Sample records for model reference learning

  1. Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

    PubMed

    Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

    2018-02-01

    This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

    NASA Astrophysics Data System (ADS)

    Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

    2017-04-01

    This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

  3. The Pursuit of Word Meanings

    PubMed Central

    Stevens, Jon Scott; Gleitman, Lila R.; Trueswell, John C.; Yang, Charles

    2016-01-01

    We evaluate here the performance of four models of cross-situational word learning; two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from naturalistic corpora of parent child-interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (2007), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest. PMID:27666335

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

    PubMed

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

    2015-11-01

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

  5. Word learning emerges from the interaction of online referent selection and slow associative learning

    PubMed Central

    McMurray, Bob; Horst, Jessica S.; Samuelson, Larissa K.

    2013-01-01

    Classic approaches to word learning emphasize the problem of referential ambiguity: in any naming situation the referent of a novel word must be selected from many possible objects, properties, actions, etc. To solve this problem, researchers have posited numerous constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative model in which referent selection is an online process that is independent of long-term learning. This two timescale approach creates significant power in the developing system. We illustrate this with a dynamic associative model in which referent selection is simulated as dynamic competition between competing referents, and learning is simulated using associative (Hebbian) learning. This model can account for a range of findings including the delay in expressive vocabulary relative to receptive vocabulary, learning under high degrees of referential ambiguity using cross-situational statistics, accelerating (vocabulary explosion) and decelerating (power-law) learning rates, fast-mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between individual differences in speed of processing and learning. Five theoretical points are illustrated. 1) Word learning does not require specialized processes – general association learning buttressed by dynamic competition can account for much of the literature. 2) The processes of recognizing familiar words are not different than those that support novel words (e.g., fast-mapping). 3) Online competition may allow the network (or child) to leverage information available in the task to augment performance or behavior despite what might be relatively slow learning or poor representations. 4) Even associative learning is more complex than previously thought – a major contributor to performance is the pruning of incorrect associations between words and referents. 5) Finally, the model illustrates that learning and referent selection/word recognition, though logically distinct, can be deeply and subtly related as phenomena like speed of processing and mutual exclusivity may derive in part from the way learning shapes the system. As a whole, this suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development and processing in children. PMID:23088341

  6. Word learning emerges from the interaction of online referent selection and slow associative learning.

    PubMed

    McMurray, Bob; Horst, Jessica S; Samuelson, Larissa K

    2012-10-01

    Classic approaches to word learning emphasize referential ambiguity: In naming situations, a novel word could refer to many possible objects, properties, actions, and so forth. To solve this, researchers have posited constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative in which referent selection is an online process and independent of long-term learning. We illustrate this theoretical approach with a dynamic associative model in which referent selection emerges from real-time competition between referents and learning is associative (Hebbian). This model accounts for a range of findings including the differences in expressive and receptive vocabulary, cross-situational learning under high degrees of ambiguity, accelerating (vocabulary explosion) and decelerating (power law) learning, fast mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between speed of processing and learning. Together it suggests that (a) association learning buttressed by dynamic competition can account for much of the literature; (b) familiar word recognition is subserved by the same processes that identify the referents of novel words (fast mapping); (c) online competition may allow the children to leverage information available in the task to augment performance despite slow learning; (d) in complex systems, associative learning is highly multifaceted; and (e) learning and referent selection, though logically distinct, can be subtly related. It suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  7. Modeling Cross-Situational Word–Referent Learning: Prior Questions

    PubMed Central

    Yu, Chen; Smith, Linda B.

    2013-01-01

    Both adults and young children possess powerful statistical computation capabilities—they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together. PMID:22229490

  8. Getting a Handle on Learning Anatomy with Interactive Three-Dimensional Graphics

    ERIC Educational Resources Information Center

    Stull, Andrew T.; Hegarty, Mary; Mayer, Richard E.

    2009-01-01

    In 2 experiments, participants learned bone anatomy by using a handheld controller to rotate an on-screen 3-dimensional bone model. The on-screen bone either included orientation references, which consisted of visible lines marking its axes (orientation reference condition), or did not include such references (no-orientation reference condition).…

  9. Justifying Alternative Models in Learning Astronomy: A Study of K-8 Science Teachers' Understanding of Frames of Reference

    ERIC Educational Resources Information Center

    Shen, Ji; Confrey, Jere

    2010-01-01

    Understanding frames of reference is critical in describing planetary motion and learning astronomy. Historically, the geocentric and heliocentric models were defended and advocated against each other. Today, there are still many people who do not understand the relationship between the two models. This topic is not adequately treated in astronomy…

  10. An associative model of adaptive inference for learning word-referent mappings.

    PubMed

    Kachergis, George; Yu, Chen; Shiffrin, Richard M

    2012-04-01

    People can learn word-referent pairs over a short series of individually ambiguous situations containing multiple words and referents (Yu & Smith, 2007, Cognition 106: 1558-1568). Cross-situational statistical learning relies on the repeated co-occurrence of words with their intended referents, but simple co-occurrence counts cannot explain the findings. Mutual exclusivity (ME: an assumption of one-to-one mappings) can reduce ambiguity by leveraging prior experience to restrict the number of word-referent pairings considered but can also block learning of non-one-to-one mappings. The present study first trained learners on one-to-one mappings with varying numbers of repetitions. In late training, a new set of word-referent pairs were introduced alongside pretrained pairs; each pretrained pair consistently appeared with a new pair. Results indicate that (1) learners quickly infer new pairs in late training on the basis of their knowledge of pretrained pairs, exhibiting ME; and (2) learners also adaptively relax the ME bias and learn two-to-two mappings involving both pretrained and new words and objects. We present an associative model that accounts for both results using competing familiarity and uncertainty biases.

  11. A Reference Model for Sustainable E-Learning Service Systems: Experiences with the Joint University/Teradata Consortium

    ERIC Educational Resources Information Center

    Demirkan, Haluk; Goul, Michael; Gros, Mary

    2010-01-01

    Many e-learning service systems fail. This is particularly true for those sponsored by joint industry/university consortia where substantial economic investments are required up-front. This article provides an industry/university consortia reference model validated through experiences with the 8-year-old Teradata University Network. The reference…

  12. Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

    PubMed

    Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

    2011-07-01

    In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Modeling Cross-Situational Word-Referent Learning: Prior Questions

    ERIC Educational Resources Information Center

    Yu, Chen; Smith, Linda B.

    2012-01-01

    Both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of…

  14. Self-Directed Learning in the Process of Work: Conceptual Considerations--Empirical Evidences.

    ERIC Educational Resources Information Center

    Straka, Gerald A.; Schaefer, Cornelia

    With reference to the literature on adult self-directed learning, a model termed the "Two-Shell Model of Motivated Self-Directed Learning" was formulated that differentiates sociohistorical environmental conditions, internal conditions, and activities related to four concepts (interest, learning strategies, control, and evaluation). The…

  15. Neurobehavioral Factors Associated with Referral for Learning Problems in a Community Sample: Evidence for an Adaptational Model for Learning Disorders.

    ERIC Educational Resources Information Center

    Waber, Deborah P.; Weiler, Michael D.; Forbes, Peter W.; Bernstein, Jane H.; Bellinger, David C.; Rappaport, Leonard

    2003-01-01

    Comparison of community children referred for learning disability evaluation (CR, n=17) with children not-referred in community general education (CGE, n=161), community special education (CSE, n=30), or from outpatient hospital referrals (HR). CR group performance was equivalent to that of CSE and HR groups. Results suggest conceptualizing…

  16. Research on Model of Student Engagement in Online Learning

    ERIC Educational Resources Information Center

    Peng, Wang

    2017-01-01

    In this study, online learning refers students under the guidance of teachers through the online learning platform for organized learning. Based on the analysis of related research results, considering the existing problems, the main contents of this paper include the following aspects: (1) Analyze and study the current student engagement model.…

  17. Computer Based Learning in Europe: A Bibliography.

    ERIC Educational Resources Information Center

    Rushby, N. J.

    This bibliography lists 172 references to papers on computer assisted learning (CAL) in European countries including the Soviet Union, Germany, Holland, Sweden, Yugoslavia, Austria, and Italy. The references which deal with such topics as teacher training, simulation, rural education, model construction, program evaluation, computer managed…

  18. The unrealized promise of infant statistical word-referent learning

    PubMed Central

    Smith, Linda B.; Suanda, Sumarga H.; Yu, Chen

    2014-01-01

    Recent theory and experiments offer a new solution as to how infant learners may break into word learning, by using cross-situational statistics to find the underlying word-referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word-referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics. PMID:24637154

  19. A Bootstrapping Model of Frequency and Context Effects in Word Learning.

    PubMed

    Kachergis, George; Yu, Chen; Shiffrin, Richard M

    2017-04-01

    Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. Copyright © 2016 Cognitive Science Society, Inc.

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

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

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

  1. Teaching Writing within the Common European Framework of Reference (CEFR): A Supplement Asynchronous Blended Learning Approach in an EFL Undergraduate Course in Egypt

    ERIC Educational Resources Information Center

    Shaarawy, Hanaa Youssef; Lotfy, Nohayer Esmat

    2013-01-01

    Based on the Common European Framework of Reference (CEFR) and following a blended learning approach (a supplement model), this article reports on a quasi-experiment where writing was taught evenly with other language skills in everyday language contexts and where asynchronous online activities were required from students to extend learning beyond…

  2. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  3. 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…

  4. Model of Distributed Learning Objects Repository for a Heterogenic Internet Environment

    ERIC Educational Resources Information Center

    Kaczmarek, Jerzy; Landowska, Agnieszka

    2006-01-01

    In this article, an extension of the existing structure of learning objects is described. The solution addresses the problem of the access and discovery of educational resources in the distributed Internet environment. An overview of e-learning standards, reference models, and problems with educational resources delivery is presented. The paper…

  5. Explorations in the Modeling of the Learning of Mathematics.

    ERIC Educational Resources Information Center

    Fuson, Karen C., Ed.; And Others

    Eleven research reports in the area of models of learning mathematics are presented in this publication of the Mathematics Education Reports series. The papers represent a mixture of theories, viewpoints, and references to other areas. Content areas addressed range from preschool to college levels. All the papers are concerned with the learning of…

  6. Tracking of multiple targets using online learning for reference model adaptation.

    PubMed

    Pernkopf, Franz

    2008-12-01

    Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

  7. Interoperability Gap Challenges for Learning Object Repositories & Learning Management Systems

    ERIC Educational Resources Information Center

    Mason, Robert T.

    2011-01-01

    An interoperability gap exists between Learning Management Systems (LMSs) and Learning Object Repositories (LORs). Learning Objects (LOs) and the associated Learning Object Metadata (LOM) that is stored within LORs adhere to a variety of LOM standards. A common LOM standard found in LORs is the Sharable Content Object Reference Model (SCORM)…

  8. An Intelligent Semantic E-Learning Framework Using Context-Aware Semantic Web Technologies

    ERIC Educational Resources Information Center

    Huang, Weihong; Webster, David; Wood, Dawn; Ishaya, Tanko

    2006-01-01

    Recent developments of e-learning specifications such as Learning Object Metadata (LOM), Sharable Content Object Reference Model (SCORM), Learning Design and other pedagogy research in semantic e-learning have shown a trend of applying innovative computational techniques, especially Semantic Web technologies, to promote existing content-focused…

  9. An Example of Competence-Based Learning: Use of Maxima in Linear Algebra for Engineers

    ERIC Educational Resources Information Center

    Diaz, Ana; Garcia, Alfonsa; de la Villa, Agustin

    2011-01-01

    This paper analyses the role of Computer Algebra Systems (CAS) in a model of learning based on competences. The proposal is an e-learning model Linear Algebra course for Engineering, which includes the use of a CAS (Maxima) and focuses on problem solving. A reference model has been taken from the Spanish Open University. The proper use of CAS is…

  10. Design Heuristics for Authentic Simulation-Based Learning Games

    ERIC Educational Resources Information Center

    Ney, Muriel; Gonçalves, Celso; Balacheff, Nicolas

    2014-01-01

    Simulation games are games for learning based on a reference in the real world. We propose a model for authenticity in this context as a result of a compromise among learning, playing and realism. In the health game used to apply this model, students interact with characters in the game through phone messages, mail messages, SMS and video.…

  11. Metaphoric Modeling of Foreign Language Teaching and Learning, with Special Reference to Teaching Philosophy Statements

    ERIC Educational Resources Information Center

    Alghbban, Mohammed I.; Ben Salamh, Sami; Maalej, Zouheir

    2017-01-01

    The current article investigates teachers' metaphoric modeling of foreign language teaching and learning at the College of Languages and Translation, King Saud University. It makes use of teaching philosophy statements as a corpus. Our objective is to analyze the underlying conceptualizations of teaching/learning, the teachers' perception of the…

  12. Stuttering Thoughts: Negative Self-Referent Thinking Is Less Sensitive to Aversive Outcomes in People with Higher Levels of Depressive Symptoms

    PubMed Central

    Iijima, Yudai; Takano, Keisuke; Boddez, Yannick; Raes, Filip; Tanno, Yoshihiko

    2017-01-01

    Learning theories of depression have proposed that depressive cognitions, such as negative thoughts with reference to oneself, can develop through a reinforcement learning mechanism. This negative self-reference is considered to be positively reinforced by rewarding experiences such as genuine support from others after negative self-disclosure, and negatively reinforced by avoidance of potential aversive situations. The learning account additionally predicts that negative self-reference would be maintained by an inability to adjust one’s behavior when negative self-reference no longer leads to such reward. To test this prediction, we designed an adapted version of the reversal-learning task. In this task, participants were reinforced to choose and engage in either negative or positive self-reference by probabilistic economic reward and punishment. Although participants were initially trained to choose negative self-reference, the stimulus-reward contingencies were reversed to prompt a shift toward positive self-reference (Study 1) and a further shift toward negative self-reference (Study 2). Model-based computational analyses showed that depressive symptoms were associated with a low learning rate of negative self-reference, indicating a high level of reward expectancy for negative self-reference even after the contingency reversal. Furthermore, the difficulty in updating outcome predictions of negative self-reference was significantly associated with the extent to which one possesses negative self-images. These results suggest that difficulty in adjusting action-outcome estimates for negative self-reference increases the chance to be faced with negative aspects of self, which may result in depressive symptoms. PMID:28824511

  13. Stuttering Thoughts: Negative Self-Referent Thinking Is Less Sensitive to Aversive Outcomes in People with Higher Levels of Depressive Symptoms.

    PubMed

    Iijima, Yudai; Takano, Keisuke; Boddez, Yannick; Raes, Filip; Tanno, Yoshihiko

    2017-01-01

    Learning theories of depression have proposed that depressive cognitions, such as negative thoughts with reference to oneself, can develop through a reinforcement learning mechanism. This negative self-reference is considered to be positively reinforced by rewarding experiences such as genuine support from others after negative self-disclosure, and negatively reinforced by avoidance of potential aversive situations. The learning account additionally predicts that negative self-reference would be maintained by an inability to adjust one's behavior when negative self-reference no longer leads to such reward. To test this prediction, we designed an adapted version of the reversal-learning task. In this task, participants were reinforced to choose and engage in either negative or positive self-reference by probabilistic economic reward and punishment. Although participants were initially trained to choose negative self-reference, the stimulus-reward contingencies were reversed to prompt a shift toward positive self-reference (Study 1) and a further shift toward negative self-reference (Study 2). Model-based computational analyses showed that depressive symptoms were associated with a low learning rate of negative self-reference, indicating a high level of reward expectancy for negative self-reference even after the contingency reversal. Furthermore, the difficulty in updating outcome predictions of negative self-reference was significantly associated with the extent to which one possesses negative self-images. These results suggest that difficulty in adjusting action-outcome estimates for negative self-reference increases the chance to be faced with negative aspects of self, which may result in depressive symptoms.

  14. A Security Framework for Online Distance Learning and Training.

    ERIC Educational Resources Information Center

    Furnell, S. M.; Onions, P. D.; Bleimann, U.; Gojny, U.; Knahl, M.; Roder, H. F.; Sanders, P. W.

    1998-01-01

    Presents a generic reference model for online distance learning and discusses security issues for each stage (enrollment, study, completion, termination, suspension). Discusses a security framework (authentication and accountability, access control, intrusion detection, network communications, nonrepudiation, learning resources provider…

  15. The Corporate University Model for Continuous Learning, Training and Development.

    ERIC Educational Resources Information Center

    El-Tannir, Akram A.

    2002-01-01

    Corporate universities typically convey corporate culture and provide systematic curriculum aimed at achieving strategic objectives. Virtual access and company-specific content combine to provide opportunities for continuous and active learning, a model that is becoming pervasive. (Contains 17 references.) (SK)

  16. Efficient model learning methods for actor-critic control.

    PubMed

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

    2012-06-01

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

  17. Toward Modeling the Learner's Personality Using Educational Games

    ERIC Educational Resources Information Center

    Essalmi, Fathi; Tlili, Ahmed; Ben Ayed, Leila Jemni; Jemmi, Mohamed

    2017-01-01

    Learner modeling is a crucial step in the learning personalization process. It allows taking into consideration the learner's profile to make the learning process more efficient. Most studies refer to an explicit method, namely questionnaire, to model learners. Questionnaires are time consuming and may not be motivating for learners. Thus, this…

  18. Paradoxes of a Long Life Learning: An Exploration of Peter Jarvis's Contribution to Experiential Learning Theory

    ERIC Educational Resources Information Center

    Dyke, Martin

    2017-01-01

    The paper explores the work of Peter Jarvis related to learning with particular reference to his definitions of learning and his models of the learning process. This exploration will consider different approaches to experiential learning and demonstrate the contribution Jarvis has made, noting how his writing on the subject has changed over time.…

  19. What Seams Do We Remove in Mobile-Assisted Seamless Learning? A Critical Review of the Literature

    ERIC Educational Resources Information Center

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2011-01-01

    Seamless learning refers to the seamless integration of the learning experiences across various dimensions including formal and informal learning contexts, individual and social learning, and physical world and cyberspace. Inspired by the exposition by Chan et al. (2006) on the seamless learning model supported by the setting of one or more mobile…

  20. Architectural and Functional Design and Evaluation of E-Learning VUIS Based on the Proposed IEEE LTSA Reference Model.

    ERIC Educational Resources Information Center

    O'Droma, Mairtin S.; Ganchev, Ivan; McDonnell, Fergal

    2003-01-01

    Presents a comparative analysis from the Institute of Electrical and Electronics Engineers (IEEE) Learning Technology Standards Committee's (LTSC) of the architectural and functional design of e-learning delivery platforms and applications, e-learning course authoring tools, and learning management systems (LMSs), with a view of assessing how…

  1. Learning by Living: Life-Altering Medical Education through Nursing Home-Based Experiential Learning

    ERIC Educational Resources Information Center

    Gugliucci, Marilyn R.; Weiner, Audrey

    2013-01-01

    The University of New England College of Osteopathic Medicine Learning by Living Project (referred to as Learning by Living) was piloted in 2006 as an experiential medical education learning model. Since its inception, medical and other health professions students have been "admitted" into nursing homes to live the life of an older adult nursing…

  2. Consultant Learning: A Model for Student-Directed Learning in Management Education.

    ERIC Educational Resources Information Center

    Kunkel, Scott W.

    2002-01-01

    Consultant learning turns the management classroom into a laboratory for free enterprise. Students determine their own grades by earning consulting fees for completing projects they design and propose. Project work becomes a portfolio for future employment. (Contains 15 references.) (SK)

  3. An E-Learning Framework for Assessment (FREMA)

    ERIC Educational Resources Information Center

    Wills, Gary B.; Bailey, Christopher P.; Davis, Hugh C.; Gilbert, Lester; Howard, Yvonne; Jeyes, Steve; Millard, David E.; Price, Joseph; Sclater, Niall; Sherratt, Robert; Tulloch, Iain; Young, Rowin

    2009-01-01

    This article reports on the e-Framework Reference Model for Assessment (FREMA) project that aimed at creating a reference model for the assessment domain: a guide to what resources (standards, projects, people, organisations, software, services and use cases) exist for the domain, aimed at helping strategists understand the state of e-learning…

  4. A Study of the Efficacy of Project-Based Learning Integrated with Computer-Based Simulation--STELLA

    ERIC Educational Resources Information Center

    Eskrootchi, Rogheyeh; Oskrochi, G. Reza

    2010-01-01

    Incorporating computer-simulation modelling into project-based learning may be effective but requires careful planning and implementation. Teachers, especially, need pedagogical content knowledge which refers to knowledge about how students learn from materials infused with technology. This study suggests that students learn best by actively…

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

    ERIC Educational Resources Information Center

    Ritchie, Graeme

    2003-01-01

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

  6. Killing the Buddha: Towards a Heretical Philosophy of Learning

    ERIC Educational Resources Information Center

    Johansson, Viktor

    2018-01-01

    This article explores how different philosophical models and pictures of learning can become dogmatic and disguise other conceptions of learning. With reference to a passage from St. Paul, I give a sense of the dogmatic teleology that underpins philosophical assumptions about learning. The Pauline assumption is exemplified through a variety of…

  7. A SCORM Odyssey.

    ERIC Educational Resources Information Center

    Shackelford, Bill

    2002-01-01

    Discusses the Shareable Content Object Reference Model (SCORM), which integrates electronic learning standards to provide a common ground for course development. Describes the Advanced Distributed Learning Co-Laboratory at the University of Wisconsin- Madison campus. (JOW)

  8. A Model of Statistics Performance Based on Achievement Goal Theory.

    ERIC Educational Resources Information Center

    Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.

    2003-01-01

    Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…

  9. Graphical Technique to Support the Teaching/Learning Process of Software Process Reference Models

    NASA Astrophysics Data System (ADS)

    Espinosa-Curiel, Ismael Edrein; Rodríguez-Jacobo, Josefina; Fernández-Zepeda, José Alberto

    In this paper, we propose a set of diagrams to visualize software process reference models (PRM). The diagrams, called dimods, are the combination of some visual and process modeling techniques such as rich pictures, mind maps, IDEF and RAD diagrams. We show the use of this technique by designing a set of dimods for the Mexican Software Industry Process Model (MoProSoft). Additionally, we perform an evaluation of the usefulness of dimods. The result of the evaluation shows that dimods may be a support tool that facilitates the understanding, memorization, and learning of software PRMs in both, software development organizations and universities. The results also show that dimods may have advantages over the traditional description methods for these types of models.

  10. A novel semi-transductive learning framework for efficient atypicality detection in chest radiographs

    NASA Astrophysics Data System (ADS)

    Alzubaidi, Mohammad; Balasubramanian, Vineeth; Patel, Ameet; Panchanathan, Sethuraman; Black, John A., Jr.

    2012-03-01

    Inductive learning refers to machine learning algorithms that learn a model from a set of training data instances. Any test instance is then classified by comparing it to the learned model. When the set of training instances lend themselves well to modeling, the use of a model substantially reduces the computation cost of classification. However, some training data sets are complex, and do not lend themselves well to modeling. Transductive learning refers to machine learning algorithms that classify test instances by comparing them to all of the training instances, without creating an explicit model. This can produce better classification performance, but at a much higher computational cost. Medical images vary greatly across human populations, constituting a data set that does not lend itself well to modeling. Our previous work showed that the wide variations seen across training sets of "normal" chest radiographs make it difficult to successfully classify test radiographs with an inductive (modeling) approach, and that a transductive approach leads to much better performance in detecting atypical regions. The problem with the transductive approach is its high computational cost. This paper develops and demonstrates a novel semi-transductive framework that can address the unique challenges of atypicality detection in chest radiographs. The proposed framework combines the superior performance of transductive methods with the reduced computational cost of inductive methods. Our results show that the proposed semitransductive approach provides both effective and efficient detection of atypical regions within a set of chest radiographs previously labeled by Mayo Clinic expert thoracic radiologists.

  11. Teaching and Learning Activity Sequencing System using Distributed Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Matsui, Tatsunori; Ishikawa, Tomotake; Okamoto, Toshio

    The purpose of this study is development of a supporting system for teacher's design of lesson plan. Especially design of lesson plan which relates to the new subject "Information Study" is supported. In this study, we developed a system which generates teaching and learning activity sequences by interlinking lesson's activities corresponding to the various conditions according to the user's input. Because user's input is multiple information, there will be caused contradiction which the system should solve. This multiobjective optimization problem is resolved by Distributed Genetic Algorithms, in which some fitness functions are defined with reference models on lesson, thinking and teaching style. From results of various experiments, effectivity and validity of the proposed methods and reference models were verified; on the other hand, some future works on reference models and evaluation functions were also pointed out.

  12. Grading for Understanding--Standards-Based Grading

    ERIC Educational Resources Information Center

    Zimmerman, Todd

    2017-01-01

    Standards-based grading (SBG), sometimes called learning objectives-based assessment (LOBA), is an assessment model that relies on students demonstrating mastery of learning objectives (sometimes referred to as standards). The goal of this grading system is to focus students on mastering learning objectives rather than on accumulating points. I…

  13. The Learning Cycle and College Science Teaching.

    ERIC Educational Resources Information Center

    Barman, Charles R.; Allard, David W.

    Originally developed in an elementary science program called the Science Curriculum Improvement Study, the learning cycle (LC) teaching approach involves students in an active learning process modeled on four elements of Jean Piaget's theory of cognitive development: physical experience, referring to the biological growth of the central nervous…

  14. Modeling Learning Processes in Lexical CALL.

    ERIC Educational Resources Information Center

    Goodfellow, Robin; Laurillard, Diana

    1994-01-01

    Studies the performance of a novice Spanish student using a Computer-assisted language learning (CALL) system designed for vocabulary enlargement. Results indicate that introspective evidence may be used to validate performance data within a theoretical framework that characterizes the learning approach as "surface" or "deep." (25 references)…

  15. Using the Longman Mini-concordancer on Tagged and Parsed Corpora, with Special Reference to Their Use as an Aid to Grammar Learning.

    ERIC Educational Resources Information Center

    Qiao, Hong Liang; Sussex, Roland

    1996-01-01

    Presents methods for using the Longman Mini-Concordancer on tagged and parsed corpora rather than plain text corpora. The article discusses several aspects with models to be applied in the classroom as an aid to grammar learning. This paper suggests exercises suitable for teaching English to both native and nonnative speakers. (13 references)…

  16. Putting the Horse Back in Front of the Cart: Using Visions and Decisions about High-Quality Learning Experiences to Drive Course Design

    ERIC Educational Resources Information Center

    Allen, Deborah; Tanner, Kimberly

    2007-01-01

    This article discusses a systematic approach to designing significant learning experiences, often referred to as the "backward design process," which has been popularized by Wiggins and McTighe (1998) and is included as a central feature of L. Dee Fink's model for integrated course design (Fink, 2003). The process is referred to as backward…

  17. Reinforcement and inference in cross-situational word learning.

    PubMed

    Tilles, Paulo F C; Fontanari, José F

    2013-01-01

    Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

  18. An Integrative Account of Constraints on Cross-Situational Learning

    PubMed Central

    Yurovsky, Daniel; Frank, Michael C.

    2015-01-01

    Word-object co-occurrence statistics are a powerful information source for vocabulary learning, but there is considerable debate about how learners actually use them. While some theories hold that learners accumulate graded, statistical evidence about multiple referents for each word, others suggest that they track only a single candidate referent. In two large-scale experiments, we show that neither account is sufficient: Cross-situational learning involves elements of both. Further, the empirical data are captured by a computational model that formalizes how memory and attention interact with co-occurrence tracking. Together, the data and model unify opposing positions in a complex debate and underscore the value of understanding the interaction between computational and algorithmic levels of explanation. PMID:26302052

  19. The Development of SCORM-Conformant Learning Content Based on the Learning Cycle Using Participatory Design

    ERIC Educational Resources Information Center

    Su, C. Y.; Chiu, C. H.; Wang, T. I.

    2010-01-01

    This study incorporates the 5E learning cycle strategy to design and develop Sharable Content Object Reference Model-conformant materials for elementary science education. The 5E learning cycle that supports the constructivist approach has been widely applied in science education. The strategy consists of five phases: engagement, exploration,…

  20. Attitudes to Formal Business Training and Learning amongst Entrepreneurs in the Cultural Industries: Situated Business Learning through "Doing with Others."

    ERIC Educational Resources Information Center

    Raffo, Carlo; O'Connor, Justin; Lovatt, Andy; Banks, Mark

    2000-01-01

    Presents arguments supporting a social model of learning linked to situated learning and cultural capital. Critiques training methods used in cultural industries (arts, publishing, broadcasting, design, fashion, restaurants). Uses case study evidence to demonstrates inadequacies of formal training in this sector. (Contains 49 references.) (SK)

  1. Online Learner Self-Regulation: Learning Presence Viewed through Quantitative Content- and Social Network Analysis

    ERIC Educational Resources Information Center

    Shea, Peter; Hayes, Suzanne; Smith, Sedef Uzuner; Vickers, Jason; Bidjerano, Temi; Gozza-Cohen, Mary; Jian, Shou-Bang; Pickett, Alexandra M.; Wilde, Jane; Tseng, Chi-Hua

    2013-01-01

    This paper presents an extension of an ongoing study of online learning framed within the community of inquiry (CoI) model (Garrison, Anderson, & Archer, 2001) in which we further examine a new construct labeled as "learning presence." We use learning presence to refer to the iterative processes of forethought and planning,…

  2. Elucidating Usage of e-Government Learning: A Perspective of the Extended Technology Acceptance Model

    ERIC Educational Resources Information Center

    Shyu, Stacy Huey-Pyng; Huang, Jen-Hung

    2011-01-01

    Learning is critical to both economic prosperity and social cohesion. E-government learning, which refers to the government's use of web-based technologies to facilitate learning about subjects that are useful to citizens, is relatively new, relevant, and potentially cost-effective. This work proposes and verifies that the technology acceptance…

  3. The Exploration of Models Regarding E-Learning Readiness: Reference Model Suggestions

    ERIC Educational Resources Information Center

    Demir, Ömer; Yurdugül, Halil

    2015-01-01

    Many studies have been conducted about readiness for e-learning, yet it is quite hard to decide which work of research from the literature to use in a specific context. Therefore, the aim of this study is to identify of which components models consist and for which stakeholders they were developed by investigating the most comprehensive and…

  4. Flexible Learning in Teacher Education: Myths, Muddles and Models

    ERIC Educational Resources Information Center

    Bigum, Chris; Rowan, Leonie

    2004-01-01

    While there has been widespread take-up of the concept 'flexible learning' within various educational environments--and equally frequent references to the flexible 'natures' of the computer and communication technologies that often underpin flexible learning initiatives--the relationship between technologies and flexibility is not a simple one. In…

  5. Metaphor, computing systems, and active learning

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

    Carroll, J.M.; Mack, R.L.

    1982-01-01

    The authors discuss the learning process that is directed towards particular goals and is initiated by the learner, through which metaphors become relevant and effective in learning. This allows an analysis of metaphors that explains why metaphors are incomplete and open-ended, and how this stimulates the construction of mental models. 9 references.

  6. Design, Development, and Validation of Learning Objects

    ERIC Educational Resources Information Center

    Nugent, Gwen; Soh, Leen-Kiat; Samal, Ashok

    2006-01-01

    A learning object is a small, stand-alone, mediated content resource that can be reused in multiple instructional contexts. In this article, we describe our approach to design, develop, and validate Shareable Content Object Reference Model (SCORM) compliant learning objects for undergraduate computer science education. We discuss the advantages of…

  7. The Actualization of Literary Learning Model Based on Verbal-Linguistic Intelligence

    ERIC Educational Resources Information Center

    Hali, Nur Ihsan

    2017-01-01

    This article is inspired by Howard Gardner's concept of linguistic intelligence and also from some authors' previous writings. All of them became the authors' reference in developing ideas on constructing a literary learning model based on linguistic intelligence. The writing of this article is not done by collecting data empirically, but by…

  8. Teaching Supply Chain Management Complexities: A SCOR Model Based Classroom Simulation

    ERIC Educational Resources Information Center

    Webb, G. Scott; Thomas, Stephanie P.; Liao-Troth, Sara

    2014-01-01

    The SCOR (Supply Chain Operations Reference) Model Supply Chain Classroom Simulation is an in-class experiential learning activity that helps students develop a holistic understanding of the processes and challenges of supply chain management. The simulation has broader learning objectives than other supply chain related activities such as the…

  9. Normalization Regression Estimation With Application to a Nonorthogonal, Nonrecursive Model of School Learning.

    ERIC Educational Resources Information Center

    Bulcock, J. W.; And Others

    Advantages of normalization regression estimation over ridge regression estimation are demonstrated by reference to Bloom's model of school learning. Theoretical concern centered on the structure of scholastic achievement at grade 10 in Canadian high schools. Data on 886 students were randomly sampled from the Carnegie Human Resources Data Bank.…

  10. A Blended Learning Approach to Teaching Project Management: A Model for Active Participation and Involvement--Insights from Norway

    ERIC Educational Resources Information Center

    Hussein, Bassam A.

    2015-01-01

    The paper demonstrates and evaluates the effectiveness of a blended learning approach to create a meaningful learning environment. We use the term blended learning approach in this paper to refer to the use of multiple or hybrid instructional methods that emphasize the role of learners as contributors to the learning process rather than recipients…

  11. A Path Less Chosen: An Assessment of the School of Advanced Military Studies

    DTIC Science & Technology

    2014-05-22

    the theory learned in course one.40 This course used theory , history, doctrine (both US and Soviet), and practical exercises to study the basic...relationships between learning domains, levels of learning and learning objectives, and the experiential learning model.96 In short, there is a major emphasis...discussion. There are multiple theories of education related to the use of discussion in learning . The most frequently cited or referred to amongst

  12. An exploration for research-oriented teaching model in biology teaching.

    PubMed

    Xing, Wanjin; Mo, Morigen; Su, Huimin

    2014-07-01

    Training innovative talents, as one of the major aims for Chinese universities, needs to reform the traditional teaching methods. The research-oriented teaching method has been introduced and its connotation and significance for Chinese university teaching have been discussed for years. However, few practical teaching methods for routine class teaching were proposed. In this paper, a comprehensive and concrete research-oriented teaching model with contents of reference value and evaluation method for class teaching was proposed based on the current teacher-guiding teaching model in China. We proposed that the research-oriented teaching model should include at least seven aspects on: (1) telling the scientific history for the skills to find out scientific questions; (2) replaying the experiments for the skills to solve scientific problems; (3) analyzing experimental data for learning how to draw a conclusion; (4) designing virtual experiments for learning how to construct a proposal; (5) teaching the lesson as the detectives solve the crime for learning the logic in scientific exploration; (6) guiding students how to read and consult the relative references; (7) teaching students differently according to their aptitude and learning ability. In addition, we also discussed how to evaluate the effects of the research-oriented teaching model in examination.

  13. Infants Encode Phonetic Detail during Cross-Situational Word Learning

    PubMed Central

    Escudero, Paola; Mulak, Karen E.; Vlach, Haley A.

    2016-01-01

    Infants often hear new words in the context of more than one candidate referent. In cross-situational word learning (XSWL), word-object mappings are determined by tracking co-occurrences of words and candidate referents across multiple learning events. Research demonstrates that infants can learn words in XSWL paradigms, suggesting that it is a viable model of real-world word learning. However, these studies have all presented infants with words that have no or minimal phonological overlap (e.g., BLICKET and GAX). Words often contain some degree of phonological overlap, and it is unknown whether infants can simultaneously encode fine phonological detail while learning words via XSWL. We tested 12-, 15-, 17-, and 20-month-olds’ XSWL of eight words that, when paired, formed non-minimal pairs (MPs; e.g., BON–DEET) or MPs (e.g., BON–TON, DEET–DIT). The results demonstrated that infants are able to learn word-object mappings and encode them with sufficient phonetic detail as to identify words in both non-minimal and MP contexts. Thus, this work suggests that infants are able to simultaneously discriminate phonetic differences between words and map words to referents in an implicit learning paradigm such as XSWL. PMID:27708605

  14. Science Centres and Science Learning.

    ERIC Educational Resources Information Center

    Rennie, Leonie J.; McClafferty, Terence P.

    1996-01-01

    Focuses on the interactive science center and its history over the last four decades. Traces the original idea to Francis Bacon. Recommends the use of cross-site studies to develop a model of learning in this setting. Contains 141 references. (DDR)

  15. Women, Work, and Learning.

    ERIC Educational Resources Information Center

    Bierema, Laura L.

    2001-01-01

    Outlines causes of women's disadvantage in the workplace and the inadequacies of career development models for women. Addresses themes related to women's learning at work: hidden curriculum in the work context, identity development, relationships and connection, and mentoring. (Contains 38 references.) (SK)

  16. The Evolution of SCORM to Tin Can API: Implications for Instructional Design

    ERIC Educational Resources Information Center

    Lindert, Lisa; Su, Bude

    2016-01-01

    Integrating and documenting formal and informal learning experiences is challenging using the current Shareable Content Object Reference Model (SCORM) eLearning standard, which limits the media and data that are obtained from eLearning. In response to SCORM's limitations, corporate, military, and academic institutions have collaborated to develop…

  17. Component Exchange Community: A Model of Utilizing Research Components to Foster International Collaboration

    ERIC Educational Resources Information Center

    Deng, Yi-Chan; Lin, Taiyu; Kinshuk; Chan, Tak-Wai

    2006-01-01

    "One-to-one" technology enhanced learning research refers to the design and investigation of learning environments and learning activities where every learner is equipped with at least one portable computing device enabled by wireless capability. G1:1 is an international research community coordinated by a network of laboratories conducting…

  18. A SCORM Compliant Courseware Authoring Tool for Supporting Pervasive Learning

    ERIC Educational Resources Information Center

    Wang, Te-Hua; Chang, Flora Chia-I

    2007-01-01

    The sharable content object reference model (SCORM) includes a representation of distance learning contents and a behavior definition of how users should interact with the contents. Generally, SCORMcompliant systems were based on multimedia and Web technologies on PCs. We further build a pervasive learning environment, which allows users to read…

  19. A Bootstrapping Model of Frequency and Context Effects in Word Learning

    ERIC Educational Resources Information Center

    Kachergis, George; Yu, Chen; Shiffrin, Richard M.

