the radial arm water maze (RAWM) for learning, short-term and long-term memory. This procedure was applied on a daily basis to all animals for 5 consecutive days or until the animal reaches days to criterion (DTC) in the 12th learning trial and memory tests. DTC is the number of days that the animal takes to make zero error in two consecutive days. Chronic stress and/or WD groups caused impaired learning, which was prevented by chronic caffeine administration. In the memory tests, chronic caffeine administration also prevented memory impairment during chronic stress conditions and/or WD. Furthermore, DTC value for caffeine treated stress, WD, and stress/WD groups indicated that caffeine normalizes memory impairment in these groups. These results showed that chronic caffeine administration prevented stress and/or WD-induced impairment of spatial learning and memory. Copyright © 2012 Elsevier B.V. All rights reserved.

Moving Beyond Misconceptions: A New Model for Learning Challenges in Cognition

NASA Astrophysics Data System (ADS)

Slater, T. F.; Slater, S. J.

2011-12-01

For over 40 years, the science education community has given its attention to cataloging the substantial body of "misconceptions" in individual's thinking about science, and to addressing the consequences of those misconceptions in the science classroom. Despite the tremendous amount of effort given to researching and disseminating information related to misconceptions, and the development of a theory of conceptual change to mitigate misconceptions, progress continues to be less than satisfying. An analysis of the literature and our own research has persuaded the CAPER Center for Astronomy and Physics Education Research to put forth model that will allow us to operate on students' learning difficulties in a more fruitful manner. Previously, much of the field's work binned erroneous student thinking into a single construct, and from that basis, curriculum developers and instructors addressed student misconceptions with a single instructional strategy. In contrast this model suggests that "misconceptions" are a mixture of at least four learning barriers: incorrect factual information, inappropriately applied mental algorithms (phenomenological primitives), insufficient cognitive structures (e.g. spatial reasoning), and affective/emotional difficulties. Each of these types of barriers should be addressed with an appropriately designed instructional strategy. Initial applications of this model to learning problems in the Earth & Space Sciences have been fruitful, suggesting that an effort towards categorizing persistent learning difficulties in the geosciences beyond the level of "misconceptions" may allow our community to craft tailored and more effective learning experiences for our students and the general public.

A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arévalo, John; Judkins, Alexander; Madabhushi, Anant; González, Fabio

2015-12-01

Convolutional neural networks (CNN) have been very successful at addressing different computer vision tasks thanks to their ability to learn image representations directly from large amounts of labeled data. Features learned from a dataset can be used to represent images from a different dataset via an approach called transfer learning. In this paper we apply transfer learning to the challenging task of medulloblastoma tumor differentiation. We compare two different CNN models which were previously trained in two different domains (natural and histopathology images). The first CNN is a state-of-the-art approach in computer vision, a large and deep CNN with 16-layers, Visual Geometry Group (VGG) CNN. The second (IBCa-CNN) is a 2-layer CNN trained for invasive breast cancer tumor classification. Both CNNs are used as visual feature extractors of histopathology image regions of anaplastic and non-anaplastic medulloblastoma tumor from digitized whole-slide images. The features from the two models are used, separately, to train a softmax classifier to discriminate between anaplastic and non-anaplastic medulloblastoma image regions. Experimental results show that the transfer learning approach produce competitive results in comparison with the state of the art approaches for IBCa detection. Results also show that features extracted from the IBCa-CNN have better performance in comparison with features extracted from the VGG-CNN. The former obtains 89.8% while the latter obtains 76.6% in terms of average accuracy.

Gamification in Action: Theoretical and Practical Considerations for Medical Educators.

PubMed

Rutledge, Chrystal; Walsh, Catharine M; Swinger, Nathan; Auerbach, Marc; Castro, Danny; Dewan, Maya; Khattab, Mona; Rake, Alyssa; Harwayne-Gidansky, Ilana; Raymond, Tia T; Maa, Tensing; Chang, Todd P

2018-02-20

Gamification involves the application of game design elements to traditionally non-game contexts. It is increasingly being used as an adjunct to traditional teaching strategies in medical education to engage the millennial learner and enhance adult learning. The extant literature has focused on determining whether the implementation of gamification results in better learning outcomes, leading to a dearth of research examining its theoretical underpinnings within the medical education context. The authors define gamification, explore how gamification works within the medical education context using self-determination theory as an explanatory mechanism for enhanced engagement and motivation, and discuss common roadblocks and challenges to implementing gamification.While previous gamification research has largely focused on determining whether implementation of gamification in medical education leads to better learning outcomes, the authors recommend that future research should explore how and under what conditions gamification is likely to be effective. Selective, purposeful gamification that aligns with learning goals has the potential to increase learner motivation and engagement and, ultimately, learning. In line with self-determination theory, game design elements can be used to enhance learners' feelings of relatedness, autonomy, and competence to foster learners' intrinsic motivation. Poorly applied game design elements, however, may undermine these basic psychological needs by the overjustification effect or through negative effects of competition. Educators must, therefore, clearly understand the benefits and pitfalls of gamification in curricular design, take a thoughtful approach when integrating game design elements, and consider the types of learners and overarching learning objectives.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

Multiplicative Multitask Feature Learning

PubMed Central

Wang, Xin; Bi, Jinbo; Yu, Shipeng; Sun, Jiangwen; Song, Minghu

2016-01-01

We investigate a general framework of multiplicative multitask feature learning which decomposes individual task’s model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks. PMID:28428735

Learning homophones in context: Easy cases are favored in the lexicon of natural languages.

PubMed

Dautriche, Isabelle; Fibla, Laia; Fievet, Anne-Caroline; Christophe, Anne

2018-08-01

Even though ambiguous words are common in languages, children find it hard to learn homophones, where a single label applies to several distinct meanings (e.g., Mazzocco, 1997). The present work addresses this apparent discrepancy between learning abilities and typological pattern, with respect to homophony in the lexicon. In a series of five experiments, 20-month-old French children easily learnt a pair of homophones if the two meanings associated with the phonological form belonged to different syntactic categories, or to different semantic categories. However, toddlers failed to learn homophones when the two meanings were distinguished only by different grammatical genders. In parallel, we analyzed the lexicon of four languages, Dutch, English, French and German, and observed that homophones are distributed non-arbitrarily in the lexicon, such that easily learnable homophones are more frequent than hard-to-learn ones: pairs of homophones are preferentially distributed across syntactic and semantic categories, but not across grammatical gender. We show that learning homophones is easier than previously thought, at least when the meanings of the same phonological form are made sufficiently distinct by their syntactic or semantic context. Following this, we propose that this learnability advantage translates into the overall structure of the lexicon, i.e., the kinds of homophones present in languages exhibit the properties that make them learnable by toddlers, thus allowing them to remain in languages. Copyright © 2018 Elsevier Inc. All rights reserved.

Problem solving based learning model with multiple representations to improve student's mental modelling ability on physics

NASA Astrophysics Data System (ADS)

Haili, Hasnawati; Maknun, Johar; Siahaan, Parsaoran

2017-08-01

Physics is a lessons that related to students' daily experience. Therefore, before the students studying in class formally, actually they have already have a visualization and prior knowledge about natural phenomenon and could wide it themselves. The learning process in class should be aimed to detect, process, construct, and use students' mental model. So, students' mental model agree with and builds in the right concept. The previous study held in MAN 1 Muna informs that in learning process the teacher did not pay attention students' mental model. As a consequence, the learning process has not tried to build students' mental modelling ability (MMA). The purpose of this study is to describe the improvement of students' MMA as a effect of problem solving based learning model with multiple representations approach. This study is pre experimental design with one group pre post. It is conducted in XI IPA MAN 1 Muna 2016/2017. Data collection uses problem solving test concept the kinetic theory of gasses and interview to get students' MMA. The result of this study is clarification students' MMA which is categorized in 3 category; High Mental Modelling Ability (H-MMA) for 7

Time-course human urine proteomics in space-flight simulation experiments.

PubMed

Binder, Hans; Wirth, Henry; Arakelyan, Arsen; Lembcke, Kathrin; Tiys, Evgeny S; Ivanisenko, Vladimir A; Kolchanov, Nikolay A; Kononikhin, Alexey; Popov, Igor; Nikolaev, Evgeny N; Pastushkova, Lyudmila; Larina, Irina M

2014-01-01

Long-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved. We studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals. The protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry.

Self-regulated learning processes of medical students during an academic learning task.

PubMed

Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

2016-10-01

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated positively with previous performance. Causal attributions and adaptive inferences measures were associated positively with learning task performance. These findings may inform remediation interventions in the early years of medical school training. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Applying Web-Enabled Problem-Based Learning and Self-Regulated Learning to Enhance Computing Skills of Taiwan's Vocational Students: A Quasi-Experimental Study of a Short-Term Module

ERIC Educational Resources Information Center

Shen, Pei-Di; Lee, Tsang-Hsiung; Tsai, Chia-Wen

2007-01-01

Contrary to conventional expectations, the reality of computing education in Taiwan's vocational schools is not so practically oriented, and thus reveals much room for improvement. In this context, we conducted a quasi-experiment to examine the effects of applying web-based problem-based learning (PBL), web-based self-regulated learning (SRL), and…

Applying Augmented Reality to a Mobile-Assisted Learning System for Martial Arts Using Kinect Motion Capture

ERIC Educational Resources Information Center

Hsu, Wen-Chun; Shih, Ju-Ling

2016-01-01

In this study, to learn the routine of Tantui, a branch of martial arts was taken as an object of research. Fitts' stages of motor learning and augmented reality (AR) were applied to a 3D mobile-assisted learning system for martial arts, which was characterized by free viewing angles. With the new system, learners could rotate the viewing angle of…

From prediction error to incentive salience: mesolimbic computation of reward motivation

PubMed Central

Berridge, Kent C.

2011-01-01

Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior. Modeling the motivational component requires recognition of additional control factors besides learning. Here I will discuss how mesocorticolimbic mechanisms generate the motivation component of incentive salience. Incentive salience takes Pavlovian learning and memory as one input and as an equally important input takes neurobiological state factors (e.g., drug states, appetite states, satiety states) that can vary independently of learning. Neurobiological state changes can produce unlearned fluctuations or even reversals in the ability of a previously-learned reward cue to trigger motivation. Such fluctuations in cue-triggered motivation can dramatically depart from all previously learned values about the associated reward outcome. Thus a consequence of the difference between incentive salience and learning can be to decouple cue-triggered motivation of the moment from previously learned values of how good the associated reward has been in the past. Another consequence can be to produce irrationally strong motivation urges that are not justified by any memories of previous reward values (and without distorting associative predictions of future reward value). Such irrationally strong motivation may be especially problematic in addiction. To comprehend these phenomena, future models of mesocorticolimbic reward function should address the neurobiological state factors that participate to control generation of incentive salience. PMID:22487042

Incorporation of local structure into kriging models for the prediction of atomistic properties in the water decamer.

PubMed

Davie, Stuart J; Di Pasquale, Nicodemo; Popelier, Paul L A

2016-10-15

Machine learning algorithms have been demonstrated to predict atomistic properties approaching the accuracy of quantum chemical calculations at significantly less computational cost. Difficulties arise, however, when attempting to apply these techniques to large systems, or systems possessing excessive conformational freedom. In this article, the machine learning method kriging is applied to predict both the intra-atomic and interatomic energies, as well as the electrostatic multipole moments, of the atoms of a water molecule at the center of a 10 water molecule (decamer) cluster. Unlike previous work, where the properties of small water clusters were predicted using a molecular local frame, and where training set inputs (features) were based on atomic index, a variety of feature definitions and coordinate frames are considered here to increase prediction accuracy. It is shown that, for a water molecule at the center of a decamer, no single method of defining features or coordinate schemes is optimal for every property. However, explicitly accounting for the structure of the first solvation shell in the definition of the features of the kriging training set, and centring the coordinate frame on the atom-of-interest will, in general, return better predictions than models that apply the standard methods of feature definition, or a molecular coordinate frame. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Identification of animal behavioral strategies by inverse reinforcement learning.

PubMed

Yamaguchi, Shoichiro; Naoki, Honda; Ikeda, Muneki; Tsukada, Yuki; Nakano, Shunji; Mori, Ikue; Ishii, Shin

2018-05-01

Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals' decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animal's behavioral strategy from behavioral time-series data. We applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, fed worms prefer, while starved worms avoid the cultivation temperature on a thermal gradient. Our IRL approach revealed that the fed worms used both the absolute temperature and its temporal derivative and that their behavior involved two strategies: directed migration (DM) and isothermal migration (IM). With DM, worms efficiently reached specific temperatures, which explains their thermotactic behavior when fed. With IM, worms moved along a constant temperature, which reflects isothermal tracking, well-observed in previous studies. In contrast to fed animals, starved worms escaped the cultivation temperature using only the absolute, but not the temporal derivative of temperature. We also investigated the neural basis underlying these strategies, by applying our method to thermosensory neuron-deficient worms. Thus, our IRL-based approach is useful in identifying animal strategies from behavioral time-series data and could be applied to a wide range of behavioral studies, including decision-making, in other organisms.

Mobile Augmented Reality as a Feature for Self-Oriented, Blended Learning in Medicine: Randomized Controlled Trial.

PubMed

Noll, Christoph; von Jan, Ute; Raap, Ulrike; Albrecht, Urs-Vito

2017-09-14

Advantages of mobile Augmented Reality (mAR) application-based learning versus textbook-based learning were already shown in a previous study. However, it was unclear whether the augmented reality (AR) component was responsible for the success of the self-developed app or whether this was attributable to the novelty of using mobile technology for learning. The study's aim was to test the hypothesis whether there is no difference in learning success between learners who employed the mobile AR component and those who learned without it to determine possible effects of mAR. Also, we were interested in potential emotional effects of using this technology. Forty-four medical students (male: 25, female: 19, mean age: 22.25 years, standard deviation [SD]: 3.33 years) participated in this study. Baseline emotional status was evaluated using the Profile of Mood States (POMS) questionnaire. Dermatological knowledge was ascertained using a single choice (SC) test (10 questions). The students were randomly assigned to learn 45 min with either a mobile learning method with mAR (group A) or without AR (group B). Afterwards, both groups were again asked to complete the previous questionnaires. AttrakDiff 2 questionnaires were used to evaluate the perceived usability as well as pragmatic and hedonic qualities. For capturing longer term effects, after 14 days, all participants were again asked to complete the SC questionnaire. All evaluations were anonymous, and descriptive statistics were calculated. For hypothesis testing, an unpaired signed-rank test was applied. For the SC tests, there were only minor differences, with both groups gaining knowledge (average improvement group A: 3.59 [SD 1.48]; group B: 3.86 [SD 1.51]). Differences between both groups were statistically insignificant (exact Mann Whitney U, U=173.5; P=.10; r=.247). However, in the follow-up SC test after 14 days, group A had retained more knowledge (average decrease of the number of correct answers group A: 0.33 [SD 1.62]; group B: 1.14 [SD 1.30]). For both groups, descriptively, there were only small variations regarding emotional involvement, and learning experiences also differed little, with both groups rating the app similar for its stimulating effect. We were unable to show significant effects for mAR on the immediate learning success of the mobile learning setting. However, the similar level of stimulation being noted for both groups is inconsistent with the previous assumption of the success of mAR-based approach being solely attributable to the excitement of using mobile technology, independent of mAR; the mAR group showed some indications for a better long-term retention of knowledge. Further studies are needed to examine this aspect. German Clinical Trials Register (DRKS): 00012980; http://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00012980 (Archived by WebCite at http://www.webcitation.org/ 6tCWoM2Jb). ©Christoph Noll, Ute von Jan, Ulrike Raap, Urs-Vito Albrecht. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 14.09.2017.

Mobile Augmented Reality as a Feature for Self-Oriented, Blended Learning in Medicine: Randomized Controlled Trial

PubMed Central

2017-01-01

Background Advantages of mobile Augmented Reality (mAR) application-based learning versus textbook-based learning were already shown in a previous study. However, it was unclear whether the augmented reality (AR) component was responsible for the success of the self-developed app or whether this was attributable to the novelty of using mobile technology for learning. Objective The study’s aim was to test the hypothesis whether there is no difference in learning success between learners who employed the mobile AR component and those who learned without it to determine possible effects of mAR. Also, we were interested in potential emotional effects of using this technology. Methods Forty-four medical students (male: 25, female: 19, mean age: 22.25 years, standard deviation [SD]: 3.33 years) participated in this study. Baseline emotional status was evaluated using the Profile of Mood States (POMS) questionnaire. Dermatological knowledge was ascertained using a single choice (SC) test (10 questions). The students were randomly assigned to learn 45 min with either a mobile learning method with mAR (group A) or without AR (group B). Afterwards, both groups were again asked to complete the previous questionnaires. AttrakDiff 2 questionnaires were used to evaluate the perceived usability as well as pragmatic and hedonic qualities. For capturing longer term effects, after 14 days, all participants were again asked to complete the SC questionnaire. All evaluations were anonymous, and descriptive statistics were calculated. For hypothesis testing, an unpaired signed-rank test was applied. Results For the SC tests, there were only minor differences, with both groups gaining knowledge (average improvement group A: 3.59 [SD 1.48]; group B: 3.86 [SD 1.51]). Differences between both groups were statistically insignificant (exact Mann Whitney U, U=173.5; P=.10; r=.247). However, in the follow-up SC test after 14 days, group A had retained more knowledge (average decrease of the number of correct answers group A: 0.33 [SD 1.62]; group B: 1.14 [SD 1.30]). For both groups, descriptively, there were only small variations regarding emotional involvement, and learning experiences also differed little, with both groups rating the app similar for its stimulating effect. Conclusions We were unable to show significant effects for mAR on the immediate learning success of the mobile learning setting. However, the similar level of stimulation being noted for both groups is inconsistent with the previous assumption of the success of mAR-based approach being solely attributable to the excitement of using mobile technology, independent of mAR; the mAR group showed some indications for a better long-term retention of knowledge. Further studies are needed to examine this aspect. Trial Registration German Clinical Trials Register (DRKS): 00012980; http://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00012980 (Archived by WebCite at http://www.webcitation.org/ 6tCWoM2Jb). PMID:28912113

Better Categorizing Misconceptions Using a Contemporary Cognitive Science Lens

NASA Astrophysics Data System (ADS)

Slater, S. J.; Slater, T. F.

2013-12-01

Much of the last three decades of discipline-based education research in the geosciences has focused on the important work of identifying the range and domain of misconceptions students bring into undergraduate science survey courses. Pinpointing students' prior knowledge is a cornerstone for developing constructivist approaches and learning environments for effective teaching. At the same time, the development of a robust a priori formula for professors to use in mitigating students' misconceptions remains elusive. An analysis of the literature and our own research has persuaded researchers at the CAPER Center for Astronomy & Physics Education Research to put forth a model that will allow professors to operate on students' various learning difficulties in a more productive manner. Previously, much of the field's work binned erroneous student thinking into a single construct, and from that basis, curriculum developers and instructors addressed student misconceptions with a single instructional strategy. In contrast, we propose a model based on the notion that 'misconceptions' are a mixture of at least four learning barriers: incorrect factual information, inappropriately applied mental algorithms (phenomenological primitives), insufficient cognitive structures (e.g. spatial reasoning), and affective/emotional difficulties (e.g. students' spiritual commitments). In this sense, each of these different types of learning barriers would be more effectively addressed with an instructional strategy purposefully targeting these different attributes. Initial applications of this model to learning problems in geosciences have been fruitful, suggesting that an effort towards categorizing persistent learning difficulties in the geosciences beyond the single generalized category of 'misconceptions' might allow our community to more effectively design learning experiences for our students and the general public

Clinical learning environment and supervision of international nursing students: A cross-sectional study.

