work-based learning model: Topics by Science.gov

Sample records for work-based learning model

Attention, Working Memory, and Long-Term Memory in Multimedia Learning: An Integrated Perspective Based on Process Models of Working Memory

ERIC Educational Resources Information Center

Schweppe, Judith; Rummer, Ralf

2014-01-01

Cognitive models of multimedia learning such as the Cognitive Theory of Multimedia Learning (Mayer 2009) or the Cognitive Load Theory (Sweller 1999) are based on different cognitive models of working memory (e.g., Baddeley 1986) and long-term memory. The current paper describes a working memory model that has recently gained popularity in basic…
Dopamine selectively remediates 'model-based' reward learning: a computational approach.

PubMed

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

2016-02-01

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

PubMed Central

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

2016-01-01

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

ERIC Educational Resources Information Center

Arani, Mohammad Reza Sarkar; Alagamandan, Jafar; Tourani, Heidar

2004-01-01

The work-based learning model of human resource development has captured a great deal of attention and has gained increasing importance in higher education in recent years. Work-based learning is a powerful phenomenon that attempts to help policy-makers, managers and curriculum developers improve the quality of the decision and organizational…
Jobs to Manufacturing Careers: Work-Based Courses. Work-Based Learning in Action

ERIC Educational Resources Information Center

Kobes, Deborah

2016-01-01

This case study, one of a series of publications exploring effective and inclusive models of work-based learning, finds that work-based courses bring college to the production line by using the job as a learning lab. Work-based courses are an innovative way to give incumbent workers access to community college credits and degrees. They are…
Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732
Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning

PubMed Central

Bath, Kevin G.; Daw, Nathaniel D.; Frank, Michael J.

2016-01-01

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by “model-free” learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by “model-based” learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. SIGNIFICANCE STATEMENT Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies. PMID:26818509
Team Based Work. Symposium.

ERIC Educational Resources Information Center

2002

This document contains three papers from a symposium on team-based work in human resource development (HRD). "Toward Transformational Learning in Organizations: Effects of Model-II Governing Variables on Perceived Learning in Teams" (Blair K. Carruth) summarizes a study that indicated that, regardless of which Model-II variable (valid…
Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

PubMed

Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

2016-01-27

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies. Copyright © 2016 the authors 0270-6474/16/361211-12$15.00/0.
Preferred strategies for workforce development: feedback from aged care workers.

PubMed

Choy, Sarojni; Henderson, Amanda

2016-11-01

Objective The aim of the present study was to investigate how aged care workers prefer to learn and be supported in continuing education and training activities. Methods Fifty-one workers in aged care facilities from metropolitan and rural settings across two states of Australia participated in a survey and interviews. Survey responses were analysed for frequencies and interview data provided explanations to the survey findings. Results The three most common ways workers were currently learning and prefer to continue to learn are: (1) everyday learning through work individually; (2) everyday learning through work individually assisted by other workers; and (3) everyday learning plus group training courses at work from the employer. The three most common types of provisions that supported workers in their learning were: (1) working and sharing with another person on the job; (2) direct teaching in a group (e.g. a trainer in a classroom at work); and (3) direct teaching by a workplace expert. Conclusions A wholly practice-based continuing education and training model is best suited for aged care workers. Two variations of this model could be considered: (1) a wholly practice-based model for individual learning; and (2) a wholly practice-based model with guidance from coworkers or other experts. Although the model is preferred by workers and convenient for employers, it needs to be well resourced. What is known about the topic? Learning needs for aged care workers are increasing significantly because of an aging population that demands more care workers. Workforce development is largely 'episodic', based on organisational requirements rather than systematic life-long learning. This study is part of a larger 3-year Australian research to investigate models of continuing education training. What does this paper add? Based on an analysis of survey and interview data from 51 workers, the present study suggests effective models of workforce development for aged care workers. What are the implications for practitioners? The effectiveness of the suggested models necessitates a culture where aged care workers' advancement in the workplace is valued and supported. Those responsible for the development of these workers need to be adequately prepared for mentoring and coaching in the workplace.
Cognitive Tools for Assessment and Learning in a High Information Flow Environment.

ERIC Educational Resources Information Center

Lajoie, Susanne P.; Azevedo, Roger; Fleiszer, David M.