    2017-01-01

    Prior research has shown that people can learn many nouns (i.e., word--object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing…

  20. Doubting Learning Outcomes in Higher Education Contexts: From Performativity towards Emergence and Negotiation

    ERIC Educational Resources Information Center

    Addison, Nicholas

    2014-01-01

    Learning Outcomes models, particularly constructive alignment, are the default 'theoretical' tool underpinning HE curriculum design in the UK despite continuing doubts as to their efficacy. With reference to the literature, this article summarises the history of the Learning Outcomes movement and charts the perceived benefits and deficits of…

  1. The Role of a Reference Synthetic Data Generator within the Field of Learning Analytics

    ERIC Educational Resources Information Center

    Berg, Alan\tM.; Mol, Stefan T.; Kismihók, Gábor; Sclater, Niall

    2016-01-01

    This paper details the anticipated impact of synthetic "big" data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at the sector-wide level (the Jisc learning analytics architecture) and…

  2. An Attempt To Design Synchronous Collaborative Learning Environments for Peer Dyads on the World Wide Web.

    ERIC Educational Resources Information Center

    Lee, Fong-Lok; Liang, Steven; Chan, Tak-Wai

    1999-01-01

    Describes the design, implementation, and preliminary evaluation of three synchronous distributed learning prototype systems: Co-Working System, Working Along System, and Hybrid System. Each supports a particular style of interaction, referred to a socio-activity learning model, between members of student dyads (pairs). All systems were…

  3. The Usability Analysis of an E-Learning Environment

    ERIC Educational Resources Information Center

    Torun, Fulya; Tekedere, Hakan

    2015-01-01

    In this research, an E-learning environment is developed for the teacher candidates taking the course on Scientific Research Methods. The course contents were adapted to one of the constructivist approach models referred to as 5E, and an expert opinion was received for the compliance of this model. An usability analysis was also performed to…

  4. "My Understanding Has Grown, My Perspective Has Switched": Linking Informal Writing to Learning Goals

    ERIC Educational Resources Information Center

    Hudd, Suzanne S.; Smart, Robert A.; Delohery, Andrew W.

    2011-01-01

    The use of informal writing is common in sociology. This article presents one model for integrating informal written work with learning goals through a theoretical framework known as concentric thinking. More commonly referred to as "the PTA model" because of the series of cognitive tasks it promotes--prioritization, translation, and analogy…

  5. Improving Teaching and Learning in Higher Education: Metaphors and Models for Partnership Consultancy

    ERIC Educational Resources Information Center

    Morrison, Keith

    2003-01-01

    The management of partnerships with external consultants is discussed with reference to seven metaphors of partnership, illuminated by an external consultancy review of teaching and learning in a University Language Centre. Shortcomings are shown in each of the seven metaphors. A model of partnership is advocated, based on Habermas' principles of…

  6. The Development Model of Knowledge Management via Web-Based Learning to Enhance Pre-Service Teacher's Competency

    ERIC Educational Resources Information Center

    Rampai, Nattaphon; Sopeerak, Saroch

    2011-01-01

    This research explores that the model of knowledge management and web technology for teachers' professional development as well as its impact in the classroom on learning and teaching, especially in pre-service teacher's competency and practices that refer to knowledge creating, analyzing, nurturing, disseminating, and optimizing process as part…

  7. The Role of Metacognition in the Language Teaching Profession

    ERIC Educational Resources Information Center

    Nodoushan, Mohammad Ali Salmani

    2008-01-01

    Metacognition is a concept in psychology that refers to a variety of self-awareness process that help learners learn better. It grew out of the developments over the past few decades of cognitive models of learning. This paper presents a brief overview of these models and discusses their main features. It begins with a discussion of behavioristic…

  8. The Role of Metacognition in the Language Teaching Profession

    ERIC Educational Resources Information Center

    Salmani Nodoushan, Mohammad Ali

    2008-01-01

    Metacognition is a concept in psychology that refers to a variety of self-awareness process that help learners learn better. It grew out of the developments over the past few decades of cognitive models of learning. This paper will present a brief overview of these models and discuss their main features. It begins with a discussion of…

  9. Implementation of Problem Based Learning Model in Concept Learning Mushroom as a Result of Student Learning Improvement Efforts Guidelines for Teachers

    ERIC Educational Resources Information Center

    Rubiah, Musriadi

    2016-01-01

    Problem based learning is a training strategy, students work together in groups, and take responsibility for solving problems in a professional manner. Instructional materials such as textbooks become the main reference of students in study of mushrooms, especially the material is considered less effective in responding to the information needs of…

  10. Do domestic dogs learn words based on humans' referential behaviour?

    PubMed

    Tempelmann, Sebastian; Kaminski, Juliane; Tomasello, Michael

    2014-01-01

    Some domestic dogs learn to comprehend human words, although the nature and basis of this learning is unknown. In the studies presented here we investigated whether dogs learn words through an understanding of referential actions by humans rather than simple association. In three studies, each modelled on a study conducted with human infants, we confronted four word-experienced dogs with situations involving no spatial-temporal contiguity between the word and the referent; the only available cues were referential actions displaced in time from exposure to their referents. We found that no dogs were able to reliably link an object with a label based on social-pragmatic cues alone in all the tests. However, one dog did show skills in some tests, possibly indicating an ability to learn based on social-pragmatic cues.

  11. The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

    NASA Astrophysics Data System (ADS)

    Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

    2017-10-01

    This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

  12. Table-sized matrix model in fractional learning

    NASA Astrophysics Data System (ADS)

    Soebagyo, J.; Wahyudin; Mulyaning, E. C.

    2018-05-01

    This article provides an explanation of the fractional learning model i.e. a Table-Sized Matrix model in which fractional representation and its operations are symbolized by the matrix. The Table-Sized Matrix are employed to develop problem solving capabilities as well as the area model. The Table-Sized Matrix model referred to in this article is used to develop an understanding of the fractional concept to elementary school students which can then be generalized into procedural fluency (algorithm) in solving the fractional problem and its operation.

  13. Mechanisms of Transfer: Modeling Motivational and Self-Regulatory Processes That Promote Transfer of Learning

    ERIC Educational Resources Information Center

    Gonzalez, John A.

    2012-01-01

    A critical goal of many school and training interventions is to provide learners with the strategies and foundational knowledge that will allow them to tackle novel problems encountered under circumstances different than the learning situations. This is also quite often referred to as the ability to transfer learning. Theories of transfer posit…

  14. Developing Managerial Learning Styles in the Context of the Strategic Application of Information and Communications Technologies.

    ERIC Educational Resources Information Center

    Holtham, Clive; Courtney, Nigel

    2001-01-01

    Training for 561 executives in the use of information and communications technologies was based on a model, the Executive Learning Ladder. Results indicated that sense making was accelerated when conducted in peer groups before being extended to less-experienced managers. Learning preference differences played a role. (Contains 38 references.) (SK)

  15. Stochastic Online Learning in Dynamic Networks under Unknown Models

    DTIC Science & Technology

    2016-08-02

    Repeated Game with Incomplete Information, IEEE International Conference on Acoustics, Speech, and Signal Processing. 20-MAR-16, Shanghai, China...in a game theoretic framework for the application of multi-seller dynamic pricing with unknown demand models. We formulated the problem as an...infinitely repeated game with incomplete information and developed a dynamic pricing strategy referred to as Competitive and Cooperative Demand Learning

  16. Orbital-free bond breaking via machine learning

    NASA Astrophysics Data System (ADS)

    Snyder, John C.; Rupp, Matthias; Hansen, Katja; Blooston, Leo; Müller, Klaus-Robert; Burke, Kieron

    2013-12-01

    Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately dissociate a diatomic, (ii) be systematically improved with increased reference data and (iii) generate accurate self-consistent densities via a projection method that avoids directions with no data. With relatively few densities, the error due to the interpolation is smaller than typical errors in standard exchange-correlation functionals.

  17. Using speakers' referential intentions to model early cross-situational word learning.

    PubMed

    Frank, Michael C; Goodman, Noah D; Tenenbaum, Joshua B

    2009-05-01

    Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

  18. Outdoor Experiences and Sustainability

    ERIC Educational Resources Information Center

    Prince, Heather E.

    2017-01-01

    Positive outdoor teaching and learning experiences and sound pedagogical approaches undoubtedly have contributed towards an understanding of environmental sustainability but it is not always clear how, and to what extent, education can translate into action. This article argues, with reference to social learning theory, that role modelling,…

  19. New Dental Accreditation Standard on Critical Thinking: A Call for Learning Models, Outcomes, Assessments.

    PubMed

    Johnsen, David C; Williams, John N; Baughman, Pauletta Gay; Roesch, Darren M; Feldman, Cecile A

    2015-10-01

    This opinion article applauds the recent introduction of a new dental accreditation standard addressing critical thinking and problem-solving, but expresses a need for additional means for dental schools to demonstrate they are meeting the new standard because articulated outcomes, learning models, and assessments of competence are still being developed. Validated, research-based learning models are needed to define reference points against which schools can design and assess the education they provide to their students. This article presents one possible learning model for this purpose and calls for national experts from within and outside dental education to develop models that will help schools define outcomes and assess performance in educating their students to become practitioners who are effective critical thinkers and problem-solvers.

  20. Deep supervised dictionary learning for no-reference image quality assessment

    NASA Astrophysics Data System (ADS)

    Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

    2018-03-01

    We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

  1. Do Domestic Dogs Learn Words Based on Humans’ Referential Behaviour?

    PubMed Central

    Tempelmann, Sebastian; Kaminski, Juliane; Tomasello, Michael

    2014-01-01

    Some domestic dogs learn to comprehend human words, although the nature and basis of this learning is unknown. In the studies presented here we investigated whether dogs learn words through an understanding of referential actions by humans rather than simple association. In three studies, each modelled on a study conducted with human infants, we confronted four word-experienced dogs with situations involving no spatial-temporal contiguity between the word and the referent; the only available cues were referential actions displaced in time from exposure to their referents. We found that no dogs were able to reliably link an object with a label based on social-pragmatic cues alone in all the tests. However, one dog did show skills in some tests, possibly indicating an ability to learn based on social-pragmatic cues. PMID:24646732

  2. Vineyard water status assessment using on-the-go thermal imaging and machine learning.

    PubMed

    Gutiérrez, Salvador; Diago, María P; Fernández-Novales, Juan; Tardaguila, Javier

    2018-01-01

    The high impact of irrigation in crop quality and yield in grapevine makes the development of plant water status monitoring systems an essential issue in the context of sustainable viticulture. This study presents an on-the-go approach for the estimation of vineyard water status using thermal imaging and machine learning. The experiments were conducted during seven different weeks from July to September in season 2016. A thermal camera was embedded on an all-terrain vehicle moving at 5 km/h to take on-the-go thermal images of the vineyard canopy at 1.2 m of distance and 1.0 m from the ground. The two sides of the canopy were measured for the development of side-specific and global models. Stem water potential was acquired and used as reference method. Additionally, reference temperatures Tdry and Twet were determined for the calculation of two thermal indices: the crop water stress index (CWSI) and the Jones index (Ig). Prediction models were built with and without considering the reference temperatures as input of the training algorithms. When using the reference temperatures, the best models casted determination coefficients R2 of 0.61 and 0.58 for cross validation and prediction (RMSE values of 0.190 MPa and 0.204 MPa), respectively. Nevertheless, when the reference temperatures were not considered in the training of the models, their performance statistics responded in the same way, returning R2 values up to 0.62 and 0.65 for cross validation and prediction (RMSE values of 0.190 MPa and 0.184 MPa), respectively. The outcomes provided by the machine learning algorithms support the use of thermal imaging for fast, reliable estimation of a vineyard water status, even suppressing the necessity of supervised acquisition of reference temperatures. The new developed on-the-go method can be very useful in the grape and wine industry for assessing and mapping vineyard water status.

  3. Vineyard water status assessment using on-the-go thermal imaging and machine learning

    PubMed Central

    Gutiérrez, Salvador; Diago, María P.; Fernández-Novales, Juan

    2018-01-01

    The high impact of irrigation in crop quality and yield in grapevine makes the development of plant water status monitoring systems an essential issue in the context of sustainable viticulture. This study presents an on-the-go approach for the estimation of vineyard water status using thermal imaging and machine learning. The experiments were conducted during seven different weeks from July to September in season 2016. A thermal camera was embedded on an all-terrain vehicle moving at 5 km/h to take on-the-go thermal images of the vineyard canopy at 1.2 m of distance and 1.0 m from the ground. The two sides of the canopy were measured for the development of side-specific and global models. Stem water potential was acquired and used as reference method. Additionally, reference temperatures Tdry and Twet were determined for the calculation of two thermal indices: the crop water stress index (CWSI) and the Jones index (Ig). Prediction models were built with and without considering the reference temperatures as input of the training algorithms. When using the reference temperatures, the best models casted determination coefficients R2 of 0.61 and 0.58 for cross validation and prediction (RMSE values of 0.190 MPa and 0.204 MPa), respectively. Nevertheless, when the reference temperatures were not considered in the training of the models, their performance statistics responded in the same way, returning R2 values up to 0.62 and 0.65 for cross validation and prediction (RMSE values of 0.190 MPa and 0.184 MPa), respectively. The outcomes provided by the machine learning algorithms support the use of thermal imaging for fast, reliable estimation of a vineyard water status, even suppressing the necessity of supervised acquisition of reference temperatures. The new developed on-the-go method can be very useful in the grape and wine industry for assessing and mapping vineyard water status. PMID:29389982

  4. Comparing the Differences and Commonalities between Service-Learning in the USA and Social Practice in China

    ERIC Educational Resources Information Center

    Xu, Ruifang

    2010-01-01

    Service-learning as a popular term refers to an educational model that combines academic study with social activism and civic service. However, some countries, such as China, use different terms. This article explores the differences and commonalities between service-learning in the USA and social practice in China in the following areas:…

  5. An Analysis of Implementation Issues for the Searchable Content Object Reference Model (SCORM) in Navy Education and Training

    DTIC Science & Technology

    2003-09-01

    content objects to be used and reused within civilian and military education and training Learning Management Systems (LMS) across the World Wide Web...to be used and reused within civilian and military education and training Learning Management Systems (LMS) across the World Wide Web. vi...1998, SUBJECT: ENHANCING LEARNING AND EDUCATION THROUGH TECHNOLOGY

  6. The Effect of Mobile Portfolio (M-Portfolio) Supported Mastery Learning Model on Students' Achievement and Their Attitudes towards Using Internet

    ERIC Educational Resources Information Center

    Ozdemir, Oguzhan; Erdemci, Husamettin

    2017-01-01

    The term mobile portfolio refers to creating, evaluating and sharing portfolios in mobile environments. Many of the states that pose an obstacle for portfolio usage are now extinguished through mobile portfolios. The aim in this research is to determine the effect of mobile portfolio supported mastery learning model on students' success and…

  7. Concepts of a Higher Hierarchical Level Require More Dual Situated Learning Events for Conceptual Change: A Study of Air Pressure and Buoyancy.

    ERIC Educational Resources Information Center

    She, Hsiao-Ching

    2002-01-01

    Examines the process of students' conceptual changes with regard to air pressure and buoyancy as a result of teaching with the dual situated learning model. Uses a model designed according to the students' ontological viewpoint on science concepts as well as the nature of these concepts. (Contains 40 references.) (Author/YDS)

  8. Recent developments in learning control and system identification for robots and structures

    NASA Technical Reports Server (NTRS)

    Phan, M.; Juang, J.-N.; Longman, R. W.

    1990-01-01

    This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.

  9. Inter-University Collaboration for Online Teaching Innovation: An Emerging Model

    ERIC Educational Resources Information Center

    Nerlich, Andrea Perkins; Soldner, James L.; Millington, Michael J.

    2012-01-01

    Distance education is constantly evolving and improving. To stay current, effective online instructors must utilize the most innovative, evidence-based teaching methods available to promote student learning and satisfaction in their courses. One emerging teaching method, referred to as blended online learning (BOL), involves collaborative…

  10. Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

    ERIC Educational Resources Information Center

    Steinley, Douglas; Brusco, Michael J.

    2011-01-01

    This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

  11. Effectiveness of e-learning in hospitals.

    PubMed

    Chuo, Yinghsiang; Liu, Chuangchun; Tsai, Chunghung

    2015-01-01

    Because medical personnel share different work shifts (i.e., three work shifts) and do not have a fixed work schedule, implementing timely, flexible, and quick e-learning methods for their continued education is imperative. Hospitals are currently focusing on developing e-learning. This study aims to explore the key factors that influence the effectiveness of e-learning in medical personnel. This study recruited medical personnel as the study participants and collected sample data by using the questionnaire survey method. This study is based on the information systems success model (IS success model), a significant model in MIS research. This study found that the factors (i.e., information quality, service quality, convenience, and learning climate) influence the e-learning satisfaction and in turn influence effectiveness in medical personnel. This study provided recommendations to medical institutions according to the derived findings, which can be used as a reference when establishing e-learning systems in the future.

  12. Software-Realized Scaffolding to Facilitate Programming for Science Learning.

    ERIC Educational Resources Information Center

    Guzdial, Mark

    1994-01-01

    Discussion of the use of programming as a learning activity focuses on software-realized scaffolding. Emile, software that facilitates programming for modeling and simulation in physics, is described, and results of an evaluation of the use of Emile with high school students are reported. (Contains 95 references.) (LRW)

  13. Learning Portfolio Analysis and Mining for SCORM Compliant Environment

    ERIC Educational Resources Information Center

    Su, Jun-Ming; Tseng, Shian-Shyong; Wang, Wei; Weng, Jui-Feng; Yang, Jin Tan David; Tsai, Wen-Nung

    2006-01-01

    With vigorous development of the Internet, e-learning system has become more and more popular. Sharable Content Object Reference Model (SCORM) 2004 provides the Sequencing and Navigation (SN) Specification to define the course sequencing behavior, control the sequencing, selecting and delivering of course, and organize the content into a…

  14. The Pursuit of Word Meanings

    ERIC Educational Resources Information Center

    Stevens, Jon Scott; Gleitman, Lila R.; Trueswell, John C.; Yang, Charles

    2017-01-01

    We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed "Pursuit," uses an associative…

  15. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    ERIC Educational Resources Information Center

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  16. Development of a machine learning potential for graphene

    NASA Astrophysics Data System (ADS)

    Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

    2018-02-01

    We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

  17. Deep learning based syndrome diagnosis of chronic gastritis.

    PubMed

    Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

    2014-01-01

    In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice.

  18. Deep Learning Based Syndrome Diagnosis of Chronic Gastritis

    PubMed Central

    Liu, Guo-Ping; Wang, Yi-Qin; Zheng, Wu; Zhong, Tao; Lu, Xiong; Qian, Peng

    2014-01-01

    In Traditional Chinese Medicine (TCM), most of the algorithms used to solve problems of syndrome diagnosis are superficial structure algorithms and not considering the cognitive perspective from the brain. However, in clinical practice, there is complex and nonlinear relationship between symptoms (signs) and syndrome. So we employed deep leaning and multilabel learning to construct the syndrome diagnostic model for chronic gastritis (CG) in TCM. The results showed that deep learning could improve the accuracy of syndrome recognition. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:24734118

  19. On the complex interplay between learning and dynamics in life sciences. Comment on the paper "Collective learning modeling based on the kinetic theory of active particles" by Burini et al.

    NASA Astrophysics Data System (ADS)

    Bellomo, Nicola; Elaiw, Ahmed; Alghamdi, Mohamed Ali

    2016-03-01

    The paper by Burini, De Lillo, and Gibelli [8] presents an overview and critical analysis of the literature on the modeling of learning dynamics. The first reference is the celebrated paper by Cucker and Smale [9]. Then, the authors also propose their own approach, based on suitable development of methods of the kinetic theory [6] and theoretical tools of evolutionary game theory [12,13], recently developed on graphs [2].

  20. Customer Satisfaction with Training Programs.

    ERIC Educational Resources Information Center

    Mulder, Martin

    2001-01-01

    A model for evaluating customer satisfaction with training programs was tested with training purchasers. The model confirmed two types of projects: training aimed at achieving learning results and at changing job performance. The model did not fit for training intended to support organizational change. (Contains 31 references.) (SK)

  1. From particle systems to learning processes. Comment on "Collective learning modeling based on the kinetic theory of active particles" by Diletta Burini, Silvana De Lillo, and Livio Gibelli

    NASA Astrophysics Data System (ADS)

    Lachowicz, Mirosław

    2016-03-01

    The very stimulating paper [6] discusses an approach to perception and learning in a large population of living agents. The approach is based on a generalization of kinetic theory methods in which the interactions between agents are described in terms of game theory. Such an approach was already discussed in Ref. [2-4] (see also references therein) in various contexts. The processes of perception and learning are based on the interactions between agents and therefore the general kinetic theory is a suitable tool for modeling them. However the main question that rises is how the perception and learning processes may be treated in the mathematical modeling. How may we precisely deliver suitable mathematical structures that are able to capture various aspects of perception and learning?

  2. Postgraduate Education for Nurses: The Middlesex Model.

    ERIC Educational Resources Information Center

    Caldwell, Kay

    2001-01-01

    A British university's curriculum model for master's and postgraduate diploma nursing education is characterized by structured collaboration among students, clinical mentors, and academic supervisors. A professional development portfolio individualizes the program and facilitates autonomous learning. (Contains 21 references.) (SK)

  3. Evolution of an Implementation-Ready Interprofessional Pain Assessment Reference Model

    PubMed Central

    Collins, Sarah A; Bavuso, Karen; Swenson, Mary; Suchecki, Christine; Mar, Perry; Rocha, Roberto A.

    2017-01-01

    Standards to increase consistency of comprehensive pain assessments are important for safety, quality, and analytics activities, including meeting Joint Commission requirements and learning the best management strategies and interventions for the current prescription Opioid epidemic. In this study we describe the development and validation of a Pain Assessment Reference Model ready for implementation on EHR forms and flowsheets. Our process resulted in 5 successive revisions of the reference model, which more than doubled the number of data elements to 47. The organization of the model evolved during validation sessions with panels totaling 48 subject matter experts (SMEs) to include 9 sets of data elements, with one set recommended as a minimal data set. The reference model also evolved when implemented into EHR forms and flowsheets, indicating specifications such as cascading logic that are important to inform secondary use of data. PMID:29854125

  4. Dynamic Assessment and Its Implications for RTI Models

    ERIC Educational Resources Information Center

    Wagner, Richard K.; Compton, Donald L.

    2011-01-01

    Dynamic assessment refers to assessment that combines elements of instruction for the purpose of learning something about an individual that cannot be learned as easily or at all from conventional assessment. The origins of dynamic assessment can be traced to Thorndike (1924), Rey (1934), and Vygotsky (1962), who shared three basic assumptions.…

  5. The Negotiation Model in Asynchronous Computer-Mediated Communication (CMC): Negotiation in Task-Based Email Exchanges

    ERIC Educational Resources Information Center

    Kitade, Keiko

    2006-01-01

    Based on recent studies, computer-mediated communication (CMC) has been considered a tool to aid in language learning on account of its distinctive interactional features. However, most studies have referred to "synchronous" CMC and neglected to investigate how "asynchronous" CMC contributes to language learning. Asynchronous CMC possesses…

  6. Adopting SCORM 1.2 Standards in a Courseware Production Environment

    ERIC Educational Resources Information Center

    Barker, Bradley

    2004-01-01

    The Sharable Content Object Reference Model (SCORM) is a technology framework for Web-based learning technology. Originated by the Department of Defense and accelerated by the Advanced Distributed Learning initiative SCORM was released in January of 2000 (ADL, 2003). The goals of SCORM are to decrease the cost of training, while increasing the…

  7. A Framework for Authenticity in the Mathematics and Statistics Classroom

    ERIC Educational Resources Information Center

    Garrett, Lauretta; Huang, Li; Charleton, Maria Calhoun

    2016-01-01

    Authenticity is a term commonly used in reference to pedagogical and curricular qualities of mathematics teaching and learning, but its use lacks a coherent framework. The work of researchers in engineering education provides such a framework. Authentic qualities of mathematics teaching and learning are fit within a model described by Strobel,…

  8. The ampakine, Org 26576, bolsters early spatial reference learning and retrieval in the Morris water maze: a subchronic, dose-ranging study in rats.

    PubMed

    Hamlyn, Eugene; Brand, Linda; Shahid, Mohammed; Harvey, Brian H

    2009-10-01

    Ampakines have shown beneficial effects on cognition in selected animal models of learning. However, their ability to modify long-term spatial memory tasks has not been studied yet. This would lend credence to their possible value in treating disorders of cognition. We evaluated the actions of subchronic Org 26576 administration on spatial reference memory performance in the 5-day Morris water maze task in male Sprague-Dawley rats, at doses of 1, 3 and 10 mg/kg twice daily through intraperitoneal injection over 12 days. Org 26576 exerted a dose and time-dependent effect on spatial learning, with dosages of 3 and 10 mg/kg significantly enhancing acquisition on day 1. Globally, escape latency decreased significantly as the training days progressed in the saline and Org 26576-treated groups, indicating that significant and equal learning had taken place over the learning period. However, at the end of the learning period, all doses of Org 26576 significantly improved spatial memory storage/retrieval without confounding effects in the cued version of the task. Org 26576 offers early phase spatial memory benefits in rats, but particularly enhances search accuracy during reference memory retrieval. These results support its possible utility in treating disorders characterized by deficits in cognitive performance.

  9. Learning To Lead: How Winston Churchill And George Marshall Harvested Their WWI Experience

    DTIC Science & Technology

    2014-04-01

    Borton’s developmental model. Rolfe and Borton references are provided in Bibliography. 4 Kolb , and Kolb . “ Experiential Learning Theory ,” 39. 5...electronically at http://www2.glos.ac.uk/ gdn/gibbs/index.htm Kolb , Alice Y. and David A. Kolb . “ Experiential Learning Theory : A Dynamic, Holistic...1 EXPERIENTIAL LEARNING THEORY ………………………………………………………..2 THE POWER OF SIX—WINSTON

  10. Deep learning applications in ophthalmology.

    PubMed

    Rahimy, Ehsan

    2018-05-01

    To describe the emerging applications of deep learning in ophthalmology. Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the initial studies have centered around detection of referable diabetic retinopathy, age-related macular degeneration, and glaucoma. Deep learning has shown promising results in automated image analysis of fundus photographs and optical coherence tomography images. Additional testing and research is required to clinically validate this technology.

  11. 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.

  12. Using Machine Learning as a fast emulator of physical processes within the Met Office's Unified Model

    NASA Astrophysics Data System (ADS)

    Prudden, R.; Arribas, A.; Tomlinson, J.; Robinson, N.

    2017-12-01

    The Unified Model is a numerical model of the atmosphere used at the UK Met Office (and numerous partner organisations including Korean Meteorological Agency, Australian Bureau of Meteorology and US Air Force) for both weather and climate applications.Especifically, dynamical models such as the Unified Model are now a central part of weather forecasting. Starting from basic physical laws, these models make it possible to predict events such as storms before they have even begun to form. The Unified Model can be simply described as having two components: one component solves the navier-stokes equations (usually referred to as the "dynamics"); the other solves relevant sub-grid physical processes (usually referred to as the "physics"). Running weather forecasts requires substantial computing resources - for example, the UK Met Office operates the largest operational High Performance Computer in Europe - and the cost of a typical simulation is spent roughly 50% in the "dynamics" and 50% in the "physics". Therefore there is a high incentive to reduce cost of weather forecasts and Machine Learning is a possible option because, once a machine learning model has been trained, it is often much faster to run than a full simulation. This is the motivation for a technique called model emulation, the idea being to build a fast statistical model which closely approximates a far more expensive simulation. In this paper we discuss the use of Machine Learning as an emulator to replace the "physics" component of the Unified Model. Various approaches and options will be presented and the implications for further model development, operational running of forecasting systems, development of data assimilation schemes, and development of ensemble prediction techniques will be discussed.

  13. Feature weighting using particle swarm optimization for learning vector quantization classifier

    NASA Astrophysics Data System (ADS)

    Dongoran, A.; Rahmadani, S.; Zarlis, M.; Zakarias

    2018-03-01

    This paper discusses and proposes a method of feature weighting in classification assignments on competitive learning artificial neural network LVQ. The weighting feature method is the search for the weight of an attribute using the PSO so as to give effect to the resulting output. This method is then applied to the LVQ-Classifier and tested on the 3 datasets obtained from the UCI Machine Learning repository. Then an accuracy analysis will be generated by two approaches. The first approach using LVQ1, referred to as LVQ-Classifier and the second approach referred to as PSOFW-LVQ, is a proposed model. The result shows that the PSO algorithm is capable of finding attribute weights that increase LVQ-classifier accuracy.

  14. The prefrontal cortex and hybrid learning during iterative competitive games.

    PubMed

    Abe, Hiroshi; Seo, Hyojung; Lee, Daeyeol

    2011-12-01

    Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning. © 2011 New York Academy of Sciences.

  15. Word learning in adults with second-language experience: effects of phonological and referent familiarity.

    PubMed

    Kaushanskaya, Margarita; Yoo, Jeewon; Van Hecke, Stephanie

    2013-04-01

    The goal of this research was to examine whether phonological familiarity exerts different effects on novel word learning for familiar versus unfamiliar referents and whether successful word learning is associated with increased second-language experience. Eighty-one adult native English speakers with various levels of Spanish knowledge learned phonologically familiar novel words (constructed using English sounds) or phonologically unfamiliar novel words (constructed using non-English and non-Spanish sounds) in association with either familiar or unfamiliar referents. Retention was tested via a forced-choice recognition task. A median-split procedure identified high-ability and low-ability word learners in each condition, and the two groups were compared on measures of second-language experience. Findings suggest that the ability to accurately match newly learned novel names to their appropriate referents is facilitated by phonological familiarity only for familiar referents but not for unfamiliar referents. Moreover, more extensive second-language learning experience characterized superior learners primarily in one word-learning condition: in which phonologically unfamiliar novel words were paired with familiar referents. Together, these findings indicate that phonological familiarity facilitates novel word learning only for familiar referents and that experience with learning a second language may have a specific impact on novel vocabulary learning in adults.

  16. Word learning in adults with second language experience: Effects of phonological and referent familiarity

    PubMed Central

    Kaushanskaya, Margarita; Yoo, Jeewon; Van Hecke, Stephanie

    2014-01-01

    Purpose The goal of this research was to examine whether phonological familiarity exerts different effects on novel word learning for familiar vs. unfamiliar referents, and whether successful word-learning is associated with increased second-language experience. Method Eighty-one adult native English speakers with various levels of Spanish knowledge learned phonologically-familiar novel words (constructed using English sounds) or phonologically-unfamiliar novel words (constructed using non-English and non-Spanish sounds) in association with either familiar or unfamiliar referents. Retention was tested via a forced-choice recognition-task. A median-split procedure identified high-ability and low-ability word-learners in each condition, and the two groups were compared on measures of second-language experience. Results Findings suggest that the ability to accurately match newly-learned novel names to their appropriate referents is facilitated by phonological familiarity only for familiar referents but not for unfamiliar referents. Moreover, more extensive second-language learning experience characterized superior learners primarily in one word-learning condition: Where phonologically-unfamiliar novel words were paired with familiar referents. Conclusions Together, these findings indicate that phonological familiarity facilitates novel word learning only for familiar referents, and that experience with learning a second language may have a specific impact on novel vocabulary learning in adults. PMID:22992709

  17. MQAPRank: improved global protein model quality assessment by learning-to-rank.

    PubMed

    Jing, Xiaoyang; Dong, Qiwen

    2017-05-25

    Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

  18. Testing the limits of long-distance learning: Learning beyond a three-segment window

    PubMed Central

    Finley, Sara

    2012-01-01

    Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones since long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] ('sh') and triggered a suffix that was either [−su] or [−∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. PMID:22303815

  19. The (kinetic) theory of active particles applied to learning dynamics. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

    NASA Astrophysics Data System (ADS)

    Nieto, J.

    2016-03-01

    The learning phenomena, their complexity, concepts, structure, suitable theories and models, have been extensively treated in the mathematical literature in the last century, and [4] contains a very good introduction to the literature describing the many approaches and lines of research developed about them. Two main schools have to be pointed out [5] in order to understand the two -not exclusive- kinds of existing models: the stimulus sampling models and the stochastic learning models. Also [6] should be mentioned as a survey where two methods of learning are pointed out, the cognitive and the social, and where the knowledge looks like a mathematical unknown. Finally, as the authors do, we refer to the works [9,10], where the concept of population thinking was introduced and which motivate the game theory rules as a tool (both included in [4] to develop their theory) and [7], where the ideas of developing a mathematical kinetic theory of perception and learning were proposed.

  20. Syntax "and" Semantics: A Teaching Model.

    ERIC Educational Resources Information Center

    Wolfe, Frank

    In translating perception into written language, a child must learn an encoding process which is a continuation of the process of improving sensing of the world around him or her. To verbalize an object (a perception) we use frames which name a referent, locate the referent in space and time, identify its appearance and behavior, and define terms…

  1. Models as Feedback: Developing Representational Competence in Chemistry

    ERIC Educational Resources Information Center

    Padalkar, Shamin; Hegarty, Mary

    2015-01-01

    Spatial information in science is often expressed through representations such as diagrams and models. Learning the strengths and limitations of these representations and how to relate them are important aspects of developing scientific understanding, referred to as "representational competence." Diagram translation is particularly…

  2. Motivation, Interest, and Attention: Re-Defining Learning in the Autism Spectrum?

    ERIC Educational Resources Information Center

    Lequia, Jenna

    2011-01-01

    In "The Passionate Mind: How People with Autism Learn", Wendy Lawson presents readers with various cognitive theories of autism spectrum disorders (ASD). In this book, Lawson makes reference to the medical and social models of disability, urging readers to consider disability from a social rather than a medical or deficit-driven perspective. Each…

  3. Problems of Implementing SCORM in an Enterprise Distance Learning Architecture: SCORM Incompatibility across Multiple Web Domains.

    ERIC Educational Resources Information Center

    Engelbrecht, Jeffrey C.

    2003-01-01

    Delivering content to distant users located in dispersed networks, separated by firewalls and different web domains requires extensive customization and integration. This article outlines some of the problems of implementing the Sharable Content Object Reference Model (SCORM) in the Marine Corps' Distance Learning System (MarineNet) and extends…

  4. Mind the Gap: Enabling Online Faculty and Instructional Designers in Mapping New Models for Quality Online Courses

    ERIC Educational Resources Information Center

    Scoppio, Grazia; Luyt, Ilka

    2017-01-01

    Distance education has provided the foundation for new generations of learning, including courses delivered through various web-based educational technologies, also referred to as online learning. Many post-secondary institutions face the challenge of creating processes and systems to support instructors who are required to design, deliver, and…

  5. Commentary: Student Cognition, the Situated Learning Context, and Test Score Interpretation

    ERIC Educational Resources Information Center

    La Marca, Paul M.

    2006-01-01

    Although it is assumed that student cognition contributes to student performance on achievement tests, it may be that current testing models lack the degree of specification necessary to warrant such inferences. With test score interpretations as the referent, the authors in this special issue address the role of student cognition in learning and…

  6. Critical Success Factor for Implementing Vocational Blended Learning

    NASA Astrophysics Data System (ADS)

    Dewi, K. C.; Ciptayani, P. I.; Surjono, H. D.; Priyanto

    2018-01-01

    Blended learning provides many benefits to the flexibility of time, place and situation constraints. The research’s objectives was describing the factors that determine the successful implementation of blended learning in vocational higher education. The research used a qualitative approach, data collected through observations and interviews by questionnare based on the CSFs indicators refers to TAM and Kliger. Data analysis was inductive method. The result provided an illustration that the success of vocational blended learning implementation was largely determined by the selection of instructional models that are inline with learning achievement target. The effectiveness of blended learning required the existence of policy support, readiness of IT infrastructure. Changing lecturer’s culture by utilizing ICT can also encourage the accelerated process of successful implementation. It can concluded that determinant factor of successful implementation of blended learning in vocational education is determined by teacher’s ability in mastering the pedagogical knowledge of designing instructional models.

  7. On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

    PubMed

    Yamazaki, Keisuke

    2012-07-01

    Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

  8. Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

    PubMed

    Yao, Chen; Zhu, Xiaojin; Weigel, Kent A

    2016-11-07

    Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle. We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes. Initially, a SVM model was trained using data from 792 animals with measured RFI phenotypes. Then, the resulting SVM was used to generate self-trained phenotypes for 3000 animals for which RFI measurements were not available. Finally, the SVM model was re-trained using data from up to 3792 animals, including those with measured and self-trained RFI phenotypes. Incorporation of additional animals with self-trained phenotypes enhanced the accuracy of genomic predictions compared to that of predictions that were derived from the subset of animals with measured phenotypes. The optimal ratio of animals with self-trained phenotypes to animals with measured phenotypes (2.5, 2.0, and 1.8) and the maximum increase achieved in prediction accuracy measured as the correlation between predicted and actual RFI phenotypes (5.9, 4.1, and 2.4%) decreased as the size of the initial training set (300, 400, and 500 animals with measured phenotypes) increased. The optimal number of animals with self-trained phenotypes may be smaller when prediction accuracy is measured as the mean squared error rather than the correlation between predicted and actual RFI phenotypes. Our results demonstrate that semi-supervised learning models that incorporate self-trained phenotypes can achieve genomic prediction accuracies that are comparable to those obtained with models using larger training sets that include only animals with measured phenotypes. Semi-supervised learning can be helpful for genomic prediction of novel traits, such as RFI, for which the size of reference population is limited, in particular, when the animals to be predicted and the animals in the reference population originate from the same herd-environment.

  9. Model-Based Reasoning: Using Visual Tools to Reveal Student Learning

    ERIC Educational Resources Information Center

    Luckie, Douglas; Harrison, Scott H.; Ebert-May, Diane

    2011-01-01

    Using visual models is common in science and should become more common in classrooms. Our research group has developed and completed studies on the use of a visual modeling tool, the Concept Connector. This modeling tool consists of an online concept mapping Java applet that has automatic scoring functions we refer to as Robograder. The Concept…

  10. Making Inexpensive 3-D Models

    ERIC Educational Resources Information Center

    Manos, Harry

    2016-01-01

    Visual aids are important to student learning, and they help make the teacher's job easier. Keeping with the "TPT" theme of "The Art, Craft, and Science of Physics Teaching," the purpose of this article is to show how teachers, lacking equipment and funds, can construct a durable 3-D model reference frame and a model gravity…

  11. Tips on Creating Complex Geometry Using Solid Modeling Software

    ERIC Educational Resources Information Center

    Gow, George

    2008-01-01

    Three-dimensional computer-aided drafting (CAD) software, sometimes referred to as "solid modeling" software, is easy to learn, fun to use, and becoming the standard in industry. However, many users have difficulty creating complex geometry with the solid modeling software. And the problem is not entirely a student problem. Even some teachers and…

  12. The influence of learning and updating speed on the growth of commercial websites

    NASA Astrophysics Data System (ADS)

    Wan, Xiaoji; Deng, Guishi; Bai, Yang; Xue, Shaowei

    2012-08-01

    In this paper, we study the competition model of commercial websites with learning and updating speed, and further analyze the influence of learning and updating speed on the growth of commercial websites from a nonlinear dynamics perspective. Using the center manifold theory and the normal form method, we give the explicit formulas determining the stability and periodic fluctuation of commercial sites. Numerical simulations reveal that sites periodically fluctuate as the speed of learning and updating crosses one threshold. The study provides reference and evidence for website operators to make decisions.

  13. 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.