PubMed

Mikkonen, Kristina; Elo, Satu; Miettunen, Jouko; Saarikoski, Mikko; Kääriäinen, Maria

2017-05-01

Previously, it has been shown that the clinical learning environment causes challenges for international nursing students, but there is a lack of empirical evidence relating to the background factors explaining and influencing the outcomes. To describe international and national students' perceptions of their clinical learning environment and supervision, and explain the related background factors. An explorative cross-sectional design was used in a study conducted in eight universities of applied sciences in Finland during September 2015-May 2016. All nursing students studying English language degree programs were invited to answer a self-administered questionnaire based on both the clinical learning environment, supervision and nurse teacher scale and Cultural and Linguistic Diversity scale with additional background questions. Participants (n=329) included international (n=231) and Finnish (n=98) nursing students. Binary logistic regression was used to identify background factors relating to the clinical learning environment and supervision. International students at a beginner level in Finnish perceived the pedagogical atmosphere as worse than native speakers. In comparison to native speakers, these international students generally needed greater support from the nurse teacher at their university. Students at an intermediate level in Finnish reported two times fewer negative encounters in cultural diversity at their clinical placement than the beginners. To facilitate a successful learning experience, international nursing students require a sufficient level of competence in the native language when conducting clinical placements. Educational interventions in language education are required to test causal effects on students' success in the clinical learning environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Early motor learning changes in upper-limb dynamics and shoulder complex loading during handrim wheelchair propulsion.

PubMed

Vegter, Riemer J K; Hartog, Johanneke; de Groot, Sonja; Lamoth, Claudine J; Bekker, Michel J; van der Scheer, Jan W; van der Woude, Lucas H V; Veeger, Dirkjan H E J

2015-03-10

To propel in an energy-efficient manner, handrim wheelchair users must learn to control the bimanually applied forces onto the rims, preserving both speed and direction of locomotion. Previous studies have found an increase in mechanical efficiency due to motor learning associated with changes in propulsion technique, but it is unclear in what way the propulsion technique impacts the load on the shoulder complex. The purpose of this study was to evaluate mechanical efficiency, propulsion technique and load on the shoulder complex during the initial stage of motor learning. 15 naive able-bodied participants received 12-minutes uninstructed wheelchair practice on a motor driven treadmill, consisting of three 4-minute blocks separated by two minutes rest. Practice was performed at a fixed belt speed (v = 1.1 m/s) and constant low-intensity power output (0.2 W/kg). Energy consumption, kinematics and kinetics of propulsion technique were continuously measured. The Delft Shoulder Model was used to calculate net joint moments, muscle activity and glenohumeral reaction force. With practice mechanical efficiency increased and propulsion technique changed, reflected by a reduced push frequency and increased work per push, performed over a larger contact angle, with more tangentially applied force and reduced power losses before and after each push. Contrary to our expectations, the above mentioned propulsion technique changes were found together with an increased load on the shoulder complex reflected by higher net moments, a higher total muscle power and higher peak and mean glenohumeral reaction forces. It appears that the early stages of motor learning in handrim wheelchair propulsion are indeed associated with improved technique and efficiency due to optimization of the kinematics and dynamics of the upper extremity. This process goes at the cost of an increased muscular effort and mechanical loading of the shoulder complex. This seems to be associated with an unchanged stable function of the trunk and could be due to the early learning phase where participants still have to learn to effectively use the full movement amplitude available within the wheelchair-user combination. Apparently whole body energy efficiency has priority over mechanical loading in the early stages of learning to propel a handrim wheelchair.

The Effect of Absorptive Capacity Perceptions on the Context Aware Ubiquitous Learning Acceptance

ERIC Educational Resources Information Center

Lin, Hsiu-Fen

2013-01-01

Purpose: The purpose of this study is to examine the impact of absorptive capacity (understanding, assimilating and applying u-learning) perceptions on behavioral intention to use u-learning through path analysis and applies the technology acceptance model (TAM) as a theoretical foundation, simultaneously improving the model by adopting prior…

The Experiential Learning Cycle in Visual Design

ERIC Educational Resources Information Center

Arsoy, Aysu; Özad, Bahire Efe

2004-01-01

Experiential Learning Cycle has been applied to the Layout and Graphics Design in Computer Course provided by the Faculty of Communication and Media Studies to the students studying at the Public Relations and Advertising Department. It is hoped that by applying the Experiential Learning Cycle, the creativity and problem solving strategies of the…

From Add-On to Mainstream: Applying Distance Learning Models for ALL Students

ERIC Educational Resources Information Center

Zai, Robert, III.; Wesley, Threasa L.

2013-01-01

The use of distance learning technology has allowed Northern Kentucky University's W. Frank Steely Library to remove traditional boundaries between both distance and on-campus students. An emerging model that applies these distance learning methodologies to all students has proven effective for enhancing reference and instructional services. This…

An Investigation of Employees' Use of E-Learning Systems: Applying the Technology Acceptance Model

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; Hsieh, Yi-Chuan; Chen, Yen-Hsun

2013-01-01

The purpose of this study is to apply the technology acceptance model to examine the employees' attitudes and acceptance of electronic learning (e-learning) systems in organisations. This study examines four factors (organisational support, computer self-efficacy, prior experience and task equivocality) that are believed to influence employees'…

Active Learning through Role Playing: Virtual Babies in a Child Development Course

ERIC Educational Resources Information Center

Poling, Devereaux A.; Hupp, Julie M.

2009-01-01

The authors designed an active learning project for a child development course in which students apply core concepts to a hypothetical baby they "raise" during the term. Students applied developmental topics to their unique, developing child. The project fostered student learning and enthusiasm for the material. The project's versatility makes it…

45 CFR 2516.120 - Who may apply for funding a subgrant?

Code of Federal Regulations, 2010 CFR

2010-10-01

... NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Eligibility To Apply § 2516.120 Who...-learning programs. (b) A local partnership, for a grant from a State to implement, operate, or expand a school-based service learning program. (1) The local partnership must include an LEA and one or more...

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…

Time-Extended Policies in Mult-Agent Reinforcement Learning

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian K.

2004-01-01

Reinforcement learning methods perform well in many domains where a single agent needs to take a sequence of actions to perform a task. These methods use sequences of single-time-step rewards to create a policy that tries to maximize a time-extended utility, which is a (possibly discounted) sum of these rewards. In this paper we build on our previous work showing how these methods can be extended to a multi-agent environment where each agent creates its own policy that works towards maximizing a time-extended global utility over all agents actions. We show improved methods for creating time-extended utilities for the agents that are both "aligned" with the global utility and "learnable." We then show how to crate single-time-step rewards while avoiding the pi fall of having rewards aligned with the global reward leading to utilities not aligned with the global utility. Finally, we apply these reward functions to the multi-agent Gridworld problem. We explicitly quantify a utility's learnability and alignment, and show that reinforcement learning agents using the prescribed reward functions successfully tradeoff learnability and alignment. As a result they outperform both global (e.g., team games ) and local (e.g., "perfectly learnable" ) reinforcement learning solutions by as much as an order of magnitude.

Learning and liking an artificial musical system: Effects of set size and repeated exposure

PubMed Central

Loui, Psyche; Wessel, David

2009-01-01

We report an investigation of humans' musical learning ability using a novel musical system. We designed an artificial musical system based on the Bohlen-Pierce scale, a scale very different from Western music. Melodies were composed from chord progressions in the new scale by applying the rules of a finite-state grammar. After exposing participants to sets of melodies, we conducted listening tests to assess learning, including recognition tests, generalization tests, and subjective preference ratings. In Experiment 1, participants were presented with 15 melodies 27 times each. Forced choice results showed that participants were able to recognize previously encountered melodies and generalize their knowledge to new melodies, suggesting internalization of the musical grammar. Preference ratings showed no differentiation among familiar, new, and ungrammatical melodies. In Experiment 2, participants were given 10 melodies 40 times each. Results showed superior recognition but unsuccessful generalization. Additionally, preference ratings were significantly higher for familiar melodies. Results from the two experiments suggest that humans can internalize the grammatical structure of a new musical system following exposure to a sufficiently large set size of melodies, but musical preference results from repeated exposure to a small number of items. This dissociation between grammar learning and preference will be further discussed. PMID:20151034

Metacognitive Confidence Increases with, but Does Not Determine, Visual Perceptual Learning.

PubMed

Zizlsperger, Leopold; Kümmel, Florian; Haarmeier, Thomas

2016-01-01

While perceptual learning increases objective sensitivity, the effects on the constant interaction of the process of perception and its metacognitive evaluation have been rarely investigated. Visual perception has been described as a process of probabilistic inference featuring metacognitive evaluations of choice certainty. For visual motion perception in healthy, naive human subjects here we show that perceptual sensitivity and confidence in it increased with training. The metacognitive sensitivity-estimated from certainty ratings by a bias-free signal detection theoretic approach-in contrast, did not. Concomitant 3Hz transcranial alternating current stimulation (tACS) was applied in compliance with previous findings on effective high-low cross-frequency coupling subserving signal detection. While perceptual accuracy and confidence in it improved with training, there were no statistically significant tACS effects. Neither metacognitive sensitivity in distinguishing between their own correct and incorrect stimulus classifications, nor decision confidence itself determined the subjects' visual perceptual learning. Improvements of objective performance and the metacognitive confidence in it were rather determined by the perceptual sensitivity at the outset of the experiment. Post-decision certainty in visual perceptual learning was neither independent of objective performance, nor requisite for changes in sensitivity, but rather covaried with objective performance. The exact functional role of metacognitive confidence in human visual perception has yet to be determined.

Learning and liking an artificial musical system: Effects of set size and repeated exposure.

PubMed

Loui, Psyche; Wessel, David

2008-10-01

We report an investigation of humans' musical learning ability using a novel musical system. We designed an artificial musical system based on the Bohlen-Pierce scale, a scale very different from Western music. Melodies were composed from chord progressions in the new scale by applying the rules of a finite-state grammar. After exposing participants to sets of melodies, we conducted listening tests to assess learning, including recognition tests, generalization tests, and subjective preference ratings. In Experiment 1, participants were presented with 15 melodies 27 times each. Forced choice results showed that participants were able to recognize previously encountered melodies and generalize their knowledge to new melodies, suggesting internalization of the musical grammar.Preference ratings showed no differentiation among familiar, new, and ungrammatical melodies. In Experiment 2, participants were given 10 melodies 40 times each. Results showed superior recognition but unsuccessful generalization. Additionally, preference ratings were significantly higher for familiar melodies. Results from the two experiments suggest that humans can internalize the grammatical structure of a new musical system following exposure to a sufficiently large set size of melodies, but musical preference results from repeated exposure to a small number of items. This dissociation between grammar learning and preference will be further discussed.

[Critical reading aptitude of clinical research texts in teaching specialist doctors].

PubMed

Carranza Lira, Sebastián; Varela, Alejandro

2007-11-01

Learning can be divided in two types: the unconscious learning and the significant learning. The critical aptitude for reading clinical research articles is a learning experience that reflects the doctor's active participation in article reading. To know the degree of aptitude for critical reading of clinical research articles in specialists under training. To all the specialist that were under training in the different services of the Hospital, a previous validated evaluation instrument for critical reading of clinical research studies was applied. Kruskal-Wallis and Mann-Whitney's U test were used for statistical analysis. After the application of the evaluation instrument, it was found that the global score had a median of 42.5 (12-89) points. In the results obtained by indicator it was found that there was a greater score for to interpret, than for to judge and for to propose. In the analysis of domain degrees according to the interpret indicator, the greater proportion was in low level. According to the indicators to judge and to propose, most of the results were in the by chance expected level. The critical reading aptitude it's not developed in specialized physicians that are under training. The development of this aptitude will allow them to have a greater profit in their courses.

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 .

A deep learning framework for causal shape transformation.

PubMed

Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

2018-02-01

Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

OGLE II Eclipsing Binaries In The LMC: Analysis With Class

NASA Astrophysics Data System (ADS)

Devinney, Edward J.; Prsa, A.; Guinan, E. F.; DeGeorge, M.

2011-01-01

The Eclipsing Binaries (EBs) via Artificial Intelligence (EBAI) Project is applying machine learning techniques to elucidate the nature of EBs. Previously, Prsa, et al. applied artificial neural networks (ANNs) trained on physically-realistic Wilson-Devinney models to solve the light curves of the 1882 detached EBs in the LMC discovered by the OGLE II Project (Wyrzykowski, et al.) fully automatically, bypassing the need for manually-derived starting solutions. A curious result is the non-monotonic distribution of the temperature ratio parameter T2/T1, featuring a subsidiary peak noted previously by Mazeh, et al. in an independent analysis using the EBOP EB solution code (Tamuz, et al.). To explore this and to gain a fuller understanding of the multivariate EBAI LMC observational plus solutions data, we have employed automatic clustering and advanced visualization (CAV) techniques. Clustering the OGLE II data aggregates objects that are similar with respect to many parameter dimensions. Measures of similarity for example, could include the multidimensional Euclidean Distance between data objects, although other measures may be appropriate. Applying clustering, we find good evidence that the T2/T1 subsidiary peak is due to evolved binaries, in support of Mazeh et al.'s speculation. Further, clustering suggests that the LMC detached EBs occupying the main sequence region belong to two distinct classes. Also identified as a separate cluster in the multivariate data are stars having a Period-I band relation. Derekas et al. had previously found a Period-K band relation for LMC EBs discovered by the MACHO Project (Alcock, et al.). We suggest such CAV techniques will prove increasingly useful for understanding the large, multivariate datasets increasingly being produced in astronomy. We are grateful for the support of this research from NSF/RUI Grant AST-05-75042 f.

A Course on Applied Superconductivity Shared by Four Departments

ERIC Educational Resources Information Center

Jensen, Bogi B.; Abrahamsen, Asger B.; Sorensen, Mads P.; Hansen, Jorn B.

2013-01-01

In this paper, a course on applied superconductivity is described. The course structure is outlined and the learning objectives and the learning activities are described. The teaching was multidisciplinary given by four departments each contributing with their expertise. Being applied superconductivity, the focus was on an application, which could…

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Parser Combinators: a Practical Application for Generating Parsers for NMR Data

PubMed Central

Fenwick, Matthew; Weatherby, Gerard; Ellis, Heidi JC; Gryk, Michael R.

2013-01-01

Nuclear Magnetic Resonance (NMR) spectroscopy is a technique for acquiring protein data at atomic resolution and determining the three-dimensional structure of large protein molecules. A typical structure determination process results in the deposition of a large data sets to the BMRB (Bio-Magnetic Resonance Data Bank). This data is stored and shared in a file format called NMR-Star. This format is syntactically and semantically complex making it challenging to parse. Nevertheless, parsing these files is crucial to applying the vast amounts of biological information stored in NMR-Star files, allowing researchers to harness the results of previous studies to direct and validate future work. One powerful approach for parsing files is to apply a Backus-Naur Form (BNF) grammar, which is a high-level model of a file format. Translation of the grammatical model to an executable parser may be automatically accomplished. This paper will show how we applied a model BNF grammar of the NMR-Star format to create a free, open-source parser, using a method that originated in the functional programming world known as “parser combinators”. This paper demonstrates the effectiveness of a principled approach to file specification and parsing. This paper also builds upon our previous work [1], in that 1) it applies concepts from Functional Programming (which is relevant even though the implementation language, Java, is more mainstream than Functional Programming), and 2) all work and accomplishments from this project will be made available under standard open source licenses to provide the community with the opportunity to learn from our techniques and methods. PMID:24352525

Neural mechanisms of cue-approach training

PubMed Central

Bakkour, Akram; Lewis-Peacock, Jarrod A.; Poldrack, Russell A.; Schonberg, Tom

2016-01-01

Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training. PMID:27677231

Implications of memory modulation for post-traumatic stress and fear disorders

PubMed Central

Parsons, Ryan G; Ressler, Kerry J

2013-01-01

Post-traumatic stress disorder, panic disorder and phobia manifest in ways that are consistent with an uncontrollable state of fear. Their development involves heredity, previous sensitizing experiences, association of aversive events with previous neutral stimuli, and inability to inhibit or extinguish fear after it is chronic and disabling. We highlight recent progress in fear learning and memory, differential susceptibility to disorders of fear, and how these findings are being applied to the understanding, treatment and possible prevention of fear disorders. Promising advances are being translated from basic science to the clinic, including approaches to distinguish risk versus resilience before trauma exposure, methods to interfere with fear development during memory consolidation after a trauma, and techniques to inhibit fear reconsolidation and to enhance extinction of chronic fear. It is hoped that this new knowledge will translate to more successful, neuroscientifically informed and rationally designed approaches to disorders of fear regulation. PMID:23354388

A mixed methods evaluation of team-based learning for applied pathophysiology in undergraduate nursing education.

PubMed

Branney, Jonathan; Priego-Hernández, Jacqueline

2018-02-01

It is important for nurses to have a thorough understanding of the biosciences such as pathophysiology that underpin nursing care. These courses include content that can be difficult to learn. Team-based learning is emerging as a strategy for enhancing learning in nurse education due to the promotion of individual learning as well as learning in teams. In this study we sought to evaluate the use of team-based learning in the teaching of applied pathophysiology to undergraduate student nurses. A mixed methods observational study. In a year two, undergraduate nursing applied pathophysiology module circulatory shock was taught using Team-based Learning while all remaining topics were taught using traditional lectures. After the Team-based Learning intervention the students were invited to complete the Team-based Learning Student Assessment Instrument, which measures accountability, preference and satisfaction with Team-based Learning. Students were also invited to focus group discussions to gain a more thorough understanding of their experience with Team-based Learning. Exam scores for answers to questions based on Team-based Learning-taught material were compared with those from lecture-taught material. Of the 197 students enrolled on the module, 167 (85% response rate) returned the instrument, the results from which indicated a favourable experience with Team-based Learning. Most students reported higher accountability (93%) and satisfaction (92%) with Team-based Learning. Lectures that promoted active learning were viewed as an important feature of the university experience which may explain the 76% exhibiting a preference for Team-based Learning. Most students wanted to make a meaningful contribution so as not to let down their team and they saw a clear relevance between the Team-based Learning activities and their own experiences of teamwork in clinical practice. Exam scores on the question related to Team-based Learning-taught material were comparable to those related to lecture-taught material. Most students had a preference for, and reported higher accountability and satisfaction with Team-based Learning. Through contextualisation and teamwork, Team-based Learning appears to be a strategy that confers strong pedagogical benefits for teaching applied pathophysiology (bioscience) to student nurses. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time with the ultimate goal to inform patient care decisions, and that the performance of these techniques with this particular dataset may be on par with that of classical methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Engineering Design Theory: Applying the Success of the Modern World to Campaign Creation

DTIC Science & Technology

2009-05-21

and school of thought) to the simple methods of design.6 This progression is analogous to Peter Senge’s levels of learning disciplines.7 Senge...iterative learning and adaptive action that develops and employs critical and creative thinking , enabling leaders to apply the necessary logic to...overcome mental rigidity and develop group insight, the Army must learn to utilize group learning and thinking , through a fluid and creative open process

Contributions of Medial Temporal Lobe and Striatal Memory Systems to Learning and Retrieving Overlapping Spatial Memories

PubMed Central

Brown, Thackery I.; Stern, Chantal E.