1998-01-01

Describes the development of a simulation-based intelligent tutoring system for nurses working in a surgical intensive care unit. Highlights include situative learning theories and models of instruction, modeling expertise, complex decision making, linking theories of learning to the design of computer-based learning environments, cognitive task…
Work-Based Learning: In Search of an Effective Model

ERIC Educational Resources Information Center

Sobiechowska, Paula; Maisch, Maire

2006-01-01

This article draws upon 10 years of pedagogic experience in developing and delivering work-based learning programmes within the United Kingdom national social work post-qualifying framework. The article is a retrospective, reflective and thematic account of our work. It briefly outlines the history of post-qualifying social work education and…
Making a Virtue out of a Necessity: Part Time Work as a Site for Undergraduate Work-Based Learning

ERIC Educational Resources Information Center

Shaw, Sue; Ogilvie, Chrissy

2010-01-01

Purpose: This paper seeks to challenge the view that student part time employment detracts from academic attainment and presents evidence that when linked to formal undergraduate study provides rich learning experiences. It also explores the extent to which formerly accepted pre-requisites for work based learning (WBL) apply in this model and how…
Characterising Work-Based Learning as a Triadic Learning Endeavour

ERIC Educational Resources Information Center

Dalrymple, Roger; Kemp, Chris; Smith, Patrick

2014-01-01

With work-based learning (WBL) forming an increasingly prevalent dimension of modern higher education practice, conceptual models of the pedagogies underpinning WBL are increasingly emerging. There is broadening recognition of the need to capture and represent the values and presuppositions underlying WBL in order to support facilitators and…
Working-memory capacity protects model-based learning from stress.

PubMed

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

2013-12-24

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

PubMed Central

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

2013-01-01

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

PubMed Central

Lloyd, Kevin; Becker, Nadine; Jones, Matthew W.; Bogacz, Rafal

2012-01-01

Learning to form appropriate, task-relevant working memory representations is a complex process central to cognition. Gating models frame working memory as a collection of past observations and use reinforcement learning (RL) to solve the problem of when to update these observations. Investigation of how gating models relate to brain and behavior remains, however, at an early stage. The current study sought to explore the ability of simple RL gating models to replicate rule learning behavior in rats. Rats were trained in a maze-based spatial learning task that required animals to make trial-by-trial choices contingent upon their previous experience. Using an abstract version of this task, we tested the ability of two gating algorithms, one based on the Actor-Critic and the other on the State-Action-Reward-State-Action (SARSA) algorithm, to generate behavior consistent with the rats'. Both models produced rule-acquisition behavior consistent with the experimental data, though only the SARSA gating model mirrored faster learning following rule reversal. We also found that both gating models learned multiple strategies in solving the initial task, a property which highlights the multi-agent nature of such models and which is of importance in considering the neural basis of individual differences in behavior. PMID:23115551
Working To Learn: A Holistic Approach to Young People's Education and Training.

ERIC Educational Resources Information Center

Senker, Peter; Rainbird, Helen; Evans, Karen; Hodkinson, Phil; Keep, Ewart; Maguire, Malcolm; Raffe, David; Unwin, Lorna

2000-01-01

Highlights deficiencies in current British policies on work-based learning for 16-19 year-olds. Discusses problems arising from employers' voluntary participation. Outlines a holistic approach based on the community of practice model. (SK)
Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

PubMed Central

Konovalov, Arkady; Krajbich, Ian

2016-01-01

Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time. PMID:27511383

Mobile Learning Based Worked Example in Electric Circuit (WEIEC) Application to Improve the High School Students' Electric Circuits Interpretation Ability

ERIC Educational Resources Information Center

Yadiannur, Mitra; Supahar

2017-01-01

This research aims to determine the feasibility and effectivity of mobile learning based Worked Example in Electric Circuits (WEIEC) application in improving the high school students' electric circuits interpretation ability on Direct Current Circuits materials. The research method used was a combination of Four-D Models and ADDIE model. The…
A Standard-Based Model for Adaptive E-Learning Platform for Mauritian Academic Institutions

ERIC Educational Resources Information Center

Kanaksabee, P.; Odit, M. P.; Ramdoyal, A.