  14. Teaching English or Producing Docility? Foucauldian Analysis of Pakistani State-Mandated English Textbooks

    ERIC Educational Resources Information Center

    Channa, Liaquat Ali; Gilhooly, Daniel; Channa, Abdul Razaque; Manan, Syed Abdul

    2017-01-01

    The scholarship of language education, particularly with reference to learning and use of English, is marked by varieties of English. One may note two broad models: (1) ENL, ESL, and EFL; (2) EIL, ELF, and WEs. Although the scholarship is replete with debates, the debates seem to only construct and maintain that learning English and its use are…

  15. Writing: The Research Paper. Postsecondary Intervention Model for Learning Disabilities. Study Manual #3.

    ERIC Educational Resources Information Center

    Friedman, Brenda G.; And Others

    The manual is intended to help students with language learning disabilities master the academic task of research paper writing. A seven-step procedure is advocated for students and their tutors: (1) select a workable topic, then limit and focus it; (2) use library references to identify sources from which to prepare a working bibliography; (3)…

  16. On the necessity of U-shaped learning.

    PubMed

    Carlucci, Lorenzo; Case, John

    2013-01-01

    A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central topic in the Cognitive Science debate about learning models. Antagonist models (e.g., connectionism versus nativism) are often judged on their ability of modeling or accounting for U-shaped behavior. The prior literature is mostly occupied with explaining how U-shaped behavior occurs. Instead, we are interested in the necessity of this kind of apparently inefficient strategy. We present and discuss a body of results in the abstract mathematical setting of (extensions of) Gold-style computational learning theory addressing a mathematically precise version of the following question: Are there learning tasks that require U-shaped behavior? All notions considered are learning in the limit from positive data. We present results about the necessity of U-shaped learning in classical models of learning as well as in models with bounds on the memory of the learner. The pattern emerges that, for parameterized, cognitively relevant learning criteria, beyond very few initial parameter values, U-shapes are necessary for full learning power! We discuss the possible relevance of the above results for the Cognitive Science debate about learning models as well as directions for future research. Copyright © 2013 Cognitive Science Society, Inc.

  17. Towards a Theoretical Framework for Educational Simulations.

    ERIC Educational Resources Information Center

    Winer, Laura R.; Vazquez-Abad, Jesus

    1981-01-01

    Discusses the need for a sustained and systematic effort toward establishing a theoretical framework for educational simulations, proposes the adaptation of models borrowed from the natural and applied sciences, and describes three simulations based on such a model adapted using Brunerian learning theory. Sixteen references are listed. (LLS)

  18. Teaching RFID Information Systems Security

    ERIC Educational Resources Information Center

    Thompson, Dale R.; Di, Jia; Daugherty, Michael K.

    2014-01-01

    The future cyber security workforce needs radio frequency identification (RFID) information systems security (INFOSEC) and threat modeling educational materials. A complete RFID security course with new learning materials and teaching strategies is presented here. A new RFID Reference Model is used in the course to organize discussion of RFID,…

  19. Testing the limits of long-distance learning: learning beyond a three-segment window.

    PubMed

    Finley, Sara

    2012-01-01

    Traditional flat-structured bigram and trigram models of phonotactics are useful because they capture a large number of facts about phonological processes. Additionally, these models predict that local interactions should be easier to learn than long-distance ones because long-distance dependencies are difficult to capture with these models. Long-distance phonotactic patterns have been observed by linguists in many languages, who have proposed different kinds of models, including feature-based bigram and trigram models, as well as precedence models. Contrary to flat-structured bigram and trigram models, these alternatives capture unbounded dependencies because at an abstract level of representation, the relevant elements are locally dependent, even if they are not adjacent at the observable level. Using an artificial grammar learning paradigm, we provide additional support for these alternative models of phonotactics. Participants in two experiments were exposed to a long-distance consonant-harmony pattern in which the first consonant of a five-syllable word was [s] or [∫] ("sh") and triggered a suffix that was either [-su] or [-∫u] depending on the sibilant quality of this first consonant. Participants learned this pattern, despite the large distance between the trigger and the target, suggesting that when participants learn long-distance phonological patterns, that pattern is learned without specific reference to distance. Copyright © 2012 Cognitive Science Society, Inc.

  20. The Computational Science Education Reference Desk: A tool for increasing inquiry based learning in the science classroom

    NASA Astrophysics Data System (ADS)

    Joiner, D. A.; Stevenson, D. E.; Panoff, R. M.

    2000-12-01

    The Computational Science Reference Desk is an online tool designed to provide educators in math, physics, astronomy, biology, chemistry, and engineering with information on how to use computational science to enhance inquiry based learning in the undergraduate and pre college classroom. The Reference Desk features a showcase of original content exploration activities, including lesson plans and background materials; a catalog of websites which contain models, lesson plans, software, and instructional resources; and a forum to allow educators to communicate their ideas. Many of the recent advances in astronomy rely on the use of computer simulation, and tools are being developed by CSERD to allow students to experiment with some of the models that have guided scientific discovery. One of these models allows students to study how scientists use spectral information to determine the makeup of the interstellar medium by modeling the interstellar extinction curve using spherical grains of silicate, amorphous carbon, or graphite. Students can directly compare their model to the average interstellar extinction curve, and experiment with how small changes in their model alter the shape of the interstellar extinction curve. A simpler model allows students to visualize spatial relationships between the Earth, Moon, and Sun to understand the cause of the phases of the moon. A report on the usefulness of these models in two classes, the Computational Astrophysics workshop at The Shodor Education Foundation and the Conceptual Astronomy class at the University of North Carolina at Greensboro, will be presented.

  1. Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

    NASA Astrophysics Data System (ADS)

    Wang, Wei-Cheng; Tai, Cheng-Chi

    2017-07-01

    The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

  2. Inferring interventional predictions from observational learning data.

    PubMed

    Meder, Bjorn; Hagmayer, York; Waldmann, Michael R

    2008-02-01

    Previous research has shown that people are capable of deriving correct predictions for previously unseen actions from passive observations of causal systems (Waldmann & Hagmayer, 2005). However, these studies were limited, since learning data were presented as tabulated data only, which may have turned the task more into a reasoning rather than a learning task. In two experiments, we therefore presented learners with trial-by-trial observational learning input referring to a complex causal model consisting of four events. To test the robustness of the capacity to derive correct observational and interventional inferences, we pitted causal order against the temporal order of learning events. The results show that people are, in principle, capable of deriving correct predictions after purely observational trial-by-trial learning, even with relatively complex causal models. However, conflicting temporal information can impair performance, particularly when the inferences require taking alternative causal pathways into account.

  3. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    NASA Astrophysics Data System (ADS)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  4. Teaching Aids and Work with Models in E-Learning Environments

    ERIC Educational Resources Information Center

    Jancaríková, Katerina; Jancarík, Antonín

    2017-01-01

    PISA study has defined several key areas to be paid attention to by teachers. One of these areas is work with models. The term model can be understood very broadly, it can refer to a drawing of a chemical reaction, a plastic model, a permanent mount (taxidermy) to advanced 3D projections. Teachers are no longer confined to teaching materials and…

  5. [Construction and Application of Innovative Education Technology Strategies in Nursing].

    PubMed

    Chao, Li-Fen; Huang, Hsiang-Ping; Ni, Lee-Fen; Tsai, Chia-Lan; Huang, Tsuey-Yuan

    2017-12-01

    The evolution of information and communication technologies has deeply impacted education reform, promoted the development of digital-learning models, and stimulated the development of diverse nursing education strategies in order to better fulfill needs and expand in new directions. The present paper introduces the intelligent-learning resources that are available for basic medical science education, problem-based learning, nursing scenario-based learning, objective structured clinical examinations, and other similar activities in the Department of Nursing at Chang Gung University of Science and Technology. The program is offered in two parts: specialized classroom facilities and cloud computing / mobile-learning. The latter includes high-fidelity simulation classrooms, online e-books, and virtual interactive simulation and augmented reality mobile-learning materials, which are provided through multimedia technology development, learning management systems, web-certificated examinations, and automated teaching and learning feedback mechanisms. It is expected that the teaching experiences that are shared in this article may be used as a reference for applying professional wisdom teaching models into nursing education.

  6. INDIVIDUAL DIFFERENCES IN TASK-SPECIFIC PAIRED ASSOCIATES LEARNING IN OLDER ADULTS: THE ROLE OF PROCESSING SPEED AND WORKING MEMORY

    PubMed Central

    Kurtz, Tanja; Mogle, Jacqueline; Sliwinski, Martin J.; Hofer, Scott M.

    2013-01-01

    Background The role of processing speed and working memory was investigated in terms of individual differences in task-specific paired associates learning in a sample of older adults. Task-specific learning, as distinct from content-oriented item-specific learning, refers to gains in performance due to repeated practice on a learning task in which the to-be-learned material changes over trials. Methods Learning trajectories were modeled within an intensive repeated-measures design based on participants obtained from an opt-in internet-based sampling service (Mage = 65.3, SD = 4.81). Participants completed an eight-item paired associates task daily over a seven-day period. Results Results indicated that a three-parameter hyperbolic model (i.e., initial level, learning rate, and asymptotic performance) best described learning trajectory. After controlling for age-related effects, both higher working memory and higher processing speed had a positive effect on all three learning parameters. Conclusion These results emphasize the role of cognitive abilities for individual differences in task-specific learning of older adults. PMID:24151913

  7. Evaluating Innovation and Navigating Unseen Boundaries: Systems, Processes and People

    ERIC Educational Resources Information Center

    Fleet, Alma; De Gioia, Katey; Madden, Lorraine; Semann, Anthony

    2018-01-01

    This paper illustrates an evaluation model emerging from Australian research. With reference to a range of contexts, its usefulness is demonstrated through application to two professional development initiatives designed to improve continuity of learning in the context of the transition to school. The model reconceptualises approaches to…

  8. Modeling Educational Content: The Cognitive Approach of the PALO Language

    ERIC Educational Resources Information Center

    Rodriguez-Artacho, Miguel; Verdejo Maillo, M. Felisa

    2004-01-01

    This paper presents a reference framework to describe educational material. It introduces the PALO Language as a cognitive based approach to Educational Modeling Languages (EML). In accordance with recent trends for reusability and interoperability in Learning Technologies, EML constitutes an evolution of the current content-centered…

  9. Physical Models that Provide Guidance in Visualization Deconstruction in an Inorganic Context

    ERIC Educational Resources Information Center

    Schiltz, Holly K.; Oliver-Hoyo, Maria T.

    2012-01-01

    Three physical model systems have been developed to help students deconstruct the visualization needed when learning symmetry and group theory. The systems provide students with physical and visual frames of reference to facilitate the complex visualization involved in symmetry concepts. The permanent reflection plane demonstration presents an…

  10. Buns, Scissors and Strawberry Laces--A Model of Science Education?

    ERIC Educational Resources Information Center

    Walsh, Ed; Edwards, Rebecca

    2009-01-01

    Models are included in the science National Curriculum because modelling is a key tool for scientists and an integral part of how science works. Modelling is explicitly referred to in the Programmes of Study for Science at Key Stage 3 and 4 (age 11-16) and in Assessing Pupil's Progress (APP). Pupils need to learn how to use models because they are…

  11. A Neural Network Model of Retrieval-Induced Forgetting

    ERIC Educational Resources Information Center

    Norman, Kenneth A.; Newman, Ehren L.; Detre, Greg

    2007-01-01

    Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular…

  12. Students as Partners: Reflections on a Conceptual Model

    ERIC Educational Resources Information Center

    Healey, Mick; Flint, Abbi; Harrington, Kathy

    2016-01-01

    This article reflects on a conceptual model for mapping the work that fits under the broad heading of students as partners in learning and teaching in higher education (Healey, Flint, & Harrington, 2014). We examine the nature and purpose of the model with reference to specific examples, and reflect on the potential and actual uses of the…

  13. Learning to Learn in the European Reference Framework for Lifelong Learning

    ERIC Educational Resources Information Center

    Pirrie, Anne; Thoutenhoofd, Ernst D.

    2013-01-01

    This article explores the construction of learning to learn that is implicit in the document "Key Competences for Lifelong Learning--European Reference Framework" and related education policy from the European Commission. The authors argue that the hallmark of learning to learn is the development of a fluid sociality rather than the…

  14. Word Learning Emerges from the Interaction of Online Referent Selection and Slow Associative Learning

    ERIC Educational Resources Information Center

    McMurray, Bob; Horst, Jessica S.; Samuelson, Larissa K.

    2012-01-01

    Classic approaches to word learning emphasize referential ambiguity: In naming situations, a novel word could refer to many possible objects, properties, actions, and so forth. To solve this, researchers have posited constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We…

  15. Reference frames in allocentric representations are invariant across static and active encoding

    PubMed Central

    Chan, Edgar; Baumann, Oliver; Bellgrove, Mark A.; Mattingley, Jason B.

    2013-01-01

    An influential model of spatial memory—the so-called reference systems account—proposes that relationships between objects are biased by salient axes (“frames of reference”) provided by environmental cues, such as the geometry of a room. In this study, we sought to examine the extent to which a salient environmental feature influences the formation of spatial memories when learning occurs via a single, static viewpoint and via active navigation, where information has to be integrated across multiple viewpoints. In our study, participants learned the spatial layout of an object array that was arranged with respect to a prominent environmental feature within a virtual arena. Location memory was tested using judgments of relative direction. Experiment 1A employed a design similar to previous studies whereby learning of object-location information occurred from a single, static viewpoint. Consistent with previous studies, spatial judgments were significantly more accurate when made from an orientation that was aligned, as opposed to misaligned, with the salient environmental feature. In Experiment 1B, a fresh group of participants learned the same object-location information through active exploration, which required integration of spatial information over time from a ground-level perspective. As in Experiment 1A, object-location information was organized around the salient environmental cue. Taken together, the findings suggest that the learning condition (static vs. active) does not affect the reference system employed to encode object-location information. Spatial reference systems appear to be a ubiquitous property of spatial representations, and might serve to reduce the cognitive demands of spatial processing. PMID:24009595

  16. Forum on Workforce Development

    NASA Technical Reports Server (NTRS)

    Hoffman, Edward

    2010-01-01

    APPEL Mission: To support NASA's mission by promoting individual, team, and organizational excellence in program/project management and engineering through the application of learning strategies, methods, models, and tools. Goals: a) Provide a common frame of reference for NASA s technical workforce. b) Provide and enhance critical job skills. c) Support engineering, program and project teams. d) Promote organizational learning across the agency. e) Supplement formal educational programs.

  17. A Three-Attribute Transfer Skills Framework--Part I: Establishing the Model and Its Relation to Chemical Education

    ERIC Educational Resources Information Center

    Dori, Yehudit Judy; Sasson, Irit

    2013-01-01

    This paper presents Part I of a two-part study. This first part reviews the literature of transfer of learning as one of the major goals of instruction. Transfer refers to students' ability to apply knowledge and skills in new learning contexts. The literature suggests partially or non-overlapping definitions, and empirical studies on transfer…

  18. Learning to Look beyond the Boundaries of Representation: Using Technology to Examine Teaching (Overview for a Digital Exhibition: Learning from the Practice of Teaching)

    ERIC Educational Resources Information Center

    Hatch, Thomas; Grossman, Pam

    2009-01-01

    Leading a classroom discussion involves multiple components, including establishing norms for participation, assisting students in engaging in careful readings of text ahead of time, and modeling features of academic discourse. In other work, Grossman and her colleagues refer to this as the "decomposition" of practice--breaking down complex…

  19. Computational Modeling for Language Acquisition: A Tutorial With Syntactic Islands.

    PubMed

    Pearl, Lisa S; Sprouse, Jon

    2015-06-01

    Given the growing prominence of computational modeling in the acquisition research community, we present a tutorial on how to use computational modeling to investigate learning strategies that underlie the acquisition process. This is useful for understanding both typical and atypical linguistic development. We provide a general overview of why modeling can be a particularly informative tool and some general considerations when creating a computational acquisition model. We then review a concrete example of a computational acquisition model for complex structural knowledge referred to as syntactic islands. This includes an overview of syntactic islands knowledge, a precise definition of the acquisition task being modeled, the modeling results, and how to meaningfully interpret those results in a way that is relevant for questions about knowledge representation and the learning process. Computational modeling is a powerful tool that can be used to understand linguistic development. The general approach presented here can be used to investigate any acquisition task and any learning strategy, provided both are precisely defined.

  20. Developing Learning Model P3E to Improve Students’ Critical Thinking Skills of Islamic Senior High School

    NASA Astrophysics Data System (ADS)

    Bahtiar; Rahayu, Y. S.; Wasis

    2018-01-01

    This research aims to produce P3E learning model to improve students’ critical thinking skills. The developed model is named P3E, consisting of 4 (four) stages namely; organization, inquiry, presentation, and evaluation. This development research refers to the development stage by Kemp. The design of the wide scale try-out used pretest-posttest group design. The wide scale try-out was conducted in grade X of 2016/2017 academic year. The analysis of the results of this development research inludes three aspects, namely: validity, practicality, and effectiveness of the model developed. The research results showed; (1) the P3E learning model was valid, according to experts with an average value of 3.7; (2) The completion of the syntax of the learning model developed obtained 98.09% and 94.39% for two schools based on the assessment of the observers. This shows that the developed model is practical to be implemented; (3) the developed model is effective for improving students’ critical thinking skills, although the n-gain of the students’ critical thinking skills was 0.54 with moderate category. Based on the results of the research above, it can be concluded that the developed P3E learning model is suitable to be used to improve students’ critical thinking skills.

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

    NASA Astrophysics Data System (ADS)

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

    2017-09-01

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

  2. On valuing peers: theories of learning and intercultural competence

    NASA Astrophysics Data System (ADS)

    Cajander, Åsa; Daniels, Mats; McDermott, Roger

    2012-12-01

    This paper investigates the links between the contributing student pedagogy and other forms of peer-mediated learning models, e.g. open-ended group projects and communities of practice. We find that a fundamental concern in each of these models is the attribution of value; specifically, recognition of the value of learning that is enabled by peer interaction, and the way in which value is created and assessed within a learning community. Value is also central to theories of intercultural competence. We examine the role that the concept of value plays in the development cycle of intercultural competence and relate it to its function in peer-mediated learning models. We also argue that elements of social learning theory, principally recent work on value creation in communities of practice, are very relevant to the construction and assessment of the type of activities proposed within the contributing student pedagogy. Our theoretical analysis is situated within the context of a globally distributed open-ended group project course unit and our conclusions are illustrated with reference to student practice in this environment.

  3. Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.

    PubMed

    Larson, David B; Chen, Matthew C; Lungren, Matthew P; Halabi, Safwan S; Stence, Nicholas V; Langlotz, Curtis P

    2018-04-01

    Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model. Results The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model. Conclusion A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of existing automated models. © RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on January 19, 2018.

  4. Prototype-Incorporated Emotional Neural Network.

    PubMed

    Oyedotun, Oyebade K; Khashman, Adnan

    2017-08-15

    Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

  5. Modeling on the grand scale: LANDFIRE lessons learned

    Treesearch

    Kori Blankenship; Jim Smith; Randy Swaty; Ayn J. Shlisky; Jeannie Patton; Sarah Hagen

    2012-01-01

    Between 2004 and 2009, the LANDFIRE project facilitated the creation of approximately 1,200 unique state-andtransition models (STMs) for all major ecosystems in the United States. The primary goal of the modeling effort was to create a consistent and comprehensive set of STMs describing reference conditions and to inform the mapping of a subset of LANDFIRE’s spatial...

  6. Applications of the Functional Writing Model in Technical and Professional Writing.

    ERIC Educational Resources Information Center

    Brostoff, Anita

    The functional writing model is a method by which students learn to devise and organize a written argument. Salient features of functional writing include the organizing idea (a component that logically unifies a paragraph or sequence of paragraphs), the reader's frame of reference, forecasting (prediction of the sequence by which the organizing…

  7. Semantic Coherence Facilitates Distributional Learning.

    PubMed

    Ouyang, Long; Boroditsky, Lera; Frank, Michael C

    2017-04-01

    Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.

  8. 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.

  9. Designing Production Based Learning as a Basic Strategy for Creating Income Generating Units at Universitas Pendidikan Indonesia

    NASA Astrophysics Data System (ADS)

    Suryadi, D.; Supriatna, N.

    2018-02-01

    The establishment of Universitas Pendidikan Indonesia (later to be referred as UPI) Statute as a State-Owned State University (PTN-BH) has implications for UPI requirements. One of them is the need for UPI to generate an Income Generating Unit (IGU) of at least IDR 100 Billion (one hundred billion rupiah). This requirement is considered difficult since UPI is one of the universities whose focus is on the world of education and not the business and industry. Surely this becomes the thinking of the entire academic community to make a breakthrough by optimizing their potential. This study aims to find the pattern of learning practice that produces economic value products as one indicator of IGU value achievement as an effort to support UPI as PTN-BH. Learning strategy is done by designing and implementing the production base learning (PBL) approach as the basis strategy for the development of production units capable of becoming IGU in UPI. The research method used refers to research and development methods with adjustments taking into account the effectiveness in validating and conducting field model trials. The result of this research is the basic design of PBL model as the development strategy of production unit in the achievement of IGU UPI PTN-BH.

  10. Learning of spatial relationships between observed and imitated actions allows invariant inverse computation in the frontal mirror neuron system.

    PubMed

    Oh, Hyuk; Gentili, Rodolphe J; Reggia, James A; Contreras-Vidal, José L

    2011-01-01

    It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator's frontal mirror neuron system can be trained to provide the motor plans for the imitated actions.

  11. Beyond naïve cue combination: salience and social cues in early word learning.

    PubMed

    Yurovsky, Daniel; Frank, Michael C

    2017-03-01

    Children learn their earliest words through social interaction, but it is unknown how much they rely on social information. Some theories argue that word learning is fundamentally social from its outset, with even the youngest infants understanding intentions and using them to infer a social partner's target of reference. In contrast, other theories argue that early word learning is largely a perceptual process in which young children map words onto salient objects. One way of unifying these accounts is to model word learning as weighted cue combination, in which children attend to many potential cues to reference, but only gradually learn the correct weight to assign each cue. We tested four predictions of this kind of naïve cue combination account, using an eye-tracking paradigm that combines social word teaching and two-alternative forced-choice testing. None of the predictions were supported. We thus propose an alternative unifying account: children are sensitive to social information early, but their ability to gather and deploy this information is constrained by domain-general cognitive processes. Developmental changes in children's use of social cues emerge not from learning the predictive power of social cues, but from the gradual development of attention, memory, and speed of information processing. © 2015 John Wiley & Sons Ltd.

  12. Beyond Naïve Cue Combination: Salience and Social Cues in Early Word Learning

    PubMed Central

    Yurovsky, Daniel

    2015-01-01

    Children learn their earliest words through social interaction, but it is unknown how much they rely on social information. Some theories argue that word learning is fundamentally social from its outset, with even the youngest infants understanding intentions and using them to infer a social partner’s target of reference. In contrast, other theories argue that early word learning is largely a perceptual process in which young children map words onto salient objects. One way of unifying these accounts is to model word learning as weighted cue-combination, in which children attend to many potential cues to reference, but only gradually learn the correct weight to assign each cue. We tested four predictions of this kind of naïve cue-combination account, using an eye-tracking paradigm that combines social word-teaching and two-alternative forced-choice testing. None of the predictions were supported. We thus propose an alternative unifying account: children are sensitive to social information early, but their ability to gather and deploy this information is constrained by domain-general cognitive processes. Developmental changes in children’s use of social cues emerge not from learning the predictive power of social cues, but from the gradual development of attention, memory, and speed of information processing. PMID:26575408

  13. Competition between multiple words for a referent in cross-situational word learning

    PubMed Central

    Benitez, Viridiana L.; Yurovsky, Daniel; Smith, Linda B.

    2016-01-01

    Three experiments investigated competition between word-object pairings in a cross-situational word-learning paradigm. Adults were presented with One-Word pairings, where a single word labeled a single object, and Two-Word pairings, where two words labeled a single object. In addition to measuring learning of these two pairing types, we measured competition between words that refer to the same object. When the word-object co-occurrences were presented intermixed in training (Experiment 1), we found evidence for direct competition between words that label the same referent. Separating the two words for an object in time eliminated any evidence for this competition (Experiment 2). Experiment 3 demonstrated that adding a linguistic cue to the second label for a referent led to different competition effects between adults who self-reported different language learning histories, suggesting both distinctiveness and language learning history affect competition. Finally, in all experiments, competition effects were unrelated to participants’ explicit judgments of learning, suggesting that competition reflects the operating characteristics of implicit learning processes. Together, these results demonstrate that the role of competition between overlapping associations in statistical word-referent learning depends on time, the distinctiveness of word-object pairings, and language learning history. PMID:27087742

  14. A knowledge-based system for patient image pre-fetching in heterogeneous database environments--modeling, design, and evaluation.

    PubMed

    Wei, C P; Hu, P J; Sheng, O R

    2001-03-01

    When performing primary reading on a newly taken radiological examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation or comparison purposes. Support of such image references is of clinical importance and may have significant effects on radiologists' examination reading efficiency, service quality, and work satisfaction. To effectively support such image reference needs, we proposed and developed a knowledge-based patient image pre-fetching system, addressing several challenging requirements of the application that include representation and learning of image reference heuristics and management of data-intensive knowledge inferencing. Moreover, the system demands an extensible and maintainable architecture design capable of effectively adapting to a dynamic environment characterized by heterogeneous and autonomous data source systems. In this paper, we developed a synthesized object-oriented entity- relationship model, a conceptual model appropriate for representing radiologists' prior image reference heuristics that are heuristic oriented and data intensive. We detailed the system architecture and design of the knowledge-based patient image pre-fetching system. Our architecture design is based on a client-mediator-server framework, capable of coping with a dynamic environment characterized by distributed, heterogeneous, and highly autonomous data source systems. To adapt to changes in radiologists' patient prior image reference heuristics, ID3-based multidecision-tree induction and CN2-based multidecision induction learning techniques were developed and evaluated. Experimentally, we examined effects of the pre-fetching system we created on radiologists' examination readings. Preliminary results show that the knowledge-based patient image pre-fetching system more accurately supports radiologists' patient prior image reference needs than the current practice adopted at the study site and that radiologists may become more efficient, consultatively effective, and better satisfied when supported by the pre-fetching system than when relying on the study site's pre-fetching practice.

  15. Instrumental learning and relearning in individuals with psychopathy and in patients with lesions involving the amygdala or orbitofrontal cortex.

    PubMed

    Mitchell, D G V; Fine, C; Richell, R A; Newman, C; Lumsden, J; Blair, K S; Blair, R J R

    2006-05-01

    Previous work has shown that individuals with psychopathy are impaired on some forms of associative learning, particularly stimulus-reinforcement learning (Blair et al., 2004; Newman & Kosson, 1986). Animal work suggests that the acquisition of stimulus-reinforcement associations requires the amygdala (Baxter & Murray, 2002). Individuals with psychopathy also show impoverished reversal learning (Mitchell, Colledge, Leonard, & Blair, 2002). Reversal learning is supported by the ventrolateral and orbitofrontal cortex (Rolls, 2004). In this paper we present experiments investigating stimulus-reinforcement learning and relearning in patients with lesions of the orbitofrontal cortex or amygdala, and individuals with developmental psychopathy without known trauma. The results are interpreted with reference to current neurocognitive models of stimulus-reinforcement learning, relearning, and developmental psychopathy. Copyright (c) 2006 APA, all rights reserved.

  16. Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning

    NASA Astrophysics Data System (ADS)

    Li, Xue-yan; Li, Xue-mei; Yang, Lingrun; Li, Jing

    2018-07-01

    Most of the previous studies on dynamic traffic assignment are based on traditional analytical framework, for instance, the idea of Dynamic User Equilibrium has been widely used in depicting both the route choice and the departure time choice. However, some recent studies have demonstrated that the dynamic traffic flow assignment largely depends on travelers' rationality degree, travelers' heterogeneity and what the traffic information the travelers have. In this paper, we develop a new self-adaptive multi agent model to depict travelers' behavior in Dynamic Traffic Assignment. We use Cumulative Prospect Theory with heterogeneous reference points to illustrate travelers' bounded rationality. We use reinforcement-learning model to depict travelers' route and departure time choosing behavior under the condition of imperfect information. We design the evolution rule of travelers' expected arrival time and the algorithm of traffic flow assignment. Compared with the traditional model, the self-adaptive multi agent model we proposed in this paper can effectively help travelers avoid the rush hour. Finally, we report and analyze the effect of travelers' group behavior on the transportation system, and give some insights into the relation between travelers' group behavior and the performance of transportation system.

  17. Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

    ERIC Educational Resources Information Center

    Gisburne, Jaclyn M.; Fairchild, Patricia J.

    2004-01-01

    This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

  18. Learner Perception of Personal Spaces of Information (PSIs): A Mental Model Analysis

    ERIC Educational Resources Information Center

    Hardof-Jaffe, Sharon; Aladjem, Ruthi

    2018-01-01

    A personal space of information (PSI) refers to the collection of digital information items created, saved and organized, on digital devices. PSIs play a central and significant role in learning processes. This study explores the mental models and perceptions of PSIs by learners, using drawing analysis. Sixty-three graduate students were asked to…

  19. Sentence-Based Attentional Mechanisms in Word Learning: Evidence from a Computational Model

    PubMed Central

    Alishahi, Afra; Fazly, Afsaneh; Koehne, Judith; Crocker, Matthew W.

    2012-01-01

    When looking for the referents of novel nouns, adults and young children are sensitive to cross-situational statistics (Yu and Smith, 2007; Smith and Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau and Gleitman, 1985; Altmann and Kamide, 1999; Kako and Trueswell, 2000). Koehne and Crocker (2010, 2011) investigate the interaction between cross-situational evidence and guidance from the sentential context in an adult language learning scenario. Their studies reveal that these learning mechanisms interact in a complex manner: they can be used in a complementary way when context helps reduce referential uncertainty; they influence word learning about equally strongly when cross-situational and contextual evidence are in conflict; and contextual cues block aspects of cross-situational learning when both mechanisms are independently applicable. To address this complex pattern of findings, we present a probabilistic computational model of word learning which extends a previous cross-situational model (Fazly et al., 2010) with an attention mechanism based on sentential cues. Our model uses a framework that seamlessly combines the two sources of evidence in order to study their emerging pattern of interaction during the process of word learning. Simulations of the experiments of (Koehne and Crocker, 2010, 2011) reveal an overall pattern of results that are in line with their findings. Importantly, we demonstrate that our model does not need to explicitly assign priority to either source of evidence in order to produce these results: learning patterns emerge as a result of a probabilistic interaction between the two clue types. Moreover, using a computational model allows us to examine the developmental trajectory of the differential roles of cross-situational and sentential cues in word learning. PMID:22783211

  20. The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

    PubMed

    Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

    2017-01-01

    The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

  1. The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

    PubMed Central

    Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

    2017-01-01

    The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

  2. Reciprocity Family Counseling: A Multi-Ethnic Model.

    ERIC Educational Resources Information Center

    Penrose, David M.

    The Reciprocity Family Counseling Method involves learning principles of behavior modification including selective reinforcement, behavioral contracting, self-correction, and over-correction. Selective reinforcement refers to the recognition and modification of parent/child responses and reinforcers. Parents and children are asked to identify…

  3. Machine learning-based dual-energy CT parametric mapping

    NASA Astrophysics Data System (ADS)

    Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W.; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Helo, Rose Al; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C.; Rassouli, Negin; Gilkeson, Robert C.; Traughber, Bryan J.; Cheng, Chee-Wai; Muzic, Raymond F., Jr.

    2018-06-01

    The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

  4. Machine learning-based dual-energy CT parametric mapping.

    PubMed

    Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

    2018-06-08

    The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z eff ), relative electron density (ρ e ), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

  5. Superior Cognitive Mapping through Single Landmark-Related Learning than through Boundary-Related Learning

    ERIC Educational Resources Information Center

    Zhou, Ruojing; Mou, Weimin

    2016-01-01

    Cognitive mapping is assumed to be through hippocampus-dependent place learning rather than striatum-dependent response learning. However, we proposed that either type of spatial learning, as long as it involves encoding metric relations between locations and reference points, could lead to a cognitive map. Furthermore, the fewer reference points…

  6. Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data.

    PubMed

    Gu, Ke; Tao, Dacheng; Qiao, Jun-Fei; Lin, Weisi

    2018-04-01

    In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.

  7. Representational Distance Learning for Deep Neural Networks

    PubMed Central

    McClure, Patrick; Kriegeskorte, Nikolaus

    2016-01-01

    Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains. PMID:28082889

  8. Representational Distance Learning for Deep Neural Networks.

    PubMed

    McClure, Patrick; Kriegeskorte, Nikolaus

    2016-01-01

    Deep neural networks (DNNs) provide useful models of visual representational transformations. We present a method that enables a DNN (student) to learn from the internal representational spaces of a reference model (teacher), which could be another DNN or, in the future, a biological brain. Representational spaces of the student and the teacher are characterized by representational distance matrices (RDMs). We propose representational distance learning (RDL), a stochastic gradient descent method that drives the RDMs of the student to approximate the RDMs of the teacher. We demonstrate that RDL is competitive with other transfer learning techniques for two publicly available benchmark computer vision datasets (MNIST and CIFAR-100), while allowing for architectural differences between student and teacher. By pulling the student's RDMs toward those of the teacher, RDL significantly improved visual classification performance when compared to baseline networks that did not use transfer learning. In the future, RDL may enable combined supervised training of deep neural networks using task constraints (e.g., images and category labels) and constraints from brain-activity measurements, so as to build models that replicate the internal representational spaces of biological brains.

  9. An Approach to Building a Learning Management System that Emphasizes on Incorporating Individualized Dissemination with Intelligent Tutoring

    NASA Astrophysics Data System (ADS)

    Ghosh, Sreya

    2017-02-01

    This article proposes a new six-model architecture for an intelligent tutoring system to be incorporated in a learning management system with domain-independence feature and individualized dissemination. The present six model architecture aims to simulate a human tutor. Some recent extensions of using intelligent tutoring system (ITS) explores learning management systems to behave as a real teacher during a teaching-learning process, by taking care of, mainly, the dynamic response system. However, the present paper argues that to mimic a human teacher it needs not only the dynamic response but also the incorporation of the teacher's dynamic review of students' performance and keeping track of their current level of understanding. Here, the term individualization has been used to refer to tailor making of contents and its dissemination fitting to the individual needs and capabilities of learners who is taking a course online and is subjected to teaching in absentia. This paper describes how the individual models of the proposed architecture achieves the features of ITS.

  10. Exploring the changing learning environment of the gross anatomy lab.

    PubMed

    Hopkins, Robin; Regehr, Glenn; Wilson, Timothy D

    2011-07-01

    The objective of this study was to assess the impact of virtual models and prosected specimens in the context of the gross anatomy lab. In 2009, student volunteers from an undergraduate anatomy class were randomly assigned to study groups in one of three learning conditions. All groups studied the muscles of mastication and completed identical learning objectives during a 45-minute lab. All groups were provided with two reference atlases. Groups were distinguished by the type of primary tools they were provided: gross prosections, three-dimensional stereoscopic computer model, or both resources. The facilitator kept observational field notes. A prepost multiple-choice knowledge test was administered to evaluate students' learning. No significant effect of the laboratory models was demonstrated between groups on the prepost assessment of knowledge. Recurring observations included students' tendency to revert to individual memorization prior to the posttest, rotation of models to match views in the provided atlas, and dissemination of groups into smaller working units. The use of virtual lab resources seemed to influence the social context and learning environment of the anatomy lab. As computer-based learning methods are implemented and studied, they must be evaluated beyond their impact on knowledge gain to consider the effect technology has on students' social development.

  11. Virtual Reality for Collaborative E-Learning

    ERIC Educational Resources Information Center

    Monahan, Teresa; McArdle, Gavin; Bertolotto, Michela

    2008-01-01

    In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved…

  12. The development of learning model for natural science based on environmental in conservation area of Bengkulu University

    NASA Astrophysics Data System (ADS)

    Karyadi, B.; Susanta, A.; Winari, E. W.; Ekaputri, R. Z.; Enersi, D.

    2018-05-01

    Research on development of a learning model for Natural Science base on conservation area in Bengkulu University has been conducted. The research methods were referred to the standard steps of Research and Development. Stage activities were (a) analysis of needs, (b) observation of the ecological aspects of conservation area as a learning resource, and (c) instructional design based on conservation area for secondary school students. The observation results on the ecological aspects revealed that the diversity of plants and animals, at the conservation area were sufficient as a source for learning. The instructional design was prepared in three phase activities namely Introduction-Exploration-Interpretation (IEI), and then it was compiled in a teaching material Based on Surrounding Natural Environment” (BSNE). The results of a limited scale trial at secondary school students in two districts of Bengkulu province showed that, the students who learned using the IEI model at the conservation area have a good performance and critical thinking. The product from the research is a book named BSNE that can be used for teachers and conservation practitioners in doing the learning activities on environmental conservation which involved public participation.

  13. A One-System Theory Which is Not Propositional.

    PubMed

    Witnauer, James E; Urcelay, Gonzalo P; Miller, Ralph R

    2009-04-01

    We argue that the propositional and link-based approaches to human contingency learning represent different levels of analysis because propositional reasoning requires a basis, which is plausibly provided by a link-based architecture. Moreover, in their attempt to compare two general classes of models (link-based and propositional), Mitchell et al. have referred to only two generic models and ignore the large variety of different models within each class.

  14. Causal relationships among academic delay of gratification, motivation, and self-regulated learning in elementary school children.

    PubMed

    Zhang, Lili; Maruno, Shun'ichi

    2010-10-01

    Academic delay of gratification refers to the postponement of immediate rewards by students and the pursuit of more important, temporally remote academic goals. A path model was designed to identify the causal relationships among academic delay of gratification and motivation, self-regulated learning strategies (as specified in the Motivated Strategies for Learning Questionnaire), and grades among 386 Chinese elementary school children. Academic delay of gratification was found to be positively related to motivation and metacognition. Cognitive strategy, resource management, and grades mediated these two factors and were indirectly related to academic delay of gratification.

  15. The Development of Cooperative Learning Model Based on Local Wisdom of Bali for Physical Education, Sport and Health Subject in Junior High School

    NASA Astrophysics Data System (ADS)

    Yoda, I. K.

    2017-03-01

    The purpose of this research is to develop a cooperative learning model based on local wisdom (PKBKL) of Bali (Tri Pramana’s concept), for physical education, sport, and health learning in VII grade of Junior High School in Singaraja-Buleleng Bali. This research is the development research of the development design chosen refers to the development proposed by Dick and Carey. The development of model and learning devices was conducted through four stages, namely: (1) identification and needs analysis stage (2) the development of design and draft of PKBKL and RPP models, (3) testing stage (expert review, try out, and implementation). Small group try out was conducted on VII-3 grade of Undiksha Laboratory Junior High School in the academic year 2013/2014, large group try out was conducted on VIIb of Santo Paulus Junior High School Singaraja in the academic year 2014/2015, and the implementation of the model was conducted on three (3) schools namely SMPN 2 Singaraja, SMPN 3 Singaraja, and Undiksha laboratory Junior High School in the academic year 2014/2015. Data were collected using documentation, testing, non-testing, questionnaire, and observation. The data were analyzed descriptively. The findings of this research indicate that: (1) PKBKL model has met the criteria of the operation of a learning model namely: syntax, social system, principles of reaction, support system, as well as instructional and nurturing effects, (2) PKBKL model is a valid, practical, and effective model, (3) the practicality of the learning devices (RPP), is at the high category. Based on the research results, there are two things recommended: (1) in order that learning stages (syntax) of PKBKL model can be performed well, then teachers need to have an understanding of the cooperative learning model of Student Team Achievement Division (STAD) type and the concepts of scientifically approach well, (2) PKBKL model can be performed well on physical education, sport and health learning, if the teachers understand the concept of Tri Pramana, therefore if the physical education, sport and health teachers want to apply this PKBKL model, they must first learn and master the concept of Tri Pramana well.