2014-01-01

Many life experiences share information with other memories. In order to make decisions based on overlapping memories, we need to distinguish between experiences to determine the appropriate behavior for the current situation. Previous work suggests that the medial temporal lobe (MTL) and medial caudate interact to support the retrieval of overlapping navigational memories in different contexts. The present study used functional magnetic resonance imaging (fMRI) in humans to test the prediction that the MTL and medial caudate play complementary roles in learning novel mazes that cross paths with, and must be distinguished from, previously learned routes. During fMRI scanning, participants navigated virtual routes that were well learned from prior training while also learning new mazes. Critically, some routes learned during scanning shared hallways with those learned during pre-scan training. Overlap between mazes required participants to use contextual cues to select between alternative behaviors. Results demonstrated parahippocampal cortex activity specific for novel spatial cues that distinguish between overlapping routes. The hippocampus and medial caudate were active for learning overlapping spatial memories, and increased their activity for previously learned routes when they became context dependent. Our findings provide novel evidence that the MTL and medial caudate play complementary roles in the learning, updating, and execution of context-dependent navigational behaviors. PMID:23448868

Flipping my environmental geochemistry classroom using Team-Based Learning

NASA Astrophysics Data System (ADS)

Griffith, E. M.

2016-02-01

Recent studies indicate that active learning disproportionately benefits STEM students from disadvantaged backgrounds and women in male-dominated fields (Lorenzo et al., 2006; Haak et al., 2011). Freeman et al. (2014) went so far as to suggest that increasing the number of STEM graduates could be done, at least in part, by "abandoning traditional lecturing in favor of active learning". Motivated in part by these previous studies and working at a Hispanic-Serving Institution, I decided to flip my environmental geochemistry course, using Team-Based Learning (TBL) - an instructional strategy for using active learning in small groups (Michaelsen et al., 1982). The course is taught over a 3 hour long class period (once a week) with a mix of upper division undergraduate and graduate students from environmental science, geology, engineering, chemistry, and biological sciences. One of the major learning outcomes of my course is that students "will be able to explain and discuss environmental geochemical data and its significance with their peers." This is practiced each class period throughout the course using TBL, where both undergraduate and graduate students learn from each other and uncover misconceptions. It is essentially one version of a flipped classroom where the students' experience changes from acquiring course content in the classroom to applying course content in the classroom in teams. I will share an overview of the teaching and learning strategy and my experience as well as examples of activities done in the classroom. Cited references: Freeman et al. (2014) PNAS 111: 8410-8415; Haak et al. (2011) Science 332: 1213-1216; Lorenzo et al. (2006) Am J Phys 74: 118-122; Michaelsen et al. (1982) Organ Behav Teaching 7: 13-22.

The association between dopamine receptor (DRD4) gene polymorphisms and second language learning style and behavioral variability in undergraduate students in Turkey.

PubMed

Maras Atabay, Meltem; Safi Oz, Zehra; Kurtman, Elvan

2014-08-01

The dopamine D4 receptor gene (DRD4) encodes a receptor for dopamine, a chemical messenger used in the brain. One variant of the DRD4 gene, the 7R allele, is believed to be associated with attention deficit hyperactivity disorder (ADHD). The aim of this study was to investigate the relationships between repeat polymorphisms in dopamine DRD4 and second language learning styles such as visual (seeing), tactile (touching), auditory (hearing), kinesthetic (moving) and group/individual learning styles, as well as the relationships among DRD4 gene polymorphisms and ADHD in undergraduate students. A total of 227 students between the ages of 17-21 years were evaluated using the Wender Utah rating scale and DSM-IV diagnostic criteria for ADHD. Additionally, Reid's perceptual learning style questionnaire for second language learning style was applied. In addition, these students were evaluated for social distress factors using the list of Threatening Events (TLE); having had no TLE, having had just one TLE or having had two or more TLEs within the previous 6 months before the interview. For DRD4 gene polymorphisms, DNA was extracted from whole blood using the standard phenol/chloroform method and genotyped using polymerase chain reaction. Second language learners with the DRD4.7+ repeats showed kinaesthetic and auditory learning styles, while students with DRD4.7-repeats showed visual, tactile and group learning, and also preferred the more visual learning styles [Formula: see text]. We also demonstrated that the DRD4 polymorphism significantly affected the risk effect conferred by an increasing level of exposure to TLE.

Anatomical entity mention recognition at literature scale

PubMed Central

Pyysalo, Sampo; Ananiadou, Sophia

2014-01-01

Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24162468

Spectral Learning for Supervised Topic Models.

PubMed

Ren, Yong; Wang, Yining; Zhu, Jun

2018-03-01

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning

NASA Astrophysics Data System (ADS)

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-01

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

PubMed Central

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-01-01

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate. PMID:28418043

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

PubMed

Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao

2017-04-18

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Rational and mechanistic perspectives on reinforcement learning.

PubMed

Chater, Nick

2009-12-01

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: mechanistic and rational. Reinforcement learning is often viewed in mechanistic terms--as describing the operation of aspects of an agent's cognitive and neural machinery. Yet it can also be viewed as a rational level of description, specifically, as describing a class of methods for learning from experience, using minimal background knowledge. This paper considers how rational and mechanistic perspectives differ, and what types of evidence distinguish between them. Reinforcement learning research in the cognitive and brain sciences is often implicitly committed to the mechanistic interpretation. Here the opposite view is put forward: that accounts of reinforcement learning should apply at the rational level, unless there is strong evidence for a mechanistic interpretation. Implications of this viewpoint for reinforcement-based theories in the cognitive and brain sciences are discussed.

Learning during processing Word learning doesn’t wait for word recognition to finish

PubMed Central

Apfelbaum, Keith S.; McMurray, Bob

2017-01-01

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the result of learning may incorporate spurious, partial information. For example, during word recognition, words take time to be identified, and competing words are often active in parallel. If learning proceeds before this competition resolves, representations may be influenced by the preliminary activations present at the time of learning. In three experiments using word learning as a model domain, we provide evidence that learning reflects the ongoing dynamics of auditory and visual processing during a learning event. These results show that learning can occur before stimulus recognition processes are complete; learning does not wait for ongoing perceptual processing to complete. PMID:27471082

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

NASA Astrophysics Data System (ADS)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

Implementation of ICARE learning model using visualization animation on biotechnology course

NASA Astrophysics Data System (ADS)

Hidayat, Habibi

2017-12-01

ICARE is a learning model that directly ensure the students to actively participate in the learning process using animation media visualization. ICARE have five key elements of learning experience from children and adult that is introduction, connection, application, reflection and extension. The use of Icare system to ensure that participants have opportunity to apply what have been they learned. So that, the message delivered by lecture to students can be understood and recorded by students in a long time. Learning model that was deemed capable of improving learning outcomes and interest to learn in following learning process Biotechnology with applying the ICARE learning model using visualization animation. This learning model have been giving motivation to participate in the learning process and learning outcomes obtained becomes more increased than before. From the results of student learning in subjects Biotechnology by applying the ICARE learning model using Visualization Animation can improving study results of student from the average value of middle test amounted to 70.98 with the percentage of 75% increased value of final test to be 71.57 with the percentage of 68.63%. The interest to learn from students more increasing visits of student activities at each cycle, namely the first cycle obtained average value by 33.5 with enough category. The second cycle is obtained an average value of 36.5 to good category and third cycle the average value of 36.5 with a student activity to good category.

Developing Students' Twenty-First Century Skills through a Service Learning Project

ERIC Educational Resources Information Center

Sabat, Isaac E.; Morgan, Whitney B.; Perry, Sara J.; Wang, Ying C.

2015-01-01

It is increasingly important for students to develop practiced and applied knowledge, teamwork skills, and civic engagement in addition to core curriculum knowledge in order to be prepared for the demands of the 21st century workforce. We propose that service-learning, or learning through an applied community service project, can uniquely address…

Increasing Capacity To Learn in the Learning Organization. Innovative Session 4. [AHRD Conference, 2001].

ERIC Educational Resources Information Center

Chalofsky, Neal E.

A workshop was conducted to give participants an opportunity to explore how to apply a different paradigm for learning in organizations. The workshop agenda was as follows: presentation of the theory and supporting research; experiential activities to apply the paradigm in academic and organizational settings; small group discussion aimed at…

Applying Item Response Theory Methods to Design a Learning Progression-Based Science Assessment

ERIC Educational Resources Information Center

Chen, Jing

2012-01-01

Learning progressions are used to describe how students' understanding of a topic progresses over time and to classify the progress of students into steps or levels. This study applies Item Response Theory (IRT) based methods to investigate how to design learning progression-based science assessments. The research questions of this study are: (1)…

A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets

ERIC Educational Resources Information Center

Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars

2015-01-01

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

Cognitive Diffusion Model: Facilitating EFL Learning in an Authentic Environment

ERIC Educational Resources Information Center

Shadiev, Rustam; Hwang, Wu-Yuin; Huang, Yueh-Min; Liu, Tzu-Yu

2017-01-01

For this study, we designed learning activities in which students applied newly acquired knowledge to solve meaningful daily life problems in their local community--a real, familiar, and relevant environment for students. For example, students learned about signs and rules in class and then applied this new knowledge to create their own rules for…

A Delphi Study on Technology Enhanced Learning (TEL) Applied on Computer Science (CS) Skills

ERIC Educational Resources Information Center

Porta, Marcela; Mas-Machuca, Marta; Martinez-Costa, Carme; Maillet, Katherine

2012-01-01

Technology Enhanced Learning (TEL) is a new pedagogical domain aiming to study the usage of information and communication technologies to support teaching and learning. The following study investigated how this domain is used to increase technical skills in Computer Science (CS). A Delphi method was applied, using three-rounds of online survey…

"A Dance with the Butterflies:" A Metamorphosis of Teaching and Learning through Technology

ERIC Educational Resources Information Center

McPherson, Sarah

2009-01-01

This paper describes a web-based collaborative project called "A Dance with the Butterflies" that applied the brain-based research of the Center for Applied Special Technologies (CAST) and principles of Universal Design for Learning (UDL) to Pre-K-4 science curriculum. Learning experiences were designed for students to invoke the Recognition,…

Applying Web-Based Co-Regulated Learning to Develop Students' Learning and Involvement in a Blended Computing Course

ERIC Educational Resources Information Center

Tsai, Chia-Wen

2015-01-01

This research investigated, via quasi-experiments, the effects of web-based co-regulated learning (CRL) on developing students' computing skills. Two classes of 68 undergraduates in a one-semester course titled "Applied Information Technology: Data Processing" were chosen for this research. The first class (CRL group, n = 38) received…

Transcranial Alternating Current Stimulation Attenuates Neuronal Adaptation.

PubMed

Kar, Kohitij; Duijnhouwer, Jacob; Krekelberg, Bart

2017-03-01

We previously showed that brief application of 2 mA (peak-to-peak) transcranial currents alternating at 10 Hz significantly reduces motion adaptation in humans. This is but one of many behavioral studies showing that weak currents applied to the scalp modulate neural processing. Transcranial stimulation has been shown to improve perception, learning, and a range of clinical symptoms. Few studies, however, have measured the neural consequences of transcranial current stimulation. We capitalized on the strong link between motion perception and neural activity in the middle temporal (MT) area of the macaque monkey to study the neural mechanisms that underlie the behavioral consequences of transcranial alternating current stimulation. First, we observed that 2 mA currents generated substantial intracranial fields, which were much stronger in the stimulated hemisphere (0.12 V/m) than on the opposite side of the brain (0.03 V/m). Second, we found that brief application of transcranial alternating current stimulation at 10 Hz reduced spike-frequency adaptation of MT neurons and led to a broadband increase in the power spectrum of local field potentials. Together, these findings provide a direct demonstration that weak electric fields applied to the scalp significantly affect neural processing in the primate brain and that this includes a hitherto unknown mechanism that attenuates sensory adaptation. SIGNIFICANCE STATEMENT Transcranial stimulation has been claimed to improve perception, learning, and a range of clinical symptoms. Little is known, however, how transcranial current stimulation generates such effects, and the search for better stimulation protocols proceeds largely by trial and error. We investigated, for the first time, the neural consequences of stimulation in the monkey brain. We found that even brief application of alternating current stimulation reduced the effects of adaptation on single-neuron firing rates and local field potentials; this mechanistic insight explains previous behavioral findings and suggests a novel way to modulate neural information processing using transcranial currents. In addition, by developing an animal model to help understand transcranial stimulation, this study will aid the rational design of stimulation protocols for the treatment of mental illnesses, and the improvement of perception and learning. Copyright © 2017 the authors 0270-6474/17/372325-11$15.00/0.

Applying machine learning to pattern analysis for automated in-design layout optimization

NASA Astrophysics Data System (ADS)

Cain, Jason P.; Fakhry, Moutaz; Pathak, Piyush; Sweis, Jason; Gennari, Frank; Lai, Ya-Chieh

2018-04-01

Building on previous work for cataloging unique topological patterns in an integrated circuit physical design, a new process is defined in which a risk scoring methodology is used to rank patterns based on manufacturing risk. Patterns with high risk are then mapped to functionally equivalent patterns with lower risk. The higher risk patterns are then replaced in the design with their lower risk equivalents. The pattern selection and replacement is fully automated and suitable for use for full-chip designs. Results from 14nm product designs show that the approach can identify and replace risk patterns with quantifiable positive impact on the risk score distribution after replacement.

Sensory, Cognitive, and Sensorimotor Learning Effects in Recognition Memory for Music.

PubMed

Mathias, Brian; Tillmann, Barbara; Palmer, Caroline

2016-08-01

Recent research suggests that perception and action are strongly interrelated and that motor experience may aid memory recognition. We investigated the role of motor experience in auditory memory recognition processes by musicians using behavioral, ERP, and neural source current density measures. Skilled pianists learned one set of novel melodies by producing them and another set by perception only. Pianists then completed an auditory memory recognition test during which the previously learned melodies were presented with or without an out-of-key pitch alteration while the EEG was recorded. Pianists indicated whether each melody was altered from or identical to one of the original melodies. Altered pitches elicited a larger N2 ERP component than original pitches, and pitches within previously produced melodies elicited a larger N2 than pitches in previously perceived melodies. Cortical motor planning regions were more strongly activated within the time frame of the N2 following altered pitches in previously produced melodies compared with previously perceived melodies, and larger N2 amplitudes were associated with greater detection accuracy following production learning than perception learning. Early sensory (N1) and later cognitive (P3a) components elicited by pitch alterations correlated with predictions of sensory echoic and schematic tonality models, respectively, but only for the perception learning condition, suggesting that production experience alters the extent to which performers rely on sensory and tonal recognition cues. These findings provide evidence for distinct time courses of sensory, schematic, and motoric influences within the same recognition task and suggest that learned auditory-motor associations influence responses to out-of-key pitches.

Cooperative learning combined with short periods of lecturing: A good alternative in teaching biochemistry.

PubMed

Fernández-Santander, Ana

2008-01-01

The informal activities of cooperative learning and short periods of lecturing has been combined and used in the university teaching of biochemistry as part of the first year course of Optics and Optometry in the academic years 2004-2005 and 2005-2006. The lessons were previously elaborated by the teacher and included all that is necessary to understand the topic (text, figures, graphics, diagrams, pictures, etc.). Additionally, a questionnaire was prepared for every chapter. All lessons contained three parts: objectives, approach and development, and the assessment of the topic. Team work, responsibility, and communication skills were some of the abilities developed with this new methodology. Students worked collaboratively in small groups of two or three following the teacher's instructions with short periods of lecturing that clarified misunderstood concepts. Homework was minimized. On comparing this combined methodology with the traditional one (only lecture), students were found to exhibit a higher satisfaction with the new method. They were more involved in the learning process and had a better attitude toward the subject. The use of this new methodology showed a significant increase in the mean score of the students' academic results. The rate of students who failed the subject was significantly inferior in comparison with those who failed in the previous years when only lecturing was applied. This combined methodology helped the teacher to observe the apprenticeship process of students better and to act as a facilitator in the process of building students' knowledge. Copyright © 2008 International Union of Biochemistry and Molecular Biology, Inc.

Media, Media Technologies, and Language Learning: Some Applied Linguistic Perspectives.

ERIC Educational Resources Information Center

Little, David

An applied linguistic framework is presented within which specific applications of media technologies may be applied to language learning. The first two parts of the paper focus on the impact of media on linguistic communication and the possibilities offered by media technologies such as newspapers, radio, television, telephone/telex, computer…

Actes/Proceedings, Canadian Association of Applied Linguistics, 3rd Annual Meeting.

ERIC Educational Resources Information Center

Laval Univ., Quebec (Quebec). International Center for Research on Bilingualism.

These proceedings on applied linguistics and language learning contain the following papers: (1) "Audio-visual and Applied Linguistics; The State of the Question," by V. Ferenczi (in French); (2) "Research in Fundamental Language: The State of the Question," by B. Pottier (in French); (3) "Psycholinguistic Insights into Language Learning," by F.…

Suggestopedia to SALT and a New Awareness in Education.

ERIC Educational Resources Information Center

Herr, Kay U.

SALT, suggestive-accelerative learning and teaching, is the Americanized version of a pedagogy developed in Bulgaria. While most extensively applied to foreign language teaching, the methodology may be applied to any discipline, particularly one based upon a foundation of learned facts. This document applies the method to ESL classes. The teacher…

An Aural Learning Project: Assimilating Jazz Education Methods for Traditional Applied Pedagogy

ERIC Educational Resources Information Center

Gamso, Nancy M.

2011-01-01

The Aural Learning Project (ALP) was developed to incorporate jazz method components into the author's classical practice and her applied woodwind lesson curriculum. The primary objective was to place a more focused pedagogical emphasis on listening and hearing than is traditionally used in the classical applied curriculum. The components of the…

Hippocampus NMDA receptors selectively mediate latent extinction of place learning.