2011-01-01

The key aim of this paper is to introduce a standard-based model for adaptive e-learning platform for Mauritian academic institutions and to investigate the conditions and tools required to implement this model. The main forces of the system are that it allows collaborative learning, communication among user, and reduce considerable paper work.…
Mobile Adaptive Communication Support for Vocabulary Acquisition

ERIC Educational Resources Information Center

Epp, Carrie Demmans

2014-01-01

This work explores the use of an adaptive mobile tool for language learning. A school-based deployment study showed that the tool supported learning. A second study is being conducted in informal learning environments. Current work focuses on building models that increase our understanding of the relationship between application usage and learning.
Validating the ACE Model for Evaluating Student Performance Using a Teaching-Learning Process Based on Computational Modeling Systems

ERIC Educational Resources Information Center

Louzada, Alexandre Neves; Elia, Marcos da Fonseca; Sampaio, Fábio Ferrentini; Vidal, Andre Luiz Pestana

2014-01-01

The aim of this work is to adapt and test, in a Brazilian public school, the ACE model proposed by Borkulo for evaluating student performance as a teaching-learning process based on computational modeling systems. The ACE model is based on different types of reasoning involving three dimensions. In addition to adapting the model and introducing…
The Role of Working Memory in Multimedia Instruction: Is Working Memory Working during Learning from Text and Pictures?

ERIC Educational Resources Information Center

Schuler, Anne; Scheiter, Katharina; van Genuchten, Erlijn

2011-01-01

A lot of research has focused on the beneficial effects of using multimedia, that is, text and pictures, for learning. Theories of multimedia learning are based on Baddeley's working memory model (Baddeley 1999). Despite this theoretical foundation, there is only little research that aims at empirically testing whether and more importantly how…
Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

ERIC Educational Resources Information Center

Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

2010-01-01

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

ERIC Educational Resources Information Center

Carey, Lois A.

2007-01-01

This paper presents a model for teaching an undergraduate social work macro practice course utilizing an experiential learning paradigm. The model provides a campus-based project with social work majors in simultaneous dual roles of students and grassroots leaders, focusing on rape and sexual assault prevention training for college students. This…
Testing the Model-Observer Similarity Hypothesis with Text-Based Worked Examples

ERIC Educational Resources Information Center

Hoogerheide, Vincent; Loyens, Sofie M. M.; Jadi, Fedora; Vrins, Anna; van Gog, Tamara

2017-01-01

Example-based learning is a very effective and efficient instructional strategy for novices. It can be implemented using text-based worked examples that provide a written demonstration of how to perform a task, or (video) modelling examples in which an instructor (the "model") provides a demonstration. The model-observer similarity (MOS)…
Work-based learning: a leadership development example from an action research study of shared governance implementation.

PubMed

Williamson, Tracey

2005-11-01

An empowering action research study was undertaken to evaluate and strengthen the implementation of shared governance. One aim was to identify factors that acted as aids or barriers to effective decision-making by clinical leaders. As a work-based learning approach, action research was expected to lead to integration of learning into practice by researcher and participants alike. Shared governance replaces traditional hierarchies and requires and develops clinical leaders. Strategies are needed to maximize learning from introduction of such initiatives at the individual, group and organizational level. Participant-observations and interviews were undertaken with shared governance council members from one model in north-west England. Leadership skills and knowledge and shared governance practices were significantly enhanced. Preparation for council roles was considered inadequate. Increased structured time for reflection and action planning was indicated. Implementation of shared governance has succeeded in developing leadership capacity. Evaluation findings have led to improvements in the overall shared governance model. Action research has been found to have great utility at optimizing work-based learning. Nurse Managers need to develop their coaching and facilitating skills and recognize there is no "quick fix" for developing clinical leaders. Implications include the need to support learners in identifying and implementing changes arising from work-based learning activities, the significant resource implications and the need to optimize the organizational climate if work-based learning approaches to leadership and management development are to succeed.
A Path Model of Effective Technology-Intensive Inquiry-Based Learning

ERIC Educational Resources Information Center

Avsec, Stanislav; Kocijancic, Slavko

2016-01-01

Individual aptitude, attitudes, and behavior in inquiry-based learning (IBL) settings may affect work and learning performance outcomes during activities using different technologies. To encourage multifaceted learning, factors in IBL settings must be statistically significant and effective, and not cognitively or psychomotor intensive. We…
Hierarchical Bayesian Models of Subtask Learning

ERIC Educational Resources Information Center

Anglim, Jeromy; Wynton, Sarah K. A.

2015-01-01

The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…
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
Student Responses Toward Student Worksheets Based on Discovery Learning for Students with Intrapersonal and Interpersonal Intelligence

NASA Astrophysics Data System (ADS)

Yerizon, Y.; Putra, A. A.; Subhan, M.