  16. What Models of Verbal Working Memory Can Learn from Phonological Theory: Decomposing the Phonological Similarity Effect

    ERIC Educational Resources Information Center

    Schweppe, Judith; Grice, Martine; Rummer, Ralf

    2011-01-01

    Despite developments in phonology over the last few decades, models of verbal working memory make reference to phoneme-sized phonological units, rather than to the features of which they are composed. This study investigates the influence on short-term retention of such features by comparing the serial recall of lists of syllables with varying…

  17. A Latent Variable Analysis of Continuing Professional Development Constructs Using PLS-SEM Modeling

    ERIC Educational Resources Information Center

    Yazdi, Mona Tabatabaee; Motallebzadeh, Khalil; Ashraf, Hamid; Baghaei, Purya

    2017-01-01

    Continuing Professional Development (CPD), in the area of teacher education, refers to the procedures, programs or strategies that help teachers encounter the challenges of their work and accomplish their own and their learning center's goals. To this aim, the purpose of this study is to propose and validate an appropriate model of EFL teachers'…

  18. The company objects keep: Linking referents together during cross-situational word learning.

    PubMed

    Zettersten, Martin; Wojcik, Erica; Benitez, Viridiana L; Saffran, Jenny

    2018-04-01

    Learning the meanings of words involves not only linking individual words to referents but also building a network of connections among entities in the world, concepts, and words. Previous studies reveal that infants and adults track the statistical co-occurrence of labels and objects across multiple ambiguous training instances to learn words. However, it is less clear whether, given distributional or attentional cues, learners also encode associations amongst the novel objects. We investigated the consequences of two types of cues that highlighted object-object links in a cross-situational word learning task: distributional structure - how frequently the referents of novel words occurred together - and visual context - whether the referents were seen on matching backgrounds. Across three experiments, we found that in addition to learning novel words, adults formed connections between frequently co-occurring objects. These findings indicate that learners exploit statistical regularities to form multiple types of associations during word learning.

  19. 3,4-Methylenedioxymethamphetamine in Adult Rats Produces Deficits in Path Integration and Spatial Reference Memory

    PubMed Central

    Able, Jessica A.; Gudelsky, Gary A.; Vorhees, Charles V.; Williams, Michael T.

    2010-01-01

    Background ±3,4-Methylenedioxymethamphetamine (MDMA) is a recreational drug that causes cognitive deficits in humans. A rat model for learning and memory deficits has not been established, although some cognitive deficits have been reported. Methods Male Sprague-Dawley rats were treated with MDMA (15 mg/kg × 4 doses) or saline (SAL) (n = 20/treatment group) and tested in different learning paradigms: 1) path integration in the Cincinnati water maze (CWM), 2) spatial learning in the Morris water maze (MWM), and 3) novel object recognition (NOR). One week after drug administration, testing began in the CWM, then four phases of MWM, and finally NOR. Following behavioral testing, monoamine levels were assessed. Results ±3,4-Methylenedioxymethamphetamine-treated rats committed more CWM errors than did SAL-treated rats. ±3,4-Methylenedioxymethamphetamine-treated animals were further from the former platform position during each 30-second MWM probe trial but showed no differences during learning trials with the platform present. There were no group differences in NOR. ± 3,4-Methylenedioxymethamphetamine depleted serotonin in all brain regions and dopamine in the striatum. Conclusions ±3,4-Methylenedioxymethamphetamine produced MWM reference memory deficits even after complex learning in the CWM, where deficits in path integration learning occurred. Assessment of path integration may provide a sensitive index of MDMA-induced learning deficits. PMID:16324685

  20. Understanding Probabilistic Interpretations of Physical Systems: A Prerequisite to Learning Quantum Physics.

    ERIC Educational Resources Information Center

    Bao, Lei; Redish, Edward F.

    2002-01-01

    Explains the critical role of probability in making sense of quantum physics and addresses the difficulties science and engineering undergraduates experience in helping students build a model of how to think about probability in physical systems. (Contains 17 references.) (Author/YDS)

  1. How Geographic Maps Increase Recall of Instructional Text.

    ERIC Educational Resources Information Center

    Kulhavy, Raymond W.; And Others

    1993-01-01

    Reviews research on how geographic maps influence the recall of associated text and describes a theoretical model of map-text learning based on dual-coding theory and working memory operations. Instructional implications are explained, and recommendations for instructional applications are given. (Contains 84 references.) (LRW)

  2. On the Effectiveness of a Neural Network for Adaptive External Pacing.

    ERIC Educational Resources Information Center

    Montazemi, Ali R.; Wang, Feng

    1995-01-01

    Proposes a neural network model for an intelligent tutoring system featuring adaptive external control of student pacing. An experiment was conducted, and students using adaptive external pacing experienced improved mastery learning and increased motivation for time management. Contains 66 references. (JKP)

  3. Neural network-based model reference adaptive control system.

    PubMed

    Patino, H D; Liu, D

    2000-01-01

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

  4. Propose but verify: Fast mapping meets cross-situational word learning

    PubMed Central

    Trueswell, John C.; Medina, Tamara Nicol; Hafri, Alon; Gleitman, Lila R.

    2012-01-01

    We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical-associative learning mechanism to gradually converge on a single mapping across learning instances. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing – more a ‘fast mapping’ procedure than a gradual statistical one. We provide experimental evidence for this Propose-but-Verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty. The findings, using both explicit (referent selection) and implicit (eye movement) measures, show that even in these artificial learning contexts, which are far simpler than those encountered by a language learner in a natural environment, participants do not retain multiple meaning hypotheses across learning instances. As we discuss, these findings challenge ‘gradualist’ accounts of word learning and are consistent with the known rapid course of vocabulary learning in a first language. PMID:23142693

  5. Development of machine learning models for diagnosis of glaucoma.

    PubMed

    Kim, Seong Jae; Cho, Kyong Jin; Oh, Sejong

    2017-01-01

    The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.

  6. Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models

    PubMed Central

    Grün, Sonja; Helias, Moritz

    2017-01-01

    Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations, given only the time-averaged correlations of the neuron activities. This paper provides evidence that the pairwise model, applied to experimental recordings, would produce a bimodal distribution for the population-averaged activity, and for some population sizes the second mode would peak at high activities, that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds. Several problems are connected with this bimodality: 1. The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds. 2. Boltzmann learning becomes non-ergodic, hence the pairwise maximum-entropy distribution cannot be found: in fact, Boltzmann learning would produce an incorrect distribution; similarly, common variants of mean-field approximations also produce an incorrect distribution. 3. The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data. This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey. Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons. The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition. To eliminate this problem a modified maximum-entropy model is presented, which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure. This model does not lead to unrealistic bimodalities, can be found with Boltzmann learning, and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition. PMID:28968396

  7. Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models.

    PubMed

    Rostami, Vahid; Porta Mana, PierGianLuca; Grün, Sonja; Helias, Moritz

    2017-10-01

    Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations, given only the time-averaged correlations of the neuron activities. This paper provides evidence that the pairwise model, applied to experimental recordings, would produce a bimodal distribution for the population-averaged activity, and for some population sizes the second mode would peak at high activities, that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds. Several problems are connected with this bimodality: 1. The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds. 2. Boltzmann learning becomes non-ergodic, hence the pairwise maximum-entropy distribution cannot be found: in fact, Boltzmann learning would produce an incorrect distribution; similarly, common variants of mean-field approximations also produce an incorrect distribution. 3. The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data. This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey. Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons. The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition. To eliminate this problem a modified maximum-entropy model is presented, which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure. This model does not lead to unrealistic bimodalities, can be found with Boltzmann learning, and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition.

  8. Prescription n-3 fatty acids, but not eicosapentaenoic acid alone, improve reference memory-related learning ability by increasing brain-derived neurotrophic factor levels in SHR.Cg-Lepr(cp)/NDmcr rats, a metabolic syndrome model.

    PubMed

    Hashimoto, Michio; Inoue, Takayuki; Katakura, Masanori; Tanabe, Yoko; Hossain, Shahdat; Tsuchikura, Satoru; Shido, Osamu

    2013-10-01

    Metabolic syndrome is implicated in the decline of cognitive ability. We investigated whether the prescription n-3 fatty acid administration improves cognitive learning ability in SHR.Cg-Lepr(cp)/NDmcr (SHR-cp) rats, a metabolic syndrome model, in comparison with administration of eicosapentaenoic acid (EPA, C20:5, n-3) alone. Administration of TAK-085 [highly purified and concentrated n-3 fatty acid formulation containing EPA ethyl ester and docosahexaenoic acid (DHA, C22:6, n-3) ethyl ester] at 300 mg/kg body weight per day for 13 weeks reduced the number of reference memory-related errors in SHR-cp rats, but EPA alone had no effect, suggesting that long-term TAK-085 administration improves cognitive learning ability in a rat model of metabolic syndrome. However, the working memory-related errors were not affected in either of the rat groups. TAK-085 and EPA administration increased plasma EPA and DHA levels of SHR-cp rats, associating with an increase in EPA and DHA in the cerebral cortex. The TAK-085 administration decreased the lipid peroxide levels and reactive oxygen species in the cerebral cortex and hippocampus of SHR-cp rats, suggesting that TAK-085 increases antioxidative defenses. Its administration also increased the brain-derived neurotrophic factor levels in the cortical and hippocampal tissues of TAK-085-administered rats. The present study suggests that long-term TAK-085 administration is a possible therapeutic strategy for protecting against metabolic syndrome-induced learning decline.

  9. Dissociation of learned helplessness and fear conditioning in mice: a mouse model of depression.

    PubMed

    Landgraf, Dominic; Long, Jaimie; Der-Avakian, Andre; Streets, Margo; Welsh, David K

    2015-01-01

    The state of being helpless is regarded as a central aspect of depression, and therefore the learned helplessness paradigm in rodents is commonly used as an animal model of depression. The term 'learned helplessness' refers to a deficit in escaping from an aversive situation after an animal is exposed to uncontrollable stress specifically, with a control/comparison group having been exposed to an equivalent amount of controllable stress. A key feature of learned helplessness is the transferability of helplessness to different situations, a phenomenon called 'trans-situationality'. However, most studies in mice use learned helplessness protocols in which training and testing occur in the same environment and with the same type of stressor. Consequently, failures to escape may reflect conditioned fear of a particular environment, not a general change of the helpless state of an animal. For mice, there is no established learned helplessness protocol that includes the trans-situationality feature. Here we describe a simple and reliable learned helplessness protocol for mice, in which training and testing are carried out in different environments and with different types of stressors. We show that with our protocol approximately 50% of mice develop learned helplessness that is not attributable to fear conditioning.

  10. Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions

    PubMed Central

    Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

    2017-01-01

    An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs) that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as “not,” “and,” and “or” simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human–robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as “true,” “false,” and “not” work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word “and,” which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word “or,” which required action generation that looked apparently random, was represented as an unstable space of the network's dynamical system. PMID:29311891

  11. Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions.

    PubMed

    Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

    2017-01-01

    An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs) that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as "not," "and," and "or" simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human-robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as "true," "false," and "not" work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word "and," which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word "or," which required action generation that looked apparently random, was represented as an unstable space of the network's dynamical system.

  12. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

    NASA Astrophysics Data System (ADS)

    Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

    2017-10-01

    Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

  13. The role of reference in cross-situational word learning.

    PubMed

    Wang, Felix Hao; Mintz, Toben H

    2018-01-01

    Word learning involves massive ambiguity, since in a particular encounter with a novel word, there are an unlimited number of potential referents. One proposal for how learners surmount the problem of ambiguity is that learners use cross-situational statistics to constrain the ambiguity: When a word and its referent co-occur across multiple situations, learners will associate the word with the correct referent. Yu and Smith (2007) propose that these co-occurrence statistics are sufficient for word-to-referent mapping. Alternative accounts hold that co-occurrence statistics alone are insufficient to support learning, and that learners are further guided by knowledge that words are referential (e.g., Waxman & Gelman, 2009). However, no behavioral word learning studies we are aware of explicitly manipulate subjects' prior assumptions about the role of the words in the experiments in order to test the influence of these assumptions. In this study, we directly test whether, when faced with referential ambiguity, co-occurrence statistics are sufficient for word-to-referent mappings in adult word-learners. Across a series of cross-situational learning experiments, we varied the degree to which there was support for the notion that the words were referential. At the same time, the statistical information about the words' meanings was held constant. When we overrode support for the notion that words were referential, subjects failed to learn the word-to-referent mappings, but otherwise they succeeded. Thus, cross-situational statistics were useful only when learners had the goal of discovering mappings between words and referents. We discuss the implications of these results for theories of word learning in children's language acquisition. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Validity of Learning Module Natural Sciences Oriented Constructivism with the Contain of Character Education for Students of Class VIII at Yunior Hight School

    NASA Astrophysics Data System (ADS)

    Oktarina, K.; Lufri, L.; Chatri, M.

    2018-04-01

    Referring to primary data collected through observation and interview to natural science teachers and some students, it is found that there is no natural science teaching materials in the form of learning modules that can make learners learn independently, build their own knowledge, and construct good character in themselves. In order to address this problem, then it is developed natural science learning module oriented to constructivism with the contain of character education. The purpose of this study is to reconstruct valid module of natural science learning materials. This type of research is a development research using the Plomp model. The development phase of the Plomp model consists of 3 stages, namely 1) preliminary research phase, 2) development or prototyping phase, and 3) assessment phase. The result of the study shows that natural science learning module oriented to constructivism with the contain of character education for students class VIII of Yunior High School 11 Sungai Penuh is valid. In future work, practicality and effectiveness will be investigated.

  15. Performance of conventional pigs and Göttingen miniature pigs in a spatial holeboard task: effects of the putative muscarinic cognition impairer Biperiden

    PubMed Central

    2013-01-01

    Background The pig is emerging as a model species that bridges the gap between rodents and humans in research. In particular, the miniature pig (referred to hereafter as the minipig) is increasingly being used as non-rodent species in pharmacological and toxicological studies. However, there is as yet a lack of validated behavioral tests for pigs, although there is evidence that the spatial holeboard task can be used to assess the working and reference memory of pigs. In the present study, we compared the learning performance of commercial pigs and Göttingen minipigs in a holeboard task. Methods Biperiden, a muscarinic M1 receptor blocker, is used to induce impairments in cognitive function in animal research. The two groups of pigs were treated orally with increasing doses of biperiden (0.05 – 20 mg.kg-1) after they had reached asymptotic performance in the holeboard task. Results Both the conventional pigs and the Göttingen minipigs learned the holeboard task, reaching nearly errorless asymptotic working and reference memory performance within approximately 100 acquisition trials. Biperiden treatment affected reference, but not working, memory, increasing trial duration and the latency to first hole visit at doses ≥ 5 mg.kg-1. Conclusion Both pig breeds learned the holeboard task and had a comparable performance. Biperiden had only a minor effect on holeboard performance overall, and mainly on reference memory performance. The effectiveness needs to be evaluated further before definitive conclusions can be drawn about the ability of this potential cognition impairer in pigs. PMID:23305134

  16. DSLM Instructional Approach to Conceptual Change Involving Thermal Expansion.

    ERIC Educational Resources Information Center

    She, Hsiao-Ching

    2003-01-01

    Examines the process of student conceptual change regarding thermal expansion using the Dual Situated Learning Model (DSLM) as an instructional approach. Indicates that DSLM promotes conceptual change and holds great potential to facilitate the process through classroom instruction at all levels. (Contains 38 references.) (Author/NB)

  17. A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

    PubMed

    Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

    2018-05-01

    Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

  18. Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents

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

    Sanyal, Jibonananda; New, Joshua Ryan; Edwards, Richard

    2014-01-01

    Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the Autotune research which employs machine learning algorithms to generate agents for the different kinds of standardmore » reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.« less

  19. Learning representations for the early detection of sepsis with deep neural networks.

    PubMed

    Kam, Hye Jin; Kim, Ha Young

    2017-10-01

    Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Multi-model blending

    DOEpatents

    Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

    2016-10-18

    A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

  1. Compound Stimulus Presentation Does Not Deepen Extinction in Human Causal Learning

    PubMed Central

    Griffiths, Oren; Holmes, Nathan; Westbrook, R. Fred

    2017-01-01

    Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training. PMID:28232809

  2. Learning relative values in the striatum induces violations of normative decision making

    PubMed Central

    Klein, Tilmann A.; Ullsperger, Markus; Jocham, Gerhard

    2017-01-01

    To decide optimally between available options, organisms need to learn the values associated with these options. Reinforcement learning models offer a powerful explanation of how these values are learnt from experience. However, human choices often violate normative principles. We suggest that seemingly counterintuitive decisions may arise as a natural consequence of the learning mechanisms deployed by humans. Here, using fMRI and a novel behavioural task, we show that, when suddenly switched to novel choice contexts, participants’ choices are incongruent with values learnt by standard learning algorithms. Instead, behaviour is compatible with the decisions of an agent learning how good an option is relative to an option with which it had previously been paired. Striatal activity exhibits the characteristics of a prediction error used to update such relative option values. Our data suggest that choices can be biased by a tendency to learn option values with reference to the available alternatives. PMID:28631734

  3. Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

    NASA Astrophysics Data System (ADS)

    Fujikake, So; Deringer, Volker L.; Lee, Tae Hoon; Krynski, Marcin; Elliott, Stephen R.; Csányi, Gábor

    2018-06-01

    We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li-C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture "effective" Li-Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials.

  4. Cognitive theories and the design of e-learning environments.

    PubMed

    Gillani, Bijan; O'Guinn, Christina

    2004-01-01

    Cognitive development refers to a mental process by which knowledge is acquired, stored, and retrieved to solve problems. Therefore, cognitive developmental theories attempt to explain cognitive activities that contribute to students' intellectual development and their capacity to learn and solve problems. Cognitive developmental research has had a great impact on the constructivism movement in education and educational technology. In order to appreciate how cognitive developmental theories have contributed to the design, process and development of constructive e-learning environments, we shall first present Piaget's cognitive theory and derive an inquiry training model from it that will support a constructivism approach to teaching and learning. Second, we will discuss an example developed by NASA that used the Web as an appropriate instructional delivery medium to apply Piaget's cognitive theory to create e-learning environments.

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

    NASA Technical Reports Server (NTRS)

    Murphy, Patrick C.; Brandon, Jay M.

    2017-01-01

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

  6. Comparison of 3 estimation methods of mycophenolic acid AUC based on a limited sampling strategy in renal transplant patients.

    PubMed

    Hulin, Anne; Blanchet, Benoît; Audard, Vincent; Barau, Caroline; Furlan, Valérie; Durrbach, Antoine; Taïeb, Fabrice; Lang, Philippe; Grimbert, Philippe; Tod, Michel

    2009-04-01

    A significant relationship between mycophenolic acid (MPA) area under the plasma concentration-time curve (AUC) and the risk for rejection has been reported. Based on 3 concentration measurements, 3 approaches have been proposed for the estimation of MPA AUC, involving either a multilinear regression approach model (MLRA) or a Bayesian estimation using either gamma absorption or zero-order absorption population models. The aim of the study was to compare the 3 approaches for the estimation of MPA AUC in 150 renal transplant patients treated with mycophenolate mofetil and tacrolimus. The population parameters were determined in 77 patients (learning study). The AUC estimation methods were compared in the learning population and in 73 patients from another center (validation study). In the latter study, the reference AUCs were estimated by the trapezoidal rule on 8 measurements. MPA concentrations were measured by liquid chromatography. The gamma absorption model gave the best fit. In the learning study, the AUCs estimated by both Bayesian methods were very similar, whereas the multilinear approach was highly correlated but yielded estimates about 20% lower than Bayesian methods. This resulted in dosing recommendations differing by 250 mg/12 h or more in 27% of cases. In the validation study, AUC estimates based on the Bayesian method with gamma absorption model and multilinear regression approach model were, respectively, 12% higher and 7% lower than the reference values. To conclude, the bicompartmental model with gamma absorption rate gave the best fit. The 3 AUC estimation methods are highly correlated but not concordant. For a given patient, the same estimation method should always be used.

  7. Learning Computational Models of Video Memorability from fMRI Brain Imaging.

    PubMed

    Han, Junwei; Chen, Changyuan; Shao, Ling; Hu, Xintao; Han, Jungong; Liu, Tianming

    2015-08-01

    Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects' brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.

  8. Cross-Sensory Transfer of Reference Frames in Spatial Memory

    ERIC Educational Resources Information Center

    Kelly, Jonathan W.; Avraamides, Marios N.

    2011-01-01

    Two experiments investigated whether visual cues influence spatial reference frame selection for locations learned through touch. Participants experienced visual cues emphasizing specific environmental axes and later learned objects through touch. Visual cues were manipulated and haptic learning conditions were held constant. Imagined perspective…

  9. Developmental Approach for Behavior Learning Using Primitive Motion Skills.

    PubMed

    Dawood, Farhan; Loo, Chu Kiong

    2018-05-01

    Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration. In designing such learning system, several key issues will be addressed: automatic segmentation of the observed actions into motion primitives using raw images acquired from the camera without requiring any kinematic model; incremental learning of spatio-temporal motion sequences to dynamically generates a topological structure in a self-stabilizing manner; organization of the learned data for easy and efficient retrieval using a dynamic associative memory; and utilizing segmented motion primitives to generate complex behavior by the combining these motion primitives. In our experiment, the self-posture is acquired through observing the image of its own body posture while performing the action in front of a mirror through body babbling. The complete architecture was evaluated by simulation and real robot experiments performed on DARwIn-OP humanoid robot.

  10. Visuo-vestibular interaction: predicting the position of a visual target during passive body rotation.

    PubMed

    Mackrous, I; Simoneau, M

    2011-11-10

    Following body rotation, optimal updating of the position of a memorized target is attained when retinal error is perceived and corrective saccade is performed. Thus, it appears that these processes may enable the calibration of the vestibular system by facilitating the sharing of information between both reference frames. Here, it is assessed whether having sensory information regarding body rotation in the target reference frame could enhance an individual's learning rate to predict the position of an earth-fixed target. During rotation, participants had to respond when they felt their body midline had crossed the position of the target and received knowledge of result. During practice blocks, for two groups, visual cues were displayed in the same reference frame of the target, whereas a third group relied on vestibular information (vestibular-only group) to predict the location of the target. Participants, unaware of the role of the visual cues (visual cues group), learned to predict the location of the target and spatial error decreased from 16.2 to 2.0°, reflecting a learning rate of 34.08 trials (determined from fitting a falling exponential model). In contrast, the group aware of the role of the visual cues (explicit visual cues group) showed a faster learning rate (i.e. 2.66 trials) but similar final spatial error 2.9°. For the vestibular-only group, similar accuracy was achieved (final spatial error of 2.3°), but their learning rate was much slower (i.e. 43.29 trials). Transferring to the Post-test (no visual cues and no knowledge of result) increased the spatial error of the explicit visual cues group (9.5°), but it did not change the performance of the vestibular group (1.2°). Overall, these results imply that cognition assists the brain in processing the sensory information within the target reference frame. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

  11. On Meaningful Measurement: Concepts, Technology and Examples.

    ERIC Educational Resources Information Center

    Cheung, K. C.

    This paper discusses how concepts and procedural skills in problem-solving tasks, as well as affects and emotions, can be subjected to meaningful measurement (MM), based on a multisource model of learning and a constructivist information-processing theory of knowing. MM refers to the quantitative measurement of conceptual and procedural knowledge…

  12. Socio-Cultural Environments and Suggestopedia.

    ERIC Educational Resources Information Center

    Bayuk, Milla

    The suggestopedic model of accelerated learning as developed by Lozanov is referred to by him as a set of attitudes inherent to sociocultural behavior common to the Soviet Bloc countries. The theoretical base accounts for a built-in obedience reflex, acceptance of authority, lack of competitiveness, promotion of collective growth, and a…

  13. Pedagogical Leadership in the 21st Century: Evidence from the Field

    ERIC Educational Resources Information Center

    Male, Trevor; Palaiologou, Ioanna

    2015-01-01

    Literature examining effective leadership in education describe a number of models such as Transformational, Learner-Centred, Distributed and Situational. A similar example is "pedagogical leadership", a phrase that frequently appears in literature and one referring to forms of practice that shape and form teaching and learning to be…

  14. Engaging Students with Disabilities: Using Student Response Technology in Elementary Classrooms

    ERIC Educational Resources Information Center

    Watson, Tiffany

    2017-01-01

    Student engagement refers to the behaviors that suggest whether a student is interested in the learning process. Finn (1989) developed the participation-identification model to explain the correlation between student engagement and identification with school, suggesting that increased participation leads to an increased sense of belongingness in…

  15. From SCORM to Common Cartridge: A Step Forward

    ERIC Educational Resources Information Center

    Gonzalez-Barbone, Victor; Anido-Rifon, Luis

    2010-01-01

    Shareable Content Object Reference Model (SCORM) was proposed as a standard for sharable learning object packaging, delivering and sequencing. Several years later, Common Cartridge (CC) is proposed as an enhancement of SCORM offering more flexibility and addressing needs not originally envisioned, namely assessment and web 2.0 standards, content…

  16. PBL and the Postmodern Condition--Knowledge Production in University Education

    ERIC Educational Resources Information Center

    Ravn, Ole; Jensen, Annie Aarup

    2016-01-01

    In this article we discuss the contemporary conditions for running the Aalborg Problem Based Learning-model (PBL). We try to pinpoint key characteristics of these conditions emphasising Lyotard's conception of knowledge production referred to as the move towards a postmodern condition for knowledge. Through discussions of this alleged condition…

  17. Applying AI to the Writer's Learning Environment.

    ERIC Educational Resources Information Center

    Houlette, Forrest

    1991-01-01

    Discussion of current applications of artificial intelligence (AI) to writing focuses on how to represent knowledge of the writing process in a way that links procedural knowledge to other types of knowledge. A model is proposed that integrates the subtasks of writing into the process of writing itself. (15 references) (LRW)

  18. Products and Processes: Synergistic Relationships

    ERIC Educational Resources Information Center

    Wallace, Virginia; Husid, Whitney

    2013-01-01

    Most people agree that products are the culmination of what students have studied. For this article, "product" will refer to students' abilities to create outcomes and design artifacts. Those abilities are guided by four processes: inquiry-based learning, use of a research model, use of Web 2.0 tools, and appropriate assessments.…

  19. The Relationship between Simultaneous-Successive Processing and Academic Achievement.

    ERIC Educational Resources Information Center

    Merritt, Frank M.; McCallum, Steve

    The Luria-Das Information Processing Model of human learning holds that information is analysed and coded within the brain in either a simultaneous or a successive fashion. Simultaneous integration refers to the synthesis of separate elements into groups, often with spatial characteristics; successive integration means that information is…

  20. Machine learning in cardiovascular medicine: are we there yet?

    PubMed

    Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

    2018-01-19

    Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  1. Towards a model of temporal attention for on-line learning in a mobile robot

    NASA Astrophysics Data System (ADS)

    Marom, Yuval; Hayes, Gillian

    2001-06-01

    We present a simple attention system, capable of bottom-up signal detection adaptive to subjective internal needs. The system is used by a robotic agent, learning to perform phototaxis and obstacle avoidance by following a teacher agent around a simulated environment, and deciding when to form associations between perceived information and imitated actions. We refer to this kind of decision-making as on-line temporal attention. The main role of the attention system is perception of change; the system is regulated through feedback about cognitive effort. We show how different levels of effort affect both the ability to learn a task, and to execute it.

  2. The Benefits and Future of Standards: Metadata and Beyond

    NASA Astrophysics Data System (ADS)

    Stracke, Christian M.

    This article discusses the benefits and future of standards and presents the generic multi-dimensional Reference Model. First the importance and the tasks of interoperability as well as quality development and their relationship are analyzed. Especially in e-Learning their connection and interdependence is evident: Interoperability is one basic requirement for quality development. In this paper, it is shown how standards and specifications are supporting these crucial issues. The upcoming ISO metadata standard MLR (Metadata for Learning Resource) will be introduced and used as example for identifying the requirements and needs for future standardization. In conclusion a vision of the challenges and potentials for e-Learning standardization is outlined.

  3. Enhancing Learning through Human Computer Interaction

    ERIC Educational Resources Information Center

    McKay, Elspeth, Ed.

    2007-01-01

    Enhancing Learning Through Human Computer Interaction is an excellent reference source for human computer interaction (HCI) applications and designs. This "Premier Reference Source" provides a complete analysis of online business training programs and e-learning in the higher education sector. It describes a range of positive outcomes for linking…

  4. Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

    NASA Astrophysics Data System (ADS)

    Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

    2017-05-01

    A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

  5. Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data.

    PubMed

    Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

    2017-05-07

    A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

  6. Slope And Equation of Line: Teach And Analysis In Terms of Emotional Intelligence

    NASA Astrophysics Data System (ADS)

    Dewi, A. C.; Budiyono; Riyadi

    2017-09-01

    Slope and equation of line is a sub-material of algebra and is one that is difficult for students to understand. The purpose of this study is to understand and explore the slope and equation so that students feel easy and ultimately improve their academic achievement. Experimental research was conducted by applying Jigsaw II learning model and Teams Games Tournament (TGT) and improvement in emotional intelligence (EI). The study sample was students from 3 different schools who were selected stratified cluster random sampling. The results showed that there is no influence of learning model and EI on academic achievement. This can happen even in the learning process students feel happy and interest. Although research shows different results with most theories, but it is expected that this research can be a good reference for students, teachers, and other researchers.

  7. Integrated model of multiple kernel learning and differential evolution for EUR/USD trading.

    PubMed

    Deng, Shangkun; Sakurai, Akito

    2014-01-01

    Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.

  8. Towards Contextualized Learning Services

    NASA Astrophysics Data System (ADS)

    Specht, Marcus

    Personalization of feedback and instruction has often been considered as a key feature in learning support. The adaptations of the instructional process to the individual and its different aspects have been investigated from different research perspectives as learner modelling, intelligent tutoring systems, adaptive hypermedia, adaptive instruction and others. Already in the 1950s first commercial systems for adaptive instruction for trainings of keyboard skills have been developed utilizing adaptive configuration of feedback based on user performance and interaction footprints (Pask 1964). Around adaptive instruction there is a variety of research issues bringing together interdisciplinary research from computer science, engineering, psychology, psychotherapy, cybernetics, system dynamics, instructional design, and empirical research on technology enhanced learning. When classifying best practices of adaptive instruction different parameters of the instructional process have been identified which are adapted to the learner, as: sequence and size of task difficulty, time of feedback, pace of learning speed, reinforcement plan and others these are often referred to the adaptation target. Furthermore Aptitude Treatment Interaction studies explored the effect of adapting instructional parameters to different characteristics of the learner (Tennyson and Christensen 1988) as task performance, personality characteristics, or cognitive abilities, this is information is referred to as adaptation mean.

  9. Phonological and Semantic Cues to Learning from Word-Types

    PubMed Central

    Richtsmeier, Peter

    2017-01-01

    Word-types represent the primary form of data for many models of phonological learning, and they often predict performance in psycholinguistic tasks. Word-types are often tacitly defined as phonologically unique words. Yet, an explicit test of this definition is lacking, and natural language patterning suggests that word meaning could also act as a cue to word-type status. This possibility was tested in a statistical phonotactic learning experiment in which phonological and semantic properties of word-types varied. During familiarization, the learning targets—word-medial consonant sequences—were instantiated either by four related word-types or by just one word-type (the experimental frequency factor). The expectation was that more word-types would lead participants to generalize the target sequences. Regarding semantic cues, related word-types were either associated with different referents or all with a single referent. Regarding phonological cues, related word-types differed from each other by one, two, or more phonemes. At test, participants rated novel wordforms for their similarity to the familiarization words. When participants heard four related word-types, they gave higher ratings to test words with the same consonant sequences, irrespective of the phonological and semantic manipulations. The results support the existing phonological definition of word-types. PMID:29187914

  10. They CAN and They SHOULD: Undergraduates Providing Peer Reference and Instruction

    ERIC Educational Resources Information Center

    Bodemer, Brett B.

    2014-01-01

    Peer learning dynamics have proven powerful in collegiate contexts. These dynamics should be leveraged at the undergraduate level in academic libraries for reference provision and basic information literacy instruction. Drawing on the literature of peer learning, documented examples of peer reference and instruction in academic libraries, and…

  11. A computerized procedure for teaching the relationship between graphic symbols and their referents.

    PubMed

    Isaacson, Mick; Lloyd, Lyle L

    2013-01-01

    Many individuals with little or no functional speech communicate through graphic symbols. Communication is enhanced when the relationship between symbols and their referents are learned to such a degree that retrieval is effortless, resulting in fluent communication. Developing fluency is a time consuming endeavor for special educators and speech-language pathologists (SLPs). It would be beneficial for these professionals to have an automated procedure based on the most efficacious method for teaching the relationship between symbols and referent. Hence, this study investigated whether a procedure based on the generation effect would promote learning the association between symbols and their referents. Results show that referent generation produces the best long-term retention of this relationship. These findings provide evidence that software based on referent generation would provide special educators and SLPs with an efficacious automated procedure, requiring minimal direct supervision, to facilitate symbol/referent learning and the development of communicative fluency.

  12. Effects of Referent Token Variability on L2 Vocabulary Learning

    ERIC Educational Resources Information Center

    Sommers, Mitchell S.; Barcroft, Joe

    2013-01-01

    Previous research has demonstrated substantially improved second language (L2) vocabulary learning when spoken word forms are varied using multiple talkers, speaking styles, or speaking rates. In contrast, the present study varied visual representations of referents for target vocabulary. English speakers learned Spanish words in formats of no…

  13. Conservative orientation as a determinant of hopelessness.

    PubMed

    Cheung, C K; Kwok, S T

    1996-06-01

    Conservative orientation is identified with reference to authoritarianism, work ethic belief, just world belief, and endorsement of individualistic causes of social problems. This over-arching orientation is hypothesized to affect hopelessness and to be affected by self-esteem with reference to ego development theory and learned helplessness theory. Causal modeling of data obtained from 1st-year college students (N = 556) in Hong Kong supports the hypotheses, showing that a student's hopelessness relates to his or her conservative orientation, even when self-esteem is controlled. This relationship can be interpreted by ego development and learned helplessness theories and cannot be explained as a spurious effect. Through the theories, hopelessness is interpreted as a result of maladaptive development and fatalistic, alienated, and helpless outlooks. This maladaptive development and the outlooks in turn result from students' conservative and individualistic orientation.

  14. Learning of spatio-temporal codes in a coupled oscillator system.

    PubMed

    Orosz, Gábor; Ashwin, Peter; Townley, Stuart

    2009-07-01

    In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.

  15. Contextual modulation of value signals in reward and punishment learning

    PubMed Central

    Palminteri, Stefano; Khamassi, Mehdi; Joffily, Mateus; Coricelli, Giorgio

    2015-01-01

    Compared with reward seeking, punishment avoidance learning is less clearly understood at both the computational and neurobiological levels. Here we demonstrate, using computational modelling and fMRI in humans, that learning option values in a relative—context-dependent—scale offers a simple computational solution for avoidance learning. The context (or state) value sets the reference point to which an outcome should be compared before updating the option value. Consequently, in contexts with an overall negative expected value, successful punishment avoidance acquires a positive value, thus reinforcing the response. As revealed by post-learning assessment of options values, contextual influences are enhanced when subjects are informed about the result of the forgone alternative (counterfactual information). This is mirrored at the neural level by a shift in negative outcome encoding from the anterior insula to the ventral striatum, suggesting that value contextualization also limits the need to mobilize an opponent punishment learning system. PMID:26302782

  16. Contextual modulation of value signals in reward and punishment learning.

    PubMed

    Palminteri, Stefano; Khamassi, Mehdi; Joffily, Mateus; Coricelli, Giorgio

    2015-08-25

    Compared with reward seeking, punishment avoidance learning is less clearly understood at both the computational and neurobiological levels. Here we demonstrate, using computational modelling and fMRI in humans, that learning option values in a relative--context-dependent--scale offers a simple computational solution for avoidance learning. The context (or state) value sets the reference point to which an outcome should be compared before updating the option value. Consequently, in contexts with an overall negative expected value, successful punishment avoidance acquires a positive value, thus reinforcing the response. As revealed by post-learning assessment of options values, contextual influences are enhanced when subjects are informed about the result of the forgone alternative (counterfactual information). This is mirrored at the neural level by a shift in negative outcome encoding from the anterior insula to the ventral striatum, suggesting that value contextualization also limits the need to mobilize an opponent punishment learning system.

  17. Contextual cueing of tactile search is coded in an anatomical reference frame.

    PubMed

    Assumpção, Leonardo; Shi, Zhuanghua; Zang, Xuelian; Müller, Hermann J; Geyer, Thomas

    2018-04-01

    This work investigates the reference frame(s) underlying tactile context memory, a form of statistical learning in a tactile (finger) search task. In this task, if a searched-for target object is repeatedly encountered within a stable spatial arrangement of task-irrelevant distractors, detecting the target becomes more efficient over time (relative to nonrepeated arrangements), as learned target-distractor spatial associations come to guide tactile search, thus cueing attention to the target location. Since tactile search displays can be represented in several reference frames, including multiple external and an anatomical frame, in Experiment 1 we asked whether repeated search displays are represented in tactile memory with reference to an environment-centered or anatomical reference frame. In Experiment 2, we went on examining a hand-centered versus anatomical reference frame of tactile context memory. Observers performed a tactile search task, divided into a learning and test session. At the transition between the two sessions, we introduced postural manipulations of the hands (crossed ↔ uncrossed in Expt. 1; palm-up ↔ palm-down in Expt. 2) to determine the reference frame of tactile contextual cueing. In both experiments, target-distractor associations acquired during learning transferred to the test session when the placement of the target and distractors was held constant in anatomical, but not external, coordinates. In the latter, RTs were even slower for repeated displays. We conclude that tactile contextual learning is coded in an anatomical reference frame. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  18. Dissociation of Learned Helplessness and Fear Conditioning in Mice: A Mouse Model of Depression

    PubMed Central

    Landgraf, Dominic; Long, Jaimie; Der-Avakian, Andre; Streets, Margo; Welsh, David K.