PubMed

Goodman, Jarid; Gabriele, Amanda; Packard, Mark G

2016-09-01

Extinction of maze learning may be achieved with or without the animal performing the previously acquired response. In typical "response extinction," animals are given the opportunity to make the previously acquired approach response toward the goal location of the maze without reinforcement. In "latent extinction," animals are not given the opportunity to make the previously acquired response and instead are confined to the previous goal location without reinforcement. Previous evidence indicates that the effectiveness of these protocols may depend on the type of memory being extinguished. Thus, one aim of the present study was to further examine the effectiveness of response and latent extinction protocols across dorsolateral striatum (DLS)-dependent response learning and hippocampus-dependent place learning tasks. In addition, previous neural inactivation experiments indicate a selective role for the hippocampus in latent extinction, but have not investigated the precise neurotransmitter mechanisms involved. Thus, the present study also examined whether latent extinction of place learning might depend on NMDA receptor activity in the hippocampus. In experiment 1, adult male Long-Evans rats were trained in a response learning task in a water plus-maze, in which animals were reinforced to make a consistent body-turn response to reach an invisible escape platform. Results indicated that response extinction, but not latent extinction, was effective at extinguishing memory in the response learning task. In experiment 2, rats were trained in a place learning task, in which animals were reinforced to approach a consistent spatial location containing the hidden escape platform. In experiment 2, animals also received intra-hippocampal infusions of the NMDA receptor antagonist 2-amino-5-phosphopentanoic acid (AP5; 5.0 or 7.5 ug/0.5 µg) or saline vehicle immediately before response or latent extinction training. Results indicated that both extinction protocols were effective at extinguishing memory in the place learning task. In addition, intra-hippocampal AP5 (7.5 µg) impaired latent extinction, but not response extinction, suggesting that hippocampal NMDA receptors are selectively involved in latent extinction. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Learning Spaces Framework: Learning in an Online World

ERIC Educational Resources Information Center

Ministerial Council on Education, Employment, Training and Youth Affairs (NJ1), 2008

2008-01-01

"Contemporary learning--learning in an online world" describes the integrated nature of the highly technological world in which young people live and learn. A key priority is to design learning spaces that integrate technologies: engaging students in ways not previously possible; creating new learning and teaching possibilities;…

From prediction error to incentive salience: mesolimbic computation of reward motivation.

PubMed

Berridge, Kent C

2012-04-01

Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior. Modeling the motivational component requires recognition of additional control factors besides learning. Here I discuss how mesocorticolimbic mechanisms generate the motivation component of incentive salience. Incentive salience takes Pavlovian learning and memory as one input and as an equally important input takes neurobiological state factors (e.g. drug states, appetite states, satiety states) that can vary independently of learning. Neurobiological state changes can produce unlearned fluctuations or even reversals in the ability of a previously learned reward cue to trigger motivation. Such fluctuations in cue-triggered motivation can dramatically depart from all previously learned values about the associated reward outcome. Thus, one consequence of the difference between incentive salience and learning can be to decouple cue-triggered motivation of the moment from previously learned values of how good the associated reward has been in the past. Another consequence can be to produce irrationally strong motivation urges that are not justified by any memories of previous reward values (and without distorting associative predictions of future reward value). Such irrationally strong motivation may be especially problematic in addiction. To understand these phenomena, future models of mesocorticolimbic reward function should address the neurobiological state factors that participate to control generation of incentive salience. © 2012 The Author. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Applying Neurological Learning Research to an Intro Astronomy Online Lab Course

NASA Astrophysics Data System (ADS)

Byrd, Gene G.; Byrd, Dana

2015-01-01

The neurological research used the 'Tower of London', a well-tested puzzle requiring multi-step planning toward a solution. Four and five year-olds are starting multistep reasoning and provide good puzzle subjects. Preschoolers who talked to themselves about future moves had greatly improved performance over those who did not. Adults given preplanning time prior to solving the same puzzle showed more neural activation during preplanning, especially in brain areas which serve higher level thinking. Applying these results to teaching astronomy, we modified an online introductory lab course in which students take a multiple choice final exam. We composed questions related to the learning objectives of the course modules (LOQs). Students could 'talk to themselves' by discursively answering these for extra credit prior to the final. Results were compared to an otherwise identical previous unmodified class. Modified classes showed statistically much better final exam average scores (78% vs. 66%). This modification helped those students who most need help. Students in the lower third of the class preferentially answered the LOQs to improve their scores and the class average on the exam. These results also show the effectiveness of relevant extra credit work. For more details plus an application to a lecture course, see Byrd and Byrd http://www.ncolr.org/issues/jiol/v12/n2/3 (Journal of Interactive Online Learning). The online lab course emphasized real photographic and quantitative astronomical observations. We also discuss and show equipment found to be most useful for the online lab course, including a 'pin-hole protractor', telescope kit and "AL-henge" telescope mount..

Producing or reproducing reasoning? Socratic dialog is very effective, but only for a few

PubMed Central

Goldin, Andrea Paula; Pedroncini, Olivia; Sigman, Mariano

2017-01-01

Successful communication between a teacher and a student is at the core of pedagogy. A well known example of a pedagogical dialog is ‘Meno’, a socratic lesson of geometry in which a student learns (or ‘discovers’) how to double the area of a given square ‘in essence, a demonstration of Pythagoras’ theorem. In previous studies we found that after engaging in the dialog participants can be divided in two kinds: those who can only apply a rule to solve the problem presented in the dialog and those who can go beyond and generalize that knowledge to solve any square problems. Here we study the effectiveness of this socratic dialog in an experimental and a control high-school classrooms, and we explore the boundaries of what is learnt by testing subjects with a set of 9 problems of varying degrees of difficulty. We found that half of the adolescents did not learn anything from the dialog. The other half not only learned to solve the problem, but could abstract something more: the geometric notion that the diagonal can be used to solve diverse area problems. Conceptual knowledge is critical for achievement in geometry, and it is not clear whether geometric concepts emerge spontaneously on the basis of universal experience with space, or reflect intrinsic properties of the human mind. We show that, for half of the learners, an exampled-based Socratic dialog in lecture form can give rise to formal geometric knowledge that can be applied to new, different problems. PMID:28333955

A single session of prefrontal cortex transcranial direct current stimulation does not modulate implicit task sequence learning and consolidation.

PubMed

Savic, Branislav; Müri, René; Meier, Beat

Transcranial direct current stimulation (tDCS) is assumed to affect cortical excitability and dependent on the specific stimulation conditions either to increase or decrease learning. The purpose of this study was to modulate implicit task sequence learning with tDCS. As cortico-striatal loops are critically involved in implicit task sequence learning, tDCS was applied above the dorsolateral prefrontal cortex (DLPFC). In Experiment 1, anodal, cathodal, or sham tDCS was applied before the start of the sequence learning task. In Experiment 2, stimulation was applied during the sequence learning task. Consolidation of learning was assessed after 24 h. The results of both experiments showed that implicit task sequence learning occurred consistently but it was not modulated by different tDCS conditions. Similarly, consolidation measured after a 24 h-interval including sleep was also not affected by stimulation. These results indicate that a single session of DLPFC tDCS is not sufficient to modulate implicit task sequence learning. This study adds to the accumulating evidence that tDCS may not be as effective as originally thought. Copyright © 2017 Elsevier Inc. All rights reserved.

Student perceptions about learning anatomy

NASA Astrophysics Data System (ADS)

Notebaert, Andrew John

This research study was conducted to examine student perceptions about learning anatomy and to explore how these perceptions shape the learning experience. This study utilized a mixed-methods design in order to better understand how students approach learning anatomy. Two sets of data were collected at two time periods; one at the beginning and one at the end of the academic semester. Data consisted of results from a survey instrument that contained open-ended questions and a questionnaire and individual student interviews. The questionnaire scored students on a surface approach to learning (relying on rote memorization and knowing factual information) scale and a deep approach to learning (understanding concepts and deeper meaning behind the material) scale. Students were asked to volunteer from four different anatomy classes; two entry-level undergraduate courses from two different departments, an upper-level undergraduate course, and a graduate level course. Results indicate that students perceive that they will learn anatomy through memorization regardless of the level of class being taken. This is generally supported by the learning environment and thus students leave the classroom believing that anatomy is about memorizing structures and remembering anatomical terminology. When comparing this class experience to other academic classes, many students believed that anatomy was more reliant on memorization techniques for learning although many indicated that memorization is their primary learning method for most courses. Results from the questionnaire indicate that most students had decreases in both their deep approach and surface approach scores with the exception of students that had no previous anatomy experience. These students had an average increase in surface approach and so relied more on memorization and repetition for learning. The implication of these results is that the learning environment may actually amplify students' perceptions of the anatomy course at all levels and experiences of enrolled students. Instructors wanting to foster deeper approaches to learning may need to apply instructional techniques that both support deeper approaches to learning and strive to change students' perceptions away from believing that anatomy is strictly memorization and thus utilizing surface approaches to learning.

The Impact of Secondary School Students' Preconceptions on the Evolution of their Mental Models of the Greenhouse effect and Global Warming

NASA Astrophysics Data System (ADS)

Reinfried, Sibylle; Tempelmann, Sebastian

2014-01-01

This paper provides a video-based learning process study that investigates the kinds of mental models of the atmospheric greenhouse effect 13-year-old learners have and how these mental models change with a learning environment, which is optimised in regard to instructional psychology. The objective of this explorative study was to observe and analyse the learners' learning pathways according to their previous knowledge in detail and to understand the mental model formation processes associated with them more precisely. For the analysis of the learning pathways, drawings, texts, video and interview transcripts from 12 students were studied using qualitative methods. The learning pathways pursued by the learners significantly depend on their domain-specific previous knowledge. The learners' preconceptions could be typified based on specific characteristics, whereby three preconception types could be formed. The 'isolated pieces of knowledge' type of learners, who have very little or no previous knowledge about the greenhouse effect, build new mental models that are close to the target model. 'Reduced heat output' type of learners, who have previous knowledge that indicates compliances with central ideas of the normative model, reconstruct their knowledge by reorganising and interpreting their existing knowledge structures. 'Increasing heat input' type of learners, whose previous knowledge consists of subjective worldly knowledge, which has a greater personal explanatory value than the information from the learning environment, have more difficulties changing their mental models. They have to fundamentally reconstruct their mental models.

Applying Consumer and Homemaking Skills to Jobs and Careers. Secondary Learning Guide 13. Project Connect. Linking Self-Family-Work.

ERIC Educational Resources Information Center

Emily Hall Tremaine Foundation, Inc., Hartford, CT.

This competency-based secondary learning guide on applying consumer and homemaking skills to jobs and careers is part of a series that are adaptations of guides developed for adult consumer and homemaking education programs. The guides provide students with experiences that help them learn to do the following: make decisions; use creative…

Comparison of Science-Technology-Society Approach and Textbook Oriented Instruction on Students' Abilities to Apply Science Concepts

ERIC Educational Resources Information Center

Kapici, Hasan Ozgur; Akcay, Hakan; Yager, Robert E.

2017-01-01

It is important for students to learn concepts and using them for solving problems and further learning. Within this respect, the purpose of this study is to investigate students' abilities to apply science concepts that they have learned from Science-Technology-Society based approach or textbook oriented instruction. Current study is based on…

A Microworld-Based Role-Playing Game Development Approach to Engaging Students in Interactive, Enjoyable, and Effective Mathematics Learning

ERIC Educational Resources Information Center

Wang, Sheng-Yuan; Chang, Shao-Chen; Hwang, Gwo-Jen; Chen, Pei-Ying

2018-01-01

In traditional teacher-centered mathematics instruction, students might show low learning motivation owing to the lack of applied contexts. Game-based learning has been recognized as a potential approach to addressing this issue; however, without proper alignment between the gaming and math-applied contexts, the benefits of game-based learning…

Make Learning Stick: Best Practices to Get the Most Out of Leadership Development

ERIC Educational Resources Information Center

Reinhold, Diane; Patterson, Tracy; Hegel, Peter

2015-01-01

In this white paper, Diane Reinhold, Tracy Patterson, and Peter Hegel assert that there is no magic bullet to ensure people apply what they learn. There are, however, steps that can be taken to create leadership programs, experiences, and supports that improve the likelihood that lessons will be learned and applied. Over time, new skills,…

TU-C-218-01: Effective Medical Imaging Physics Education.

PubMed

Sprawls, P

2012-06-01

A practical and applied knowledge of physics and the associated technology is required for the clinically effective and safe use of the various medical imaging modalities. This is needed by all involved in the imaging process, including radiologists, especially residents in training, technologists, and physicists who provide consultation on optimum and safe procedures and as educators for the other imaging professionals. This area of education is undergoing considerable change and evolution for three reasons: 1. Increasing capabilities and complexity of medical imaging technology and procedures, 2.Expanding scope and availability of educational resources, especially on the internet, and 3. A significant increase in our knowledge of the mental learning process and the design of learning activities to optimize effectiveness and efficiency, especially for clinically applied physics. This course will address those three issues by providing guidance on establishing appropriate clinically focused learning outcomes, a review of the brain function for enhancing clinically applied physics, and the design and delivery of effective learning activities beginning with the classroom and continuing through learning physics during the clinical practice of radiology. Characteristics of each type of learning activity will be considered with respect to effectiveness and efficiency in achieving appropriate learning outcomes. A variety of available resources will be identified and demonstrated for use in the different phases of learning process. A major focus is on enhancing the role of the medical physicist in clinical radiology both as a resource and educator with contemporary technology being the tool, but not the teacher. 1. Develop physics learning objectives that will support effective and safe medical imaging procedures. 2. Understand specific brain functions that are involved in learning and applying physics. 3. Describe the characteristics and development of mental knowledge structures for applied clinical physics. 4. List the established levels of learning and associate each with specific functions that can be performed. 5. Analyze the different types of learning activities (classroom, individual study, clinical, etc.) with respect to effectiveness and efficiency. 6. Design and Provide a comprehensive physics education program with each activity optimized with respect to outcomes and available resources. © 2012 American Association of Physicists in Medicine.

Learning to apply models of materials while explaining their properties

NASA Astrophysics Data System (ADS)

Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

2014-09-01

Background:Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose:This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials. Sample:An experimental group is 27 Finnish upper secondary school students and control group included 18 students from the same school. Design and methods:In quasi-experimental setting, students were guided through predict, observe, explain activities in four practical work situations. It was intended that the structural models would encourage students to learn how to identify and apply appropriate models when predicting and explaining situations. The lessons, organised over a one-week period, began with a teacher's demonstration and continued with student experiments in which they described the properties and behaviours of six household products representing three different materials. Results:Most students in the experimental group learned to apply the models correctly, as demonstrated by post-test scores that were significantly higher than pre-test scores. The control group showed no significant difference between pre- and post-test scores. Conclusions:The findings indicate that the intervention where students engage in predict, observe, explain activities while several materials and models are confronted at the same time, had a positive effect on learning outcomes.

Learning "While" Working: Success Stories on Workplace Learning in Europe

ERIC Educational Resources Information Center

Lardinois, Rocio

2011-01-01

Cedefop's report "Learning while working: success stories on workplace learning in Europe" presents an overview of key trends in adult learning in the workplace. It takes stock of previous research carried out by Cedefop between 2003 and 2010 on key topics for adult learning: governance and the learning regions; social partner roles in…

Observational learning by individuals with autism: a review of teaching strategies.

PubMed

Plavnick, Joshua B; Hume, Kara A

2014-05-01

Observational learning is the process used to explain the acquisition of novel behaviors or performance of previously acquired behaviors under novel conditions after observing the behavior of another person and the consequences that follow the behavior. Many learners with autism do not attend to environmental stimuli at a level sufficient to learn a range of prosocial behaviors through observation of others. Modeling, group or dyadic instruction, and explicit observation training can improve the extent to which individuals with autism learn through observation. This article reviews previous research that involved observational learning by individuals with autism and outlines future research that could benefit instructional practices.

Automated Knowledge Discovery From Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael; DeCoste, Dennis; Mazzoni, Dominic; Scharenbroich, Lucas; Enke, Brian; Merline, William

2007-01-01

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Landcover Classification Using Deep Fully Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Li, X.; Zhou, S.; Tang, J.

2017-12-01

Land cover classification has always been an essential application in remote sensing. Certain image features are needed for land cover classification whether it is based on pixel or object-based methods. Different from other machine learning methods, deep learning model not only extracts useful information from multiple bands/attributes, but also learns spatial characteristics. In recent years, deep learning methods have been developed rapidly and widely applied in image recognition, semantic understanding, and other application domains. However, there are limited studies applying deep learning methods in land cover classification. In this research, we used fully convolutional networks (FCN) as the deep learning model to classify land covers. The National Land Cover Database (NLCD) within the state of Kansas was used as training dataset and Landsat images were classified using the trained FCN model. We also applied an image segmentation method to improve the original results from the FCN model. In addition, the pros and cons between deep learning and several machine learning methods were compared and explored. Our research indicates: (1) FCN is an effective classification model with an overall accuracy of 75%; (2) image segmentation improves the classification results with better match of spatial patterns; (3) FCN has an excellent ability of learning which can attains higher accuracy and better spatial patterns compared with several machine learning methods.

Experiential learning: transforming theory into practice.

PubMed

Yardley, Sarah; Teunissen, Pim W; Dornan, Tim

2012-01-01

Whilst much is debated about the importance of experiential learning in curriculum development, the concept only becomes effective if it is applied in an appropriate way. We believe that this effectiveness is directly related to a sound understanding of the theory, supporting the learning. The purpose of this article is to introduce readers to the theories underpinning experiential learning, which are then expanded further in an AMEE Guide, which considers the theoretical basis of experiential learning from a social learning, constructionist perspective and applies it to three stages of medical education: early workplace experience, clerkships and residency. This article argues for the importance and relevance of experiential learning and addresses questions that are commonly asked about it. First, we answer the questions 'what is experiential learning?' and 'how does it relate to social learning theory?' to orientate readers to the principles on which our arguments are based. Then, we consider why those ideas (theories) are relevant to educators--ranging from those with responsibilities for curriculum design to 'hands-on' teachers and workplace supervisors. The remainder of this article discusses how experiential learning theories and a socio-cultural perspective can be applied in practice. We hope that this will give readers a taste for our more detailed AMEE Guide and the further reading recommended at the end of it.

45 CFR 2516.110 - Who may apply for a direct grant from the Corporation?

Code of Federal Regulations, 2010 CFR

2010-10-01

...) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Eligibility To Apply...-learning programs. (2) An Indian Tribe. (3) For activities in a nonparticipating State or Indian Tribe, a...

45 CFR 2516.110 - Who may apply for a direct grant from the Corporation?

Code of Federal Regulations, 2011 CFR

2011-10-01

...) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Eligibility To Apply...-learning programs. (2) An Indian Tribe. (3) For activities in a nonparticipating State or Indian Tribe, a...

Developing an Experiential Learning Program: Milestones and Challenges

ERIC Educational Resources Information Center

Austin, M. Jill; Rust, Dianna Zeh

2015-01-01

College and University faculty members have increasingly adopted experiential learning teaching methods that are designed to engage students in the learning process. Experiential learning is simply defined as "hands-on" learning and may involve any of the following activities: service learning, applied learning in the discipline,…

Improving galaxy morphologies for SDSS with Deep Learning

NASA Astrophysics Data System (ADS)

Domínguez Sánchez, H.; Huertas-Company, M.; Bernardi, M.; Tuccillo, D.; Fischer, J. L.

2018-05-01

We present a morphological catalogue for ˜670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-types and a series of GZ2 type questions (disc/features, edge-on galaxies, bar signature, bulge prominence, roundness, and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-type model is not so efficient. For the T-type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (>97 per cent), precision and recall values (>90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.

Evaluation of an Instructional Model to Teach Clinically Relevant Medicinal Chemistry in a Campus and a Distance Pathway

PubMed Central

Galt, Kimberly A.