2018-04-01

Students have a low mathematical ability because they are used to learning to hear the teacher's explanation. For that students are given activities to sharpen his ability in math. One way to do that is to create discovery learning based work sheet. The development of this worksheet took into account specific student learning styles including in schools that have classified students based on multiple intelligences. The dominant learning styles in the classroom were intrapersonal and interpersonal. The purpose of this study was to discover students’ responses to the mathematics work sheets of the junior high school with a discovery learning approach suitable for students with Intrapersonal and Interpersonal Intelligence. This tool was developed using a development model adapted from the Plomp model. The development process of this tools consists of 3 phases: front-end analysis/preliminary research, development/prototype phase and assessment phase. From the results of the research, it is found that students have good response to the resulting work sheet. The worksheet was understood well by students and its helps student in understanding the concept learned.
Writing the Book…Literally: The Convergence of Authentic Intellectual Work (AIW) and Project-Based Learning (PBL)

ERIC Educational Resources Information Center

Buckmiller, Tom M.; Kruse, Jerrid W.

2015-01-01

Using the Project-Based Learning (PBL) and Authentic Intellectual Work (AIW) models, we sought to create coursework that had value beyond the classroom. Refinements in the self-publishing book industry provide the opportunity to present student work to a larger audience and in a different, more engaging format. With the help of free software, our…
Sandboxes for Model-Based Inquiry

ERIC Educational Resources Information Center

Brady, Corey; Holbert, Nathan; Soylu, Firat; Novak, Michael; Wilensky, Uri

2015-01-01

In this article, we introduce a class of constructionist learning environments that we call "Emergent Systems Sandboxes" ("ESSs"), which have served as a centerpiece of our recent work in developing curriculum to support scalable model-based learning in classroom settings. ESSs are a carefully specified form of virtual…
Quality specifications in postgraduate medical e-learning: an integrative literature review leading to a postgraduate medical e-learning model.

PubMed

De Leeuw, R A; Westerman, Michiel; Nelson, E; Ket, J C F; Scheele, F

2016-07-08

E-learning is driving major shifts in medical education. Prioritizing learning theories and quality models improves the success of e-learning programs. Although many e-learning quality standards are available, few are focused on postgraduate medical education. We conducted an integrative review of the current postgraduate medical e-learning literature to identify quality specifications. The literature was thematically organized into a working model. Unique quality specifications (n = 72) were consolidated and re-organized into a six-domain model that we called the Postgraduate Medical E-learning Model (Postgraduate ME Model). This model was partially based on the ISO-19796 standard, and drew on cognitive load multimedia principles. The domains of the model are preparation, software design and system specifications, communication, content, assessment, and maintenance. This review clarified the current state of postgraduate medical e-learning standards and specifications. It also synthesized these specifications into a single working model. To validate our findings, the next-steps include testing the Postgraduate ME Model in controlled e-learning settings.
Teaching evidence-based social work in foundation practice courses: learning from pedagogical choices of allied fields.

PubMed

Traube, Dorian E; Pohle, Cara E; Barley, Melissa

2012-01-01

The field of social work is attuned to the need to incorporate evidence-based practice education into masters-level curriculum. One question remaining is how to integrate evidence-based practice in the foundation practice courses. Integration of evidence-based practice across the foundation-level curriculum coincides with the Council on Social Work Education's mandate that student's engage in research-informed practice and practice-informed research. Through a discussion of definitions, criticisms, and pedagogy across the allied fields of medicine, nursing, and social work the authors address the current status of evidence-based practice curriculum in foundation-level education. The authors incorporate the lessons learned from allied fields and a Masters of Social Work student's analyses of their experience of evidence-based practice learning to propose an adult-learner model to improve evidence-based practice pedagogy in Social Work.
The Role of Acquired Shared Mental Models in Improving the Process of Team-Based Learning

ERIC Educational Resources Information Center

Johnson, Tristan E.; Khalil, Mohammed K.; Spector, J. Michael, Ed.

2008-01-01

Working in teams is an important aspect of learning in various educational settings. Although education has embraced instructional strategies that use multiple learners to facilitate learning, the benefits of team-based learning need to be substantiated. There are limited efforts to evaluate the efficacy of learning processes associated with…
An Intelligent Learning Diagnosis System for Web-Based Thematic Learning Platform

ERIC Educational Resources Information Center

Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun

2007-01-01

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…
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.