    2015-01-01

    The state of being helpless is regarded as a central aspect of depression, and therefore the learned helplessness paradigm in rodents is commonly used as an animal model of depression. The term ‘learned helplessness’ refers to a deficit in escaping from an aversive situation after an animal is exposed to uncontrollable stress specifically, with a control/comparison group having been exposed to an equivalent amount of controllable stress. A key feature of learned helplessness is the transferability of helplessness to different situations, a phenomenon called ‘trans-situationality’. However, most studies in mice use learned helplessness protocols in which training and testing occur in the same environment and with the same type of stressor. Consequently, failures to escape may reflect conditioned fear of a particular environment, not a general change of the helpless state of an animal. For mice, there is no established learned helplessness protocol that includes the trans-situationality feature. Here we describe a simple and reliable learned helplessness protocol for mice, in which training and testing are carried out in different environments and with different types of stressors. We show that with our protocol approximately 50% of mice develop learned helplessness that is not attributable to fear conditioning. PMID:25928892

  19. The diabolo classifier

    PubMed

    Schwenk

    1998-11-15

    We present a new classification architecture based on autoassociative neural networks that are used to learn discriminant models of each class. The proposed architecture has several interesting properties with respect to other model-based classifiers like nearest-neighbors or radial basis functions: it has a low computational complexity and uses a compact distributed representation of the models. The classifier is also well suited for the incorporation of a priori knowledge by means of a problem-specific distance measure. In particular, we will show that tangent distance (Simard, Le Cun, & Denker, 1993) can be used to achieve transformation invariance during learning and recognition. We demonstrate the application of this classifier to optical character recognition, where it has achieved state-of-the-art results on several reference databases. Relations to other models, in particular those based on principal component analysis, are also discussed.

  20. Validity of Students Worksheet Based Problem-Based Learning for 9th Grade Junior High School in living organism Inheritance and Food Biotechnology.

    NASA Astrophysics Data System (ADS)

    Jefriadi, J.; Ahda, Y.; Sumarmin, R.

    2018-04-01

    Based on preliminary research of students worksheet used by teachers has several disadvantages such as students worksheet arranged directly drove learners conduct an investigation without preceded by directing learners to a problem or provide stimulation, student's worksheet not provide a concrete imageand presentation activities on the students worksheet not refer to any one learning models curicullum recommended. To address problems Reviews these students then developed a worksheet based on problem-based learning. This is a research development that using Ploom models. The phases are preliminary research, development and assessment. The instruments used in data collection that includes pieces of observation/interviews, instrument self-evaluation, instruments validity. The results of the validation expert on student worksheets get a valid result the average value 80,1%. Validity of students worksheet based problem-based learning for 9th grade junior high school in living organism inheritance and food biotechnology get valid category.

  1. Assisting the integration of social media in problem-based learning sessions in the Faculty of Medicine at King Abdulaziz University.

    PubMed

    Awan, Zuhier A; Awan, Almuatazbellah A; Alshawwa, Lana; Tekian, Ara; Park, Yoon Soo

    2018-05-07

    Issues related to traditional Problem-Based Learning (PBL) at King Abdulaziz University Faculty of Medicine (KAU-FOM), including lack of student interaction between sessions and outdated instructional materials have led to the examining the use of social media. This study examines factors affecting the implementation of social media into PBL sessions Methods: Mentored social media activities were incorporated between PBL sessions to third year medical students. Ground rules were set, and students were kept on track with learning objectives and authentic references. An online survey consisting of 18 questions were administered to measure the impact of the social media model embedded between PBL sessions. Feedback showed major improvements in students' learning process as well as identifying areas for improvement. The highest ratings were in participation and communication, knowledge and information gathering, and cooperation and team-building. This paper indicates that incorporating social media could facilitate learning between PBL sessions. Furthermore, guidelines are proposed to help educators implement a social media model into their PBL sessions.

  2. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

    PubMed

    Grisafi, Andrea; Wilkins, David M; Csányi, Gábor; Ceriotti, Michele

    2018-01-19

    Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

  3. Space coding for sensorimotor transformations can emerge through unsupervised learning.

    PubMed

    De Filippo De Grazia, Michele; Cutini, Simone; Lisi, Matteo; Zorzi, Marco

    2012-08-01

    The posterior parietal cortex (PPC) is fundamental for sensorimotor transformations because it combines multiple sensory inputs and posture signals into different spatial reference frames that drive motor programming. Here, we present a computational model mimicking the sensorimotor transformations occurring in the PPC. A recurrent neural network with one layer of hidden neurons (restricted Boltzmann machine) learned a stochastic generative model of the sensory data without supervision. After the unsupervised learning phase, the activity of the hidden neurons was used to compute a motor program (a population code on a bidimensional map) through a simple linear projection and delta rule learning. The average motor error, calculated as the difference between the expected and the computed output, was less than 3°. Importantly, analyses of the hidden neurons revealed gain-modulated visual receptive fields, thereby showing that space coding for sensorimotor transformations similar to that observed in the PPC can emerge through unsupervised learning. These results suggest that gain modulation is an efficient coding strategy to integrate visual and postural information toward the generation of motor commands.

  4. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

    NASA Astrophysics Data System (ADS)

    Grisafi, Andrea; Wilkins, David M.; Csányi, Gábor; Ceriotti, Michele

    2018-01-01

    Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

  5. Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain.

    PubMed

    Tan, W Katherine; Hassanpour, Saeed; Heagerty, Patrick J; Rundell, Sean D; Suri, Pradeep; Huhdanpaa, Hannu T; James, Kathryn; Carrell, David S; Langlotz, Curtis P; Organ, Nancy L; Meier, Eric N; Sherman, Karen J; Kallmes, David F; Luetmer, Patrick H; Griffith, Brent; Nerenz, David R; Jarvik, Jeffrey G

    2018-03-28

    To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC. Copyright © 2018 The Association of University Radiologists. All rights reserved.

  6. The Role of Competition in Word Learning via Referent Selection

    ERIC Educational Resources Information Center

    Horst, Jessica S.; Scott, Emilly J.; Pollard, Jessica A.

    2010-01-01

    Previous research suggests that competition among the objects present during referent selection influences young children's ability to learn words in fast mapping tasks. The present study systematically explored this issue with 30-month-old children. Children first received referent selection trials with a target object and either two, three or…

  7. The Knowledge Base as an Extension of Distance Learning Reference Service

    ERIC Educational Resources Information Center

    Casey, Anne Marie

    2012-01-01

    This study explores knowledge bases as extension of reference services for distance learners. Through a survey and follow-up interviews with distance learning librarians, this paper discusses their interest in creating and maintaining a knowledge base as a resource for reference services to distance learners. It also investigates their perceptions…

  8. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system.

    PubMed

    Born, Jannis; Galeazzi, Juan M; Stringer, Simon M

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet.

  9. Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

    PubMed Central

    Born, Jannis; Stringer, Simon M.

    2017-01-01

    A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet. PMID:28562618

  10. Student Restraints to Reform: Conceptual Change Issues in Enhancing Students' Learning Processes.

    ERIC Educational Resources Information Center

    Thomas, Gregory P.

    1999-01-01

    Describes a teacher-researcher's investigation into barriers to student adoption of an alternative referent for learning and its consequential learning strategies in an 11th-grade chemistry class. Suggests that various contextual factors influenced students' willingness to adopt the alternative referent, and that students' beliefs, trust of the…

  11. The Characteristics of Learning Disabled and Other Handicapped Students Referred for Evaluation in the State of Iowa.

    ERIC Educational Resources Information Center

    Wilson, Lonny; And Others

    1986-01-01

    Demographic data, IQ, achievement, perceptual-motor, behavior ratings, and diagnostic classification (learning, mental, emotional disability or no handicap) were analyzed for all children (N=2002) referred for complete psychological evaluation during one school year in Iowa. Learning disabled children showed a distinct pattern different from…

  12. Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement

    ERIC Educational Resources Information Center

    Krumm, Andrew E.; Beattie, Rachel; Takahashi, Sola; D'Angelo, Cynthia; Feng, Mingyu; Cheng, Britte

    2016-01-01

    This paper outlines the development of practical measures of productive persistence using digital learning system data. Practical measurement refers to data collection and analysis approaches originating from improvement science; productive persistence refers to the combination of academic and social mindsets as well as learning behaviours that…

  13. Effectiveness of Asynchronous Reference Services for Distance Learning Students within Florida's Community College System

    ERIC Educational Resources Information Center

    Profeta, Patricia C.

    2007-01-01

    The provision of equitable library services to distance learning students emerged as a critical area during the 1990s. Library services available to distance learning students included digital reference and instructional services, remote access to online research tools, database and research tutorials, interlibrary loan, and document delivery.…

  14. Word Learning in Adults with Second-Language Experience: Effects of Phonological and Referent Familiarity

    ERIC Educational Resources Information Center

    Kaushanskaya, Margarita; Yoo, Jeewon; Van Hecke, Stephanie

    2013-01-01

    Purpose: The goal of this research was to examine whether phonological familiarity exerts different effects on novel word learning for familiar versus unfamiliar referents and whether successful word learning is associated with increased second-language experience. Method: Eighty-one adult native English speakers with various levels of Spanish…

  15. Open Distant Learning: Pedagogical Terms of Reference and Dilemmas

    ERIC Educational Resources Information Center

    Tatkovic, Nevenka; Ruzic, Maja; Tatkovic, Sanja

    2006-01-01

    The paper first presents the essential viewpoints of general characteristics of open distance learning (OLD) and the short historical origins. The second part presents some pedagogical terms of reference for Open distance learning as the quality of ODL, the criteria of successful ODL (planning, successful interaction, work and emotional climate,…

  16. Preliminary Analysis of Assessment Instrument Design to Reveal Science Generic Skill and Chemistry Literacy

    ERIC Educational Resources Information Center

    Sumarni, Woro; Sudarmin; Supartono, Wiyanto

    2016-01-01

    The purpose of this research is to design assessment instrument to evaluate science generic skill (SGS) achievement and chemistry literacy in ethnoscience-integrated chemistry learning. The steps of tool designing refers to Plomp models including 1) Investigation Phase (Prelimenary Investigation); 2) Designing Phase (Design); 3)…

  17. Beyond Broca's and Wernicke's Areas: A New Perspective on the Neurobiology of Language.

    ERIC Educational Resources Information Center

    Lem, Lawrence

    1992-01-01

    Proposes a neurobiological model in which a greater number of brain structures than previously indicated are involved in language functions, with particular reference to second language learning. The study examines three areas of the brain rarely associated with language: the anterior cingulate gyrus, the prefrontal cortex, and the basal temporal…

  18. Developing and Validating Technological Pedagogical Content Knowledge-Practical (TPACK-Practical) through the Delphi Survey Technique

    ERIC Educational Resources Information Center

    Yeh, Yi-Fen; Hsu, Ying-Shao; Wu, Hsin-Kai; Hwang, Fu-Kwun; Lin, Tzu-Chiang

    2014-01-01

    Technological pedagogical content knowledge TPACK refers to the knowledge set that teachers currently use to further improve the quality of their teaching and assist their students in learning. Several TPACK models have been proposed, either for discussing TPACK's possible composition or its practical applications. Considering that…

  19. Computer-Based Tutoring of Visual Concepts: From Novice to Experts.

    ERIC Educational Resources Information Center

    Sharples, Mike

    1991-01-01

    Description of ways in which computers might be used to teach visual concepts discusses hypermedia systems; describes computer-generated tutorials; explains the use of computers to create learning aids such as concept maps, feature spaces, and structural models; and gives examples of visual concept teaching in medical education. (10 references)…

  20. Moving Toward SCORM Compliant Content Production at Educational Software Company: Technical and Administrative Challenges

    ERIC Educational Resources Information Center

    Kultur, Can; Oytun, Erden; Cagiltay, Kursat; Ozden, M. Yasar; Kucuk, Mehmet Emin

    2004-01-01

    The Shareable Content Object Reference Model (SCORM) aims to standardize electronic course content, its packaging and delivery. Instructional designers and e-learning material producer organizations accept SCORM?s significant impact on instructional design/delivery process, however not much known about how such standards will be implemented to…

  1. Transactional, Cooperative, and Communal: Relating the Structure of Engineering Engagement Programs with the Nature of Partnerships

    ERIC Educational Resources Information Center

    Thompson, Julia D.; Jesiek, Brent K.

    2017-01-01

    This paper examines how the structural features of engineering engagement programs (EEPs) are related to the nature of their service-learning partnerships. "Structure" refers to formal and informal models, processes, and operations adopted or used to describe engagement programs, while "nature" signifies the quality of…

  2. Unifying Psychology and Experiential Education: Toward an Integrated Understanding of "Why" It Works

    ERIC Educational Resources Information Center

    Houge Mackenzie, Susan; Son, Julie S.; Hollenhorst, Steve

    2014-01-01

    This article examines the significance of psychology to experiential education (EE) and critiques EE models that have developed in isolation from larger psychological theories and developments. Following a review of literature and current issues, select areas of psychology are explored with reference to experiential learning processes. The state…

  3. Teaching Only the Essentials--The Thirty-Minute Stand.

    ERIC Educational Resources Information Center

    Engeldinger, Eugene A.

    1988-01-01

    Describes an instructional model for teaching library skills which consists of a 30-minute lecture followed by a 20-minute exercise. Assumptions about learning and the educational process are discussed as well as goal-setting for the class and exercise. It is suggested that this format could be applied to other disciplines. (10 references) (MES)

  4. Endogenous Market-Clearing Prices and Reference Point Adaptation

    NASA Astrophysics Data System (ADS)

    Dragicevic, Arnaud Z.

    When prices depend on the submitted bids, i.e. with endogenous market-clearing prices in repeated-round auction mechanisms, the assumption of independent private values that underlines the property of incentive-compatibility is to be brought into question; even if these mechanisms provide active involvement and market learning. In its orthodox view, adaptive bidding behavior imperils incentive-compatibility. We relax the assumption of private values' independence in the repeated-round auctions, when the market-clearing prices are made public at the end of each round. Instead of using game-theory learning models, we introduce a behavioral model that shows that bidders bid according to the anchoring-and-adjustment heuristic, which neither ignores the rationality and incentive-compatibility constraints, nor rejects the posted prices issued from others' bids. Bidders simply weight information at their disposal and adjust their discovered value using reference points encoded in the sequential price weighting function. Our model says that bidders and offerers are sincere boundedly rational utility maximizers. It lies between evolutionary dynamics and adaptive heuristics and we model the concept of inertia as high weighting of the anchor, which stands for truthful bidding and high regard to freshly discovered preferences. Adjustment means adaptive rule based on adaptation of the reference point in the direction of the posted price. It helps a bidder to maximize her expected payoff, which is after all the only purpose that matters to rationality. The two components simply suggest that sincere bidders are boundedly rational. Furthermore, by deviating from their anchor in the direction of the public signal, bidders operate in a correlated equilibrium. The correlation between bids comes from the commonly observed history of play and each bidder's actions are determined by the history. Bidders are sincere if they have limited memory and confine their reference point adaptation to their anchor and the latest posted price. S-shaped weighting mechanism reflects such a bidding strategy.

  5. The Educational Kanban: promoting effective self-directed adult learning in medical education.

    PubMed

    Goldman, Stuart

    2009-07-01

    The author reviews the many forces that have driven contemporary medical education approaches to evaluation and places them in an adult learning theory context. After noting their strengths and limitations, the author looks to lessons learned from manufacturing on both efficacy and efficiency and explores how these can be applied to the process of trainee assessment in medical education.Building on this, the author describes the rationale for and development of the Educational Kanban (EK) at Children's Hospital Boston--specifically, how it was designed to integrate adult learning theory, Japanese manufacturing models, and educator observations into a unique form of teacher-student collaboration that allows for continuous improvement. It is a formative tool, built on the Accreditation Council for Graduate Medical Education's six core competencies, that guides educational efforts to optimize teaching and learning, promotes adult learner responsibility and efficacy, and takes advantage of the labor-intensive clinical educational setting. The author discusses how this model, which will be implemented in July 2009, will lead to training that is highly individualized, optimizes faculty and student educational efforts, and ultimately conserves faculty resources. A model EK is provided for general reference.The EK represents a novel approach to adult learning that will enhance educational effectiveness and efficiency and complement existing evaluative models. Described here in a specific graduate medical setting, it can readily be adapted and integrated into a wide range of undergraduate and graduate clinical educational environments.

  6. Development of Mathematics Learning Strategy Module, Based on Higher Order Thinking Skill (Hots) To Improve Mathematic Communication And Self Efficacy On Students Mathematics Department

    NASA Astrophysics Data System (ADS)

    Andriani, Ade; Dewi, Izwita; Halomoan, Budi

    2018-03-01

    In general, this research is conducted to improve the quality of lectures on mathematics learning strategy in Mathematics Department. The specific objective of this research is to develop learning instrument of mathematics learning strategy based on Higher Order Thinking Skill (HOTS) that can be used to improve mathematical communication and self efficacy of mathematics education students. The type of research is development research (Research & Development), where this research aims to develop a new product or improve the product that has been made. This development research refers to the four-D Model, which consists of four stages: defining, designing, developing, and disseminating. The instrument of this research is the validation sheet and the student response sheet of the instrument.

  7. dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs.

    PubMed

    Ma, Kede; Liu, Wentao; Liu, Tongliang; Wang, Zhou; Tao, Dacheng

    2017-05-26

    Objective assessment of image quality is fundamentally important in many image processing tasks. In this work, we focus on learning blind image quality assessment (BIQA) models which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIP) can be obtained automatically at low cost by exploiting largescale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation (gMAD) competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an additional performance gain.

  8. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    NASA Astrophysics Data System (ADS)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  9. Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks.

    PubMed

    Lee, Young Han

    2018-04-04

    The purposes of this study are to evaluate the feasibility of protocol determination with a convolutional neural networks (CNN) classifier based on short-text classification and to evaluate the agreements by comparing protocols determined by CNN with those determined by musculoskeletal radiologists. Following institutional review board approval, the database of a hospital information system (HIS) was queried for lists of MRI examinations, referring department, patient age, and patient gender. These were exported to a local workstation for analyses: 5258 and 1018 consecutive musculoskeletal MRI examinations were used for the training and test datasets, respectively. The subjects for pre-processing were routine or tumor protocols and the contents were word combinations of the referring department, region, contrast media (or not), gender, and age. The CNN Embedded vector classifier was used with Word2Vec Google news vectors. The test set was tested with each classification model and results were output as routine or tumor protocols. The CNN determinations were evaluated using the receiver operating characteristic (ROC) curves. The accuracies were evaluated by a radiologist-confirmed protocol as the reference protocols. The optimal cut-off values for protocol determination between routine protocols and tumor protocols was 0.5067 with a sensitivity of 92.10%, a specificity of 95.76%, and an area under curve (AUC) of 0.977. The overall accuracy was 94.2% for the ConvNet model. All MRI protocols were correct in the pelvic bone, upper arm, wrist, and lower leg MRIs. Deep-learning-based convolutional neural networks were clinically utilized to determine musculoskeletal MRI protocols. CNN-based text learning and applications could be extended to other radiologic tasks besides image interpretations, improving the work performance of the radiologist.

  10. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition

    DTIC Science & Technology

    2006-04-05

    classes. In this study we evaluate the effectiveness of one of these formulations that was developed by Crammer and Singer [9], which leads to...significantly more complex model can be learned by directly applying the Crammer -Singer multiclass formulation on the outputs of the binary classifiers...will refer to this as the Crammer -Singer (CS) model. Comparing the scaling approach to the Crammer -Singer approach we can see that the Crammer -Singer

  11. Comparison of Scores on Two Visual-Motor Tests for Children Referred for Learning or Adjustment Difficulties.

    ERIC Educational Resources Information Center

    DeMers, Stephen T.; And Others

    1981-01-01

    This study compared the performance of school-aged children referred for learning or adjustment difficulties on Beery's Developmental Test of Visual-Motor Integration and Koppitz's version of the Bender-Gestalt test. Results indicated that the tests are related but not equivalent when administered to referred populations. (Author/AL)

  12. Extending E-Book with Contextual Knowledge Recommender for Reading Support on a Web-Based Learning System

    ERIC Educational Resources Information Center

    Chen, Gwo-Dong; Wei, Fu-Hsiang; Wang, Chin-Yeh; Lee, Jih-Hsien

    2007-01-01

    Reading content of the Web is increasingly popular. When students read the same material, each student has a unique comprehension of the text and requires individual support from appropriate references. Most references in typical web learning systems are unorganized. Students are often required to disrupt their reading to locate references. This…

  13. On Learning to Write Those **** References

    ERIC Educational Resources Information Center

    Hartley, James

    2014-01-01

    In this article, the author discusses how difficult it is for psychology college students to learn to write multiple disciplines of references. It is hard for students to understand why all details have to be written in the right order and the right type-style--depending upon which reference system is used. In this article, the author proposes…

  14. Tone of voice guides word learning in informative referential contexts.

    PubMed

    Reinisch, Eva; Jesse, Alexandra; Nygaard, Lynne C

    2013-06-01

    Listeners infer which object in a visual scene a speaker refers to from the systematic variation of the speaker's tone of voice (ToV). We examined whether ToV also guides word learning. During exposure, participants heard novel adjectives (e.g., "daxen") spoken with a ToV representing hot, cold, strong, weak, big, or small while viewing picture pairs representing the meaning of the adjective and its antonym (e.g., elephant-ant for big-small). Eye fixations were recorded to monitor referent detection and learning. During test, participants heard the adjectives spoken with a neutral ToV, while selecting referents from familiar and unfamiliar picture pairs. Participants were able to learn the adjectives' meanings, and, even in the absence of informative ToV, generalize them to new referents. A second experiment addressed whether ToV provides sufficient information to infer the adjectival meaning or needs to operate within a referential context providing information about the relevant semantic dimension. Participants who saw printed versions of the novel words during exposure performed at chance during test. ToV, in conjunction with the referential context, thus serves as a cue to word meaning. ToV establishes relations between labels and referents for listeners to exploit in word learning.

  15. Android-assisted physics mobile learning to improve senior high school students' divergent thinking skills and physics HOTS

    NASA Astrophysics Data System (ADS)

    Mardiana, Nana; Kuswanto, Heru

    2017-08-01

    The aims of the research concerned here were to reveal (1) the characteristics of Android-assisted PML (physics mobile learning) to improve SMA (sekolah menengah atas, Indonesian senior high school) students' divergent thinking skills and physics HOTS (higher order thinking skills); (2) the feasibility of the Android-assisted PML; and (3) the influence of using the Android-assisted PML on improvement in SMA students' divergent thinking skills and physics HOTS. The7 research was of the R&D (research and development) type, adapted from theBorg-&-Gall development model. The research data were analyzed by means of MANOVA with the significance level of 5%. The results are as follows. (1) The product of the development, a learning media in software form with the android package(apk) format, is named PML (to refer to Physics Mobile Learning), which has such characterictics as being operable with use of Android devicesand being very good in quality in the aspect oflearning, material, software technology, and audiovisual appearance. 2) The developed learning media referred to as PML is appropriate for learning activity according to evaluation by a material expert, a media expert, peer reviewers, and physics teachers as well as according to results of students' tryouts. (3) The use of the Android-assisted PML media product could improve SMA students' divergent thinking skillsand physics HOTS with the respective high-category gain scores of 0.701 and 0.759.

  16. Machine learning molecular dynamics for the simulation of infrared spectra.

    PubMed

    Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

    2017-10-01

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

  17. Mathematizing Process of Junior High School Students to Improve Mathematics Literacy Refers PISA on RCP Learning

    NASA Astrophysics Data System (ADS)

    Wardono; Mariani, S.; Hendikawati, P.; Ikayani

    2017-04-01

    Mathematizing process (MP) is the process of modeling a phenomenon mathematically or establish the concept of a phenomenon. There are two mathematizing that is Mathematizing Horizontal (MH) and Mathematizing Vertical (MV). MH as events changes contextual problems into mathematical problems, while MV is the process of formulation of the problem into a variety of settlement mathematics by using some appropriate rules. Mathematics Literacy (ML) is the ability to formulate, implement and interpret mathematics in various contexts, including the capacity to perform reasoning mathematically and using the concepts, procedures, and facts to describe, explain or predict phenomena incident. If junior high school students are conditioned continuously to conduct mathematizing activities on RCP (RME-Card Problem) learning, it will be able to improve ML that refers PISA. The purpose of this research is to know the capability of the MP grade VIII on ML content shape and space with the matter of the cube and beams with RCP learning better than the scientific learning, upgrade MP grade VIII in the issue of the cube and beams with RCP learning better than the scientific learning in terms of cognitive styles reflective and impulsive the MP grade VIII with the approach of the RCP learning in terms of cognitive styles reflective and impulsive This research is the mixed methods model concurrent embedded. The population in this study, i.e., class VIII SMPN 1 Batang with sample two class. Data were taken with the observation, interviews, and tests and analyzed with a different test average of one party the right qualitative and descriptive. The results of this study demonstrate the capability of the MP student with RCP learning better than the scientific learning, upgrade MP with RCP learning better compare with scientific learning in term cognitive style of reflective and impulsive. The subject of the reflective group top, middle, and bottom can meet all the process of MH indicators are then the subject of the reflective upper and intermediate group can meet all the MV indicators but to lower groups can only fulfill some MV indicators. The subject is impulsive upper and middle group can meet all the MH indicators but to lower groups can only meet some MH indicator, then the subject is impulsive group can meet all the MV indicators but for middle and the bottom group can only fulfill some MV indicators.

  18. The effectiveness of physics learning material based on South Kalimantan local wisdom

    NASA Astrophysics Data System (ADS)

    Hartini, Sri; Misbah, Helda, Dewantara, Dewi

    2017-08-01

    The local wisdom is essential element incorporated into learning process. However, there are no learning materials in Physics learning process which contain South Kalimantan local wisdom. Therefore, it is necessary to develop a Physics learning material based on South Kalimantan local wisdom. The objective of this research is to produce products in the form of learning material based on South Kalimantan local wisdom that is feasible and effective based on the validity, practicality, effectiveness of learning material and achievement of waja sampai kaputing (wasaka) character. This research is a research and development which refers to the ADDIE model. Data were obtained through the validation sheet of learning material, questionnaire, the test of learning outcomes and the sheet of character assesment. The research results showed that (1) the validity category of the learning material was very valid, (2) the practicality category of the learning material was very practical, (3) the effectiveness category of thelearning material was very effective, and (4) the achivement of wasaka characters was very good. In conclusion, the Physics learning materials based on South Kalimantan local wisdom are feasible and effective to be used in learning activities.

  19. Robust reinforcement learning.

    PubMed

    Morimoto, Jun; Doya, Kenji

    2005-02-01

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

  20. The importance of witnessed agency in chimpanzee social learning of tool use☆

    PubMed Central

    Hopper, Lydia M.; Lambeth, Susan P.; Schapiro, Steven J.; Whiten, Andrew

    2015-01-01

    Social learning refers to individuals learning from others, including information gained through indirect social influences, such as the results of others’ actions and changes in the physical environment. One method to determine the relative influence of these varieties of information is the ‘ghost display’, in which no model is involved, but subjects can watch the results that a model would produce. Previous research has shown mixed success by chimpanzees (Pan troglodytes) learning from ghost displays, with some studies suggesting learning only in relatively simple tasks. To explore whether the failure of chimpanzees to learn from a ghost display may be due to neophobia when tested singly or a requirement for more detailed information for complex tasks, we presented ghost displays of a tool-use task to chimpanzees in their home social groups. Previous tests have revealed that chimpanzees are unable to easily solve this tool-use task asocially, or learn from ghost displays when tested singly, but can learn after observing conspecifics in a group setting. In the present study, despite being tested in a group situation, chimpanzees still showed no success in solving the task via trial-and-error learning, in a baseline condition, nor in learning the task from the ghost display. Simply being in the presence of their group mates and being shown the affordances of the task was not sufficient to encourage learning. Following this, in an escalating series of tests, we examined the chimpanzees’ ability to learn from a demonstration by models with agency: (1) a human; (2) video footage of a chimpanzee; (3) a live chimpanzee model. In the first two of these ‘social’ conditions, subjects showed limited success. By the end of the final open diffusion phase, which was run to determine whether this new behavior would be transmitted among the group after seeing a successful chimpanzee use the task, 83% of chimpanzees were now successful. This confirmed a marked overall effect of observing animate conspecific modeling, in contrast to the ghost condition. This article is part of a Special Issue entitled: insert SI title. PMID:25444770

  1. The importance of witnessed agency in chimpanzee social learning of tool use.

    PubMed

    Hopper, Lydia M; Lambeth, Susan P; Schapiro, Steven J; Whiten, Andrew

    2015-03-01

    Social learning refers to individuals learning from others, including information gained through indirect social influences, such as the results of others' actions and changes in the physical environment. One method to determine the relative influence of these varieties of information is the 'ghost display', in which no model is involved, but subjects can watch the results that a model would produce. Previous research has shown mixed success by chimpanzees (Pan troglodytes) learning from ghost displays, with some studies suggesting learning only in relatively simple tasks. To explore whether the failure of chimpanzees to learn from a ghost display may be due to neophobia when tested singly or a requirement for more detailed information for complex tasks, we presented ghost displays of a tool-use task to chimpanzees in their home social groups. Previous tests have revealed that chimpanzees are unable to easily solve this tool-use task asocially, or learn from ghost displays when tested singly, but can learn after observing conspecifics in a group setting. In the present study, despite being tested in a group situation, chimpanzees still showed no success in solving the task via trial-and-error learning, in a baseline condition, nor in learning the task from the ghost display. Simply being in the presence of their group mates and being shown the affordances of the task was not sufficient to encourage learning. Following this, in an escalating series of tests, we examined the chimpanzees' ability to learn from a demonstration by models with agency: (1) a human; (2) video footage of a chimpanzee; (3) a live chimpanzee model. In the first two of these 'social' conditions, subjects showed limited success. By the end of the final open diffusion phase, which was run to determine whether this new behavior would be transmitted among the group after seeing a successful chimpanzee use the task, 83% of chimpanzees were now successful. This confirmed a marked overall effect of observing animate conspecific modeling, in contrast to the ghost condition. This article is part of a Special Issue entitled: insert SI title. Copyright © 2014 Elsevier B.V. All rights reserved.

  2. The Chicken or the Egg? The Direction of the Relationship Between Mathematics Anxiety and Mathematics Performance.

    PubMed

    Carey, Emma; Hill, Francesca; Devine, Amy; Szücs, Dénes

    2015-01-01

    This review considers the two possible causal directions between mathematics anxiety (MA) and poor mathematics performance. Either poor maths performance may elicit MA (referred to as the Deficit Theory), or MA may reduce future maths performance (referred to as the Debilitating Anxiety Model). The evidence is in conflict: the Deficit Theory is supported by longitudinal studies and studies of children with mathematical learning disabilities, but the Debilitating Anxiety Model is supported by research which manipulates anxiety levels and observes a change in mathematics performance. It is suggested that this mixture of evidence might indicate a bidirectional relationship between MA and mathematics performance (the Reciprocal Theory), in which MA and mathematics performance can influence one another in a vicious cycle.

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

    PubMed

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

    2010-02-01

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

  4. An Interprofessional Collaborative Practice model for preparation of clinical educators.

    PubMed

    Scarvell, Jennie M; Stone, Judy

    2010-07-01

    Work-integrated learning is essential to health professional education, but faces increasing academic and industry resource pressures. The aim of this pilot "Professional Practice Project" was to develop and implement an innovative education intervention for clinical educators across several health disciplines. The project used interprofessional collaboration as its underlying philosophy, and a participatory action research methodology in four cycles: Cycle 1: Formation of an interprofessional project executive and working party from academic staff. Data collection of student insights into work integrated learning. Cycle 2: Formation of an interprofessional reference group to inform curriculum development for a series of clinical education workshops. Cycle 3: Delivery of workshops; 174 clinical educators, supervisors and preceptors attended two workshops: "Introduction to experiential learning" and " utilizing available resources for learning". Cycle 4: Seminar discussion of the Professional Practice Project at a national health-education conference. This pilot project demonstrated the advantages of using collaborative synergies to allow innovation around clinical education, free from the constraints of traditional discipline-specific education models. The planning, delivery and evaluation of clinical education workshops describe the benefits of interprofessional collaboration through enhanced creative thinking, sharing of clinical education models and a broadening of experience for both learners and facilitators.

  5. Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading

    PubMed Central

    Deng, Shangkun; Sakurai, Akito

    2014-01-01

    Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits. PMID:25097891

  6. iSentenizer-μ: multilingual sentence boundary detection model.

    PubMed

    Wong, Derek F; Chao, Lidia S; Zeng, Xiaodong

    2014-01-01

    Sentence boundary detection (SBD) system is normally quite sensitive to genres of data that the system is trained on. The genres of data are often referred to the shifts of text topics and new languages domains. Although new detection models can be retrained for different languages or new text genres, previous model has to be thrown away and the creation process has to be restarted from scratch. In this paper, we present a multilingual sentence boundary detection system (iSentenizer-μ) for Danish, German, English, Spanish, Dutch, French, Italian, Portuguese, Greek, Finnish, and Swedish languages. The proposed system is able to detect the sentence boundaries of a mixture of different text genres and languages with high accuracy. We employ i (+)Learning algorithm, an incremental tree learning architecture, for constructing the system. iSentenizer-μ, under the incremental learning framework, is adaptable to text of different topics and Roman-alphabet languages, by merging new data into existing model to learn the new knowledge incrementally by revision instead of retraining. The system has been extensively evaluated on different languages and text genres and has been compared against two state-of-the-art SBD systems, Punkt and MaxEnt. The experimental results show that the proposed system outperforms the other systems on all datasets.

  7. Color, Reference, and Expertise in Language Acquisition

    ERIC Educational Resources Information Center

    Clark, Eve V.

    2006-01-01

    In learning the meaning of a new term, children need to fix its reference, learn its conventional meaning, and discover the meanings with which it contrasts. To do this, children must attend to adult speakers--the experts--and to their patterns of use. In the domain of color, children need to identify color terms as such, fix the reference of each…

  8. Harriet Tubman Integrated Unit. ArtsEdge Curricula, Lessons and Activities.

    ERIC Educational Resources Information Center

    Van Der Woude, Gladys

    Harriet Tubman, a famous Civil War freedom fighter from Maryland, is the focus of this unit that integrates the arts and history. Students will learn about Harriet Tubman through music, art, dance, literature, and reference materials. The five lessons will be models and a springboard for the research projects that the students will complete about…

  9. A Machine Learning Approach to Measurement of Text Readability for EFL Learners Using Various Linguistic Features

    ERIC Educational Resources Information Center

    Kotani, Katsunori; Yoshimi, Takehiko; Isahara, Hitoshi

    2011-01-01

    The present paper introduces and evaluates a readability measurement method designed for learners of EFL (English as a foreign language). The proposed readability measurement method (a regression model) estimates the text readability based on linguistic features, such as lexical, syntactic and discourse features. Text readability refers to the…

  10. Implementing RtI with Gifted Students

    ERIC Educational Resources Information Center

    Coleman, Mary Ruth, Ed.; Johnsen, Susan K., Ed.

    2012-01-01

    "Implementing RtI With Gifted Students" shares how RtI can fit within the framework of gifted education programming models. This edited book will serve as a reference guide for those interested in learning more about RtI and how it might be effectively implemented to meet the needs of all gifted students. Chapters contributed by top gifted…

  11. An Online Peer Assisted Learning Community Model and its Application in ZJNU

    ERIC Educational Resources Information Center

    Gaofeng, Ruan; Yeyu, Lin

    2007-01-01

    Peer coaching, or peer assisting, was established in 1970s by Joyce and Showers. Initially used in teachers' professional development, it refers to a process that two or more teacher peers evaluate current practice mutually; expand skills, extract and build new skills; share ideas, and review & solve problems of classroom teaching in a way of…

  12. The Role of Observation and Emulation in the Development of Athletic Self-Regulation.

    ERIC Educational Resources Information Center

    Kitsantas, Anastasia; Zimmerman, Barry J.; Cleary, Tim

    2000-01-01

    Studies the influences of modeling and social feedback in acquisition of dart-throwing skill with 60 high school girls. Discusses results in terms of a social-cognitive view of athletic skill acquisition in which vicarious abstraction of a skill prepares students to learn self-regulatively during practice efforts. (Contains 20 references, 4…

  13. In the Midst of a Shift: Undergraduate STEM Education and "PBL" Enactment

    ERIC Educational Resources Information Center

    Wallace, Maria F. G.; Webb, Angela W.

    2016-01-01

    In the engineering field, problem- and project-based learning, both of which are often referred to as PBL, are the dominant instructional models called for by accreditation agencies. The aim of this qualitative case study is to analyze and capture a holistic perspective of PBL course design and its implementation in two communication-intensive…

  14. The Development of Anti-Corruption Education Course for Primary School Teacher Education Students

    ERIC Educational Resources Information Center

    Indawati, Ninik

    2015-01-01

    The purpose of this research was to develop learning tools as well as test the effectiveness of the implementation of anti-corruption education course for Primary School Teacher Education students, who must be able to transfer anti-corruption values to learners. The research method refers to the development of procedural models, which is…

  15. Toward a Unified Modeling of Learner's Growth Process and Flow Theory

    ERIC Educational Resources Information Center

    Challco, Geiser C.; Andrade, Fernando R. H.; Borges, Simone S.; Bittencourt, Ig I.; Isotani, Seiji

    2016-01-01

    Flow is the affective state in which a learner is so engaged and involved in an activity that nothing else seems to matter. In this sense, to help students in the skill development and knowledge acquisition (referred to as learners' growth process) under optimal conditions, the instructional designers should create learning scenarios that favor…

  16. Toward a Summative System for the Assessment of Teaching Quality in Higher Education

    ERIC Educational Resources Information Center

    Murphy, Timothy; MacLaren, Iain; Flynn, Sharon

    2009-01-01

    This study examines various aspects of an effective teaching evaluation system. In particular, reference is made to the potential of Fink's (2008) four main dimensions of teaching as a summative evaluation model for effective teaching and learning. It is argued that these dimensions can be readily accommodated in a Teaching Portfolio process. The…

  17. Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling

    PubMed Central

    van der Toolen, Yaloe; Vrij, Aldert; Arntz, Arnoud; Verschuere, Bruno

    2018-01-01

    Summary Recently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth‐tellers. Experiment 2 examined whether these findings replicated on independent‐sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth‐tellers' statements. Together, these findings suggest that liars may over‐prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data. PMID:29861544

  18. Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems

    NASA Astrophysics Data System (ADS)

    Raksincharoensak, Pongsathorn; Khaisongkram, Wathanyoo; Nagai, Masao; Shimosaka, Masamichi; Mori, Taketoshi; Sato, Tomomasa

    2010-12-01

    This paper describes the modelling of naturalistic driving behaviour in real-world traffic scenarios, based on driving data collected via an experimental automobile equipped with a continuous sensing drive recorder. This paper focuses on the longitudinal driving situations which are classified into five categories - car following, braking, free following, decelerating and stopping - and are referred to as driving states. Here, the model is assumed to be represented by a state flow diagram. Statistical machine learning of driver-vehicle-environment system model based on driving database is conducted by a discriminative modelling approach called boosting sequential labelling method.