2008-01-01

Objectives To evaluate an instructional model for teaching clinically relevant medicinal chemistry. Methods An instructional model that uses Bloom's cognitive and Krathwohl's affective taxonomy, published and tested concepts in teaching medicinal chemistry, and active learning strategies, was introduced in the medicinal chemistry courses for second-professional year (P2) doctor of pharmacy (PharmD) students (campus and distance) in the 2005-2006 academic year. Student learning and the overall effectiveness of the instructional model were assessed. Student performance after introducing the instructional model was compared to that in prior years. Results Student performance on course examinations improved compared to previous years. Students expressed overall enthusiasm about the course and better understood the value of medicinal chemistry to clinical practice. Conclusion The explicit integration of the cognitive and affective learning objectives improved student performance, student ability to apply medicinal chemistry to clinical practice, and student attitude towards the discipline. Testing this instructional model provided validation to this theoretical framework. The model is effective for both our campus and distance-students. This instructional model may also have broad-based applications to other science courses. PMID:18483599

Combination of a Flipped Classroom Format and a Virtual Patient Case to Enhance Active Learning in a Required Therapeutics Course

PubMed Central

Lichvar, Alicia Beth; Hedges, Ashley; Benedict, Neal J.

2016-01-01

Objective. To design and evaluate the integration of a virtual patient activity in a required therapeutics course already using a flipped-classroom teaching format. Design. A narrative-branched, dynamic virtual-patient case was designed to replace the static written cases that students worked through during the class, which was dedicated to teaching the complications of liver disease. Students completed pre- and posttests before and after completing the virtual patient case. Examination scores were compared to those in the previous year. Assessment. Students’ posttest scores were higher compared to pretest scores (33% vs 50%). Overall median examination scores were higher compared to the historical control group (70% vs 80%), as well as scores on questions assessing higher-level learning (67% vs 83%). A majority of students (68%) felt the virtual patient helped them apply knowledge gained in the pre-class video lecture. Students preferred this strategy to usual in-class activities (33%) or indicated it was of equal value (37%). Conclusion. The combination of a pre-class video lecture with an in-class virtual patient case is an effective active-learning strategy. PMID:28179724

Metacognitive and multimedia support of experiments in inquiry learning for science teacher preparation

NASA Astrophysics Data System (ADS)

Bruckermann, Till; Aschermann, Ellen; Bresges, André; Schlüter, Kirsten

2017-04-01

Promoting preservice science teachers' experimentation competency is required to provide a basis for meaningful learning through experiments in schools. However, preservice teachers show difficulties when experimenting. Previous research revealed that cognitive scaffolding promotes experimentation competency by structuring the learning process, while metacognitive and multimedia support enhance reflection. However, these support measures have not yet been tested in combination. Therefore, we decided to use cognitive scaffolding to support students' experimental achievements and supplement it by metacognitive and multimedia scaffolds in the experimental groups. Our research question is to what extent supplementing cognitive support by metacognitive and multimedia scaffolding further promotes experimentation competency. The intervention has been applied in a two-factorial design to a two-month experimental course for 63 biology teacher students in their first bachelor year. Pre-post-test measured experimentation competency in a performance assessment. Preservice teachers worked in groups of four. Therefore, measurement took place at group level (N = 16). Independent observers rated preservice teachers' group performance qualitatively on a theory-based system of categories. Afterwards, experimentation competency levels led to quantitative frequency analysis. The results reveal differing gains in experimentation competency but contrary to our hypotheses. Implications of combining scaffolding measures on promoting experimentation competency are discussed.

Data Mining for Anomaly Detection

NASA Technical Reports Server (NTRS)

Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

2013-01-01

The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

Fall classification by machine learning using mobile phones.

PubMed

Albert, Mark V; Kording, Konrad; Herrmann, Megan; Jayaraman, Arun

2012-01-01

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls-left and right lateral, forward trips, and backward slips-while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.

Combination of a Flipped Classroom Format and a Virtual Patient Case to Enhance Active Learning in a Required Therapeutics Course.

PubMed

Lichvar, Alicia Beth; Hedges, Ashley; Benedict, Neal J; Donihi, Amy C

2016-12-25

Objective. To design and evaluate the integration of a virtual patient activity in a required therapeutics course already using a flipped-classroom teaching format. Design. A narrative-branched, dynamic virtual-patient case was designed to replace the static written cases that students worked through during the class, which was dedicated to teaching the complications of liver disease. Students completed pre- and posttests before and after completing the virtual patient case. Examination scores were compared to those in the previous year. Assessment. Students' posttest scores were higher compared to pretest scores (33% vs 50%). Overall median examination scores were higher compared to the historical control group (70% vs 80%), as well as scores on questions assessing higher-level learning (67% vs 83%). A majority of students (68%) felt the virtual patient helped them apply knowledge gained in the pre-class video lecture. Students preferred this strategy to usual in-class activities (33%) or indicated it was of equal value (37%). Conclusion. The combination of a pre-class video lecture with an in-class virtual patient case is an effective active-learning strategy.

A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks.

PubMed

Wang, Changhan; Yan, Xinchen; Smith, Max; Kochhar, Kanika; Rubin, Marcie; Warren, Stephen M; Wrobel, James; Lee, Honglak

2015-01-01

Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system.

History matching through dynamic decision-making

PubMed Central

Maschio, Célio; Santos, Antonio Alberto; Schiozer, Denis; Rocha, Anderson

2017-01-01

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy. This work introduces a dynamic decision-making optimization framework for history matching problems in which new models are generated based on, and guided by, the dynamic analysis of the data of available solutions. The optimization framework follows a ‘learning-from-data’ approach, and includes two optimizer components that use machine learning techniques, such as unsupervised learning and statistical analysis, to uncover patterns of input attributes that lead to good output responses. These patterns are used to support the decision-making process while generating new, and better, history matched solutions. The proposed framework is applied to a benchmark model (UNISIM-I-H) based on the Namorado field in Brazil. Results show the potential the dynamic decision-making optimization framework has for improving the quality of history matching solutions using a substantial smaller number of simulations when compared with a previous work on the same benchmark. PMID:28582413

Spectral Unmixing Based Construction of Lunar Mineral Abundance Maps

NASA Astrophysics Data System (ADS)

Bernhardt, V.; Grumpe, A.; Wöhler, C.

2017-07-01

In this study we apply a nonlinear spectral unmixing algorithm to a nearly global lunar spectral reflectance mosaic derived from hyper-spectral image data acquired by the Moon Mineralogy Mapper (M3) instrument. Corrections for topographic effects and for thermal emission were performed. A set of 19 laboratory-based reflectance spectra of lunar samples published by the Lunar Soil Characterization Consortium (LSCC) were used as a catalog of potential endmember spectra. For a given spectrum, the multi-population population-based incremental learning (MPBIL) algorithm was used to determine the subset of endmembers actually contained in it. However, as the MPBIL algorithm is computationally expensive, it cannot be applied to all pixels of the reflectance mosaic. Hence, the reflectance mosaic was clustered into a set of 64 prototype spectra, and the MPBIL algorithm was applied to each prototype spectrum. Each pixel of the mosaic was assigned to the most similar prototype, and the set of endmembers previously determined for that prototype was used for pixel-wise nonlinear spectral unmixing using the Hapke model, implemented as linear unmixing of the single-scattering albedo spectrum. This procedure yields maps of the fractional abundances of the 19 endmembers. Based on the known modal abundances of a variety of mineral species in the LSCC samples, a conversion from endmember abundances to mineral abundances was performed. We present maps of the fractional abundances of plagioclase, pyroxene and olivine and compare our results with previously published lunar mineral abundance maps.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Inter- and Intra-Dimensional Dependencies in Implicit Phonotactic Learning

ERIC Educational Resources Information Center

Moreton, Elliott

2012-01-01

Is phonological learning subject to the same inductive biases as learning in other domains? Previous studies of non-linguistic learning found that intra-dimensional dependencies (between two instances of the same feature) were learned more easily than inter-dimensional ones. This study compares implicit learning of intra- and inter-dimensional…

The Learning Context: Influence on Learning to Program

ERIC Educational Resources Information Center

Govender, Irene

2009-01-01

In this paper the influence of the learning context is considered when learning to program. For the purposes of this study, the lectures, study process, previous knowledge or teaching experience and tests comprised the learning context. The article argues that students' experiences of the learning context have important implications for teaching…

Personalized Virtual Learning Environment from the Detection of Learning Styles

ERIC Educational Resources Information Center

Martínez Cartas, M. L.; Cruz Pérez, N.; Deliche Quesada, D.; Mateo Quero, S.

2013-01-01

Through the previous detection of existing learning styles in a classroom, a Virtual Learning Environment (VLE) has been designed for students of several Engineering degrees, using the Learning Management System (LMS) utilized in the University of Jaen, ILIAS. Learning styles of three different Knowledge Areas; Chemical Engineering, Materials…

Development of an Adaptive Learning System with Two Sources of Personalization Information

ERIC Educational Resources Information Center

Tseng, J. C. R.; Chu, H. C.; Hwang, G. J.; Tsai, C. C.

2008-01-01

Previous research of adaptive learning mainly focused on improving student learning achievements based only on single-source of personalization information, such as learning style, cognitive style or learning achievement. In this paper, an innovative adaptive learning approach is proposed by basing upon two main sources of personalization…

The Professor-Student Learning Relationship in Higher Education: Wisdom from Students with Learning Disabilities

ERIC Educational Resources Information Center

Fox, Laurie; McNally, Jennifer Ceven

2018-01-01

This mixed-methods study addressed the professor-student learning relationship in higher education and perception of its helpfulness in learning among college students with diagnosed learning disabilities. The Relationship and Learning Questionnaire drew items from four previously researched instruments. Results identify students' preference for…

Network mechanisms of intentional learning

PubMed Central

Hampshire, Adam; Hellyer, Peter J.; Parkin, Beth; Hiebert, Nole; MacDonald, Penny; Owen, Adrian M.; Leech, Robert; Rowe, James

2016-01-01

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated. PMID:26658925

An efficient approach to improve the usability of e-learning resources: the role of heuristic evaluation.

PubMed

Davids, Mogamat Razeen; Chikte, Usuf M E; Halperin, Mitchell L

2013-09-01

Optimizing the usability of e-learning materials is necessary to maximize their potential educational impact, but this is often neglected when time and other resources are limited, leading to the release of materials that cannot deliver the desired learning outcomes. As clinician-teachers in a resource-constrained environment, we investigated whether heuristic evaluation of our multimedia e-learning resource by a panel of experts would be an effective and efficient alternative to testing with end users. We engaged six inspectors, whose expertise included usability, e-learning, instructional design, medical informatics, and the content area of nephrology. They applied a set of commonly used heuristics to identify usability problems, assigning severity scores to each problem. The identification of serious problems was compared with problems previously found by user testing. The panel completed their evaluations within 1 wk and identified a total of 22 distinct usability problems, 11 of which were considered serious. The problems violated the heuristics of visibility of system status, user control and freedom, match with the real world, intuitive visual layout, consistency and conformity to standards, aesthetic and minimalist design, error prevention and tolerance, and help and documentation. Compared with user testing, heuristic evaluation found most, but not all, of the serious problems. Combining heuristic evaluation and user testing, with each involving a small number of participants, may be an effective and efficient way of improving the usability of e-learning materials. Heuristic evaluation should ideally be used first to identify the most obvious problems and, once these are fixed, should be followed by testing with typical end users.

Change in the relative contributions of habit and working memory facilitates serial reversal learning expertise in rhesus monkeys.

PubMed

Hassett, Thomas C; Hampton, Robert R

2017-05-01

Functionally distinct memory systems likely evolved in response to incompatible demands placed on learning by distinct environmental conditions. Working memory appears adapted, in part, for conditions that change frequently, making rapid acquisition and brief retention of information appropriate. In contrast, habits form gradually over many experiences, adapting organisms to contingencies of reinforcement that are stable over relatively long intervals. Serial reversal learning provides an opportunity to simultaneously examine the processes involved in adapting to rapidly changing and relatively stable contingencies. In serial reversal learning, selecting one of the two simultaneously presented stimuli is positively reinforced, while selection of the other is not. After a preference for the positive stimulus develops, the contingencies of reinforcement reverse. Naïve subjects adapt to such reversals gradually, perseverating in selection of the previously rewarded stimulus. Experts reverse rapidly according to a win-stay, lose-shift response pattern. We assessed whether a change in the relative control of choice by habit and working memory accounts for the development of serial reversal learning expertise. Across three experiments, we applied manipulations intended to attenuate the contribution of working memory but leave the contribution of habit intact. We contrasted performance following long and short intervals in Experiments 1 and 2, and we interposed a competing cognitive load between trials in Experiment 3. These manipulations slowed the acquisition of reversals in expert subjects, but not naïve subjects, indicating that serial reversal learning expertise is facilitated by a shift in the control of choice from passively acquired habit to actively maintained working memory.

Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

NASA Astrophysics Data System (ADS)

Bauer, Johannes; Dávila-Chacón, Jorge; Wermter, Stefan

2015-10-01

Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multi-sensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the self-organising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.

What Campuses Need to Know about Organizational Learning and the Learning Organization

ERIC Educational Resources Information Center

Kezar, Adrianna

2005-01-01

This chapter provides an overview of the literature on organizational learning and the learning organization, sets out key concepts in each area, and reviews the way that organizational learning and the learning organization have been applied within higher education.

Motivation, students' needs and learning outcomes: a hybrid game-based app for enhanced language learning.

PubMed

Berns, Anke; Isla-Montes, José-Luis; Palomo-Duarte, Manuel; Dodero, Juan-Manuel

2016-01-01

In the context of European Higher Education students face an increasing focus on independent, individual learning-at the expense of face-to-face interaction. Hence learners are, all too often, not provided with enough opportunities to negotiate in the target language. The current case study aims to address this reality by going beyond conventional approaches to provide students with a hybrid game-based app, combining individual and collaborative learning opportunities. The 4-week study was carried out with 104 German language students (A1.2 CEFR) who had previously been enrolled in a first-semester A1.1 level course at a Spanish university. The VocabTrainerA1 app-designed specifically for this study-harnesses the synergy of combining individual learning tasks and a collaborative murder mystery game in a hybrid level-based architecture. By doing so, the app provides learners with opportunities to apply their language skills to real-life-like communication. The purpose of the study was twofold: on one hand we aimed to measure learner motivation, perceived usefulness and added value of hybrid game-based apps; on the other, we sought to determine their impact on language learning. To this end, we conducted focus group interviews and an anonymous Technology Acceptance Model survey (TAM). In addition, students took a pre-test and a post-test. Scores from both tests were compared with the results obtained in first-semester conventional writing tasks, with a view to measure learning outcomes. The study provides qualitative and quantitative data supporting our initial hypotheses. Our findings suggest that hybrid game-based apps like VocabTrainerA1-which seamlessly combine individual and collaborative learning tasks-motivate learners, stimulate perceived usefulness and added value, and better meet the language learning needs of today's digital natives. In terms of acceptance, outcomes and sustainability, the data indicate that hybrid game-based apps significantly improve proficiency, hence are indeed, effective tools for enhanced language learning.

Deficits in hippocampal-dependent transfer generalization learning accompany synaptic dysfunction in a mouse model of amyloidosis.

PubMed

Montgomery, Karienn S; Edwards, George; Levites, Yona; Kumar, Ashok; Myers, Catherine E; Gluck, Mark A; Setlow, Barry; Bizon, Jennifer L

2016-04-01

Elevated β-amyloid and impaired synaptic function in hippocampus are among the earliest manifestations of Alzheimer's disease (AD). Most cognitive assessments employed in both humans and animal models, however, are insensitive to this early disease pathology. One critical aspect of hippocampal function is its role in episodic memory, which involves the binding of temporally coincident sensory information (e.g., sights, smells, and sounds) to create a representation of a specific learning epoch. Flexible associations can be formed among these distinct sensory stimuli that enable the "transfer" of new learning across a wide variety of contexts. The current studies employed a mouse analog of an associative "transfer learning" task that has previously been used to identify risk for prodromal AD in humans. The rodent version of the task assesses the transfer of learning about stimulus features relevant to a food reward across a series of compound discrimination problems. The relevant feature that predicts the food reward is unchanged across problems, but an irrelevant feature (i.e., the context) is altered. Experiment 1 demonstrated that C57BL6/J mice with bilateral ibotenic acid lesions of hippocampus were able to discriminate between two stimuli on par with control mice; however, lesioned mice were unable to transfer or apply this learning to new problem configurations. Experiment 2 used the APPswe PS1 mouse model of amyloidosis to show that robust impairments in transfer learning are evident in mice with subtle β-amyloid-induced synaptic deficits in the hippocampus. Finally, Experiment 3 confirmed that the same transfer learning impairments observed in APPswePS1 mice were also evident in the Tg-SwDI mouse, a second model of amyloidosis. Together, these data show that the ability to generalize learned associations to new contexts is disrupted even in the presence of subtle hippocampal dysfunction and suggest that, across species, this aspect of hippocampal-dependent learning may be useful for early identification of AD-like pathology. © 2015 Wiley Periodicals, Inc.

A Predictive Study of Learner Attitudes Toward Open Learning in a Robotics Class

NASA Astrophysics Data System (ADS)

Avsec, Stanislav; Rihtarsic, David; Kocijancic, Slavko

2014-10-01

Open learning (OL) strives to transform teaching and learning by applying learning science and emerging technologies to increase student success, improve learning productivity, and lower barriers to access. OL of robotics has a significant growth rate in secondary and/or high schools, but failures exist. Little is known about why many users stop their OL after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with different types of OL. In this study, we tested a regression model for student satisfaction involving students' attitudes toward OL usage. A survey was conducted to investigate the critical factors affecting students' achievements and satisfaction in OL of robotics with use of own developed direct manipulation learning environment as learning context. A multiple regression analyses were carried out to investigate how different facets of students' expectations and experiences are related to perceived learning achievements and course satisfaction. Descriptive statistics and analysis of variance was performed to determine the effect of predictor variables to student satisfaction. The results demonstrate that students have significantly positive perceptions toward using OL of robotics as a learning-assisted tool. Furthermore, behavioral intention to use OL is influenced by perceived usefulness and self-efficacy. The following five major categories of satisfaction factors with OL course were revealed during analysis of the studies (effect sizes in parentheses): organization (0.69); implementation (0.61); professional content (0.53); interaction (0.43); self-efficacy (0.14). All these effect sizes were judged to be significant and large. The results also showed that learner-mentor/instructor interaction, learner-professional content interaction, and online and offline self-efficacy were good predictors of student satisfaction and course quality. Peer interactions and self-regulated learning have to be considered carefully. A learner-mentor/instructor and learner-professional content interaction are indicated as most significant interactions.

Endocannabinoid signaling and memory dynamics: A synaptic perspective.