The Industrial Manufacturing Technician Apprenticeship. Work-Based Learning in Action

ERIC Educational Resources Information Center

Scott, Geri

2016-01-01

This case study, one of a series of publications exploring effective and inclusive models of work-based learning, finds that entry-level occupations in manufacturing have historically been considered unskilled jobs for which little or no training is necessary. As a consequence, employers have experienced high turnover among new-hires, and…
Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets

NASA Astrophysics Data System (ADS)

Goel, Amit; Montgomery, Michele

2015-08-01

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

ERIC Educational Resources Information Center

Douglas, Elliot P.; Chiu, Chu-Chuan

2013-01-01

This paper describes implementation and testing of an active learning, team-based pedagogical approach to instruction in engineering. This pedagogy has been termed Process Oriented Guided Inquiry Learning (POGIL), and is based upon the learning cycle model. Rather than sitting in traditional lectures, students work in teams to complete worksheets…
Knowledge Management through the Equilibrium Pattern Model for Learning

NASA Astrophysics Data System (ADS)

Sarirete, Akila; Noble, Elizabeth; Chikh, Azeddine

Contemporary students are characterized by having very applied learning styles and methods of acquiring knowledge. This behavior is consistent with the constructivist models where students are co-partners in the learning process. In the present work the authors developed a new model of learning based on the constructivist theory coupled with the cognitive development theory of Piaget. The model considers the level of learning based on several stages and the move from one stage to another requires learners' challenge. At each time a new concept is introduced creates a disequilibrium that needs to be worked out to return back to its equilibrium stage. This process of "disequilibrium/equilibrium" has been analyzed and validated using a course in computer networking as part of Cisco Networking Academy Program at Effat College, a women college in Saudi Arabia. The model provides a theoretical foundation for teaching especially in a complex knowledge domain such as engineering and can be used in a knowledge economy.
Perceptions of the Impact of Online Learning as a Distance-Based Learning Model on the Professional Practices of Working Nurses in Northern Ontario

ERIC Educational Resources Information Center

Carter, Lorraine; Hanna, Mary; Warry, Wayne

2016-01-01

Nurses in Canada face diverse challenges to their ongoing educational pursuits. As a result, they have been early adopters of courses and programs based on distance education principles and, in particular, online learning models. In the study described in this paper, nurses studying at two northern universities, in programs involving online…
Small Private Online Research: A Proposal for A Numerical Methods Course Based on Technology Use and Blended Learning

ERIC Educational Resources Information Center

Cepeda, Francisco Javier Delgado

2017-01-01

This work presents a proposed model in blended learning for a numerical methods course evolved from traditional teaching into a research lab in scientific visualization. The blended learning approach sets a differentiated and flexible scheme based on a mobile setup and face to face sessions centered on a net of research challenges. Model is…
Can model-free reinforcement learning explain deontological moral judgments?

PubMed

Ayars, Alisabeth

2016-05-01

Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. Copyright © 2016 Elsevier B.V. All rights reserved.
Exploring Conditions for Transformative Learning in Work-Integrated Education

ERIC Educational Resources Information Center

McRae, Norah

2015-01-01

A qualitative study was undertaken that explored the conditions for transformative learning in cooperative education as a form of work-integrated learning (WIL), towards the development of a theoretical model. Four case studies were analyzed based on interviews with WIL students, supervisors and their co-op coordinator. The findings revealed that…
Learning from Analyzing Linguistically Diverse Students' Work: A Contribution of Preservice Teacher Inquiry

ERIC Educational Resources Information Center

Athanases, Steven Z.; Wong, Joanna W.

2018-01-01

One task of Feiman-Nemser's teacher learning model--develop tools and dispositions to study teaching--frames how we organized learning opportunities during teacher preparation. We explored how and to what degree preservice teachers used teacher inquiry to analyze linguistically diverse students' work through an asset-based lens, beyond deficit…
Learning at work: competence development or competence-stress.