  19. Learning to Explain Astronomy across Moving Frames of Reference: Exploring the Role of Classroom and Planetarium-Based Instructional Contexts

    ERIC Educational Resources Information Center

    Plummer, Julia Diane; Kocareli, Alicia; Slagle, Cynthia

    2014-01-01

    Learning astronomy involves significant spatial reasoning, such as learning to describe Earth-based phenomena and understanding space-based explanations for those phenomena as well as using the relevant size and scale information to interpret these frames of reference. This study examines daily celestial motion (DCM) as one case of how children…

  20. ERP evidence for conflict in contingency learning.

    PubMed

    Whitehead, Peter S; Brewer, Gene A; Blais, Chris

    2017-07-01

    The proportion congruency effect refers to the observation that the magnitude of the Stroop effect increases as the proportion of congruent trials in a block increases. Contemporary work shows that proportion effects can be driven by both context and individual items, and are referred to as context-specific proportion congruency (CSPC) and item-specific proportion congruency (ISPC) effects, respectively. The conflict-modulated Hebbian learning account posits that these effects manifest from the same mechanism, while the parallel episodic processing model posits that the ISPC can occur by simple associative learning. Our prior work showed that the neural correlates of the CSPC is an N2 over frontocentral electrode sites approximately 300 ms after stimulus onset that predicts behavioral performance. There is strong consensus in the field that this N2 signal is associated with conflict detection in the medial frontal cortex. The experiment reported here assesses whether the same qualitative electrophysiological pattern of results holds for the ISPC. We find that the spatial topography of the N2 is similar but slightly delayed with a peak onset of approximately 300 ms after stimulus onset. We argue that this provides strong evidence that a single common mechanism-conflict-modulated Hebbian learning-drives both the ISPC and CSPC. © 2017 Society for Psychophysiological Research.

  1. Demonizing in children's television cartoons and Disney animated films.

    PubMed

    Fouts, Gregory; Callan, Mitchell; Piasentin, Kelly; Lawson, Andrea

    2006-01-01

    The purpose of this study was to assess the prevalence of demonizing in the two major media that young children use (television and movies). Two content analyses were conducted using the animated feature films (n = 34) of the Walt Disney Company and after-school cartoons (n = 41). Each was coded for the modeling of the use of "evil" words when referring to a person, e.g., monster, devil, demon, wicked. Seventy-four percent of the Disney films contained "evil" references, with an average of 5.6 references per film. Forty-four percent of the after-school cartoons contained "evil" references, with an average of one per cartoon. The results are discussed within the context of children's repeated exposure to popular animated movies and cartoons and their learning to demonize people who engage in perceived "bad" behaviors.

  2. An evolutionary morphological approach for software development cost estimation.

    PubMed

    Araújo, Ricardo de A; Oliveira, Adriano L I; Soares, Sergio; Meira, Silvio

    2012-08-01

    In this work we present an evolutionary morphological approach to solve the software development cost estimation (SDCE) problem. The proposed approach consists of a hybrid artificial neuron based on framework of mathematical morphology (MM) with algebraic foundations in the complete lattice theory (CLT), referred to as dilation-erosion perceptron (DEP). Also, we present an evolutionary learning process, called DEP(MGA), using a modified genetic algorithm (MGA) to design the DEP model, because a drawback arises from the gradient estimation of morphological operators in the classical learning process of the DEP, since they are not differentiable in the usual way. Furthermore, an experimental analysis is conducted with the proposed model using five complex SDCE problems and three well-known performance metrics, demonstrating good performance of the DEP model to solve SDCE problems. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. The Design of Collaborative Learning for Teaching Physics in Vocational Secondary School

    NASA Astrophysics Data System (ADS)

    Ismayati, Euis

    2018-04-01

    Vocational secondary school (Sekolah Menengah Kejuruan or SMK) is a vocational education that is based on the principle of human resource investment (human capital investment) referring to the quality of education and productivity to compete in the global job market. Therefore, vocational education relates directly to business world/industry which fulfills the needs of the skilled worker. According to the results of some researches, the work ethics of vocational graduates are still unsatisfying. Most of them are less able to perform their works, to adapt to the changes and development of technology and science, to be retrained, to develop themselves, to collaborate, and to argue. Meanwhile, the employers in the world of work and industries require their employees to have abilities to think creatively and working collaboratively. In addition, the students’ abilities to adapt to the technology in working environment are greatly influenced by the learning process in their schools, especially in science learning. The process of science learning which can help the students to think and act scientifically should be implemented by teachers using a learning approach which is appropriate to the students’ need and the material taught to the students. To master technology and industry needs science mastery. Physics, as a part of science, has an important role in the development of technology since the products of technology strongly support further development of science. In order to develop the abilities to think critically and working collaboratively, education should be given to the students through the learning process using learning model which refers to a collaborative group discussion system called Collaborative Learning. Moreover, Collaborative learning for teaching Physics in vocational secondary school should be designed in such a way that the goal of teaching and learning can be achieved. Collaborative Learning is advantageous to improve the students’ creative thinking and collaborative working.

  4. Learning from other people’s fear: amygdala-based social reference learning in social anxiety disorder

    PubMed Central

    Blair, K. S.; Otero, M.; Teng, C.; Geraci, M.; Lewis, E.; Hollon, N.; Blair, R. J. R.; Ernst, Monique; Grillon, C.; Pine, D. S.

    2016-01-01

    Background Social anxiety disorder involves fear of social objects or situations. Social referencing may play an important role in the acquisition of this fear and could be a key determinant in future biomarkers and treatment pathways. However, the neural underpinnings mediating such learning in social anxiety are unknown. Using event-related functional magnetic resonance imaging, we examined social reference learning in social anxiety disorder. Specifically, would patients with the disorder show increased amygdala activity during social reference learning, and further, following social reference learning, show particularly increased response to objects associated with other people’s negative reactions? Method A total of 32 unmedicated patients with social anxiety disorder and 22 age-, intelligence quotient- and gender-matched healthy individuals responded to objects that had become associated with others’ fearful, angry, happy or neutral reactions. Results During the social reference learning phase, a significant group × social context interaction revealed that, relative to the comparison group, the social anxiety group showed a significantly greater response in the amygdala, as well as rostral, dorsomedial and lateral frontal and parietal cortices during the social, relative to non-social, referencing trials. In addition, during the object test phase, relative to the comparison group, the social anxiety group showed increased bilateral amygdala activation to objects associated with others’ fearful reactions, and a trend towards decreased amygdala activation to objects associated with others’ happy and neutral reactions. Conclusions These results suggest perturbed observational learning in social anxiety disorder. In addition, they further implicate the amygdala and dorsomedial prefrontal cortex in the disorder, and underscore their importance in future biomarker developments. PMID:27476529

  5. The teacher's role in college level classes for non-science majors: A constructivist approach for teaching prospective science teachers

    NASA Astrophysics Data System (ADS)

    Abbas, Abdullah Othman

    1997-12-01

    This interpretive research set out to investigate the characteristics of an exemplary college science instructor who endeavors to improve teaching and learning in a physical science course for prospective teachers. The course was innovative in the sense that it was designed to meet the specific needs of prospective elementary teachers who needed to have models of how to teach science in a way that employed materials and small group activities. The central purpose for this study is to understand the metaphors that Mark (a pseudonym), the chemistry instructor in the course, used as referents to conceptualize his roles and frame actions and interactions in the classroom. Within the theoretical frame of constructivism, human cognitive interests, and co-participation theories, an ethnographic research design, described by Erickson (1986), Guba and Lincoln (1989), and Gallagher (1991), was employed in the study. The main sources of data for this study were field notes, transcript analysis of interviews with the instructor and students, and analyses of videotaped excerpts. Additional data sources, such as student journals and the results of students' responses to the University/Community College Student Questionnaire which was developed by a group science education researchers at Florida State University, were employed to maximize that the assertions I constructed were consistent with the variety of data. Data analyses and interpretation in the study focused on identifying the aspects which the instructor and the researcher might find useful in reflecting to understand what was happening and why that was happening in the classroom. The analysis reveals how the instructor used constructivism as a referent for his teaching and the learning of his students. To be consistent with his beliefs and goals that prospective teachers should enjoy their journey of learning chemistry, Mark, the driver in the journey, used the roles of controller, facilitator, learner, and entertainer as referents for actions to create conducive learning environments. He was able to switch his actions based on which of the constituent metaphors he used as a referent to frame his actions and interactions, and thereby, to create an exciting environment for learning.

  6. Merging curriculum design with chemical epistemology: A case of teaching and learning chemistry through modeling

    NASA Astrophysics Data System (ADS)

    Erduran, Sibel

    The central problem underlying this dissertation is the design of learning environments that enable the teaching and learning of chemistry through modeling. Significant role of models in chemistry knowledge is highlighted with a shift in emphasis from conceptual to epistemological accounts of models. Research context is the design and implementation of student centered Acids & Bases Curriculum, developed as part of Project SEPIA. Qualitative study focused on 3 curriculum activities conducted in one 7th grade class of 19 students in an urban, public middle school in eastern United States. Questions guiding the study were: (a) How can learning environments be designed to promote growth of chemistry knowledge through modeling? (b) What epistemological criteria facilitate learning of growth of chemistry knowledge through modeling? Curriculum materials, and verbal data from whole class conversations and student group interviews were analyzed. Group interviews consisted of same 4 students, selected randomly before curriculum implementation, and were conducted following each activity to investigate students' developing understandings of models. Theoretical categories concerning definition, properties and kinds of models as well as educational and chemical models informed curriculum design, and were redefined as codes in the analysis of verbal data. Results indicate more diversity of codes in student than teacher talk across all activities. Teacher concentrated on educational and chemical models. A significant finding is that model properties such as 'compositionality' and 'projectability' were not present in teacher talk as expected by curriculum design. Students did make reference to model properties. Another finding is that students demonstrate an understanding of models characterized by the seventeenth century Lemery model of acids and bases. Two students' developing understandings of models across curriculum implementation suggest that curriculum bears some change in students' understanding of models. The tension between students' everyday knowledge and teacher's scientific knowledge is highlighted relative to the patterns in codes observed in data. Implications for theory of learning, curriculum design and teacher education are discussed. It is argued that future educational research should acknowledge and incorporate perspectives from chemical epistemology.

  7. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    PubMed

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  8. Collaborative innovations with rural and regional secondary teachers: enhancing student learning in mathematics

    NASA Astrophysics Data System (ADS)

    Pegg, John; Panizzon, Debra

    2011-06-01

    When questioned, secondary mathematics teachers in rural and regional schools in Australia refer to their limited opportunities to engage and share experiences with peers in other schools as an under-utilised and cost-effective mechanism to support their professional learning and enhance their students' learning. The paper reports on the creation and evaluation of a network of learning communities of rural secondary mathematics teachers around a common purpose—enhancement and increased engagement of student learning in mathematics. To achieve this goal, teams of teachers from six rural schools identified an issue hindering improved student learning of mathematics in their school. Working collaboratively with support from university personnel with expertise in curriculum, assessment and quality pedagogy, teachers developed and implemented strategies to address an identified issue in ways that were relevant to their teaching contexts. The research study identifies issues in mathematics of major concern to rural teachers of mathematics, the successes and challenges the teachers faced in working in learning communities on the issue they identified, and the efficacy of the professional learning model.

  9. Tone of voice guides word learning in informative referential contexts

    PubMed Central

    Reinisch, Eva; Jesse, Alexandra; Nygaard, Lynne C.

    2012-01-01

    Listeners infer which object in a visual scene a speaker refers to from the systematic variation of the speaker’s tone of voice (ToV). We examined whether ToV also guides word learning. During exposure, participants heard novel adjectives (e.g., “daxen”) spoken with a ToV representing hot, cold, strong, weak, big, or small while viewing picture pairs representing the meaning of the adjective and its antonym (e.g., elephant-ant for big-small). Eye fixations were recorded to monitor referent detection and learning. During test, participants heard the adjectives spoken with a neutral ToV, while selecting referents from familiar and unfamiliar picture pairs. Participants were able to learn the adjectives’ meanings, and, even in the absence of informative ToV, generalise them to new referents. A second experiment addressed whether ToV provides sufficient information to infer the adjectival meaning or needs to operate within a referential context providing information about the relevant semantic dimension. Participants who saw printed versions of the novel words during exposure performed at chance during test. ToV, in conjunction with the referential context, thus serves as a cue to word meaning. ToV establishes relations between labels and referents for listeners to exploit in word learning. PMID:23134484

  10. What's in a Name? Interlocutors Dynamically Update Expectations about Shared Names.

    PubMed

    Gegg-Harrison, Whitney M; Tanenhaus, Michael K

    2016-01-01

    In order to refer using a name, speakers must believe that their addressee knows about the link between the name and the intended referent. In cases where speakers and addressees learned a subset of names together, speakers are adept at using only the names their partner knows. But speakers do not always share such learning experience with their conversational partners. In these situations, what information guides speakers' choice of referring expression? A speaker who is uncertain about a names' common ground (CG) status often uses a name and description together. This N+D form allows speakers to demonstrate knowledge of a name, and could provide, even in the absence of miscommunication, useful evidence to the addressee regarding the speaker's knowledge. In cases where knowledge of one name is associated with knowledge of other names, this could provide indirect evidence regarding knowledge of other names that could support generalizations used to update beliefs about CG. Using Bayesian approaches to language processing as a guiding framework, we predict that interlocutors can use their partner's choice of referring expression, in particular their use of an N+D form, to generate more accurate beliefs regarding their partner's knowledge of other names. In Experiment 1, we find that domain experts are able to use their partner's referring expression choices to generate more accurate estimates of CG. In Experiment 2, we find that interlocutors are able to infer from a partner's use of an N+D form which other names that partner is likely to know or not know. Our results suggest that interlocutors can use the information conveyed in their partner's choice of referring expression to make generalizations that contribute to more accurate beliefs about what is shared with their partner, and further, that models of CG for reference need to account not just for the status of referents, but the status of means of referring to those referents.

  11. What's in a Name? Interlocutors Dynamically Update Expectations about Shared Names

    PubMed Central

    Gegg-Harrison, Whitney M.; Tanenhaus, Michael K.

    2016-01-01

    In order to refer using a name, speakers must believe that their addressee knows about the link between the name and the intended referent. In cases where speakers and addressees learned a subset of names together, speakers are adept at using only the names their partner knows. But speakers do not always share such learning experience with their conversational partners. In these situations, what information guides speakers' choice of referring expression? A speaker who is uncertain about a names' common ground (CG) status often uses a name and description together. This N+D form allows speakers to demonstrate knowledge of a name, and could provide, even in the absence of miscommunication, useful evidence to the addressee regarding the speaker's knowledge. In cases where knowledge of one name is associated with knowledge of other names, this could provide indirect evidence regarding knowledge of other names that could support generalizations used to update beliefs about CG. Using Bayesian approaches to language processing as a guiding framework, we predict that interlocutors can use their partner's choice of referring expression, in particular their use of an N+D form, to generate more accurate beliefs regarding their partner's knowledge of other names. In Experiment 1, we find that domain experts are able to use their partner's referring expression choices to generate more accurate estimates of CG. In Experiment 2, we find that interlocutors are able to infer from a partner's use of an N+D form which other names that partner is likely to know or not know. Our results suggest that interlocutors can use the information conveyed in their partner's choice of referring expression to make generalizations that contribute to more accurate beliefs about what is shared with their partner, and further, that models of CG for reference need to account not just for the status of referents, but the status of means of referring to those referents. PMID:26955361

  12. The Cannon: A data-driven approach to Stellar Label Determination

    NASA Astrophysics Data System (ADS)

    Ness, M.; Hogg, David W.; Rix, H.-W.; Ho, Anna. Y. Q.; Zasowski, G.

    2015-07-01

    New spectroscopic surveys offer the promise of stellar parameters and abundances (“stellar labels”) for hundreds of thousands of stars; this poses a formidable spectral modeling challenge. In many cases, there is a subset of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new data-driven approach for determining stellar labels from spectroscopic data. The Cannon learns from the “known” labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with {T}{eff}, {log} g, and [{Fe}/{{H}}] as the labels, and then applying it to the spectra of 55,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one-ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.

  13. Validity of "Hi_Science" as instructional media based-android refer to experiential learning model

    NASA Astrophysics Data System (ADS)

    Qamariah, Jumadi, Senam, Wilujeng, Insih

    2017-08-01

    Hi_Science is instructional media based-android in learning science on material environmental pollution and global warming. This study is aimed: (a) to show the display of Hi_Science that will be applied in Junior High School, and (b) to describe the validity of Hi_Science. Hi_Science as instructional media created with colaboration of innovative learning model and development of technology at the current time. Learning media selected is based-android and collaborated with experiential learning model as an innovative learning model. Hi_Science had adapted student worksheet by Taufiq (2015). Student worksheet had very good category by two expert lecturers and two science teachers (Taufik, 2015). This student worksheet is refined and redeveloped in android as an instructional media which can be used by students for learning science not only in the classroom, but also at home. Therefore, student worksheet which has become instructional media based-android must be validated again. Hi_Science has been validated by two experts. The validation is based on assessment of meterials aspects and media aspects. The data collection was done by media assessment instrument. The result showed the assessment of material aspects has obtained the average value 4,72 with percentage of agreement 96,47%, that means Hi_Science on the material aspects is in excellent category or very valid category. The assessment of media aspects has obtained the average value 4,53 with percentage of agreement 98,70%, that means Hi_Science on the media aspects is in excellent category or very valid category. It was concluded that Hi_Science as instructional media can be applied in the junior high school.

  14. Help me if I can't: Social interaction effects in adult contextual word learning.

    PubMed

    Verga, Laura; Kotz, Sonja A

    2017-11-01

    A major challenge in second language acquisition is to build up new vocabulary. How is it possible to identify the meaning of a new word among several possible referents? Adult learners typically use contextual information, which reduces the number of possible referents a new word can have. Alternatively, a social partner may facilitate word learning by directing the learner's attention toward the correct new word meaning. While much is known about the role of this form of 'joint attention' in first language acquisition, little is known about its efficacy in second language acquisition. Consequently, we introduce and validate a novel visual word learning game to evaluate how joint attention affects the contextual learning of new words in a second language. Adult learners either acquired new words in a constant or variable sentence context by playing the game with a knowledgeable partner, or by playing the game alone on a computer. Results clearly show that participants who learned new words in social interaction (i) are faster in identifying a correct new word referent in variable sentence contexts, and (ii) temporally coordinate their behavior with a social partner. Testing the learned words in a post-learning recall or recognition task showed that participants, who learned interactively, better recognized words originally learned in a variable context. While this result may suggest that interactive learning facilitates the allocation of attention to a target referent, the differences in the performance during recognition and recall call for further studies investigating the effect of social interaction on learning performance. In summary, we provide first evidence on the role joint attention in second language learning. Furthermore, the new interactive learning game offers itself to further testing in complex neuroimaging research, where the lack of appropriate experimental set-ups has so far limited the investigation of the neural basis of adult word learning in social interaction. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Conventional wisdom: negotiating conventions of reference enhances category learning.

    PubMed

    Voiklis, John; Corter, James E

    2012-01-01

    Collaborators generally coordinate their activities through communication, during which they readily negotiate a shared lexicon for activity-related objects. This social-pragmatic activity both recruits and affects cognitive and social-cognitive processes ranging from selective attention to perspective taking. We ask whether negotiating reference also facilitates category learning or might private verbalization yield comparable facilitation? Participants in three referential conditions learned to classify imaginary creatures according to combinations of functional features-nutritive and destructive-that implicitly defined four categories. Remote partners communicated in the Dialogue condition. In the Monologue condition, participants recorded audio descriptions for their own later use. Controls worked silently. Dialogue yielded better category learning, with wider distribution of attention. Monologue offered no benefits over working silently. We conclude that negotiating reference compels collaborators to find communicable structure in their shared activity; this social-pragmatic constraint accelerates category learning and likely provides much of the benefit recently ascribed to learning labeled categories. Copyright © 2012 Cognitive Science Society, Inc.

  16. 75 FR 33593 - Office of Special Education and Rehabilitative Services; Overview Information; Personnel...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-06-14

    ... ``high-incidence disabilities'' refers to learning disabilities, emotional disturbance, or mental... purposes of this priority, the term high-incidence disabilities refers to learning disabilities, emotional... Information; Personnel Development To Improve Services and Results for Children With Disabilities; Notice...

  17. Discriminative least squares regression for multiclass classification and feature selection.

    PubMed

    Xiang, Shiming; Nie, Feiping; Meng, Gaofeng; Pan, Chunhong; Zhang, Changshui

    2012-11-01

    This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

  18. Sociometric-Status-Group Classification of Mainstreamed Children Who Have Moderate Learning Difficulties: An Investigation of Personal and Environmental Factors.

    ERIC Educational Resources Information Center

    Frederickson, Norah L.; Furnham, Adrian F.

    1998-01-01

    Study examines the classes of variables identified in D. L. MacMillan and G. M. Morrison's (1984) multicomponent model for research on sociometric status in special education. Results are discussed with reference to social-exchange theory, as an integrative basis for research on children's sociometric status. Implications for mainstreaming…

  19. Learning by Explaining Examples to Oneself: A Computational Model

    DTIC Science & Technology

    1992-02-01

    rules. of which 28 rep~resented common senise phtysirs (e.g.. a taut rope tied to a object pulls onl it ) and 17 represented ()vr-gnerlizt inssuch as...the ,mii~ jduid( ii ot refer to anl examiplle to achieve tilie goal. thliu we cla-si tied lie goalI as beingp resolved bY EIIL( * llliimi v all mlv 1i e

  20. Instructional Media Production for Early Childhood Education: A. B. C. Jig-Saw Puzzle, a Model

    ERIC Educational Resources Information Center

    Yusuf, Mudashiru Olalere; Olanrewaju, Olatayo Solomon; Soetan, Aderonke K.

    2015-01-01

    In this paper, a. b. c. jig-saw puzzle was produced for early childhood education using local materials. This study was a production based type of research, to serve as a supplemental or total learning resource. Its production followed four phases of development referred to as information, design, production and evaluation. The storyboard cards,…

  1. Promoting Knowledge Creation Discourse in an Asian Primary Five Classroom: Results from an Inquiry into Life Cycles

    ERIC Educational Resources Information Center

    van Aalst, Jan; Truong, Mya Sioux

    2011-01-01

    The phrase "knowledge creation" refers to the practices by which a community advances its collective knowledge. Experience with a model of knowledge creation could help students to learn about the nature of science. This research examined how much progress a teacher and 16 Primary Five (Grade 4) students in the International…

  2. Construct Validity of the Wechsler Intelligence Scale for Children-Fourth UK Edition with a Referred Irish Sample: Wechsler and Cattell-Horn-Carroll Model Comparisons with 15 Subtests

    ERIC Educational Resources Information Center

    Canivez, Gary L.; Watkins, Marley W.; Good, Rebecca; James, Kate; James, Trevor

    2017-01-01

    Background: Irish educational psychologists frequently use the Wechsler Intelligence Scale for Children-Fourth UK Edition (WISC-IV[superscript UK]; Wechsler, 2004, Wechsler Intelligence Scale for Children-Fourth UK Edition, London, UK, Harcourt Assessment) in clinical assessments of children with learning difficulties. Unfortunately, reliability…

  3. The Concept of "English Learning Power" and Its Implication for the Design of English Curricula for Primary and Middle Schools in China

    ERIC Educational Resources Information Center

    Dai, Zhongxin

    2015-01-01

    In the research on "New Characteristics of Future Basic Education in China," Dina Pei formulates a "three-powered" model to theorize about the characteristics of future basic education in China. The three powers refer to the "Policy-making Power" of the local educational administration, the "Leading Power"…

  4. Quantitative Skills as a Graduate Learning Outcome of University Science Degree Programmes: Student Performance Explored through the "Planned-Enacted-Experienced" Curriculum Model

    ERIC Educational Resources Information Center

    Matthews, Kelly E.; Adams, Peter; Goos, Merrilyn

    2016-01-01

    Application of mathematical and statistical thinking and reasoning, typically referred to as quantitative skills, is essential for university bioscience students. First, this study developed an assessment task intended to gauge graduating students' quantitative skills. The Quantitative Skills Assessment of Science Students (QSASS) was the result,…

  5. Creating Domain Independent Adaptive E-Learning Systems Using the Sharable Content Object Reference Model

    ERIC Educational Resources Information Center

    Watson, Jason; Ahmed, Pervaiz K.; Hardaker, Glenn

    2007-01-01

    Purpose: This research aims to investigate how a generic web-based ITS can be created which will adapt the training content in real time, to the needs of the individual trainee across any domain. Design/methodology/approach: After examining the various alternatives SCORM was adopted in this project because it provided an infrastructure that makes…

  6. Factors Affecting Students' Acceptance of Tablet PCs: A Study in Italian High Schools

    ERIC Educational Resources Information Center

    Cacciamani, Stefano; Villani, Daniela; Bonanomi, Andrea; Carissoli, Claudia; Olivari, Maria Giulia; Morganti, Laura; Riva, Giuseppe; Confalonieri, Emanuela

    2018-01-01

    To maximize the advantages of the tablet personal computer (TPC) at school, this technology needs to be accepted by students as new tool for learning. With reference to the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology, the aims of this study were (a) to analyze factors influencing high school students'…

  7. Vers une Theorie de l'Education/Apprentissage

    NASA Astrophysics Data System (ADS)

    Goulet, Georges

    1986-12-01

    The author has attempted to clarify the idea of the curriculum as a distinct area of knowledge which is characteristic of educational science within the wider framework of educational sciences. Inspired by the method of a comparative analysis which was developed by George Z. Bereday, the author analyzes the data which are used referring to a typology of definitions based on the Aristotelian idea of causality. In the article a meta-theory of the curriculum is proposed and defined based on the consensus discovered among the authors whose views are analyzed. The present curriculum is examined with respect to its original meaning as proposed by John Dewey in order to establish the process of organized and intentional education/learning. However, the plan or programme of education/learning which is often identified as the notion of the curriculum, is presented here as being only one of the parametres determining the nature of the whole process of organized education/learning. The convergence of the remaining four parametres constitutes a special process of education/learning realized to suit each learner as the beneficiary agent (learner), the initiating agent (intervener), the physical and social environment as well as the knowledge or the culture to be transmitted. The theory goes on to deal with the paradigmatic nature of the process and the four levels of intentionality characterizing the planning and actualization of each process. The theory presents the curriculum as if it were a phenomenon which functions like an open system, i.e. where the total or the `Gestalt' constitutes the realized learning by each learner, referring to the laws of equifinality and homeostasis. The article closes by presenting a spiral model which seeks to represent the web of real and perceptual influences which contribute to the learning aimed at by the whole institution of education/learning.

  8. Novel word acquisition in aphasia: Facing the word-referent ambiguity of natural language learning contexts.

    PubMed

    Peñaloza, Claudia; Mirman, Daniel; Tuomiranta, Leena; Benetello, Annalisa; Heikius, Ida-Maria; Järvinen, Sonja; Majos, Maria C; Cardona, Pedro; Juncadella, Montserrat; Laine, Matti; Martin, Nadine; Rodríguez-Fornells, Antoni

    2016-06-01

    Recent research suggests that some people with aphasia preserve some ability to learn novel words and to retain them in the long-term. However, this novel word learning ability has been studied only in the context of single word-picture pairings. We examined the ability of people with chronic aphasia to learn novel words using a paradigm that presents new word forms together with a limited set of different possible visual referents and requires the identification of the correct word-object associations on the basis of online feedback. We also studied the relationship between word learning ability and aphasia severity, word processing abilities, and verbal short-term memory (STM). We further examined the influence of gross lesion location on new word learning. The word learning task was first validated with a group of forty-five young adults. Fourteen participants with chronic aphasia were administered the task and underwent tests of immediate and long-term recognition memory at 1 week. Their performance was compared to that of a group of fourteen matched controls using growth curve analysis. The learning curve and recognition performance of the aphasia group was significantly below the matched control group, although above-chance recognition performance and case-by-case analyses indicated that some participants with aphasia had learned the correct word-referent mappings. Verbal STM but not word processing abilities predicted word learning ability after controlling for aphasia severity. Importantly, participants with lesions in the left frontal cortex performed significantly worse than participants with lesions that spared the left frontal region both during word learning and on the recognition tests. Our findings indicate that some people with aphasia can preserve the ability to learn a small novel lexicon in an ambiguous word-referent context. This learning and recognition memory ability was associated with verbal STM capacity, aphasia severity and the integrity of the left inferior frontal region. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Novel word acquisition in aphasia: Facing the word-referent ambiguity of natural language learning contexts

    PubMed Central

    Peñaloza, Claudia; Mirman, Daniel; Tuomiranta, Leena; Benetello, Annalisa; Heikius, Ida-Maria; Järvinen, Sonja; Majos, Maria C.; Cardona, Pedro; Juncadella, Montserrat; Laine, Matti; Martin, Nadine; Rodríguez-Fornells, Antoni

    2017-01-01

    Recent research suggests that some people with aphasia preserve some ability to learn novel words and to retain them in the long-term. However, this novel word learning ability has been studied only in the context of single word-picture pairings. We examined the ability of people with chronic aphasia to learn novel words using a paradigm that presents new word forms together with a limited set of different possible visual referents and requires the identification of the correct word-object associations on the basis of online feedback. We also studied the relationship between word learning ability and aphasia severity, word processing abilities, and verbal short-term memory (STM). We further examined the influence of gross lesion location on new word learning. The word learning task was first validated with a group of forty-five young adults. Fourteen participants with chronic aphasia were administered the task and underwent tests of immediate and long-term recognition memory at 1 week. Their performance was compared to that of a group of fourteen matched controls using growth curve analysis. The learning curve and recognition performance of the aphasia group was significantly below the matched control group, although above-chance recognition performance and case-by-case analyses indicated that some participants with aphasia had learned the correct word-referent mappings. Verbal STM but not word processing abilities predicted word learning ability after controlling for aphasia severity. Importantly, participants with lesions in the left frontal cortex performed significantly worse than participants with lesions that spared the left frontal region both during word learning and on the recognition tests. Our findings indicate that some people with aphasia can preserve the ability to learn a small novel lexicon in an ambiguous word-referent context. This learning and recognition memory ability was associated with verbal STM capacity, aphasia severity and the integrity of the left inferior frontal region. PMID:27085892

  10. EHR-based phenotyping: Bulk learning and evaluation.

    PubMed

    Chiu, Po-Hsiang; Hripcsak, George

    2017-06-01

    In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability. In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate and informative statistical models. In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction. We use a sparse annotation set to train and evaluate many phenotypes at once, which we call bulk learning. In this batch-phenotyping framework, disease cohort definitions can be learned from within the abstract feature space established by using multiple diseases as a substrate and diagnostic codes as surrogates. In particular, using surrogate labels for model training renders possible its subsequent evaluation using only a sparse annotated sample. Moreover, statistical models can be trained and evaluated, using the same sparse annotation, from within the abstract feature space of low dimensionality that encapsulates the shared clinical traits of these target diseases, collectively referred to as the bulk learning set. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Multiple memory systems as substrates for multiple decision systems

    PubMed Central

    Doll, Bradley B.; Shohamy, Daphna; Daw, Nathaniel D.

    2014-01-01

    It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an “internal model.” Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other. Here we investigated the relationship between model-based RL and relational memory by comparing individual differences across behavioral tasks designed to measure either capacity. Human subjects performed two tasks, a learning and generalization task (acquired equivalence) which involves relational encoding and depends on the hippocampus; and a sequential RL task that could be solved by either a model-based or model-free strategy. We assessed the correlation between subjects’ use of flexible, relational memory, as measured by generalization in the acquired equivalence task, and their differential reliance on either RL strategy in the decision task. We observed a significant positive relationship between generalization and model-based, but not model-free, choice strategies. These results are consistent with the hypothesis that model-based RL, like acquired equivalence, relies on a more general-purpose relational memory system. PMID:24846190

  12. The Chicken or the Egg? The Direction of the Relationship Between Mathematics Anxiety and Mathematics Performance

    PubMed Central

    Carey, Emma; Hill, Francesca; Devine, Amy; Szücs, Dénes

    2016-01-01

    This review considers the two possible causal directions between mathematics anxiety (MA) and poor mathematics performance. Either poor maths performance may elicit MA (referred to as the Deficit Theory), or MA may reduce future maths performance (referred to as the Debilitating Anxiety Model). The evidence is in conflict: the Deficit Theory is supported by longitudinal studies and studies of children with mathematical learning disabilities, but the Debilitating Anxiety Model is supported by research which manipulates anxiety levels and observes a change in mathematics performance. It is suggested that this mixture of evidence might indicate a bidirectional relationship between MA and mathematics performance (the Reciprocal Theory), in which MA and mathematics performance can influence one another in a vicious cycle. PMID:26779093

  13. Approach to Learning of Sub-Degree Students in Hong Kong

    ERIC Educational Resources Information Center

    Chan, Yiu Man; Chan, Christine Mei Sheung

    2010-01-01

    The learning approaches and learning experiences of 404 sub-degree students were assessed by using a Study Process Questionnaire and a Learning Experience Questionnaire. While the learning approaches in this study meant whether students used a deep learning or surface learning approach, the learning experiences referred to students' perceptions…

  14. Imitation by combination: preschool age children evidence summative imitation in a novel problem-solving task.

    PubMed

    Subiaul, Francys; Krajkowski, Edward; Price, Elizabeth E; Etz, Alexander

    2015-01-01

    Children are exceptional, even 'super,' imitators but comparatively poor independent problem-solvers or innovators. Yet, imitation and innovation are both necessary components of cumulative cultural evolution. Here, we explored the relationship between imitation and innovation by assessing children's ability to generate a solution to a novel problem by imitating two different action sequences demonstrated by two different models, an example of imitation by combination, which we refer to as "summative imitation." Children (N = 181) from 3 to 5 years of age and across three experiments were tested in a baseline condition or in one of six demonstration conditions, varying in the number of models and opening techniques demonstrated. Across experiments, more than 75% of children evidenced summative imitation, opening both compartments of the problem box and retrieving the reward hidden in each. Generally, learning different actions from two different models was as good (and in some cases, better) than learning from 1 model, but the underlying representations appear to be the same in both demonstration conditions. These results show that summative imitation not only facilitates imitation learning but can also result in new solutions to problems, an essential feature of innovation and cumulative culture.

  15. Flyback CCM inverter for AC module applications: iterative learning control and convergence analysis

    NASA Astrophysics Data System (ADS)

    Lee, Sung-Ho; Kim, Minsung

    2017-12-01

    This paper presents an iterative learning controller (ILC) for an interleaved flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features small output ripple current, high efficiency, and low cost, and hence it is well suited for photovoltaic power applications. However, it exhibits the non-minimum phase behaviour, because its transfer function from control duty to output current has the right-half-plane (RHP) zero. Moreover, the flyback CCM inverter suffers from the time-varying grid voltage disturbance. Thus, conventional control scheme results in inaccurate output tracking. To overcome these problems, the ILC is first developed and applied to the flyback inverter operating in CCM. The ILC makes use of both predictive and current learning terms which help the system output to converge to the reference trajectory. We take into account the nonlinear averaged model and use it to construct the proposed controller. It is proven that the system output globally converges to the reference trajectory in the absence of state disturbances, output noises, or initial state errors. Numerical simulations are performed to validate the proposed control scheme, and experiments using 400-W AC module prototype are carried out to demonstrate its practical feasibility.

  16. The definition of insulin resistance using HOMA-IR for Americans of Mexican descent using machine learning.

    PubMed

    Qu, Hui-Qi; Li, Quan; Rentfro, Anne R; Fisher-Hoch, Susan P; McCormick, Joseph B

    2011-01-01

    The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods. This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance. Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80. Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population.

  17. The Definition of Insulin Resistance Using HOMA-IR for Americans of Mexican Descent Using Machine Learning

    PubMed Central

    Qu, Hui-Qi; Li, Quan; Rentfro, Anne R.; Fisher-Hoch, Susan P.; McCormick, Joseph B.

    2011-01-01

    Objective The lack of standardized reference range for the homeostasis model assessment-estimated insulin resistance (HOMA-IR) index has limited its clinical application. This study defines the reference range of HOMA-IR index in an adult Hispanic population based with machine learning methods. Methods This study investigated a Hispanic population of 1854 adults, randomly selected on the basis of 2000 Census tract data in the city of Brownsville, Cameron County. Machine learning methods, support vector machine (SVM) and Bayesian Logistic Regression (BLR), were used to automatically identify measureable variables using standardized values that correlate with HOMA-IR; K-means clustering was then used to classify the individuals by insulin resistance. Results Our study showed that the best cutoff of HOMA-IR for identifying those with insulin resistance is 3.80. There are 39.1% individuals in this Hispanic population with HOMA-IR>3.80. Conclusions Our results are dramatically different using the popular clinical cutoff of 2.60. The high sensitivity and specificity of HOMA-IR>3.80 for insulin resistance provide a critical fundamental for our further efforts to improve the public health of this Hispanic population. PMID:21695082

  18. From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

    PubMed

    Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

    2017-11-01

    Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Reduced variability of visual left ventricular ejection fraction assessment with reference images: The Japanese Association of Young Echocardiography Fellows multicenter study.

    PubMed

    Kusunose, Kenya; Shibayama, Kentaro; Iwano, Hiroyuki; Izumo, Masaki; Kagiyama, Nobuyuki; Kurosawa, Koji; Mihara, Hirotsugu; Oe, Hiroki; Onishi, Tetsuari; Onishi, Toshinari; Ota, Mitsuhiko; Sasaki, Shunsuke; Shiina, Yumi; Tsuruta, Hikaru; Tanaka, Hidekazu

    2018-07-01

    Visual estimation of left ventricular ejection fraction (LVEF) is widely applied to confirm quantitative EF. However, visual assessment is subjective, and variability may be influenced by observer experience. We hypothesized that a learning session might reduce the misclassification rate. Protocol 1: Visual LVEFs for 30 cases were measured by 79 readers from 13 cardiovascular tertiary care centers. Readers were divided into 3 groups by their experience: limited (1-5 years, n=28), intermediate (6-11 years, n=26), and highly experienced (12-years, n=25). Protocol 2: All readers were randomized to assess the effect of a learning session with reference images only or feedback plus reference images. After the session, 20 new cases were shown to all readers following the same methodology. To assess the concordance and accuracy pre- and post-intervention, each visual LVEF measurement was compared to overall average values as a reference. Experience affected the concordance in visual EF values among the readers. Groups with intermediate and high experience showed significantly better mean difference (MD), standard deviation (SD), and coefficient of variation (CV) than those with limited experience at baseline. The learning session with reference image reduced the MD, SD, and CV in readers with limited experience. The learning session with reference images plus feedback also reduced proportional bias. Importantly, the misclassification rate for mid-range EF cases was reduced regardless of experience. This large multicenter study suggested that a simple learning session with reference images can successfully reduce the misclassification rate for LVEF assessment. Copyright © 2018 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

  20. Retrospective Revaluation: The Phenomenon and Its Theoretical Implications

    PubMed Central

    Miller, Ralph R.; Witnauer, James E.

    2015-01-01

    Retrospective revaluation refers to an increase (or decrease) in responding to conditioned stimulus (CS X) as a result of decreasing (or increasing) the associative strength of another CS (A) with respect to the unconditioned stimulus (i.e., A-US) that was previously trained in compound with the target CS (e.g., AX−US or just AX). We discuss the conditions under which retrospective revaluation phenomena are most apt to be observed and their implications for various models of learning that are able to account for retrospective revaluation (e.g., Dickinson and Burke, 1996; Miller and Matzel, 1988; Van Hamme and Wasserman, 1994). Although retroactive revaluation is relatively parameter specific, it is seen to be a reliable phenomenon observed across many tasks and species. As it is not anticipated by many conventional models of learning (e.g., Rescorla and Wagner, 1972), it serves as a critical benchmark for evaluating traditional and newer models. PMID:26342855

  1. The Sense of Confidence during Probabilistic Learning: A Normative Account.

    PubMed

    Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

    2015-06-01

    Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable "feeling of knowing" or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process.