PubMed

Drumond, Ana; Madeira, Natália; Fonseca, Rosalina

2017-02-01

Memory acquisition is a key brain feature in which our human nature relies on. Memories evolve over time. Initially after learning, memories are labile and sensitive to disruption by the interference of concurrent events. Later on, after consolidation, memories are resistant to disruption. However, reactivation of previously consolidated memories renders them again in an unstable state and therefore susceptible to perturbation. Additionally, and depending on the characteristics of the stimuli, a parallel process may be initiated which ultimately leads to the extinction of the previously acquired response. This dynamic aspect of memory maintenance opens the possibility for an updating of previously acquired memories but it also creates several conceptual challenges. What is the time window for memory updating? What determines whether reconsolidation or extinction is triggered? In this review, we tried to re-examine the relationship between consolidation, reconsolidation and extinction, aiming for a unifying view of memory dynamics. Since cellular models of memory share common principles, we present the evidence that similar rules apply to the maintenance of synaptic plasticity. Recently, a new function of the endocannabinoid (eCB) signaling system has been described for associative forms of synaptic plasticity in amygdala synapses. The eCB system has emerged as a key modulator of memory dynamics by adjusting the outcome to stimuli intensity. We propose a key function of eCB in discriminative forms of learning by restricting associative plasticity in amygdala synapses. Since many neuropsychiatric disorders are associated with a dysregulation in memory dynamics, understanding the rules underlying memory maintenance paves the path to better clinical interventions. Copyright © 2016 Elsevier Inc. All rights reserved.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect

PubMed Central

Knoblauch, Andreas; Körner, Edgar; Körner, Ursula; Sommer, Friedrich T.

2014-01-01

Although already William James and, more explicitly, Donald Hebb's theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect. PMID:24858841

Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning.

PubMed

Zu, Chen; Gao, Yue; Munsell, Brent; Kim, Minjeong; Peng, Ziwen; Cohen, Jessica R; Zhang, Daoqiang; Wu, Guorong

2018-06-14

The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hyperedge encodes a subnetwork with similar functional connectivity patterns between different subjects. Unlike previous learning-based methods, our approach is designed to jointly optimize the weights for all hyperedges such that the learned representation is in consensus with the distribution of phenotype data, i.e. clinical labels. In order to suppress the spurious subnetwork biomarkers, we further enforce a sparsity constraint on the hyperedge weights, where a larger hyperedge weight indicates the subnetwork with the capability of identifying the disorder condition. We apply our hypergraph learning-based method to identify subnetwork biomarkers in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A comprehensive quantitative and qualitative analysis is performed, and the results show that our approach can correctly classify ASD and ADHD subjects from normal controls with 87.65 and 65.08% accuracies, respectively.

Deep learning for EEG-Based preference classification

NASA Astrophysics Data System (ADS)

Teo, Jason; Hou, Chew Lin; Mountstephens, James

2017-10-01

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

Refocusing International Astronomy Education Research Using a Cognitive Focus

NASA Astrophysics Data System (ADS)

Slater, Timothy F.; Slater, Stephanie J.

2015-08-01

For over 40 years, the international astronomy education community has given its attention to cataloging the substantial body of "misconceptions" in individual's thinking about astronomy, and to addressing the consequences of those misconceptions in the science classroom. Despite the tremendous amount of effort given to researching and disseminating information related to misconceptions, and the development of a theory of conceptual change to mitigate misconceptions, progress continues to be less than satisfying. An analysis of the literature and our own research has motivated the CAPER Center for Astronomy & Physics Education Research to advance a new model that allowing us to operate on students' astronomical learning difficulties in a more fruitful manner. Previously, much of the field's work binned erroneous student thinking into a single construct, and from that basis, curriculum developers and instructors addressed student misconceptions with a single instructional strategy. In contrast this model suggests that "misconceptions" are a mixture of at least four learning barriers: incorrect factual information, inappropriately applied mental algorithms (e.g., phenomenological primitives), insufficient cognitive structures (e.g., spatial reasoning), and affective/emotional difficulties. Each of these types of barriers should be addressed with an appropriately designed instructional strategy. Initial applications of this model to learning problems in astronomy and the space sciences have been fruitful, suggesting that an effort towards categorizing persistent learning difficulties in astronomy beyond the level of "misconceptions" may allow our community to craft tailored and more effective learning experiences for our students and the general public.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

Seamless Language Learning: Second Language Learning with Social Media

ERIC Educational Resources Information Center

Wong, Lung-Hsiang; Chai, Ching Sing; Aw, Guat Poh

2017-01-01

This conceptual paper describes a language learning model that applies social media to foster contextualized and connected language learning in communities. The model emphasizes weaving together different forms of language learning activities that take place in different learning contexts to achieve seamless language learning. it promotes social…

Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis.

PubMed

Collins, Ryan L; Hu, Ting; Wejse, Christian; Sirugo, Giorgio; Williams, Scott M; Moore, Jason H

2013-02-18

Identifying high-order genetics associations with non-additive (i.e. epistatic) effects in population-based studies of common human diseases is a computational challenge. Multifactor dimensionality reduction (MDR) is a machine learning method that was designed specifically for this problem. The goal of the present study was to apply MDR to mining high-order epistatic interactions in a population-based genetic study of tuberculosis (TB). The study used a previously published data set consisting of 19 candidate single-nucleotide polymorphisms (SNPs) in 321 pulmonary TB cases and 347 healthy controls from Guniea-Bissau in Africa. The ReliefF algorithm was applied first to generate a smaller set of the five most informative SNPs. MDR with 10-fold cross-validation was then applied to look at all possible combinations of two, three, four and five SNPs. The MDR model with the best testing accuracy (TA) consisted of SNPs rs2305619, rs187084, and rs11465421 (TA = 0.588) in PTX3, TLR9 and DC-Sign, respectively. A general 1000-fold permutation test of the null hypothesis of no association confirmed the statistical significance of the model (p = 0.008). An additional 1000-fold permutation test designed specifically to test the linear null hypothesis that the association effects are only additive confirmed the presence of non-additive (i.e. nonlinear) or epistatic effects (p = 0.013). An independent information-gain measure corroborated these results with a third-order epistatic interaction that was stronger than any lower-order associations. We have identified statistically significant evidence for a three-way epistatic interaction that is associated with susceptibility to TB. This interaction is stronger than any previously described one-way or two-way associations. This study highlights the importance of using machine learning methods that are designed to embrace, rather than ignore, the complexity of common diseases such as TB. We recommend future studies of the genetics of TB take into account the possibility that high-order epistatic interactions might play an important role in disease susceptibility.

Unsupervised Feature Learning With Winner-Takes-All Based STDP

PubMed Central

Ferré, Paul; Mamalet, Franck; Thorpe, Simon J.

2018-01-01

We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. We show equivalence between rank order coding Leaky-Integrate-and-Fire neurons and ReLU artificial neurons when applied to non-temporal data. We apply this to images using rank-order coding, which allows us to perform a full network simulation with a single feed-forward pass using GPU hardware. Next we introduce a binary STDP learning rule compatible with training on batches of images. Two mechanisms to stabilize the training are also presented : a Winner-Takes-All (WTA) framework which selects the most relevant patches to learn from along the spatial dimensions, and a simple feature-wise normalization as homeostatic process. This learning process allows us to train multi-layer architectures of convolutional sparse features. We apply our method to extract features from the MNIST, ETH80, CIFAR-10, and STL-10 datasets and show that these features are relevant for classification. We finally compare these results with several other state of the art unsupervised learning methods. PMID:29674961

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

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

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2011-02-18

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant linksmore » between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.« less

Action research on the development of a caring curriculum in Taiwan: Part II.

PubMed

Lee-Hsieh, Jane; Kuo, Chien-Lin; Turton, Michael A; Hsu, Chin-Lung; Chu, Hsiu-Chi

2007-12-01

This article presents the development, design, implementation, and evaluation of the third-year course of a caring curriculum being developed for a 5-year associate degree nursing program in Taiwan. The course, titled Application of Caring Concepts, was taught to more than 800 students by 16 instructors recruited from various departments. The instructors attended workshops and seminars on caring and then developed the course materials and teaching strategies. Instructional strategies included role modeling, dialogue, discussions, journaling, simulations, readings, and projects that involved students' applying caring skills outside of the classroom. Students were evaluated by patients in clinical practice using the Caring Behavior Measurement, developed in a previous study, and the course was evaluated by qualitative analysis of student feedback. Student responses to course content and instructional strategies were positive. Patients generally indicated that students always or normally performed caring behaviors. The study showed that with an appropriate curriculum and learning strategies, students can learn caring skills.

Porosity estimation by semi-supervised learning with sparsely available labeled samples

NASA Astrophysics Data System (ADS)

Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi

2017-09-01

This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation

PubMed Central

Brzosko, Zuzanna; Zannone, Sara; Schultz, Wolfram

2017-01-01

Spike timing-dependent plasticity (STDP) is under neuromodulatory control, which is correlated with distinct behavioral states. Previously, we reported that dopamine, a reward signal, broadens the time window for synaptic potentiation and modulates the outcome of hippocampal STDP even when applied after the plasticity induction protocol (Brzosko et al., 2015). Here, we demonstrate that sequential neuromodulation of STDP by acetylcholine and dopamine offers an efficacious model of reward-based navigation. Specifically, our experimental data in mouse hippocampal slices show that acetylcholine biases STDP toward synaptic depression, whilst subsequent application of dopamine converts this depression into potentiation. Incorporating this bidirectional neuromodulation-enabled correlational synaptic learning rule into a computational model yields effective navigation toward changing reward locations, as in natural foraging behavior. Thus, temporally sequenced neuromodulation of STDP enables associations to be made between actions and outcomes and also provides a possible mechanism for aligning the time scales of cellular and behavioral learning. DOI: http://dx.doi.org/10.7554/eLife.27756.001 PMID:28691903

Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

NASA Astrophysics Data System (ADS)

Ibragimov, Bulat; Toesca, Diego; Chang, Daniel; Koong, Albert; Xing, Lei

2017-12-01

Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was DSC= 0.83 and \

Program theory-driven evaluation science in a youth development context.

PubMed

Deane, Kelsey L; Harré, Niki

2014-08-01

Program theory-driven evaluation science (PTDES) provides a useful framework for uncovering the mechanisms responsible for positive change resulting from participation in youth development (YD) programs. Yet it is difficult to find examples of PTDES that capture the complexity of such experiences. This article offers a much-needed example of PTDES applied to Project K, a youth development program with adventure, service-learning and mentoring components. Findings from eight program staff focus groups, 351 youth participants' comments, four key program documents, and results from six previous Project K research projects were integrated to produce a theory of change for the program. A direct logic analysis was then conducted to assess the plausibility of the proposed theory against relevant research literature. This demonstrated that Project K incorporates many of the best practice principles discussed in the literature that covers the three components of the program. The contributions of this theory-building process to organizational learning and development are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Struggling to understand abstract science topics: a Roundhouse diagram-based study

NASA Astrophysics Data System (ADS)

Ward, Robin E.; Wandersee, James H.

2002-06-01

This study explored the effects of Roundhouse diagram construction on a previously low-performing middle school science student's struggles to understand abstract science concepts and principles. It is based on a metacognition-based visual learning model proposed by Wandersee in 1994. Ward and Wandersee introduced the Roundhouse diagram strategy and showed how it could be applied in science education. This article aims at elucidating the process by which Roundhouse diagramming helps learners bootstrap their current understandings to reach the intended meaningful understanding of complex science topics. The main findings of this study are that (a) it is crucial that relevant prior knowledge and dysfunctional alternative conceptions not be ignored during new learning if low-performing science students are to understand science well; (b) as the student's mastery of the Roundhouse diagram construction improved, so did science achievement; and (c) the student's apt choice of concept-related visual icons aided progress toward meaningful understanding of complex science concepts.

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.

Feature Discovery by Competitive Learning.

ERIC Educational Resources Information Center

Rumelhart, David E.; Zipser, David

1985-01-01

Reports results of studies with an unsupervised learning paradigm called competitive learning which is examined using computer simulation and formal analysis. When competitive learning is applied to parallel networks of neuron-like elements, many potentially useful learning tasks can be accomplished. (Author)

Learning to predict slip for ground robots

NASA Technical Reports Server (NTRS)

Angelova, Anelia; Matthies, Larry; Helmick, Daniel; Sibley, Gabe; Perona, Pietro

2006-01-01

In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only.

Tonic or Phasic Stimulation of Dopaminergic Projections to Prefrontal Cortex Causes Mice to Maintain or Deviate from Previously Learned Behavioral Strategies

PubMed Central

Ellwood, Ian T.; Patel, Tosha; Wadia, Varun; Lee, Anthony T.; Liptak, Alayna T.

2017-01-01

Dopamine neurons in the ventral tegmental area (VTA) encode reward prediction errors and can drive reinforcement learning through their projections to striatum, but much less is known about their projections to prefrontal cortex (PFC). Here, we studied these projections and observed phasic VTA–PFC fiber photometry signals after the delivery of rewards. Next, we studied how optogenetic stimulation of these projections affects behavior using conditioned place preference and a task in which mice learn associations between cues and food rewards and then use those associations to make choices. Neither phasic nor tonic stimulation of dopaminergic VTA–PFC projections elicited place preference. Furthermore, substituting phasic VTA–PFC stimulation for food rewards was not sufficient to reinforce new cue–reward associations nor maintain previously learned ones. However, the same patterns of stimulation that failed to reinforce place preference or cue–reward associations were able to modify behavior in other ways. First, continuous tonic stimulation maintained previously learned cue–reward associations even after they ceased being valid. Second, delivering phasic stimulation either continuously or after choices not previously associated with reward induced mice to make choices that deviated from previously learned associations. In summary, despite the fact that dopaminergic VTA–PFC projections exhibit phasic increases in activity that are time locked to the delivery of rewards, phasic activation of these projections does not necessarily reinforce specific actions. Rather, dopaminergic VTA–PFC activity can control whether mice maintain or deviate from previously learned cue–reward associations. SIGNIFICANCE STATEMENT Dopaminergic inputs from ventral tegmental area (VTA) to striatum encode reward prediction errors and reinforce specific actions; however, it is currently unknown whether dopaminergic inputs to prefrontal cortex (PFC) play similar or distinct roles. Here, we used bulk Ca2+ imaging to show that unexpected rewards or reward-predicting cues elicit phasic increases in the activity of dopaminergic VTA–PFC fibers. However, in multiple behavioral paradigms, we failed to observe reinforcing effects after stimulation of these fibers. In these same experiments, we did find that tonic or phasic patterns of stimulation caused mice to maintain or deviate from previously learned cue–reward associations, respectively. Therefore, although they may exhibit similar patterns of activity, dopaminergic inputs to striatum and PFC can elicit divergent behavioral effects. PMID:28739583

The Interplay of Perceptions of the Learning Environment, Personality and Learning Strategies: A Study amongst International Business Studies Students

ERIC Educational Resources Information Center

Nijhuis, Jan; Segers, Mien; Gijselaers, Wim

2007-01-01

Previous research on students' learning strategies has examined the relationships between either perceptions of the learning environment or personality and learning strategies. The focus of this study was on the joint relationships between the students' perceptions of the learning environment, their personality, and the learning strategies they…

The Nature of Self-Directed Learning and Transformational Learning in Self-Managing Bipolar Disorder to Stay Well

ERIC Educational Resources Information Center

Francik, Wendy A.

2012-01-01

The purpose of the research was to explore the self-directed learning and transformational learning experiences among persons with bipolar disorder. A review of previous research pointed out how personal experiences with self-directed learning and transformational learning facilitated individuals' learning to manage HIV, Methicillan-resitant…

Improving Health with Science: Exploring Community-Driven Science Education in Kenya

NASA Astrophysics Data System (ADS)

Leak, Anne Emerson

This study examines the role of place-based science education in fostering student-driven health interventions. While literature shows the need to connect science with students' place and community, there is limited understanding of strategies for doing so. Making such connections is important for underrepresented students who tend to perceive learning science in school as disconnected to their experiences out of school (Aikenhead, Calabrese-Barton, & Chinn, 2006). To better understand how students can learn to connect place and community with science and engineering practices in a village in Kenya, I worked with community leaders, teachers, and students to develop and study an education program (a school-based health club) with the goal of improving knowledge of health and sanitation in a Kenyan village. While students selected the health topics and problems they hoped to address through participating in the club, the topics were taught with a focus on providing opportunities for students to learn the practices of science and health applications of these practices. Students learned chemistry, physics, environmental science, and engineering to help them address the health problems they had identified in their community. Surveys, student artifacts, ethnographic field notes, and interview data from six months of field research were used to examine the following questions: (1) In what ways were learning opportunities planned for using science and engineering practices to improve community health? (2) In what ways did students apply science and engineering practices and knowledge learned from the health club in their school, homes, and community? and (3) What factors seemed to influence whether students applied or intended to apply what they learned in the health club? Drawing on place-based science education theory and community-engagement models of health, process and structural coding (Saldana, 2013) were used to determine patterns in students' applications of their learning. Students applied learning across health topics they identified as interesting and relevant to their community: hand-washing, disease-prevention, first aid, balanced diet, and water. Students' application of their learning was influenced by internal, external, and relational factors with the community, science education factors, and cultural factors. Some factors, which may have been barriers for students to apply their learning, were turned into supports via bridging strategies used by the students and teacher. Bridging strategies allowed students to connect between their place and science in meaningful ways in the classroom. These strategies were critical in bringing students' place into the classroom and enabling students to apply their learning toward place. The model resulting from the identified factors informed existing models for sociocultural considerations in community-based health interventions. The community-engagement applied practices of science (CAPS) model serves to conceptualize findings in this study and informs an integrated method for using community-engagement education as a stimuli for students to become cultural brokers and improve community health. In addition to focusing on teaching practices of science and encouraging students to apply their learning, this research suggests that bridging strategies can be used to connect science with a students' place in meaningful ways that serve both students and their local communities.

Effects of dopamine on reinforcement learning and consolidation in Parkinson's disease.

PubMed

Grogan, John P; Tsivos, Demitra; Smith, Laura; Knight, Brogan E; Bogacz, Rafal; Whone, Alan; Coulthard, Elizabeth J

2017-07-10

Emerging evidence suggests that dopamine may modulate learning and memory with important implications for understanding the neurobiology of memory and future therapeutic targeting. An influential hypothesis posits that dopamine biases reinforcement learning. More recent data also suggest an influence during both consolidation and retrieval. Eighteen Parkinson's disease patients learned through feedback ON or OFF medication, with memory tested 24 hr later ON or OFF medication (4 conditions, within-subjects design with matched healthy control group). Patients OFF medication during learning decreased in memory accuracy over the following 24 hr. In contrast to previous studies, however, dopaminergic medication during learning and testing did not affect expression of positive or negative reinforcement. Two further experiments were run without the 24 hr delay, but they too failed to reproduce effects of dopaminergic medication on reinforcement learning. While supportive of a dopaminergic role in consolidation, this study failed to replicate previous findings on reinforcement learning.

Temporally Coordinated Deep Brain Stimulation in the Dorsal and Ventral Striatum Synergistically Enhances Associative Learning.

PubMed

Katnani, Husam A; Patel, Shaun R; Kwon, Churl-Su; Abdel-Aziz, Samer; Gale, John T; Eskandar, Emad N

2016-01-04

The primate brain has the remarkable ability of mapping sensory stimuli into motor behaviors that can lead to positive outcomes. We have previously shown that during the reinforcement of visual-motor behavior, activity in the caudate nucleus is correlated with the rate of learning. Moreover, phasic microstimulation in the caudate during the reinforcement period was shown to enhance associative learning, demonstrating the importance of temporal specificity to manipulate learning related changes. Here we present evidence that extends upon our previous finding by demonstrating that temporally coordinated phasic deep brain stimulation across both the nucleus accumbens and caudate can further enhance associative learning. Monkeys performed a visual-motor associative learning task and received stimulation at time points critical to learning related changes. Resulting performance revealed an enhancement in the rate, ceiling, and reaction times of learning. Stimulation of each brain region alone or at different time points did not generate the same effect.