PubMed

Paulsson, Katarina; Ivergård, Toni; Hunt, Brian

2005-03-01

Changes in work and the ways in which it is carried out bring a need for upgrading workplace knowledge, skills and competencies. In today's workplaces, and for a number of reasons, workloads are higher than ever and stress is a growing concern (Health Risk Soc. 2(2) (2000) 173; Educat. Psychol. Meas. 61(5) (2001) 866). Increased demand for learning brings a risk that this will be an additional stress factor and thus a risk to health. Our research study is based on the control-demand-support model of Karasek and Theorell (Health Work: Stress, Productivity and the Reconstruction of Working Life, Basic Books/Harper, New York, 1990). We have used this model for our own empirical research with the aim to evaluate the model in the modern workplace. Our research enables us to expand the model in the light of current workplace conditions-especially those relating to learning. We report empirical data from a questionnaire survey of working conditions in two different branches of industry. We are able to define differences between companies in terms of working conditions and competence development. We describe and discuss the effects these conditions have on workplace competence development. Our research results show that increased workers' control of the learning process makes competence development more stimulating, is likely to simplify the work and reduces (learning-related) stress. It is therefore important that learning at work allows employees to control their learning and also allows time for the process of learning and reflection.
Values, Predicaments, and Countermeasures for a Model Combining Learning with Working in Higher Vocational Education

ERIC Educational Resources Information Center

Cheng, Jun

2014-01-01

Combining learning with working is a fundamental way to deepen teaching reform in higher vocational education. Based on an analysis of the social value, individual value, educational value, and current predicaments of higher vocational education, this article explores effective ways to realize the value of this talent development model and…
The Group as Teacher: The Gestalt Peer-Learning Community as a Vehicle for Organisational Healing.

ERIC Educational Resources Information Center

Barber, Paul

The possibility of using a Gestalt-informed peer learning community to facilitate reflective learning and organizational change was explored. A peer learning community model exists that is based on two approaches to working with mental illness--therapeutic community practice (which is based on treating the community group rather than individuals…
Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy

PubMed Central

Gao, Yaozong; Zhan, Yiqiang

2015-01-01

Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC ∼0.89) and fast (∼4 s), which satisfies the real-world clinical requirements of IGRT. PMID:24495983
Supervised Learning Based Hypothesis Generation from Biomedical Literature.

PubMed

Sang, Shengtian; Yang, Zhihao; Li, Zongyao; Lin, Hongfei

2015-01-01

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.
A Comparative Study of the Effects of the Neurocognitive-Based Model and the Conventional Model on Learner Attention, Working Memory and Mood

ERIC Educational Resources Information Center

Srikoon, Sanit; Bunterm, Tassanee; Nethanomsak, Teerachai; Ngang, Tang Keow

2017-01-01

Purpose: The attention, working memory, and mood of learners are the most important abilities in the learning process. This study was concerned with the comparison of contextualized attention, working memory, and mood through a neurocognitive-based model (5P) and a conventional model (5E). It sought to examine the significant change in attention,…
Team Learning to Narrow the Gap between Healthcare Knowledge and Practice

ERIC Educational Resources Information Center

Anand, Tejwansh S.

2014-01-01

This study explored team-based learning in teams of healthcare professionals working on making meaning of evidence-based clinical guidelines in their field to apply them within their practice setting. The research based team learning models posited by Kasl, Marsick, and Dechant (1997) and Edmondson, Dillon, and Roloff (2007) were used as the…
The Psychology of Delivering a Psychological Service: Self-Organised Learning as a Model for Consultation

ERIC Educational Resources Information Center

Clarke, Steve; Jenner, Simon

2006-01-01

The article describes how one Educational Psychology Service in the UK developed a service delivery based on self-organised learning (SOL). This model is linked to the paradigms and discourses within which educational psychology and special educational needs work. The work described here is dedicated to the memory of Brian Roberts, academic, close…
Group-Based Active Learning of Classification Models.

PubMed

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.
Tracking student progress in a game-like physics learning environment with a Monte Carlo Bayesian knowledge tracing model

NASA Astrophysics Data System (ADS)

Gweon, Gey-Hong; Lee, Hee-Sun; Dorsey, Chad; Tinker, Robert; Finzer, William; Damelin, Daniel

2015-03-01

In tracking student learning in on-line learning systems, the Bayesian knowledge tracing (BKT) model is a popular model. However, the model has well-known problems such as the identifiability problem or the empirical degeneracy problem. Understanding of these problems remain unclear and solutions to them remain subjective. Here, we analyze the log data from an online physics learning program with our new model, a Monte Carlo BKT model. With our new approach, we are able to perform a completely unbiased analysis, which can then be used for classifying student learning patterns and performances. Furthermore, a theoretical analysis of the BKT model and our computational work shed new light on the nature of the aforementioned problems. This material is based upon work supported by the National Science Foundation under Grant REC-1147621 and REC-1435470.
Work-based learning: A training model for state wide system changes.