  2. The Sense of Confidence during Probabilistic Learning: A Normative Account

    PubMed Central

    Meyniel, Florent; Schlunegger, Daniel; Dehaene, Stanislas

    2015-01-01

    Learning in a stochastic environment consists of estimating a model from a limited amount of noisy data, and is therefore inherently uncertain. However, many classical models reduce the learning process to the updating of parameter estimates and neglect the fact that learning is also frequently accompanied by a variable “feeling of knowing” or confidence. The characteristics and the origin of these subjective confidence estimates thus remain largely unknown. Here we investigate whether, during learning, humans not only infer a model of their environment, but also derive an accurate sense of confidence from their inferences. In our experiment, humans estimated the transition probabilities between two visual or auditory stimuli in a changing environment, and reported their mean estimate and their confidence in this report. To formalize the link between both kinds of estimate and assess their accuracy in comparison to a normative reference, we derive the optimal inference strategy for our task. Our results indicate that subjects accurately track the likelihood that their inferences are correct. Learning and estimating confidence in what has been learned appear to be two intimately related abilities, suggesting that they arise from a single inference process. We show that human performance matches several properties of the optimal probabilistic inference. In particular, subjective confidence is impacted by environmental uncertainty, both at the first level (uncertainty in stimulus occurrence given the inferred stochastic characteristics) and at the second level (uncertainty due to unexpected changes in these stochastic characteristics). Confidence also increases appropriately with the number of observations within stable periods. Our results support the idea that humans possess a quantitative sense of confidence in their inferences about abstract non-sensory parameters of the environment. This ability cannot be reduced to simple heuristics, it seems instead a core property of the learning process. PMID:26076466

  3. Learning-based 3D surface optimization from medical image reconstruction

    NASA Astrophysics Data System (ADS)

    Wei, Mingqiang; Wang, Jun; Guo, Xianglin; Wu, Huisi; Xie, Haoran; Wang, Fu Lee; Qin, Jing

    2018-04-01

    Mesh optimization has been studied from the graphical point of view: It often focuses on 3D surfaces obtained by optical and laser scanners. This is despite the fact that isosurfaced meshes of medical image reconstruction suffer from both staircases and noise: Isotropic filters lead to shape distortion, while anisotropic ones maintain pseudo-features. We present a data-driven method for automatically removing these medical artifacts while not introducing additional ones. We consider mesh optimization as a combination of vertex filtering and facet filtering in two stages: Offline training and runtime optimization. In specific, we first detect staircases based on the scanning direction of CT/MRI scanners, and design a staircase-sensitive Laplacian filter (vertex-based) to remove them; and then design a unilateral filtered facet normal descriptor (uFND) for measuring the geometry features around each facet of a given mesh, and learn the regression functions from a set of medical meshes and their high-resolution reference counterparts for mapping the uFNDs to the facet normals of the reference meshes (facet-based). At runtime, we first perform staircase-sensitive Laplacian filter on an input MC (Marching Cubes) mesh, and then filter the mesh facet normal field using the learned regression functions, and finally deform it to match the new normal field for obtaining a compact approximation of the high-resolution reference model. Tests show that our algorithm achieves higher quality results than previous approaches regarding surface smoothness and surface accuracy.

  4. [Effects of chronic partial sleep deprivation on growth and learning/memory in young rats].

    PubMed

    Jiang, Fan; Shen, Xiao-Ming; Li, Sheng-Hui; Cui, Mao-Long; Zhang, Yin; Wang, Cheng; Yu, Xiao-Gang; Yan, Chong-Huai

    2009-02-01

    The effects of sleep deprivation on the immature brain remain unknown. Based on a computer controlled chronic sleep deprivation animal model, the effects of chronic partial sleep deprivation on growth, learning and memory in young rats were explored. Twelve weaned male Spraque-Dawley rats (3-week-old) were randomly divided into sleep deprivation, test control and blank control groups. Sleep deprivation was performed using computer-controlled "disc-over-water" technique at 8-11 am daily, for 14 days. The temperature and weights were measured every 7 days. Morris water maze was used to test spatial learning and memory abilities before and 7 and 14 days after sleep deprivation. After 14 days of sleep deprivation, the rats were sacrificed for weighting their major organs. After 14 days of sleep deprivation, the rats' temperature increased significantly. During the sleep deprivation, the rate of weight gain in the sleep deprivation group was much slower than that in the test control and blank control groups. The thymus of the rats subjected to sleep deprivation was much lighter than that of the blank control group. After 7 days of sleep deprivation, the rats showed slower acquisition of reference memory, but were capable of successfully performing the task by repeated exposure to the test. Such impairment of reference memory was not seen 14 days after sleep deprivation. Chronic sleep deprivation can affect growth of immature rats, as well as their abilities to acquire spatial reference memory.

  5. Virtual Reference Services.

    ERIC Educational Resources Information Center

    Brewer, Sally

    2003-01-01

    As the need to access information increases, school librarians must create virtual libraries. Linked to reliable reference resources, the virtual library extends the physical collection and library hours and lets students learn to use Web-based resources in a protected learning environment. The growing number of virtual schools increases the need…

  6. Effectiveness of Cooperative Learning Instructional Tools With Predict-Observe-Explain Strategy on the Topic of Cuboid and Cube Volume

    NASA Astrophysics Data System (ADS)

    Nurhuda; Lukito, A.; Masriyah

    2018-01-01

    This study aims to develop instructional tools and implement it to see the effectiveness. The method used in this research referred to Designing Effective Instruction. Experimental research with two-group pretest-posttest design method was conducted. The instructional tools have been developed is cooperative learning model with predict-observe-explain strategy on the topic of cuboid and cube volume which consist of lesson plans, POE tasks, and Tests. Instructional tools were of good quality by criteria of validity, practicality, and effectiveness. These instructional tools was very effective for teaching the volume of cuboid and cube. Cooperative instructional tool with predict-observe-explain (POE) strategy was good of quality because the teacher was easy to implement the steps of learning, students easy to understand the material and students’ learning outcomes completed classically. Learning by using this instructional tool was effective because learning activities were appropriate and students were very active. Students’ learning outcomes were completed classically and better than conventional learning. This study produced a good instructional tool and effectively used in learning. Therefore, these instructional tools can be used as an alternative to teach volume of cuboid and cube topics.

  7. Evolutionary signals of selection on cognition from the great tit genome and methylome

    PubMed Central

    Laine, Veronika N.; Gossmann, Toni I.; Schachtschneider, Kyle M.; Garroway, Colin J.; Madsen, Ole; Verhoeven, Koen J. F.; de Jager, Victor; Megens, Hendrik-Jan; Warren, Wesley C.; Minx, Patrick; Crooijmans, Richard P. M. A.; Corcoran, Pádraic; Adriaensen, Frank; Belda, Eduardo; Bushuev, Andrey; Cichon, Mariusz; Charmantier, Anne; Dingemanse, Niels; Doligez, Blandine; Eeva, Tapio; Erikstad, Kjell Einar; Fedorov, Slava; Hau, Michaela; Hille, Sabine; Hinde, Camilla; Kempenaers, Bart; Kerimov, Anvar; Krist, Milos; Mand, Raivo; Matthysen, Erik; Nager, Reudi; Norte, Claudia; Orell, Markku; Richner, Heinz; Slagsvold, Tore; Tilgar, Vallo; Tinbergen, Joost; Torok, Janos; Tschirren, Barbara; Yuta, Tera; Sheldon, Ben C.; Slate, Jon; Zeng, Kai; van Oers, Kees; Visser, Marcel E.; Groenen, Martien A. M.

    2016-01-01

    For over 50 years, the great tit (Parus major) has been a model species for research in evolutionary, ecological and behavioural research; in particular, learning and cognition have been intensively studied. Here, to provide further insight into the molecular mechanisms behind these important traits, we de novo assemble a great tit reference genome and whole-genome re-sequence another 29 individuals from across Europe. We show an overrepresentation of genes related to neuronal functions, learning and cognition in regions under positive selection, as well as increased CpG methylation in these regions. In addition, great tit neuronal non-CpG methylation patterns are very similar to those observed in mammals, suggesting a universal role in neuronal epigenetic regulation which can affect learning-, memory- and experience-induced plasticity. The high-quality great tit genome assembly will play an instrumental role in furthering the integration of ecological, evolutionary, behavioural and genomic approaches in this model species. PMID:26805030

  8. Recurrent Themes in E-Learning: A Narrative Analysis of Major E-Learning Reports

    ERIC Educational Resources Information Center

    Waight, Consuelo L.; Willging, Pedro; Wentling, Tim

    2004-01-01

    E-learning, sometimes referred to as online learning, Web-based learning, distance learning, and technology-based learning, among other names, is a concept that has garnered significant global attention. This broad attention to e-learning has resulted in numerous e-learning reports. In doing extensive Web searches for e-learning reports, the…

  9. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

    PubMed

    He, Dan; Kuhn, David; Parida, Laxmi

    2016-06-15

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

  10. A Review of Current Machine Learning Methods Used for Cancer Recurrence Modeling and Prediction

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

    Hemphill, Geralyn M.

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type has become a necessity in cancer research. A major challenge in cancer management is the classification of patients into appropriate risk groups for better treatment and follow-up. Such risk assessment is critically important in order to optimize the patient’s health and the use of medical resources, as well as to avoid cancer recurrence. This paper focuses on the application of machine learning methods for predicting the likelihood of a recurrence of cancer. It is not meant to bemore » an extensive review of the literature on the subject of machine learning techniques for cancer recurrence modeling. Other recent papers have performed such a review, and I will rely heavily on the results and outcomes from these papers. The electronic databases that were used for this review include PubMed, Google, and Google Scholar. Query terms used include “cancer recurrence modeling”, “cancer recurrence and machine learning”, “cancer recurrence modeling and machine learning”, and “machine learning for cancer recurrence and prediction”. The most recent and most applicable papers to the topic of this review have been included in the references. It also includes a list of modeling and classification methods to predict cancer recurrence.« less

  11. Promoting clinical competence: using scaffolded instruction for practice-based learning.

    PubMed

    Tilley, Donna Scott; Allen, Patricia; Collins, Cathie; Bridges, Ruth Ann; Francis, Patricia; Green, Alexia

    2007-01-01

    Competency-based education is essential for bridging the gap between education and practice. The attributes of competency-based education include an outcomes focus, allowance for increasing levels of competency, learner accountability, practice-based learning, self-assessment, and individualized learning experiences. One solution to this challenge is scaffolded instruction, where collaboration and knowledge facilitate learning. Collaboration refers to the role of clinical faculty who model desired clinical skills then gradually shift responsibility for nursing activity to the student. This article describes scaffolded instruction as applied in a Web-based second-degree bachelor of science in nursing (BSN) program. This second-degree BSN program uses innovative approaches to education, including a clinical component that relies on clinical coaches. Students in the program remain in their home community and complete their clinical hours with an assigned coach. The method will be described first, followed by a description of how the method was applied.

  12. Active inference, communication and hermeneutics☆

    PubMed Central

    Friston, Karl J.; Frith, Christopher D.

    2015-01-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007

  13. Active inference, communication and hermeneutics.

    PubMed

    Friston, Karl J; Frith, Christopher D

    2015-07-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Virtual Visualisation Laboratory for Science and Mathematics Content (Vlab-SMC) with Special Reference to Teaching and Learning of Chemistry

    NASA Astrophysics Data System (ADS)

    Badioze Zaman, Halimah; Bakar, Norashiken; Ahmad, Azlina; Sulaiman, Riza; Arshad, Haslina; Mohd. Yatim, Nor Faezah

    Research on the teaching of science and mathematics in schools and universities have shown that available teaching models are not effective in instilling the understanding of scientific and mathematics concepts, and the right scientific and mathematics skills required for learners to become good future scientists (mathematicians included). The extensive development of new technologies has a marked influence on education, by facilitating the design of new learning and teaching materials, that can improve the attitude of learners towards Science and Mathematics and the plausibility of advanced interactive, personalised learning process. The usefulness of the computer in Science and Mathematics education; as an interactive communication medium that permits access to all types of information (texts, images, different types of data such as sound, graphics and perhaps haptics like smell and touch); as an instrument for problem solving through simulations of scientific and mathematics phenomenon and experiments; as well as measuring and monitoring scientific laboratory experiments. This paper will highlight on the design and development of the virtual Visualisation Laboratory for Science & Mathematics Content (VLab-SMC) based on the Cognitivist- Constructivist-Contextual development life cycle model as well as the Instructional Design (ID) model, in order to achieve its objectives in teaching and learning. However, this paper with only highlight one of the virtual labs within VLab-SMC that is, the Virtual Lab for teaching Chemistry (VLab- Chem). The development life cycle involves the educational media to be used, measurement of content, and the authoring and programming involved; whilst the ID model involves the application of the cognitivist, constructivist and contextual theories in the modeling of the modules of VLab-SMC generally and Vlab-Chem specifically, using concepts such as 'learning by doing', contextual learning, experimental simulations 3D and real-time animations to create a virtual laboratory based on a real laboratory. Initial preliminary study shows positive indicators of VLab-Chem for the teaching and learning of Chemistry on the topic of 'Salts and Acids'.

  15. Early Spanish Grammatical Gender Bootstrapping: Learning Nouns through Adjectives

    ERIC Educational Resources Information Center

    Arias-Trejo, Natalia; Alva, Elda Alicia

    2013-01-01

    Research has demonstrated that children use different strategies to infer a referent. One of these strategies is to use inflectional morphology. We present evidence that toddlers learning Spanish are capable of using gender word inflections to infer word reference. Thirty-month-olds were tested in a preferential looking experiment. Participants…

  16. Why IM Me? I'm Right Here!

    ERIC Educational Resources Information Center

    Marcus, Sara

    2007-01-01

    Although the relationship between styles of learning and reference service has been taken for granted within the profession, there has been little empirical research that directly links individual learning styles to optimal reference behaviors. This paper is a call for such research, and illustrates the importance of understanding the relationship…

  17. Distributed Learning Environment: Major Functions, Implementation, and Continuous Improvement.

    ERIC Educational Resources Information Center

    Converso, Judith A.; Schaffer, Scott P.; Guerra, Ingrid J.

    The content of this paper is based on a development plan currently in design for the U.S. Navy in conjunction with the Learning Systems Institute at Florida State University. Leading research (literature review) references and case study ("best practice") references are presented as supporting evidence for the results-oriented…

  18. Cross-Situational Learning of Minimal Word Pairs

    ERIC Educational Resources Information Center

    Escudero, Paola; Mulak, Karen E.; Vlach, Haley A.

    2016-01-01

    "Cross-situational statistical learning" of words involves tracking co-occurrences of auditory words and objects across time to infer word-referent mappings. Previous research has demonstrated that learners can infer referents across sets of very phonologically distinct words (e.g., WUG, DAX), but it remains unknown whether learners can…

  19. Understanding Frame-of-Reference Training Success: A Social Learning Theory Perspective

    ERIC Educational Resources Information Center

    Sulsky, Lorne M.; Kline, Theresa J. B.

    2007-01-01

    Employing the social learning theory (SLT) perspective on training, we analysed the effects of alternative frame-of-reference (FOR) training protocols on various criteria of training effectiveness. Undergraduate participants (N = 65) were randomly assigned to one of four FOR training conditions and a control condition. Training effectiveness was…

  20. Comparing the engineering program feeders from SiF and convention models

    NASA Astrophysics Data System (ADS)

    Roongruangsri, Warawaran; Moonpa, Niwat; Vuthijumnonk, Janyawat; Sangsuwan, Kampanart

    2018-01-01

    This research aims to compare the relationship between two types of engineering program feeder models within the technical education systems of Rajamangala University of Technology Lanna (RMUTL), Chiangmai, Thailand. To illustrate, the paper refers to two typologies of feeder models, which are the convention and the school in factory (SiF) models. The new SiF model is developed through a collaborative educational process between the sectors of industry, government and academia, using work-integrated learning. The research methodology were use to compared features of the the SiF model with conventional models in terms of learning outcome, funding budget for the study, the advantages and disadvantages from the point of view of students, professors, the university, government and industrial partners. The results of this research indicate that the developed SiF feeder model is the most pertinent ones as it meet the requirements of the university, the government and the industry. The SiF feeder model showed the ability to yield positive learning outcomes with low expenditures per student for both the family and the university. In parallel, the sharing of knowledge between university and industry became increasingly important in the process, which resulted in the improvement of industrial skills for professors and an increase in industrial based research for the university. The SiF feeder model meets its demand of public policy in supporting a skilled workforce for the industry, which could be an effective tool for the triple helix educational model of Thailand.

  1. Real-world visual statistics and infants' first-learned object names

    PubMed Central

    Clerkin, Elizabeth M.; Hart, Elizabeth; Rehg, James M.; Yu, Chen

    2017-01-01

    We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present—a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872373

  2. Spatial frequency discrimination learning in normal and developmentally impaired human vision

    PubMed Central

    Astle, Andrew T.; Webb, Ben S.; McGraw, Paul V.

    2010-01-01

    Perceptual learning effects demonstrate that the adult visual system retains neural plasticity. If perceptual learning holds any value as a treatment tool for amblyopia, trained improvements in performance must generalise. Here we investigate whether spatial frequency discrimination learning generalises within task to other spatial frequencies, and across task to contrast sensitivity. Before and after training, we measured contrast sensitivity and spatial frequency discrimination (at a range of reference frequencies 1, 2, 4, 8, 16 c/deg). During training, normal and amblyopic observers were divided into three groups. Each group trained on a spatial frequency discrimination task at one reference frequency (2, 4, or 8 c/deg). Normal and amblyopic observers who trained at lower frequencies showed a greater rate of within task learning (at their reference frequency) compared to those trained at higher frequencies. Compared to normals, amblyopic observers showed greater within task learning, at the trained reference frequency. Normal and amblyopic observers showed asymmetrical transfer of learning from high to low spatial frequencies. Both normal and amblyopic subjects showed transfer to contrast sensitivity. The direction of transfer for contrast sensitivity measurements was from the trained spatial frequency to higher frequencies, with the bandwidth and magnitude of transfer greater in the amblyopic observers compared to normals. The findings provide further support for the therapeutic efficacy of this approach and establish general principles that may help develop more effective protocols for the treatment of developmental visual deficits. PMID:20832416

  3. Neural Flight Control System

    NASA Technical Reports Server (NTRS)

    Gundy-Burlet, Karen

    2003-01-01

    The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

  4. A Panel Analysis of Student Mathematics Achievement in the US in the 1990s: Does Increasing the Amount of Time in Learning Activities Affect Math Achievement?

    ERIC Educational Resources Information Center

    Aksoy, Tevfik; Link, Charles R.

    2000-01-01

    Uses panel estimation techniques to estimate econometric models of mathematics achievement determinants for a nationally representative sample of high-school students. Extra time spent on math homework increases test scores; an extra hour of TV viewing negatively affects scores. Longer math periods also help. (Contains 56 references.) (MLH)

  5. The Provision of Economics Education for Adults in the U.K. with Special Reference to the London Area. Research Papers in Economics Education.

    ERIC Educational Resources Information Center

    Freedman, L. T.

    The nature of adult economics education provided in the United Kingdom is outlined, and a framework within which it might be investigated is suggested. Androgogical learning principles are critically appraised and are compared and contrasted to pedagogical principles. An adult economics course model is developed, against which actual courses may…

  6. Common Battlefield Training for Airmen

    DTIC Science & Technology

    2007-01-01

    Independent Evaluation Analysis We developed three model courses that satisfied the training requirements for CBAT,4 based primarily on training materials ...individual subject-matter experts identified in their sorting or that the material from the Lessons Learned Database suggested9 refer to and apply the...shared experience that might or might not materialize in future operations. Finally, there have been questions regarding the best location for CBAT or

  7. Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

    USDA-ARS?s Scientific Manuscript database

    Surface soil moisture is critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purpo...

  8. Analysis and lessons learned instituting an instant messaging reference service at an academic health sciences library: the first year.

    PubMed

    Kipnis, Daniel G; Kaplan, Gary E

    2008-01-01

    In February 2006, Thomas Jefferson University went live with a new instant messaging (IM) service. This paper reviews the first 102 transcripts to examine question types and usage patterns. In addition, the paper highlights lessons learned in instituting the service. IM reference represents a small proportion of reference questions, but based on user feedback and technological improvements, the library has decided to continue the service.

  9. Using cooperative learning for a drug information assignment.

    PubMed

    Earl, Grace L

    2009-11-12

    To implement a cooperative learning activity to engage students in analyzing tertiary drug information resources in a literature evaluation course. The class was divided into 4 sections to form expert groups and each group researched a different set of references using the jigsaw technique. Each member of each expert group was reassigned to a jigsaw group so that each new group was composed of 4 students from 4 different expert groups. The jigsaw groups met to discuss search strategies and rate the usefulness of the references. In addition to group-based learning, teaching methods included students' writing an independent research paper to enhance their abilities to search and analyze drug information resources. The assignment and final course grades improved after implementation of the activity. Students agreed that class discussions were a useful learning experience and 75% (77/102) said they would use the drug information references for other courses. The jigsaw technique was successful in engaging students in cooperative learning to improve critical thinking skills regarding drug information.

  10. Making Inexpensive 3-D Models

    NASA Astrophysics Data System (ADS)

    Manos, Harry

    2016-03-01

    Visual aids are important to student learning, and they help make the teacher's job easier. Keeping with the TPT theme of "The Art, Craft, and Science of Physics Teaching," the purpose of this article is to show how teachers, lacking equipment and funds, can construct a durable 3-D model reference frame and a model gravity well tailored to specific class lessons. Most of the supplies are readily available in the home or at school: rubbing alcohol, a rag, two colors of spray paint, art brushes, and masking tape. The cost of these supplies, if you don't have them, is less than 20.

  11. E-Learning and Technologies for Open Distance Learning in Management Accounting

    ERIC Educational Resources Information Center

    Kashora, Trust; van der Poll, Huibrecht M.; van der Poll, John A.

    2016-01-01

    This research develops a knowledge acquisition and construction framework for e-learning for Management Accounting students at the University of South Africa, an Open Distance Learning institution which utilises e-learning. E-learning refers to the use of electronic applications and processes for learning, including the transfer of skills and…

  12. The Application of Learning Theories in Community College Classrooms. UCLA Community College Bibliography

    ERIC Educational Resources Information Center

    Carducci, Rozana

    2006-01-01

    The references in this document provide an overview of empirical and conceptual scholarship on the application of learning theories in community college classrooms. Specific theories discussed in the citations include: active learning, cooperative learning, multiple intelligences, problem-based learning, and self-regulated learning. In addition to…

  13. Spatio-Temporal Simulation and Analysis of Regional Ecological Security Based on Lstm

    NASA Astrophysics Data System (ADS)

    Gong, C.; Qi, L.; Heming, L.; Karimian, H.; Yuqin, M.

    2017-10-01

    Region is a complicated system, where human, nature and society interact and influence. Quantitative modeling and simulation of ecology in the region are the key to realize the strategy of regional sustainable development. Traditional machine learning methods have made some achievements in the modeling of regional ecosystems, but it is difficult to determine the learning characteristics and to realize spatio-temporal simulation. Deep learning does not need prior identification of training characteristics, have excellent feature learning ability, can improve the accuracy of model prediction, so the use of deep learning model has a significant advantage. Therefore, we use net primary productivity (NPP), atmospheric optical depth (AOD), moderate-resolution imaging spectrometer (MODIS), Normalized Difference Vegetation Index (NDVI), landcover and population data, and use LSTM to do spatio-temporal simulation. We conduct spatial analysis and driving force analysis. The conclusions are as follows: the ecological deficit of northwestern Henan and urban communities such as Zhengzhou is higher. The reason of former lies in the weak land productivity of the Loess Plateau, the irrational crop cultivation mode. The latter lies in the high consumption of resources in the large urban agglomeration; The positive trend of Henan ecological development from 2013 is mainly due to the effective environmental protection policy in the 12th five-year plan; The main driver of the sustained ecological deficit growth of Henan in 2004-2013 is high-speed urbanization, increasing population and goods consumption. This article provides relevant basic scientific support and reference for the regional ecological scientific management and construction.

  14. The Competitive Advantage of Organizational Learning.

    ERIC Educational Resources Information Center

    Appelbaum, Steven H.; Gallagher, John

    2000-01-01

    Explores theories of organizational learning and identifies the implications of the following for learning organizations: the new economy, strategic planning, management practices, and communication. (Contains 32 references.) (SK)

  15. Reference Frames during the Acquisition and Development of Spatial Memories

    ERIC Educational Resources Information Center

    Kelly, Jonathan W.; McNamara, Timothy P.

    2010-01-01

    Four experiments investigated the role of reference frames during the acquisition and development of spatial knowledge, when learning occurs incrementally across views. In two experiments, participants learned overlapping spatial layouts. Layout 1 was first studied in isolation, and Layout 2 was later studied in the presence of Layout 1. The…

  16. Language Educational Policy and Language Learning Quality Management: The "Common European Framework of Reference"

    ERIC Educational Resources Information Center

    Barenfanger, Olaf; Tschirner, Erwin

    2008-01-01

    The major goal of the Council of Europe to promote and facilitate communication and interaction among Europeans of different mother tongues has led to the development of the "Common European Framework of Reference for Languages: Learning, Teaching, Assessment" (CEFR). Among other things, the CEFR is intended to help language…

  17. Glossary of Adult Learning in Europe. A.E. Monographs.

    ERIC Educational Resources Information Center

    Federighi, Paolo, Ed.

    This document presents detailed "definitions" of more than 150 key terms covering the lexicon currently being used in the field of adult learning in 20 European countries. The document begins with an introduction that discusses the glossary's theoretical and historical references and includes 14 references and a 16-item bibliography. The…

  18. The Impact of Self-Selection and Reference Group Identification in a University Living-Learning Center

    ERIC Educational Resources Information Center

    Fisher, Ronald J.; Andrews, John J.

    1976-01-01

    A co-educational living-learning center for the arts was studied through participant observation and quantitative assessment. The results document the importance of full self-selection into a membership group and demonstrate the relationships between reference group identification, basic interests in personality, and social behavior. (Author)

  19. Braille Literacy: Resources for Instruction, Writing Equipment, and Supplies. NLS Reference Circulars

    ERIC Educational Resources Information Center

    Peaco, Freddie L., Comp.

    2004-01-01

    This reference circular lists instructional materials, supplies, and equipment currently available for learning braille, and cites sources about braille literacy. The resources given are intended to assist sighted individuals who are interested in learning braille or want to transcribe print materials into braille; instructors who teach braille;…

  20. When Practice Doesn't Make Perfect: Effects of Task Goals on Learning Computing Concepts

    ERIC Educational Resources Information Center

    Miller, Craig S.; Settle, Amber

    2011-01-01

    Specifying file references for hypertext links is an elementary competence that nevertheless draws upon core computational thinking concepts such as tree traversal and the distinction between relative and absolute references. In this article we explore the learning effects of different instructional strategies in the context of an introductory…

  1. Student Motivation, Attitude, and Approach to Learning: Notes from a Novice Teacher.

    ERIC Educational Resources Information Center

    Vivaldo-Lima, Eduardo

    2001-01-01

    Describes what young professors can do considering student motivation, learning styles, and instructional effectiveness to improve student learning. Lists recommendations from experts on how to improve student learning. Includes 25 references. (Author/YDS)

  2. Cooperative Learning in Elementary Schools

    ERIC Educational Resources Information Center

    Slavin, Robert E.

    2015-01-01

    Cooperative learning refers to instructional methods in which students work in small groups to help each other learn. Although cooperative learning methods are used for different age groups, they are particularly popular in elementary (primary) schools. This article discusses methods and theoretical perspectives on cooperative learning for the…

  3. [Dental education for college students based on WeChat public platform].

    PubMed

    Chen, Chuan-Jun; Sun, Tan

    2016-06-01

    The authors proposed a model for dental education based on WeChat public platform. In this model, teachers send various kinds of digital teaching information such as PPT，word and video to the WeChat public platform and students share the information for preview before class and differentiate the key-point knowledge from those information for in-depth learning in class. Teachers also send reference materials for expansive learning after class. Questionaire through the WeChat public platform is used to evaluate teaching effect of teachers and improvement may be taken based on the feedback questionnaire. A discussion and interaction based on WeCchat between students and teacher can be aroused on a specific topic to reach a proper solution. With technique development of mobile terminal, mobile class will come true in near future.

  4. Twelve tips for implementing whole-task curricula: how to make it work.

    PubMed

    Dolmans, Diana H J M; Wolfhagen, Ineke H A P; Van Merriënboer, Jeroen J G

    2013-10-01

    Whole-task models of learning and instructional design, such as problem-based learning, are nowadays very popular. Schools regularly encounter large problems when they implement whole-task curricula. The main aim of this article is to provide 12 tips that may help to make the implementation of a whole-task curriculum successful. Implementing whole-task curricula fails when the implementation is not well prepared. Requirements that must be met to make the implementation of whole task models into a success are described as twelve tips. The tips are organized in four clusters and refer to (1) the infrastructure, (2) the teachers, (3) the students, and (4) the management of the educational organization. Finally, the presented framework will be critically discussed and the importance of shared values and a change of culture is emphasized.

  5. Early comprehension of the Spanish plural*

    PubMed Central

    Arias-Trejo, Natalia; Cantrell, Lisa M.; Smith, Linda B.; Alva Canto, Elda A.

    2015-01-01

    Understanding how linguistic cues map to the environment is crucial for early language comprehension and may provide a way for bootstrapping and learning words. Research has suggested that learning how plural syntax maps to the perceptual environment may show a trajectory in which children first learn surrounding cues (verbs, modifiers) before a full mastery of the noun morpheme alone. The Spanish plural system of simple codas, dominated by one allomorph -s, and with redundant agreement markers, may facilitate early understanding of how plural linguistic cues map to novel referents. Two-year-old Mexican children correctly identified multiple novel object referents when multiple verbal cues in a phrase indicated plurality as well as in instances when the noun morphology in novel nouns was the ONLY indicator of plurality. These results demonstrate Spanish-speaking children’s ability to use plural noun inflectional morphology to infer novel word referents which may have implications for their word learning. PMID:24560441

  6. Exploring the Function Space of Deep-Learning Machines

    NASA Astrophysics Data System (ADS)

    Li, Bo; Saad, David

    2018-06-01

    The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we study both sparsely and densely connected architectures to discover a layerwise convergence of candidate functions, marked by a corresponding reduction in entropy when approaching the reference function, gain insight into the importance of having a large number of layers, and observe phase transitions as the error increases.

  7. Response time modeling reveals multiple contextual cuing mechanisms.

    PubMed

    Sewell, David K; Colagiuri, Ben; Livesey, Evan J

    2017-08-24

    Contextual cuing refers to a response time (RT) benefit that occurs when observers search through displays that have been repeated over the course of an experiment. Although it is generally agreed that contextual cuing arises via an associative learning mechanism, there is uncertainty about the type(s) of process(es) that allow learning to influence RT. We contrast two leading accounts of the contextual cuing effect that differ in terms of the general process that is credited with producing the effect. The first, the expedited search account, attributes the cuing effect to an increase in the speed with which the target is acquired. The second, the decision threshold account, attributes the cuing effect to a reduction in the response threshold used by observers when making a subsequent decision about the target (e.g., judging its orientation). We use the diffusion model to contrast the quantitative predictions of these two accounts at the level of individual observers. Our use of the diffusion model allows us to also explore a novel decision-level locus of the cuing effect based on perceptual learning. This novel account attributes the RT benefit to a perceptual learning process that increases the quality of information used to drive the decision process. Our results reveal both individual differences in the process(es) involved in contextual cuing but also identify several striking regularities across observers. We find strong support for both the decision threshold account as well as the novel perceptual learning account. We find relatively weak support for the expedited search account.

  8. Learning to Explain Astronomy Across Moving Frames of Reference: Exploring the role of classroom and planetarium-based instructional contexts

    NASA Astrophysics Data System (ADS)

    Plummer, Julia Diane; Kocareli, Alicia; Slagle, Cynthia

    2014-05-01

    Learning astronomy involves significant spatial reasoning, such as learning to describe Earth-based phenomena and understanding space-based explanations for those phenomena as well as using the relevant size and scale information to interpret these frames of reference. This study examines daily celestial motion (DCM) as one case of how children learn to move between frames of reference in astronomy wherein one explains Earth-based descriptions of the Sun's, Moon's, and stars' apparent motion using the Earth's daily rotation. We analysed interviews with 8-9-year-old students (N = 99) who participated in one of four instructional conditions emphasizing: the space-based perspective; the Earth-based perspective in the planetarium; constructing explanations for the Earth-based observations; and a combination of the planetarium plus constructing explanations in the classroom. We used an embodied cognition framework to analyse outcomes while also considering challenges learners face due to the high cognitive demands of spatial reasoning. Results support the hypothesis that instruction should engage students in learning both the Earth-based observations and space-based explanations, as focusing on a single frame of reference resulted in less sophisticated explanations; however, few students were able to construct a fully scientific explanation after instruction.

  9. Frames of reference in spatial language acquisition.

    PubMed

    Shusterman, Anna; Li, Peggy

    2016-08-01

    Languages differ in how they encode spatial frames of reference. It is unknown how children acquire the particular frame-of-reference terms in their language (e.g., left/right, north/south). The present paper uses a word-learning paradigm to investigate 4-year-old English-speaking children's acquisition of such terms. In Part I, with five experiments, we contrasted children's acquisition of novel word pairs meaning left-right and north-south to examine their initial hypotheses and the relative ease of learning the meanings of these terms. Children interpreted ambiguous spatial terms as having environment-based meanings akin to north and south, and they readily learned and generalized north-south meanings. These studies provide the first direct evidence that children invoke geocentric representations in spatial language acquisition. However, the studies leave unanswered how children ultimately acquire "left" and "right." In Part II, with three more experiments, we investigated why children struggle to master body-based frame-of-reference words. Children successfully learned "left" and "right" when the novel words were systematically introduced on their own bodies and extended these words to novel (intrinsic and relative) uses; however, they had difficulty learning to talk about the left and right sides of a doll. This difficulty was paralleled in identifying the left and right sides of the doll in a non-linguistic memory task. In contrast, children had no difficulties learning to label the front and back sides of a doll. These studies begin to paint a detailed account of the acquisition of spatial terms in English, and provide insights into the origins of diverse spatial reference frames in the world's languages. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  10. Traditional Instruction of Differential Equations and Conceptual Learning

    ERIC Educational Resources Information Center

    Arslan, Selahattin

    2010-01-01

    Procedural and conceptual learning are two types of learning, related to two types of knowledge, which are often referred to in mathematics education. Procedural learning involves only memorizing operations with no understanding of underlying meanings. Conceptual learning involves understanding and interpreting concepts and the relations between…

  11. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

    PubMed

    Zhang, Li; Ai, Haixin; Chen, Wen; Yin, Zimo; Hu, Huan; Zhu, Junfeng; Zhao, Jian; Zhao, Qi; Liu, Hongsheng

    2017-05-18

    Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).

  12. The "Intentionality Measurement Instrument" [or "IMI"]: A Comprehensive Psychometric Instrument Based upon the Dual Quadrant Scalar Model of Intentionality That Is Designed to Measure Intent, Motive Type, and Disposition

    ERIC Educational Resources Information Center

    Osler, James Edward, II

    2016-01-01

    The overall aim of this paper is to provide an epistemological rational for the measurement of intentionality. The purpose of this narrative is to identify "Intentionality" as an arena of action in the dispositional learning domain can be measured using an "Intentionality Measurement Instrument" [also referred by the acronym…

  13. Army Reserve Instructors' Perceptions regarding the Effectiveness of the Experiential Learning Model in Teaching Mid-Level Army Reserve Officers

    ERIC Educational Resources Information Center

    Meyer, Robert S.

    2010-01-01

    The Command and General Staff College (CGSC) at Ft. Leavenworth is a fully accredited graduate school. The primary professional development program at CGSC has been for mid-level officers. This program is referred to as ILE (Intermediate Level Education) and is taught in small cohort groups of 12 to 18 students. CGSC has embraced the principles of…

  14. Effects of Teacher Self-Disclosure on Student Learning and Perceptions of Teacher.

    ERIC Educational Resources Information Center

    McCarthy, Patricia R.; Schmeck, Ronald R.

    Researchers in the area of human learning and memory have stressed the need for systematic studies of the factors involved in information processing and their effects on the retention and recall of the information processed. One such important factor may be self-reference. A lecturer may stimulate self-reference in students through…

  15. Effects of Concreteness and Contiguity on Learning from Computer-Based Reference Maps

    ERIC Educational Resources Information Center

    Srinivasan, Sribhagyam; Lewis, Daphne D.; Crooks, Steven M.

    2006-01-01

    Today's technology has reached new heights that have not been fully implemented. One of the areas where technology has not yet reached its full potential is in education. This study examined the effects of concreteness of location names and contiguity of location names with textual information on learning from computer-based reference maps. The…

  16. Virtual Reference at a Global University: An Analysis of Patron and Question Type

    ERIC Educational Resources Information Center

    Rawson, Joseph; Davis, Megan A.; Harding, Julie; Miller, Clare

    2013-01-01

    This paper covers material presented at the 15th Annual Off-Campus conference (formerly known as the Off Campus Library Services Conference) in Memphis, Tennessee. During the course of this presentation, participants learned how both chat and instant messaging reference are being conducted and evaluated at a major online learning university. This…

  17. Learning, Adjustment and Stress Disorders: With Special Reference to Tsunami Affected Regions. Beitrage zur Padagogischen und Rehabilitationspsychologie. Volume 1

    ERIC Educational Resources Information Center

    Witruk, Evelin, Ed.; Riha, David, Ed.; Teichert, Alexandra, Ed.; Haase, Norman, Ed.; Stueck, Marcus, Ed.

    2010-01-01

    This book contains selected contributions from the international workshop Learning, "Adjustment and Stress Disorders--with special reference to Tsunami affected Regions" organised by Evelin Witruk and the team of Educational and Rehabilitative Psychology at the University of Leipzig in January 2006. The book contains new results and the…

  18. The Association between Learning Preferences and Preferred Methods of Assessment of Dental Students

    ERIC Educational Resources Information Center

    Buchanan, Phil

    2016-01-01

    This study is designed to gather information concerning a possible relationship between how dental students prefer to take in and communicate new information and how they prefer to be assessed. Though there are numerous references in the literature regarding the learning styles of students there are also references to the inaccuracy of such…

  19. Functional Cues for Position Learning Effects in Animals

    ERIC Educational Resources Information Center

    Burns, Richard A.; Johnson, Kendra S.; Harris, Brian A.; Kinney, Beth A.; Wright, Sarah E.