Bericht uber den 2. Internationalen Kongress fur Angewandte Linguistik (Report on the Second International Congress for Applied Linguistics).

ERIC Educational Resources Information Center

Mohr, Peter

This report of the 1969 Second International Congress for Applied Linguistics contains summaries of papers and speeches on the following topics: (1) linguistics applied to literary texts, (2) computer analysis of texts, (3) research in the psychology of first language learning, (4) research in the psychology of second language learning, (5) speech…

Learning-Based Just-Noticeable-Quantization- Distortion Modeling for Perceptual Video Coding.

PubMed

Ki, Sehwan; Bae, Sung-Ho; Kim, Munchurl; Ko, Hyunsuk

2018-07-01

Conventional predictive video coding-based approaches are reaching the limit of their potential coding efficiency improvements, because of severely increasing computation complexity. As an alternative approach, perceptual video coding (PVC) has attempted to achieve high coding efficiency by eliminating perceptual redundancy, using just-noticeable-distortion (JND) directed PVC. The previous JNDs were modeled by adding white Gaussian noise or specific signal patterns into the original images, which were not appropriate in finding JND thresholds due to distortion with energy reduction. In this paper, we present a novel discrete cosine transform-based energy-reduced JND model, called ERJND, that is more suitable for JND-based PVC schemes. Then, the proposed ERJND model is extended to two learning-based just-noticeable-quantization-distortion (JNQD) models as preprocessing that can be applied for perceptual video coding. The two JNQD models can automatically adjust JND levels based on given quantization step sizes. One of the two JNQD models, called LR-JNQD, is based on linear regression and determines the model parameter for JNQD based on extracted handcraft features. The other JNQD model is based on a convolution neural network (CNN), called CNN-JNQD. To our best knowledge, our paper is the first approach to automatically adjust JND levels according to quantization step sizes for preprocessing the input to video encoders. In experiments, both the LR-JNQD and CNN-JNQD models were applied to high efficiency video coding (HEVC) and yielded maximum (average) bitrate reductions of 38.51% (10.38%) and 67.88% (24.91%), respectively, with little subjective video quality degradation, compared with the input without preprocessing applied.

Testing Prepares Students to Learn Better: The Forward Effect of Testing in Category Learning

ERIC Educational Resources Information Center

Lee, Hee Seung; Ahn, Dahwi

2018-01-01

The forward effect of testing occurs when testing on previously studied information facilitates subsequent learning. The present research investigated whether interim testing on initially studied materials enhances the learning of new materials in category learning and examined the metacognitive judgments of such learning. Across the 4…

Positivity effect in healthy aging in observational but not active feedback-learning.

PubMed

Bellebaum, Christian; Rustemeier, Martina; Daum, Irene

2012-01-01

The present study investigated the impact of healthy aging on the bias to learn from positive or negative performance feedback in observational and active feedback learning. In active learning, a previous study had already shown a negative learning bias in healthy seniors older than 75 years, while no bias was found for younger seniors. However, healthy aging is accompanied by a 'positivity effect', a tendency to primarily attend to stimuli with positive valence. Based on recent findings of dissociable neural mechanisms in active and observational feedback learning, the positivity effect was hypothesized to influence older participants' observational feedback learning in particular. In two separate experiments, groups of young (mean age 27) and older participants (mean age 60 years) completed an observational or active learning task designed to differentially assess positive and negative learning. Older but not younger observational learners showed a significant bias to learn better from positive than negative feedback. In accordance with previous findings, no bias was found for active learning. This pattern of results is discussed in terms of differences in the neural underpinnings of active and observational learning from performance feedback.

Theoractive Learning towards Academic Endeavour

ERIC Educational Resources Information Center

Rajbhandari, Mani Man Singh

2018-01-01

Theoractive learning is an essential ingredient that can complement various academic theories, making them easier to apply to a learning environment. Although it appears that theoractive learning is the effect of the beneficial causes of teaching and learning in certain contextual settings; theoractive learning is, however, actions-oriented and…

No Trade-Off between Learning Speed and Associative Flexibility in Bumblebees: A Reversal Learning Test with Multiple Colonies

PubMed Central

Raine, Nigel E.; Chittka, Lars

2012-01-01

Potential trade-offs between learning speed and memory-related performance could be important factors in the evolution of learning. Here, we test whether rapid learning interferes with the acquisition of new information using a reversal learning paradigm. Bumblebees (Bombus terrestris) were trained to associate yellow with a floral reward. Subsequently the association between colour and reward was reversed, meaning bees then had to learn to visit blue flowers. We demonstrate that individuals that were fast to learn yellow as a predictor of reward were also quick to reverse this association. Furthermore, overnight memory retention tests suggest that faster learning individuals are also better at retaining previously learned information. There is also an effect of relatedness: colonies whose workers were fast to learn the association between yellow and reward also reversed this association rapidly. These results are inconsistent with a trade-off between learning speed and the reversal of a previously made association. On the contrary, they suggest that differences in learning performance and cognitive (behavioural) flexibility could reflect more general differences in colony learning ability. Hence, this study provides additional evidence to support the idea that rapid learning and behavioural flexibility have adaptive value. PMID:23028779

Assessing orientations to learning to teach.

PubMed

Oosterheert, Ida E; Vermunt, Jan D; Denessen, E

2002-03-01

An important purpose of teacher education is that student teachers develop and change their existing knowledge on learning and teaching. Research on how student teachers variously engage in this process is scarce. In a previous study of 30 student teachers, we identified five different orientations to learning to teach. Our aim was to extend the results of the previous study by developing an instrument to assess orientations to learning to teach at a larger scale. The development and psychometric properties of the instrument are discussed. The results with respect to how student teachers learn are compared to the results of the qualitative study. Participants in this study were 169 secondary student teachers from three institutes which had all adopted an initial in-service model of learning to teach. On the basis of extensive qualitative study, a questionnaire was developed to assess individual differences in learning to teach. Factor-, reliability-, and nonparametric scalability analyses were performed to identify reliable scales. Cluster analysis was used to identify groups of students with similar orientations to learning to teach. Eight scales covering cognitive, regulative and affective aspects of student teachers' learning were identified. Cluster analysis indicates that the instrument discriminates well between student teachers. Four of the five previously found patterns were found again. The four orientations found in relatively uniform learning environments indicate that student teachers need differential support in their learning. Although the instrument measures individual differences in a reliable way, it is somewhat one-sided in the sense that items representing constructive ways of learning dominate. New items forming a broader range of scales should be created.

Enhancing students’ cognitive skill in Nguyen Tat Thanh high school Hanoi Vietnam through scientific learning material of static electricity

NASA Astrophysics Data System (ADS)

Priyanto, A.; Linuwih, S.; Aji, M. P.; Bich, D. D.

2018-03-01

Scientific learning material is still needed by students at Nguyen Tat Thanh High School (NTT), Hanoi Vietnam in order to enhance the students’ cognitive skill. Cognitive skill represents the level of students’ understanding to the particular material. Students’ cognitive skill can be improved by applying the learning material based on scientific approach as a treatment. The enhancement of students’ cognitive skill can be measured by analyzing the students’ test result collected before and after treatment. The analysis is focused to measure the enhancement or the sifted of cognitive aspects including remembering aspect (C1), understanding aspect (C2), applying aspect (C3), analyzing aspect (C4), and evaluating aspect (C5). According to the analysis the enhancement of cognitive aspects are 8.26% of remembering, 3.26% of understanding, 32.94% of applying, 21.74% of analyzing, and 21.74% of evaluating. The major enhancements are occured at applying, analyzing, and evaluating aspects. Therefore it can be concluded that students’ cognitive skill is enhanced by applying scientific learning material of static electricity.

A Case for Contextual Learning.

ERIC Educational Resources Information Center

Souders, John; Prescott, Carolyn

1999-01-01

Establishing schooling/larger world connections is critical for adolescents. The contextual learning approach views learning as most effective when information is presented within a familiar framework. Employing puzzles, hands-on learning activities, project-based learning, contextual connections, applied math, mentoring, and wider audiences…

Changes to Students' Learning Processes Following Instruction on the Topic

ERIC Educational Resources Information Center

Clump, Michael A.

2005-01-01

Previous research indicates that students' learning styles, as assessed by the Inventory of Learning Processes (ILP; Schmeck, Ribich, & Ramanaiah, 1977), change during college. Additionally, prior research indicates that teaching students about their learning styles enables them to change those learning styles. The current study investigated…

Exploring the Engagement Effects of Visual Programming Language for Data Structure Courses

ERIC Educational Resources Information Center

Chang, Chih-Kai; Yang, Ya-Fei; Tsai, Yu-Tzu

2017-01-01

Previous research indicates that understanding the state of learning motivation enables researchers to deeply understand students' learning processes. Studies have shown that visual programming languages use graphical code, enabling learners to learn effectively, improve learning effectiveness, increase learning fun, and offering various other…

Understanding the Role of Academic Language on Conceptual Understanding in an Introductory Materials Science and Engineering Course

NASA Astrophysics Data System (ADS)

Kelly, Jacquelyn

Students may use the technical engineering terms without knowing what these words mean. This creates a language barrier in engineering that influences student learning. Previous research has been conducted to characterize the difference between colloquial and scientific language. Since this research had not yet been applied explicitly to engineering, conclusions from the area of science education were used instead. Various researchers outlined strategies for helping students acquire scientific language. However, few examined and quantified the relationship it had on student learning. A systemic functional linguistics framework was adopted for this dissertation which is a framework that has not previously been used in engineering education research. This study investigated how engineering language proficiency influenced conceptual understanding of introductory materials science and engineering concepts. To answer the research questions about engineering language proficiency, a convenience sample of forty-one undergraduate students in an introductory materials science and engineering course was used. All data collected was integrated with the course. Measures included the Materials Concept Inventory, a written engineering design task, and group observations. Both systemic functional linguistics and mental models frameworks were utilized to interpret data and guide analysis. A series of regression analyses were conducted to determine if engineering language proficiency predicts group engineering term use, if conceptual understanding predicts group engineering term use, and if conceptual understanding predicts engineering language proficiency. Engineering academic language proficiency was found to be strongly linked to conceptual understanding in the context of introductory materials engineering courses. As the semester progressed, this relationship became even stronger. The more engineering concepts students are expected to learn, the more important it is that they are proficient in engineering language. However, exposure to engineering terms did not influence engineering language proficiency. These results stress the importance of engineering language proficiency for learning, but warn that simply exposing students to engineering terms does not promote engineering language proficiency.

Multidisciplinary Views on Applying Explicit and Implicit Motor Learning in Practice: An International Survey

PubMed Central

Kleynen, Melanie; Braun, Susy M.; Rasquin, Sascha M. C.; Bleijlevens, Michel H. C.; Lexis, Monique A. S.; Halfens, Jos; Wilson, Mark R.; Masters, Rich S. W.; Beurskens, Anna J.

2015-01-01

Background A variety of options and techniques for causing implicit and explicit motor learning have been described in the literature. The aim of the current paper was to provide clearer guidance for practitioners on how to apply motor learning in practice by exploring experts’ opinions and experiences, using the distinction between implicit and explicit motor learning as a conceptual departure point. Methods A survey was designed to collect and aggregate informed opinions and experiences from 40 international respondents who had demonstrable expertise related to motor learning in practice and/or research. The survey was administered through an online survey tool and addressed potential options and learning strategies for applying implicit and explicit motor learning. Responses were analysed in terms of consensus (≥ 70%) and trends (≥ 50%). A summary figure was developed to illustrate a taxonomy of the different learning strategies and options indicated by the experts in the survey. Results Answers of experts were widely distributed. No consensus was found regarding the application of implicit and explicit motor learning. Some trends were identified: Explicit motor learning can be promoted by using instructions and various types of feedback, but when promoting implicit motor learning, instructions and feedback should be restricted. Further, for implicit motor learning, an external focus of attention should be considered, as well as practicing the entire skill. Experts agreed on three factors that influence motor learning choices: the learner’s abilities, the type of task, and the stage of motor learning (94.5%; n = 34/36). Most experts agreed with the summary figure (64.7%; n = 22/34). Conclusion The results provide an overview of possible ways to cause implicit or explicit motor learning, signposting examples from practice and factors that influence day-to-day motor learning decisions. PMID:26296203

Applying Learning Analytics to Investigate Timed Release in Online Learning

ERIC Educational Resources Information Center

Martin, Florence; Whitmer, John C.

2016-01-01

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

A Blended Mobile Learning Environment for Museum Learning

ERIC Educational Resources Information Center

Hou, Huei-Tse; Wu, Sheng-Yi; Lin, Peng-Chun; Sung, Yao-Ting; Lin, Jhe-Wei; Chang, Kuo-En

2014-01-01

The use of mobile devices for informal learning has gained attention over recent years. Museum learning is also regarded as an important research topic in the field of informal learning. This study explored a blended mobile museum learning environment (BMMLE). Moreover, this study applied three blended museum learning modes: (a) the traditional…

Adaptive and perceptual learning technologies in medical education and training.

PubMed

Kellman, Philip J

2013-10-01

Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

PubMed

Hepler, N Lance; Scheffler, Konrad; Weaver, Steven; Murrell, Ben; Richman, Douglas D; Burton, Dennis R; Poignard, Pascal; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-09-01

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

A WEB based approach in biomedical engineering design education.

PubMed

Enderle, J D; Browne, A F; Hallowell, M B

1997-01-01

As part of the accreditation process for university engineering programs, students are required to complete a minimum number of design credits in their course of study, typically at the senior level. Many call this the capstone course. Engineering design is a course or series of courses that bring together concepts and principles that students learn in their field of study--it involves the integration and extension of material learned in their major toward a specific project. Most often, the student is exposed to system-wide analysis, critique and evaluation for the first time. Design is an iterative, decision making process in which the student optimally applies previously learned material to meet a stated objective. At the University of Connecticut, students work in teams of 3-4 members and work on externally sponsored projects. To facilitate working with sponsors, a WEB based approach is used for reporting the progress on projects. Students are responsible for creating their own WEB sites that support both html and pdf formats. Students provide the following deliverables: weekly progress reports, project statement, specifications, project proposal, interim report, and final report. A senior design homepage also provides links to data books and other resources for use by students. We are also planning distance learning experiences between two campuses so students can work on projects that involve the use of video conferencing.

Sequential Multiple Assignment Randomized Trial (SMART) with Adaptive Randomization for Quality Improvement in Depression Treatment Program

PubMed Central

Chakraborty, Bibhas; Davidson, Karina W.

2015-01-01

Summary Implementation study is an important tool for deploying state-of-the-art treatments from clinical efficacy studies into a treatment program, with the dual goals of learning about effectiveness of the treatments and improving the quality of care for patients enrolled into the program. In this article, we deal with the design of a treatment program of dynamic treatment regimens (DTRs) for patients with depression post acute coronary syndrome. We introduce a novel adaptive randomization scheme for a sequential multiple assignment randomized trial of DTRs. Our approach adapts the randomization probabilities to favor treatment sequences having comparatively superior Q-functions used in Q-learning. The proposed approach addresses three main concerns of an implementation study: it allows incorporation of historical data or opinions, it includes randomization for learning purposes, and it aims to improve care via adaptation throughout the program. We demonstrate how to apply our method to design a depression treatment program using data from a previous study. By simulation, we illustrate that the inputs from historical data are important for the program performance measured by the expected outcomes of the enrollees, but also show that the adaptive randomization scheme is able to compensate poorly specified historical inputs by improving patient outcomes within a reasonable horizon. The simulation results also confirm that the proposed design allows efficient learning of the treatments by alleviating the curse of dimensionality. PMID:25354029

DNorm: disease name normalization with pairwise learning to rank

PubMed Central

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

2013-01-01

Motivation: 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. Methods: 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. Results: 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. Availability: 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 Contact: zhiyong.lu@nih.gov PMID:23969135

Improving the redistribution of the security lessons in healthcare: An evaluation of the Generic Security Template.

PubMed

He, Ying; Johnson, Chris

2015-11-01

The recurrence of past security breaches in healthcare showed that lessons had not been effectively learned across different healthcare organisations. Recent studies have identified the need to improve learning from incidents and to share security knowledge to prevent future attacks. Generic Security Templates (GSTs) have been proposed to facilitate this knowledge transfer. The objective of this paper is to evaluate whether potential users in healthcare organisations can exploit the GST technique to share lessons learned from security incidents. We conducted a series of case studies to evaluate GSTs. In particular, we used a GST for a security incident in the US Veterans' Affairs Administration to explore whether security lessons could be applied in a very different Chinese healthcare organisation. The results showed that Chinese security professional accepted the use of GSTs and that cyber security lessons could be transferred to a Chinese healthcare organisation using this approach. The users also identified the weaknesses and strengths of GSTs, providing suggestions for future improvements. Generic Security Templates can be used to redistribute lessons learned from security incidents. Sharing cyber security lessons helps organisations consider their own practices and assess whether applicable security standards address concerns raised in previous breaches in other countries. The experience gained from this study provides the basis for future work in conducting similar studies in other healthcare organisations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An Evaluation of the Use of Voice Boards, E-Book Readers and Virtual Worlds in a Postgraduate Distance Learning Applied Linguistics and TESOL Programme

ERIC Educational Resources Information Center

Rogerson-Revell, Pamela; Nie, Ming; Armellini, Alejandro

2012-01-01

We researched the incorporation of three learning technologies (voice boards, i.e. voice-based discussion boards, e-book readers, and Second Life virtual world), into the Master's Programme in Applied Linguistics and Teaching English to Speakers of Other Languages offered by distance learning at the University of Leicester. This small-scale study…

What You Learn is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning

PubMed Central

Smith, Linda

2016-01-01

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps to use eye movement data to infer underlying real-time processes in statistical word learning. PMID:22213894

Incorporating Problem-Based Learning in Physical Education Teacher Education

ERIC Educational Resources Information Center

Hushman, Glenn; Napper-Owen, Gloria

2011-01-01

Problem-based learning (PBL) is an educational method that identifies a problem as a context for student learning. Critical-thinking skills, deductive reasoning, knowledge, and behaviors are developed as students learn how theory can be applied to practical settings. Problem-based learning encourages self-direction, lifelong learning, and sharing…

Linking Action Learning and Inter-Organisational Learning: The Learning Journey Approach

ERIC Educational Resources Information Center

Schumacher, Thomas

2015-01-01

The article presents and illustrates the learning journey (LJ)--a new management development approach to inter-organisational learning based on observation, reflection and problem-solving. The LJ involves managers from different organisations and applies key concepts of action learning and systemic organisational development. Made up of…

Intelligent Discovery for Learning Objects Using Semantic Web Technologies

ERIC Educational Resources Information Center

Hsu, I-Ching

2012-01-01

The concept of learning objects has been applied in the e-learning field to promote the accessibility, reusability, and interoperability of learning content. Learning Object Metadata (LOM) was developed to achieve these goals by describing learning objects in order to provide meaningful metadata. Unfortunately, the conventional LOM lacks the…

Blended Learning: A Disruption that Has Found Its Time

ERIC Educational Resources Information Center

Gonzales, Lisa; Vodicka, Devin

2012-01-01

"Blended learning" is learning facilitated by the effective combination of different modes of delivery, models of teaching and styles of learning, and applying them in an interactively meaningful learning environment. There are four standard modes of blended learning that have proven to meet student academic needs and provide flexibility with…

New Definitions for New Higher Education Institutions

ERIC Educational Resources Information Center

Meyer, Katrina A.