    2004-01-01

    Using transfer methodology, several possible factors that could have affected the expression of serial position learning were examined with runway-trained rats. A 3-trial series (SNP) --for which S and P refer to series trials when sucrose (S) and plain (P) Noyes pellets were used as a reward, and N refers to a trial without reward -- was the…

  20. The Role of Elicited Verbal Imitation in Toddlers' Word Learning

    ERIC Educational Resources Information Center

    Hodges, Rosemary; Munro, Natalie; Baker, Elise; McGregor, Karla; Docking, Kimberley; Arciuli, Joanne

    2016-01-01

    This study is about the role of elicited verbal imitation in toddler word learning. Forty-eight toddlers were taught eight nonwords linked to referents. During training, they were asked to imitate the nonwords. Naming of the referents was tested at three intervals (one minute later [uncued], five minutes, and 1-7 days later [cued]) and recognition…

  1. Math for Learning, Math for Life: An Annotated Bibliography.

    ERIC Educational Resources Information Center

    Elliott, Claire

    This document presents a total of 109 references and annotations of works that are in some way related to the topic of math for learning and life. Section 1 presents 68 annotated references with keywords drawn from the Canadian Literacy Thesaurus. Selected topics covered in the listed publications are as follows: numeracy as social practice; the…

  2. The Learning Outcomes of Mentoring Library Science Students in Virtual World Reference: A Case Study

    ERIC Educational Resources Information Center

    Purpur, Geraldine; Morris, Jon Levi

    2015-01-01

    This article reports on the cognitive and affective development of students being mentored in virtual reference interview skills by professional librarians. The authors present a case study which examines the impact on student learning resulting from librarian mentor participation and collaboration with students on a course assignment. This study…

  3. Imitation by combination: preschool age children evidence summative imitation in a novel problem-solving task

    PubMed Central

    Subiaul, Francys; Krajkowski, Edward; Price, Elizabeth E.; Etz, Alexander

    2015-01-01

    Children are exceptional, even ‘super,’ imitators but comparatively poor independent problem-solvers or innovators. Yet, imitation and innovation are both necessary components of cumulative cultural evolution. Here, we explored the relationship between imitation and innovation by assessing children’s ability to generate a solution to a novel problem by imitating two different action sequences demonstrated by two different models, an example of imitation by combination, which we refer to as “summative imitation.” Children (N = 181) from 3 to 5 years of age and across three experiments were tested in a baseline condition or in one of six demonstration conditions, varying in the number of models and opening techniques demonstrated. Across experiments, more than 75% of children evidenced summative imitation, opening both compartments of the problem box and retrieving the reward hidden in each. Generally, learning different actions from two different models was as good (and in some cases, better) than learning from 1 model, but the underlying representations appear to be the same in both demonstration conditions. These results show that summative imitation not only facilitates imitation learning but can also result in new solutions to problems, an essential feature of innovation and cumulative culture. PMID:26441782

  4. Supporting Reform-Oriented Secondary Science Teaching Through the Use of a Framework to Analyze Construction of Scientific Explanations

    NASA Astrophysics Data System (ADS)

    Richmond, Gail; Parker, Joyce M.; Kaldaras, Leonora

    2016-08-01

    The Next-Generation Science Standards (NGSS) call for a different approach to learning science. They promote three-dimensional (3D) learning that blends disciplinary core ideas, crosscutting concepts and scientific practices. In this study, we examined explanations constructed by secondary science teacher candidates (TCs) as a scientific practice outlined in the NGSS necessary for supporting students' learning of science in this 3D way. We examined TCs' ability to give explanations that include explicit statements of underlying reasons for natural phenomena, as opposed to simply describing patterns or laws. In their methods courses, TCs were taught to organize explanations into a what/how/why framework, where what refers to what happens in specific cases (data or observations); how refers to how things usually happen and is equivalent to patterns or laws; and why refers to causal explanations or models. We examined TCs' ability to do this spontaneously and in a resource-rich environment as a first step in gauging their preparedness for NGSS-aligned teaching. We found that (1) the ability of TCs to articulate complete and accurate causal scientific explanations for phenomena exists along a continuum; (2) TCs in our sample whose explanations fell on the upper end of this continuum were more likely to provide complete and accurate explanations even in the absence of support from explicit standards; and (3) teacher candidate's ability to construct complete and accurate explanations did not correlate with cross-course performance or academic major. The implications of these findings for the preparation of teachers for NGSS-based science instruction are discussed.

  5. Finding models to detect Alzheimer's disease by fusing structural and neuropsychological information

    NASA Astrophysics Data System (ADS)

    Giraldo, Diana L.; García-Arteaga, Juan D.; Velasco, Nelson; Romero, Eduardo

    2015-12-01

    Alzheimer's disease (AD) is a neurodegenerative disease that affects higher brain functions. Initial diagnosis of AD is based on the patient's clinical history and a battery of neuropsychological tests. The accuracy of the diagnosis is highly dependent on the examiner's skills and on the evolution of a variable clinical frame. This work presents an automatic strategy that learns probabilistic brain models for different stages of the disease, reducing the complexity, parameter adjustment and computational costs. The proposed method starts by setting a probabilistic class description using the information stored in the neuropsychological test, followed by constructing the different structural class models using membership values from the learned probabilistic functions. These models are then used as a reference frame for the classification problem: a new case is assigned to a particular class simply by projecting to the different models. The validation was performed using a leave-one-out cross-validation, two classes were used: Normal Control (NC) subjects and patients diagnosed with mild AD. In this experiment it is possible to achieve a sensibility and specificity of 80% and 79% respectively.

  6. Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

    PubMed

    Pham-The, Hai; Casañola-Martin, Gerardo; Garrigues, Teresa; Bermejo, Marival; González-Álvarez, Isabel; Nguyen-Hai, Nam; Cabrera-Pérez, Miguel Ángel; Le-Thi-Thu, Huong

    2016-02-01

    In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.

  7. Embodied learning of a generative neural model for biological motion perception and inference

    PubMed Central

    Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V.

    2015-01-01

    Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons. PMID:26217215

  8. Embodied learning of a generative neural model for biological motion perception and inference.

    PubMed

    Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

    2015-01-01

    Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  9. Development of multimedia learning based inquiry on vibration and wave material

    NASA Astrophysics Data System (ADS)

    Madeali, H.; Prahani, B. K.

    2018-03-01

    This study aims to develop multimedia learning based inquiry that is interesting, easy to understand by students and streamline the time of teachers in bringing the teaching materials as well as feasible to be used in learning the physics subject matter of vibration and wave. This research is a Research and Development research with reference to ADDIE model that is Analysis, Design, Development, Implementation, and Evaluation. Multimedia based learning inquiry is packaged in hypertext form using Adobe Flash CS6 Software. The inquiry aspect is constructed by showing the animation of the concepts that the student wants to achieve and then followed by questions that will ask the students what is observable. Multimedia learning based inquiry is then validated by 2 learning experts, 3 material experts and 3 media experts and tested on 3 junior high school teachers and 23 students of state junior high school 5 of Kendari. The results of the study include: (1) Validation results by learning experts, material experts and media experts in valid categories; (2) The results of trials by teachers and students fall into the practical category. These results prove that the multimedia learning based inquiry on vibration and waves materials that have been developed feasible use in physics learning by students of junior high school class VIII.

  10. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

    PubMed

    Li, Zhixi; He, Yifan; Keel, Stuart; Meng, Wei; Chang, Robert T; He, Mingguang

    2018-03-02

    To assess the performance of a deep learning algorithm for detecting referable glaucomatous optic neuropathy (GON) based on color fundus photographs. A deep learning system for the classification of GON was developed for automated classification of GON on color fundus photographs. We retrospectively included 48 116 fundus photographs for the development and validation of a deep learning algorithm. This study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON. The reference standard was made until 3 graders achieved agreement. A separate validation dataset of 8000 fully gradable fundus photographs was used to assess the performance of this algorithm. The area under receiver operator characteristic curve (AUC) with sensitivity and specificity was applied to evaluate the efficacy of the deep learning algorithm detecting referable GON. In the validation dataset, this deep learning system achieved an AUC of 0.986 with sensitivity of 95.6% and specificity of 92.0%. The most common reasons for false-negative grading (n = 87) were GON with coexisting eye conditions (n = 44 [50.6%]), including pathologic or high myopia (n = 37 [42.6%]), diabetic retinopathy (n = 4 [4.6%]), and age-related macular degeneration (n = 3 [3.4%]). The leading reason for false-positive results (n = 480) was having other eye conditions (n = 458 [95.4%]), mainly including physiologic cupping (n = 267 [55.6%]). Misclassification as false-positive results amidst a normal-appearing fundus occurred in only 22 eyes (4.6%). A deep learning system can detect referable GON with high sensitivity and specificity. Coexistence of high or pathologic myopia is the most common cause resulting in false-negative results. Physiologic cupping and pathologic myopia were the most common reasons for false-positive results. Copyright © 2018 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

  11. Superior cognitive mapping through single landmark-related learning than through boundary-related learning.

    PubMed

    Zhou, Ruojing; Mou, Weimin

    2016-08-01

    Cognitive mapping is assumed to be through hippocampus-dependent place learning rather than striatum-dependent response learning. However, we proposed that either type of spatial learning, as long as it involves encoding metric relations between locations and reference points, could lead to a cognitive map. Furthermore, the fewer reference points to specify individual locations, the more accurate a cognitive map of these locations will be. We demonstrated that participants have more accurate representations of vectors between 2 locations and of configurations among 3 locations when locations are individually encoded in terms of a single landmark than when locations are encoded in terms of a boundary. Previous findings have shown that learning locations relative to a boundary involve stronger place learning and higher hippocampal activation whereas learning relative to a single landmark involves stronger response learning and higher striatal activation. Recognizing this, we have provided evidence challenging the cognitive map theory but favoring our proposal. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  12. Blended Learning

    ERIC Educational Resources Information Center

    Halan, Deepak

    2005-01-01

    Blended learning basically refers to using several methods for teaching. It can be thought to be a learning program where more than one delivery mode is being used with the ultimate goal of optimizing the learning result and cost of program delivery. Examples of blended learning could be the combination of technology-based resources and…

  13. Online Resource-Based Learning Environment: Case Studies in Primary Classrooms

    ERIC Educational Resources Information Center

    So, Winnie Wing Mui; Ching, Fiona Ngai Ying

    2012-01-01

    This paper discusses the creation of learning environments with online resources by three primary school teachers for pupil's learning of science-related topics with reference to the resource-based e-learning environments (RBeLEs) framework. Teachers' choice of contexts, resources, tools, and scaffolds in designing the learning environments are…

  14. Hybrid Learning in Enhancing Communicative Skill in English

    ERIC Educational Resources Information Center

    Singaravelu, G.

    2010-01-01

    The present study highlights the effectiveness of Hybrid-Learning in enhancing communicative skill in English among the Trainees of Bachelor of education of School of Distance Education, Bharathiar University,Coimbatore. Hybrid learning refers to mixing of different learning methods or mixing two more methods for teaching learning process. It…

  15. Mice with a fra-1 knock-in into the c-fos locus show impaired spatial but regular contextual learning and normal LTP.

    PubMed

    Gass, Peter; Fleischmann, Alexander; Hvalby, Oivind; Jensen, Vidar; Zacher, Christiane; Strekalova, Tatyana; Kvello, Ane; Wagner, Erwin F; Sprengel, Rolf

    2004-11-04

    The immediate early gene c-fos is part of the AP-1 transcription factor complex, which is involved in molecular mechanisms underlying learning and memory. Mice that lack c-Fos in the brain show impairments in spatial reference and contextual learning, and also exhibit a reduced long-term potentiation of synaptic transmission (LTP) at CA3-to-CA1 synapses. In the present study, we investigated mice in which c-fos was deleted and replaced by fra-1 (c-fos(fra-1) mice) to determine whether other members of the c-fos gene family can substitute for the functions of the c-fos gene. In c-fos(fra-1) mice, both CA3-to-CA1 LTP and contextual learning in a Pavlovian fear conditioning task were similar to wild-type littermates, indicating that Fra-1 expression restored the impairments caused by brain-specific c-Fos depletion. However, c-Fos-mediated learning deficits in a reference memory task of the Morris watermaze were also present in c-fos(fra-1) mice. These findings suggest that different c-Fos target genes are involved in LTP, contextual learning, and spatial reference memory formation.

  16. The learning curve of laparoscopic holecystectomy in general surgery resident training: old age of the patient may be a risk factor?

    PubMed

    Ferrarese, Alessia; Gentile, Valentina; Bindi, Marco; Rivelli, Matteo; Cumbo, Jacopo; Solej, Mario; Enrico, Stefano; Martino, Valter

    2016-01-01

    A well-designed learning curve is essential for the acquisition of laparoscopic skills: but, are there risk factors that can derail the surgical method? From a review of the current literature on the learning curve in laparoscopic surgery, we identified learning curve components in video laparoscopic cholecystectomy; we suggest a learning curve model that can be applied to assess the progress of general surgical residents as they learn and master the stages of video laparoscopic cholecystectomy regardless of type of patient. Electronic databases were interrogated to better define the terms "surgeon", "specialized surgeon", and "specialist surgeon"; we surveyed the literature on surgical residency programs outside Italy to identify learning curve components, influential factors, the importance of tutoring, and the role of reference centers in residency education in surgery. From the definition of acceptable error, self-efficacy, and error classification, we devised a learning curve model that may be applied to training surgical residents in video laparoscopic cholecystectomy. Based on the criteria culled from the literature, the three surgeon categories (general, specialized, and specialist) are distinguished by years of experience, case volume, and error rate; the patients were distinguished for years and characteristics. The training model was constructed as a series of key learning steps in video laparoscopic cholecystectomy. Potential errors were identified and the difficulty of each step was graded using operation-specific characteristics. On completion of each procedure, error checklist scores on procedure-specific performance are tallied to track the learning curve and obtain performance indices of measurement that chart the trainee's progress. The concept of the learning curve in general surgery is disputed. The use of learning steps may enable the resident surgical trainee to acquire video laparoscopic cholecystectomy skills proportional to the instructor's ability, the trainee's own skills, and the safety of the surgical environment. There were no patient characteristics that can derail the methods. With this training scheme, resident trainees may be provided the opportunity to develop their intrinsic capabilities without the loss of basic technical skills.

  17. The Role of Multiple Neuromodulators in Reinforcement Learning That Is Based on Competition between Eligibility Traces.

    PubMed

    Huertas, Marco A; Schwettmann, Sarah E; Shouval, Harel Z

    2016-01-01

    The ability to maximize reward and avoid punishment is essential for animal survival. Reinforcement learning (RL) refers to the algorithms used by biological or artificial systems to learn how to maximize reward or avoid negative outcomes based on past experiences. While RL is also important in machine learning, the types of mechanistic constraints encountered by biological machinery might be different than those for artificial systems. Two major problems encountered by RL are how to relate a stimulus with a reinforcing signal that is delayed in time (temporal credit assignment), and how to stop learning once the target behaviors are attained (stopping rule). To address the first problem synaptic eligibility traces were introduced, bridging the temporal gap between a stimulus and its reward. Although, these were mere theoretical constructs, recent experiments have provided evidence of their existence. These experiments also reveal that the presence of specific neuromodulators converts the traces into changes in synaptic efficacy. A mechanistic implementation of the stopping rule usually assumes the inhibition of the reward nucleus; however, recent experimental results have shown that learning terminates at the appropriate network state even in setups where the reward nucleus cannot be inhibited. In an effort to describe a learning rule that solves the temporal credit assignment problem and implements a biologically plausible stopping rule, we proposed a model based on two separate synaptic eligibility traces, one for long-term potentiation (LTP) and one for long-term depression (LTD), each obeying different dynamics and having different effective magnitudes. The model has been shown to successfully generate stable learning in recurrent networks. Although, the model assumes the presence of a single neuromodulator, evidence indicates that there are different neuromodulators for expressing the different traces. What could be the role of different neuromodulators for expressing the LTP and LTD traces? Here we expand on our previous model to include several neuromodulators, and illustrate through various examples how different these contribute to learning reward-timing within a wide set of training paradigms and propose further roles that multiple neuromodulators can play in encoding additional information of the rewarding signal.

  18. A Study on Mobile Learning as a Learning Style in Modern Research Practice

    ERIC Educational Resources Information Center

    Joan, D. R. Robert

    2013-01-01

    Mobile learning is a kind of learning that takes place via a portable handheld electronic device. It also refers to learning via other kinds of mobile devices such as tablet computers, net-books and digital readers. The objective of mobile learning is to provide the learner the ability to assimilate learning anywhere and at anytime. Mobile devices…

  19. Use of an eight-arm radial water maze to assess working and reference memory following neonatal brain injury.

    PubMed

    Penley, Stephanie C; Gaudet, Cynthia M; Threlkeld, Steven W

    2013-12-04

    Working and reference memory are commonly assessed using the land based radial arm maze. However, this paradigm requires pretraining, food deprivation, and may introduce scent cue confounds. The eight-arm radial water maze is designed to evaluate reference and working memory performance simultaneously by requiring subjects to use extra-maze cues to locate escape platforms and remedies the limitations observed in land based radial arm maze designs. Specifically, subjects are required to avoid the arms previously used for escape during each testing day (working memory) as well as avoid the fixed arms, which never contain escape platforms (reference memory). Re-entries into arms that have already been used for escape during a testing session (and thus the escape platform has been removed) and re-entries into reference memory arms are indicative of working memory deficits. Alternatively, first entries into reference memory arms are indicative of reference memory deficits. We used this maze to compare performance of rats with neonatal brain injury and sham controls following induction of hypoxia-ischemia and show significant deficits in both working and reference memory after eleven days of testing. This protocol could be easily modified to examine many other models of learning impairment.

  20. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

    PubMed

    Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

    2017-01-01

    Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

  1. Constructivist Learning Environments and Defining the Online Learning Community

    ERIC Educational Resources Information Center

    Brown, Loren

    2014-01-01

    The online learning community is frequently referred to, but ill defined. The constructivist philosophy and approach to teaching and learning is both an effective means of constructing an online learning community and it is a tool by which to define key elements of the learning community. In order to build a nurturing, self-sustaining online…

  2. Mobile Technology: Implications of Its Application on Learning

    ERIC Educational Resources Information Center

    Adeyemo, Samuel Adesola; Adedoja, Gloria Olusola; Adelore, Omobola

    2013-01-01

    Learning in Nigeria is considered to have taken a new dimension as the Distance Learning Centre (DLC) of the University of Ibadan has created wider access to learning through the application of mobile technology to learning with particular reference to mobile phones use for the teaching and learning process. By this, the Centre seeks to achieve…

  3. Metonymy and Reference-Point Errors in Novice Programming

    ERIC Educational Resources Information Center

    Miller, Craig S.

    2014-01-01

    When learning to program, students often mistakenly refer to an element that is structurally related to the element that they intend to reference. For example, they may indicate the attribute of an object when their intention is to reference the whole object. This paper examines these reference-point errors through the context of metonymy.…

  4. Genetics Home Reference: Grange syndrome

    MedlinePlus

    ... fragile bones that are prone to breakage, and learning disabilities. Most people with this disorder also have heart ... of Grange syndrome , such as bone abnormalities and learning disabilities. Learn more about the gene associated with Grange ...

  5. Color image definition evaluation method based on deep learning method

    NASA Astrophysics Data System (ADS)

    Liu, Di; Li, YingChun

    2018-01-01

    In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

  6. Reconstruction of normal forms by learning informed observation geometries from data.

    PubMed

    Yair, Or; Talmon, Ronen; Coifman, Ronald R; Kevrekidis, Ioannis G

    2017-09-19

    The discovery of physical laws consistent with empirical observations is at the heart of (applied) science and engineering. These laws typically take the form of nonlinear differential equations depending on parameters; dynamical systems theory provides, through the appropriate normal forms, an "intrinsic" prototypical characterization of the types of dynamical regimes accessible to a given model. Using an implementation of data-informed geometry learning, we directly reconstruct the relevant "normal forms": a quantitative mapping from empirical observations to prototypical realizations of the underlying dynamics. Interestingly, the state variables and the parameters of these realizations are inferred from the empirical observations; without prior knowledge or understanding, they parametrize the dynamics intrinsically without explicit reference to fundamental physical quantities.

  7. Discourse Bootstrapping: Preschoolers Use Linguistic Discourse to Learn New Words

    ERIC Educational Resources Information Center

    Sullivan, Jessica; Barner, David

    2016-01-01

    When children acquire language, they often learn words in the absence of direct instruction (e.g. "This is a ball!") or even social cues to reference (e.g. eye gaze, pointing). However, there are few accounts of how children do this, especially in cases where the referent of a new word is ambiguous. Across two experiments, we test…

  8. The Influence of Phonotactic Probability and Neighborhood Density on Children's Production of Newly Learned Words

    ERIC Educational Resources Information Center

    Heisler, Lori; Goffman, Lisa

    2016-01-01

    A word learning paradigm was used to teach children novel words that varied in phonotactic probability and neighborhood density. The effects of frequency and density on speech production were examined when phonetic forms were nonreferential (i.e., when no referent was attached) and when phonetic forms were referential (i.e., when a referent was…

  9. [Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

    PubMed

    Liu, Guo-hai; Jiang, Hui; Xiao, Xia-hong; Zhang, Dong-juan; Mei, Cong-li; Ding, Yu-han

    2012-04-01

    Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.

  10. An Improved Incremental Learning Approach for KPI Prognosis of Dynamic Fuel Cell System.

    PubMed

    Yin, Shen; Xie, Xiaochen; Lam, James; Cheung, Kie Chung; Gao, Huijun

    2016-12-01

    The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

  11. A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

    PubMed Central

    Gao, Junfeng; Wang, Zhao; Yang, Yong; Zhang, Wenjia; Tao, Chunyi; Guan, Jinan; Rao, Nini

    2013-01-01

    A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time. PMID:23755136

  12. Messy Collaboration: Learning from a Learning Study

    ERIC Educational Resources Information Center

    Adamson, Bob; Walker, Elizabeth

    2011-01-01

    Messy collaboration refers to complexity, unpredictability and management dilemmas when educators work together. Such messiness was evident in a Hong Kong English Learning Study, a structured cyclical process in which teachers and researcher-participants from a teacher education institution work collaboratively on effective student learning. This…

  13. Peter Jarvis and the Understanding of Adult Learning

    ERIC Educational Resources Information Center

    Illeris, Knud

    2017-01-01

    By comparing Peter Jarvis' understanding of learning with two other approaches--which Jarvis himself has referred to as "the most comprehensive": Etienne Wenger's "social theory of learning" and my own psychologically oriented theory of "the three dimensions of learning"--it becomes evident that Jarvis' understanding…

  14. Student Approaches to Learning and Studying. Research Monograph.

    ERIC Educational Resources Information Center

    Biggs, John B.

    A common thread in contemporary research in student learning refers to the ways in which students go about learning. A theory of learning is presented that accentuates the interaction between the person and the situation. Research evidence implies a form of meta-cognition called meta-learning, the awareness of students of their own learning…

  15. Learning Objects and Virtual Learning Environments Technical Evaluation Criteria

    ERIC Educational Resources Information Center

    Kurilovas, Eugenijus; Dagiene, Valentina

    2009-01-01

    The main scientific problems investigated in this article deal with technical evaluation of quality attributes of the main components of e-Learning systems (referred here as DLEs--Digital Libraries of Educational Resources and Services), i.e., Learning Objects (LOs) and Virtual Learning Environments (VLEs). The main research object of the work is…

  16. Building a Blended Learning Program

    ERIC Educational Resources Information Center

    McLester, Susan

    2011-01-01

    "Online learning" often serves as an umbrella term that includes the subcategory of blended learning, which might also be referred to as hybrid learning, and comprises some combination of online and face-to-face time. Spurred in part by a 2009 U.S. Department of Education study, "Evaluation of Evidence-Based Practices in Online Learning," which…

  17. Online Learning: E-Learning Fast, Cheap, and Good

    ERIC Educational Resources Information Center

    Piskurich, George M.

    2006-01-01

    There is a variation of e-learning, used mainly in academic settings, that can be a valuable intervention tool for the performance technologist. It is often referred to as online learning. In the performance improvement field, this term is often used interchangeably with synchronous e-learning, but there are some major differences between these…

  18. The Development of an E-Learning-Based Learning Service for MKDP Curriculum and Learning at the Indonesia University of Education

    ERIC Educational Resources Information Center

    Rusman

    2016-01-01

    E-learning is a general term used to refer to computer-enhanced learning based that facilitates whoever, wherever, and whenever the person is to be able to learn more fun, easier and cheaper by using Internet. In other words, E-learning is the use of network technologies to create, foster, deliver, and facilitate learning, anytime and anywhere. It…

  19. Synthesizing animal and human behavior research via neural network learning theory.

    PubMed

    Tryon, W W

    1995-12-01

    Animal and human research have been "divorced" since approximately 1968. Several recent articles have tried to persuade behavior therapists of the merits of animal research. Three reasons are given concerning why disinterest in animal research is so widespread: (1) functional explanations are given for animals, and cognitive explanations are given for humans; (2) serial symbol manipulating models are used to explain human behavior; and (3) human learning was assumed, thereby removing it as something to be explained. Brain-inspired connectionist neural networks, collectively referred to as neural network learning theory (NNLT), are briefly described, and a spectrum of their accomplishments from simple conditioning through speech is outlined. Five benefits that behavior therapists can derive from NNLT are described. They include (a) enhanced professional identity derived from a comprehensive learning theory, (b) improved interdisciplinary collaboration both clinically and scientifically, (c) renewed perceived relevance of animal research, (d) access to plausible proximal causal mechanisms capable of explaining operant conditioning, and (e) an inherently developmental perspective.

  20. Sensorimotor Learning during a Marksmanship Task in Immersive Virtual Reality

    PubMed Central

    Rao, Hrishikesh M.; Khanna, Rajan; Zielinski, David J.; Lu, Yvonne; Clements, Jillian M.; Potter, Nicholas D.; Sommer, Marc A.; Kopper, Regis; Appelbaum, Lawrence G.

    2018-01-01

    Sensorimotor learning refers to improvements that occur through practice in the performance of sensory-guided motor behaviors. Leveraging novel technical capabilities of an immersive virtual environment, we probed the component kinematic processes that mediate sensorimotor learning. Twenty naïve subjects performed a simulated marksmanship task modeled after Olympic Trap Shooting standards. We measured movement kinematics and shooting performance as participants practiced 350 trials while receiving trial-by-trial feedback about shooting success. Spatiotemporal analysis of motion tracking elucidated the ballistic and refinement phases of hand movements. We found systematic changes in movement kinematics that accompanied improvements in shot accuracy during training, though reaction and response times did not change over blocks. In particular, we observed longer, slower, and more precise ballistic movements that replaced effort spent on corrections and refinement. Collectively, these results leverage developments in immersive virtual reality technology to quantify and compare the kinematics of movement during early learning of full-body sensorimotor orienting. PMID:29467693

  1. Learning Activities for an Undergraduate Mineralogy/Petrology Course-"I Am/We Are."

    ERIC Educational Resources Information Center

    Goodell, Philip C.

    2001-01-01

    Introduces an entry level mineralogy/igneous petrology course designed for undergraduate students and presents a series of learning activities based on individual and cooperative learning. Includes 18 references. (Author/YDS)

  2. Genetics Home Reference: 48,XXYY syndrome

    MedlinePlus

    ... degree of difficulty with speech and language development. Learning disabilities, especially those that are language-based, are very ... Autism Speaks CHADD: The National Resource on ADHD Learning Disabilities Association of America National Center for Learning Disabilities ...

  3. Problem-based learning: an approach to enhancing learning and understanding of optics for first-year students

    NASA Astrophysics Data System (ADS)

    Bowe, Brian W.; Daly, Siobhan; Flynn, Cathal; Howard, Robert

    2003-03-01

    In this paper a model for the implementation of a problem-based learning (PBL) course for a typical year physics one programme is described. Reference is made to how PBL has been implemented in relation to geometrical and physical optics. PBL derives from the theory that learning is an active process in which the learner constructs new knowledge on the basis of current knowledge, unlike traditional teaching practices in higher education, where the emphasis is on the transmission of factual knowledge. The course consists of a set of optics related real life problems that are carefully constructed to meet specified learning outcomes. The students, working in groups, encounter these problem-solving situations and are facilitated to produce a solution. The PBL course promotes student engagement in order to achieve higher levels of cognitive learning. Evaluation of the course indicates that the students adopt a deep learning approach and that they attain a thorough understanding of the subject instead of the superficial understanding associated with surface learning. The methodology also helps students to develop metacognitive skills. Another outcome of this teaching methodology is the development of key skills such as the ability to work in a group and to communicate, and present, information effectively.

  4. 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.

  5. Real-world visual statistics and infants' first-learned object names.

    PubMed

    Clerkin, Elizabeth M; Hart, Elizabeth; Rehg, James M; Yu, Chen; Smith, Linda B

    2017-01-05

    We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

  6. Quantitative skills as a graduate learning outcome of university science degree programmes: student performance explored through theplanned-enacted-experiencedcurriculum model

    NASA Astrophysics Data System (ADS)

    Matthews, Kelly E.; Adams, Peter; Goos, Merrilyn

    2016-07-01

    Application of mathematical and statistical thinking and reasoning, typically referred to as quantitative skills, is essential for university bioscience students. First, this study developed an assessment task intended to gauge graduating students' quantitative skills. The Quantitative Skills Assessment of Science Students (QSASS) was the result, which examined 10 mathematical and statistical sub-topics. Second, the study established an evidential baseline of students' quantitative skills performance and confidence levels by piloting the QSASS with 187 final-year biosciences students at a research-intensive university. The study is framed within the planned-enacted-experienced curriculum model and contributes to science reform efforts focused on enhancing the quantitative skills of university graduates, particularly in the biosciences. The results found, on average, weak performance and low confidence on the QSASS, suggesting divergence between academics' intentions and students' experiences of learning quantitative skills. Implications for curriculum design and future studies are discussed.

  7. Recent advances in environmental data mining

    NASA Astrophysics Data System (ADS)

    Leuenberger, Michael; Kanevski, Mikhail

    2016-04-01

    Due to the large amount and complexity of data available nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis, modelling and visualization. An important part of these developments deals with an elaboration and application of a contemporary and coherent methodology following the process from data collection to the justification and communication of the results. Recent fundamental progress in machine learning (ML) can considerably contribute to the development of the emerging field - environmental data science. The present research highlights and investigates the different issues that can occur when dealing with environmental data mining using cutting-edge machine learning algorithms. In particular, the main attention is paid to the description of the self-consistent methodology and two efficient algorithms - Random Forest (RF, Breiman, 2001) and Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. Despite the fact that they are based on two different concepts, i.e. decision trees vs artificial neural networks, they both propose promising results for complex, high dimensional and non-linear data modelling. In addition, the study discusses several important issues of data driven modelling, including feature selection and uncertainties. The approach considered is accompanied by simulated and real data case studies from renewable resources assessment and natural hazards tasks. In conclusion, the current challenges and future developments in statistical environmental data learning are discussed. References - Breiman, L., 2001. Random Forests. Machine Learning 45 (1), 5-32. - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

  8. Issues in reflection and debriefing: how nurse educators structure experiential activities.

    PubMed

    Brackenreg, Jenni

    2004-12-01

    Experiential learning is particularly useful in vocational education programs where theory needs to be linked to practice. Although experiential learning is often advocated in nursing education and the importance of debriefing and reflection is almost always espoused, the focus in the literature has tended to be on detailed descriptions of the action phase with little close analysis of how the reflective phase is facilitated. The Lewinian model described by Kolb [Experiential Learning. Experience as Source of Learning and Development, Prentice-Hall, New Jersey, 1984] and the structuring approach suggested by Thiagarajan [Experiential Learning Packages, Prentice-Hall, Englewood Cliffs, NJ, 1980] have been used as the theoretical context for an exploration of how nurse teachers describe their facilitation of the debriefing and reflective phases of experiential learning activities. Explication of the entire planned experiential learning experience is important for increasing the chances of the student being able to close the experiential learning loop. The more covert reflective phases for facilitating experiential learning are crucial and if neglected, or inexpertly and insensitively handled, may at best lead to poor learning outcomes or at worst lead to emotional damage and ;unfinished business' for the student. Interviews with eight experienced university educators elicited descriptions of how they constructed experiential activities with special reference to their descriptions of how the debriefing or reflective phases were structured.

  9. Bayesian Estimation and Inference Using Stochastic Electronics

    PubMed Central

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

  10. Bayesian Estimation and Inference Using Stochastic Electronics.

    PubMed

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

  11. Dealing with the Archetypes Development Process for a Regional EHR System

    PubMed Central

    Santos, M.R.; Bax, M.P.; Kalra, D.

    2012-01-01

    Objectives This paper aims to present the archetype modelling process used for the Health Department of Minas Gerais State, Brazil (SES/MG), to support building its regional EHR system, and the lessons learned during this process. Methods This study was undertaken within the Minas Gerais project. The EHR system architecture was built assuming the reference model from the ISO 13606 norm. The whole archetype development process took about ten months, coordinated by a clinical team co-ordinated by three health professionals and one systems analyst from the SES/MG. They were supported by around 30 health professionals from the internal SES/MG areas, and 5 systems analysts from the PRODEMGE. Based on a bottom-up approach, the project team used technical interviews and brainstorming sessions to conduct the modelling process. Results The main steps of the archetype modelling process were identified and described, and 20 archetypes were created. Lessons learned: –The set of principles established during the selection of PCS elements helped the clinical team to keep the focus in their objectives;–The initial focus on the archetype structural organization aspects was important;–The data elements identified were subjected to a rigorous analysis aimed at determining the most suitable clinical domain;–Levelling the concepts to accommodate them within the hierarchical levels in the reference model was definitely no easy task, and the use of a mind mapping tool facilitated the modelling process;–Part of the difficulty experienced by the clinical team was related to a view focused on the original forms previously used;–The use of worksheets facilitated the modelling process by health professionals;–It was important to have a health professional that knew about the domain tables and health classifications from the Brazilian Federal Government as member in the clinical team. Conclusion The archetypes (referencing terminology, domain tables and term lists) provided a favorable condition for the use of a controlled vocabulary between the central repository and the EMR systems and, probably, will increase the chances of preserving the semantics from the knowledge domain. Finally, the reference model from the ISO 13606 norm, along with the archetypes, proved sufficient to meet the specificities for the creation of an EHR system for basic healthcare in a Brazilian state. PMID:23646075

  12. Motivation and learning physics

    NASA Astrophysics Data System (ADS)

    Fischer, Hans Ernst; Horstendahl, Michaela

    1997-09-01

    Being involved in science education we cannot avoid confronting the problem of students' waning interest in physics. Therefore, we want to focus on arguments developed by new theoretical work in the field of motivation. Especially, we are attracted by the theory of motivation featured by Deci and Ryan, because it is related to an assumptions of human development similar to our own approach. Beneath elements of cognitive development, motivation is seen as a basic concept to describe students' learning in a physics classroom. German students at lower and upper secondary level regard physics as very difficult to learn, very abstract and dominated by male students. As a result physics at school continuously loses importance and acceptance although a lot of work has been done to modernise and develop the related physics courses. We assume that knowing about the influence of motivation on learning physics may lead to new insights in the design of classroom settings. Referring to Deci and Ryan, we use a model of motivation to describe the influence of two different teaching strategies (teacher and discourse oriented) on learning. Electrostatics was taught in year 8. The outcomes of a questionnaire which is able to evaluate defined, motivational states are compared with the interpretation of the same student's interaction in the related situation of the physics classroom. The scales of the questionnaire and the categories of analysis of the video-recording are derived from the same model of motivation.

  13. Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

    PubMed

    Torkzaban, Bahareh; Kayvanjoo, Amir Hossein; Ardalan, Arman; Mousavi, Soraya; Mariotti, Roberto; Baldoni, Luciana; Ebrahimie, Esmaeil; Ebrahimi, Mansour; Hosseini-Mazinani, Mehdi

    2015-01-01

    Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

  14. Finding new perovskite halides via machine learning

    DOE PAGES

    Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; ...

    2016-04-26

    Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.« less

  15. Effects of edaravone on a rat model of punch-drunk syndrome.

    PubMed

    Nomoto, Jun; Kuroki, Takao; Nemoto, Masaaki; Kondo, Kosuke; Harada, Naoyuki; Nagao, Takeki

    2011-01-01

    Punch-drunk syndrome (PDS) refers to a pathological condition in which higher brain dysfunction occurs in a delayed fashion in boxers who have suffered repeated blows to the head. However, the underlying mechanisms remain unknown. This study attempted to elucidate the mechanism of higher brain dysfunction observed following skull vibration in two experiments involving a rat model of PDS. Experiment 1 evaluated the effects of edaravone on histological changes in the rat brain tissue after skull vibration (frequency 20 Hz, amplitude 4 mm, duration 60 minutes). The amount of free radicals formed in response to skull vibration was very small, and edaravone administration reduced the number of glial fibrillary acidic protein and advanced glycation end product-positive cells. Experiment 2 examined the time course of change in learning ability following skull vibration in Tokai High Avoider rats. The learning ability of individual rats was evaluated by the Sidman-type electric shock avoidance test 5 days after the last session of skull vibration or final anesthesia and once a month for 9 consecutive months. Delayed learning disability was not observed in rats administered edaravone immediately after skull vibration. These results suggest that free radical-induced astrocyte activation and subsequent glial scar formation contribute to the occurrence of delayed learning disabilities. Edaravone administration after skull vibration suppressed glial scar formation, thereby inhibiting the occurrence of delayed learning disabilities.

  16. Finding new perovskite halides via machine learning

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

    Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho

    Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vectormore » machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.« less

  17. Formal to Informal Learning with IT: Research Challenges and Issues for E-Learning

    ERIC Educational Resources Information Center

    Cox, M.J.

    2013-01-01

    For the purpose of clarity and consistency, the term e-learning is used throughout the paper to refer to technology-enhanced learning and information technology (IT) in teaching and learning. IT depicts computing and other IT resources. Research into e-learning has changed in focus and breadth over the last four decades as a consequence of…

  18. No-Reference Image Quality Assessment by Wide-Perceptual-Domain Scorer Ensemble Method.

    PubMed

    Liu, Tsung-Jung; Liu, Kuan-Hsien

    2018-03-01

    A no-reference (NR) learning-based approach to assess image quality is presented in this paper. The devised features are extracted from wide perceptual domains, including brightness, contrast, color, distortion, and texture. These features are used to train a model (scorer) which can predict scores. The scorer selection algorithms are utilized to help simplify the proposed system. In the final stage, the ensemble method is used to combine the prediction results from selected scorers. Two multiple-scale versions of the proposed approach are also presented along with the single-scale one. They turn out to have better performances than the original single-scale method. Because of having features from five different domains at multiple image scales and using the outputs (scores) from selected score prediction models as features for multi-scale or cross-scale fusion (i.e., ensemble), the proposed NR image quality assessment models are robust with respect to more than 24 image distortion types. They also can be used on the evaluation of images with authentic distortions. The extensive experiments on three well-known and representative databases confirm the performance robustness of our proposed model.

  19. Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization

    PubMed Central

    Masino, Aaron J.

    2016-01-01

    Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks—future forecasting and new-patient generalizations—tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task. PMID:27636203

  20. Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization.

    PubMed

    Qian, Ting; Masino, Aaron J

    2016-01-01

    Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks-future forecasting and new-patient generalizations-tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task.

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