2009-01-01

New terms were exploding early in the development of distance learning and virtual universities. Distance learning, online learning, e-learning, and distributed learning were applied to the various new forms of learning using online or Web-based materials and processes. However, largely thanks to the immediate popularity of the Western Governors'…

Shark: SQL and Rich Analytics at Scale

DTIC Science & Technology

2012-11-26

learning programs up to 100 faster than Hadoop. Unlike previous systems, Shark shows that it is possible to achieve these speedups while retaining a...Shark to run SQL queries up to 100× faster than Apache Hive, and machine learning programs up to 100× faster than Hadoop. Unlike previous systems, Shark...so using a runtime that is optimized for such workloads and a programming model that is designed to express machine learn - ing algorithms. 4.1

Sequence learning in Parkinson's disease: Focusing on action dynamics and the role of dopaminergic medication.

PubMed

Ruitenberg, Marit F L; Duthoo, Wout; Santens, Patrick; Seidler, Rachael D; Notebaert, Wim; Abrahamse, Elger L

2016-12-01

Previous studies on movement sequence learning in Parkinson's disease (PD) have produced mixed results. A possible explanation for the inconsistent findings is that some studies have taken dopaminergic medication into account while others have not. Additionally, in previous studies the response modalities did not allow for an investigation of the action dynamics of sequential movements as they unfold over time. In the current study we investigated sequence learning in PD by specifically considering the role of medication status in a sequence learning task where mouse movements were performed. The focus on mouse movements allowed us to examine the action dynamics of sequential movement in terms of initiation time, movement time, movement accuracy, and velocity. PD patients performed the sequence learning task once on their regular medication, and once after overnight withdrawal from their medication. Results showed that sequence learning as reflected in initiation times was impaired when PD patients performed the task ON medication compared to OFF medication. In contrast, sequence learning as reflected in the accuracy of movement trajectories was enhanced when performing the task ON compared to OFF medication. Our findings suggest that while medication enhances execution processes of movement sequence learning, it may at the same time impair planning processes that precede actual execution. Overall, the current study extends earlier findings on movement sequence learning in PD by differentiating between various components of performance, and further refines previous dopamine overdose effects in sequence learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

See, reflect, learn more: qualitative analysis of breaking bad news reflective narratives.

PubMed

Karnieli-Miller, Orit; Palombo, Michal; Meitar, Dafna

2018-05-01

Breaking bad news (BBN) is a challenge that requires multiple professional competencies. BBN teaching often includes didactic and group role-playing sessions. Both are useful and important, but exclude another critical component of students' learning: day-to-day role-model observation in the clinics. Given the importance of observation and the potential benefit of reflective writing in teaching, we have incorporated reflective writing into our BBN course. The aim of this study was to enhance our understanding of the learning potential in reflective writing about BBN encounters and the ability to identify components that inhibit this learning. This was a systematic qualitative immersion/crystallization analysis of 166 randomly selected BBN narratives written by 83 senior medical students. We analysed the narratives in an iterative consensus-building process to identify the issues discussed, the lessons learned and the enhanced understanding of BBN. Having previously been unaware of, not invited to or having avoided BBN encounters, the mandatory assignment led students to search for or ask their mentors to join them in BBN encounters. Observation and reflective writing enhanced students' awareness that 'bad news' is relative and subjective, while shedding light on patients', families', physicians' and their own experiences and needs, revealing the importance of the different components of the BBN protocol. We identified diversity among the narratives and the extent of students' learning. Narrative writing provided students with an opportunity for a deliberative learning process. This led to deeper understanding of BBN encounters, of how to apply the newly taught protocol, or of the need for it. This process connected the formal and informal or hidden curricula. To maximise learning through reflective writing, students should be encouraged to write in detail about a recent observed encounter, analyse it according to the protocol, address different participants' behaviours and emotions, and identify dilemmas and clear lessons learned. © 2018 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Deficits in hippocampal-dependent transfer generalization learning accompany synaptic dysfunction in a mouse model of amyloidosis

PubMed Central

Montgomery, Karienn S.; Edwards, George; Levites, Yona; Kumar, Ashok; Myers, Catherine E.; Gluck, Mark A.; Setlow, Barry; Bizon, Jennifer L.

2015-01-01

Elevated β-amyloid and impaired synaptic function in hippocampus are among the earliest manifestations of Alzheimer’s disease (AD). Most cognitive assessments employed in both humans and animal models, however, are insensitive to this early disease pathology. One critical aspect of hippocampal function is its role in episodic memory, which involves the binding of temporally coincident sensory information (e.g., sights, smells, and sounds) to create a representation of a specific learning epoch. Flexible associations can be formed among these distinct sensory stimuli that enable the “transfer” of new learning across a wide variety of contexts. The current studies employed a mouse analog of an associative “transfer learning” task that has previously been used to identify risk for prodromal AD in humans. The rodent version of the task assesses the transfer of learning about stimulus features relevant to a food reward across a series of compound discrimination problems. The relevant feature that predicts the food reward is unchanged across problems, but an irrelevant feature (i.e., the context) is altered. Experiment 1 demonstrated that C57BL6/J mice with bilateral ibotenic acid lesions of hippocampus were able to discriminate between two stimuli on par with control mice; however, lesioned mice were unable to transfer or apply this learning to new problem configurations. Experiment 2 used the APPswePS1 mouse model of amyloidosis to show that robust impairments in transfer learning are evident in mice with subtle β amyloid-induced synaptic deficits in the hippocampus. Finally, Experiment 3 confirmed that the same transfer learning impairments observed in APPswePS1 mice were also evident in the Tg-SwDI mouse, a second model of amyloidosis. Together, these data show that the ability to generalize learned associations to new contexts is disrupted even in the presence of subtle hippocampal dysfunction and suggest that, across species, this aspect of hippocampal-dependent learning may be useful for early identification of AD-like pathology. PMID:26418152

Learning in the thick of it.

PubMed

Darling, Marilyn; Parry, Charles; Moore, Joseph

2005-01-01

The U.S. Army's Opposing Force (OPFOR) is a 2,500-member brigade whose job is to help prepare soldiers for combat. Created to be the meanest, toughest foe that soldiers will ever face, OPFOR engages units-in-training in a variety of mock campaigns under a wide range of conditions. Every month, a fresh brigade of more than 4,000 soldiers takes on this standing enemy. OPFOR, which is stationed in the California desert, always has the home-court advantage. But the force being trained--called BLU FOR--is numerically and technologically superior. It possesses more resources and better, more available data. It is made up of experienced soldiers. And it knows just what to expect, because OPFOR shares its methods from previous campaigns with BLUFOR's commanders. In short, each BLUFOR brigade is given practically every edge. Yet OPFOR almost always wins. Underlying OPFOR's consistent success is the way it uses the after-action review (AAR), a method for extracting lessons from one event or project and applying them to others. AAR meetings became a popular business tool after Shell Oil began experimenting with them in 1998. Most corporate AARs, however, are faint echoes of the rigorous reviews performed by OPFOR. Companies tend to treat the process as a pro-forma wrap-up, drawing lessons from an action but rarely learning them. OPFOR's AARs, by contrast, generate raw material that is fed back into the execution cycle. And while OPFOR's reviews extract numerous lessons, the brigade does not consider a lesson to be learned until it is successfully applied and validated. It might not make sense for companies to adopt OPFOR's AAR processes in their entirety, but four fundamentals are mandatory: Lessons must benefit the team that extracts them. The AAR process must start atthe beginning of the activity. Lessons must link explicitly to future actions. And leaders must hold everyone, especially themselves, accountable for learning.

Community involvement in out of hospital cardiac arrest

PubMed Central

Shams, Ali; Raad, Mohamad; Chams, Nour; Chams, Sana; Bachir, Rana; El Sayed, Mazen J.

2016-01-01

Abstract Out of hospital cardiac arrest (OHCA) is a leading cause of death worldwide. Developing countries including Lebanon report low survival rates and poor neurologic outcomes in affected victims. Community involvement through early recognition and bystander cardiopulmonary resuscitation (CPR) can improve OHCA survival. This study assesses knowledge and attitude of university students in Lebanon and identifies potential barriers and facilitators to learning and performing CPR. A cross-sectional survey was administered to university students. The questionnaire included questions regarding the following data elements: demographics, knowledge, and awareness about sudden cardiac arrest, CPR, automated external defibrillator (AED) use, prior CPR and AED training, ability to perform CPR or use AED, barriers to performing/learning CPR/AED, and preferred location for attending CPR/AED courses. Descriptive analysis followed by multivariate analysis was carried out to identify predictors and barriers to learning and performing CPR. A total of 948 students completed the survey. Participants’ mean age was 20.1 (±2.1) years with 53.1% women. Less than half of participants (42.9%) were able to identify all the presenting signs of cardiac arrest. Only 33.7% of participants felt able to perform CPR when witnessing a cardiac arrest. Fewer participants (20.3%) reported receiving previous CPR training. Several perceived barriers to learning and performing CPR were also reported. Significant predictors of willingness to perform CPR when faced with a cardiac arrest were: earning higher income, previous CPR training and feeling confident in one's ability to apply an AED, or perform CPR. Lacking enough expertise in performing CPR was a significant barrier to willingness to perform CPR. University students in Lebanon are familiar with the symptoms of cardiac arrest, however, they are not well trained in CPR and lack confidence to perform it. The attitude towards the importance of bystander CPR and the need to learn CPR is very positive. Interventions should focus on public awareness campaigns regarding the importance of initiating bystander CPR while activating emergency medical services (EMS) and on making CPR training more available. PMID:27787361

Problems of Primary Education Today

ERIC Educational Resources Information Center

Dubova, M. V.

2014-01-01

Primary education in Russia has failed to adapt to the needs of post-Soviet society, and is still based on rote learning and memorization instead of learning through discovery and learning to use and apply what is learned.

Learning in Mental Retardation: A Comprehensive Bibliography.

ERIC Educational Resources Information Center

Gardner, James M.; And Others

The bibliography on learning in mentally handicapped persons is divided into the following topic categories: applied behavior change, classical conditioning, discrimination, generalization, motor learning, reinforcement, verbal learning, and miscellaneous. An author index is included. (KW)

Remembering Zoltan Dienes, a Maverick of Mathematics Teaching and Learning: Applying the Variability Principles to Teach Algebra

ERIC Educational Resources Information Center

Gningue, Serigne Mbaye

2016-01-01

This paper is written in honor of Zoltan Paul Dienes, an internationally renowned mathematician and educator, who passed away in January 2014. It is an attempt to describe, analyze and apply Dienes' theory on how mathematical structures can be taught by applying his four principles of learning upon which he believed a teacher can base concept…

State of Learning in Canada: A Year in Review, 2009-2010

ERIC Educational Resources Information Center

Canadian Council on Learning, 2010

2010-01-01

The 2009-2010 "State of Learning in Canada" provides the most current information on the Canadian learning landscape, contributing to a comprehensive understanding of how Canadians are faring as lifelong learners. As in previous "State of Learning" reports, this update reflects the Canadian Council on Learning's (CCL's) vision…

Optimal and Adaptive Online Learning

ERIC Educational Resources Information Center

Luo, Haipeng

2016-01-01

Online learning is one of the most important and well-established machine learning models. Generally speaking, the goal of online learning is to make a sequence of accurate predictions "on the fly," given some information of the correct answers to previous prediction tasks. Online learning has been extensively studied in recent years,…

An Examination of Learning Profiles in Physical Education

ERIC Educational Resources Information Center

Shen, Bo; Chen, Ang

2007-01-01

Using the model of domain learning as a theoretical framework, the study was designed to examine the extent to which learners' initial learning profiles based on previously acquired knowledge, learning strategy application, and interest-based motivation were distinctive in learning softball. Participants were 177 sixth-graders from three middle…

Evaluating the Effectiveness of Online Learning at the High School Level

ERIC Educational Resources Information Center

Haley, Robert

2013-01-01

United States high schools are increasingly using online learning to complement traditional classroom learning. Previous researchers of post secondary online learning have shown no significant differences between traditional and online learning. However, there has been little research at the secondary level about the effectiveness of online…

Exploring Mobile Learning in the Third Space

ERIC Educational Resources Information Center

Schuck, Sandy; Kearney, Matthew; Burden, Kevin

2017-01-01

Mobile learning is enabling educators and students to learn in ways not previously possible. The ways that portable, multi-functional mobile devices can untether the learner from formal institutional learning give scope for learning to be conceptualised in an expanded variety of places, times and ways. In this conceptual article the authors…

Individual Learning Accounts: A Strategy for Lifelong Learning?

ERIC Educational Resources Information Center

Renkema, Albert

2006-01-01

Purpose: Since the end of the previous century social partners in different branches of industry have laid down measures to stimulate individual learning and competence development of workers in collective labour agreements. Special attention is given to stimulating learning demand among traditional non-participants to lifelong learning, such as…

Conceptualizing the Essence of Presence in E-Learning through Digital Dasein

ERIC Educational Resources Information Center

Haj-Bolouri, Amir; Flensburg, Per

2017-01-01

Previous research on e-learning elucidates the notion of presence and learning. Scholars have conceptualized different concepts and theories based on the idea of distance education and learning. However, the "experience" of learning has been overshadowed with emphasizes on pedagogical models for social presence, theories on how to…

The Influences of Cognitive Styles on Individual Learning and Collaborative Learning

ERIC Educational Resources Information Center

Chen, Sherry Y.; Chang, Li-Ping

2016-01-01

Both individual learning (IL) and collaborative learning (CL) provide students with different benefits. However, previous research indicates that cognitive styles affect students' learning preferences. Thus, it is necessary to examine how cognitive styles influence students' reactions to IL and CL. Among various cognitive styles, Pask's…

Professional development for design-based learning in engineering education: a case study

NASA Astrophysics Data System (ADS)

Gómez Puente, Sonia M.; van Eijck, Michiel; Jochems, Wim

2015-01-01

Design-based learning (DBL) is an educational approach in which students gather and apply theoretical knowledge to solve design problems. In this study, we examined how critical DBL dimensions (project characteristics, design elements, the teacher's role, assessment, and social context) are applied by teachers in the redesign of DBL projects. We conducted an intervention for the professional development of the DBL teachers in the Mechanical Engineering and the Electrical Engineering departments. We used the Experiential Learning Cycle as an educational model for the professionalisation programme. The findings show that the programme encouraged teachers to apply the DBL theoretical framework. However, there are some limitations with regard to specific project characteristics. Further research into supporting teachers to develop open-ended and multidisciplinary activities in the projects that support learning is recommended.

Students' Energy Understanding Across Biology, Chemistry, and Physics Contexts

NASA Astrophysics Data System (ADS)

Opitz, S. T.; Neumann, K.; Bernholt, S.; Harms, U.

2017-07-01

Energy is considered both as a disciplinary core idea and as a concept cutting across science disciplines. Most previous approaches studied progressing energy understanding in specific disciplinary contexts, while disregarding the relation of understanding across them. Hence, this study provides a systematic analysis of cross-disciplinary energy learning. On the basis of a cross-sectional study with n = 742 students from grades 6, 8, and 10, we analyze students' progression in understanding energy across biology, chemistry, and physics contexts. The study is guided by three hypothetical scenarios that describe how the connection between energy understanding in the three disciplinary contexts changes across grade levels. These scenarios are compared using confirmatory factor analysis (CFA). The results suggest that, from grade 6 to grade 10, energy understanding in the three disciplinary contexts is highly interrelated, thus indicating a parallel progression of energy understanding in the three disciplinary contexts. In our study, students from grade 6 onwards appeared to have few problems to apply one energy understanding across the three disciplinary contexts. These findings were unexpected, as previous research concluded that students likely face difficulties in connecting energy learning across disciplinary boundaries. Potential reasons for these results and the characteristics of the observed cross-disciplinary energy understanding are discussed in the light of earlier findings and implications for future research, and the teaching of energy as a core idea and a crosscutting concept are addressed.

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

PubMed

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

2017-01-01

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

Fast Brain Plasticity during Word Learning in Musically-Trained Children

PubMed Central

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

2017-01-01

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

Measurement of Turbulent Pressure and Temperature Fluctuations in a Gas Turbine Combustor

NASA Technical Reports Server (NTRS)

Povinelli, Louis (Technical Monitor); LaGraff, John E.; Bramanti, Cristina; Pldfield, Martin; Passaro, Andrea; Biagioni, Leonardo

2004-01-01

The report summarizes the results of the redesign efforts directed towards the gas-turbine combustor rapid-injector flow diagnostic probe developed under sponsorship of NASA-GRC and earlier reported in NASA-CR-2003-212540. Lessons learned during the theoretical development, developmental testing and field-testing in the previous phase of this research were applied to redesign of both the probe sensing elements and of the rapid injection device. This redesigned probe (referred to herein as Turboprobe) has been fabricated and is ready, along with the new rapid injector, for field-testing. The probe is now designed to capture both time-resolved and mean total temperatures, total pressures and, indirectly, one component of turbulent fluctuations.

TRAFIC: fiber tract classification using deep learning

NASA Astrophysics Data System (ADS)

Ngattai Lam, Prince D.; Belhomme, Gaetan; Ferrall, Jessica; Patterson, Billie; Styner, Martin; Prieto, Juan C.

2018-03-01

We present TRAFIC, a fully automated tool for the labeling and classification of brain fiber tracts. TRAFIC classifies new fibers using a neural network trained using shape features computed from previously traced and manually corrected fiber tracts. It is independent from a DTI Atlas as it is applied to already traced fibers. This work is motivated by medical applications where the process of extracting fibers from a DTI atlas, or classifying fibers manually is time consuming and requires knowledge about brain anatomy. With this new approach we were able to classify traced fiber tracts obtaining encouraging results. In this report we will present in detail the methods used and the results achieved with our approach.

Self-calibrating models for dynamic monitoring and diagnosis

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin

1994-01-01

The present goal in qualitative reasoning is to develop methods for automatically building qualitative and semiquantitative models of dynamic systems and to use them for monitoring and fault diagnosis. The qualitative approach to modeling provides a guarantee of coverage while our semiquantitative methods support convergence toward a numerical model as observations are accumulated. We have developed and applied methods for automatic creation of qualitative models, developed two methods for obtaining tractable results on problems that were previously intractable for qualitative simulation, and developed more powerful methods for learning semiquantitative models from observations and deriving semiquantitative predictions from them. With these advances, qualitative reasoning comes significantly closer to realizing its aims as a practical engineering method.

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

PubMed Central

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting. PMID:25837826

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

PubMed

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.