PubMed

Clay, Zakia; Barrett, Nora; Reilly, Ann; Zazzarino, Anthony

2016-12-01

Despite the substantial amount of money invested in staff training each year, many people trained fail to transfer what they learn to the workplace. We document a training initiative that was implemented to develop and maintain a competent workforce. A work-based learning (WBL) model was used as a guide to teach the knowledge and skills necessary to effectively deliver psychiatric rehabilitation services. This training framework afforded practitioners an opportunity to acquire the critical knowledge and skills to improve the quality of life for individuals living with serious mental illnesses. Preliminary pre and posttest results show an overall increase in practitioner knowledge. Additionally, individualized technical assistance has the potential to increase positive learning outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Enhancing an Instructional Design Model for Virtual Reality-Based Learning

ERIC Educational Resources Information Center

Chen, Chwen Jen; Teh, Chee Siong

2013-01-01

In order to effectively utilize the capabilities of virtual reality (VR) in supporting the desired learning outcomes, careful consideration in the design of instruction for VR learning is crucial. In line with this concern, previous work proposed an instructional design model that prescribes instructional methods to guide the design of VR-based…
Enhancing Competence in Health Social Work Education through Simulation-Based Learning: Strategies from a Case Study of a Family Session

ERIC Educational Resources Information Center

Craig, Shelley L.; McInroy, Lauren B.; Bogo, Marion; Thompson, Michelle

2017-01-01

Simulation-based learning (SBL) is a powerful tool for social work education, preparing students to practice in integrated health care settings. In an educational environment addressing patient health using an integrated care model, there is growing emphasis on students developing clinical competencies prior to entering clinical placements or…
Teaching Note--An Exploration of Team-Based Learning and Social Work Education: A Natural Fit

ERIC Educational Resources Information Center

Robinson, Michael A.; Robinson, Michelle Bachelor; McCaskill, Gina M.

2013-01-01

The literature on team-based learning (TBL) as a pedagogical methodology in social work education is limited; however, TBL, which was developed as a model for business, has been successfully used as a teaching methodology in nursing, business, engineering, medical school, and many other disciplines in academia. This project examines the use of TBL…
The Adolescent Mentalization-based Integrative Treatment (AMBIT) approach to outcome evaluation and manualization: adopting a learning organization approach.

PubMed

Fuggle, Peter; Bevington, Dickon; Cracknell, Liz; Hanley, James; Hare, Suzanne; Lincoln, John; Richardson, Garry; Stevens, Nina; Tovey, Heather; Zlotowitz, Sally

2015-07-01

AMBIT (Adolescent Mentalization-Based Integrative Treatment) is a developing team approach to working with hard-to-reach adolescents. The approach applies the principle of mentalization to relationships with clients, team relationships and working across agencies. It places a high priority on the need for locally developed evidence-based practice, and proposes that outcome evaluation needs to be explicitly linked with processes of team learning using a learning organization framework. A number of innovative methods of team learning are incorporated into the AMBIT approach, particularly a system of web-based wiki-formatted AMBIT manuals individualized for each participating team. The paper describes early development work of the model and illustrates ways of establishing explicit links between outcome evaluation, team learning and manualization by describing these methods as applied to two AMBIT-trained teams; one team working with young people on the edge of care (AMASS - the Adolescent Multi-Agency Support Service) and another working with substance use (CASUS - Child and Adolescent Substance Use Service in Cambridgeshire). Measurement of the primary outcomes for each team (which were generally very positive) facilitated team learning and adaptations of methods of practice that were consolidated through manualization. © The Author(s) 2014.
Community-Based Research: From Practice to Theory and Back Again.

ERIC Educational Resources Information Center

Stoecker, Randy

2003-01-01

Explores the theoretical strands being combined in community-based research--charity service learning, social justice service learning, action research, and participatory research. Shows how different models of community-based research, based in different theories of society and different approaches to community work, may combine or conflict. (EV)
Scaffolding Teachers Integrate Social Media into a Problem-Based Learning Approach?

ERIC Educational Resources Information Center

Buus, Lillian

2012-01-01

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

ERIC Educational Resources Information Center

dos Santos, Simone C.

2017-01-01

The problem-based learning (PBL) approach has been successfully applied to teaching software engineering thanks to its principles of group work, learning by solving real problems, and learning environments that match the market realities. However, the lack of well-defined methodologies and processes for implementing the PBL approach represents a…
3D interactive augmented reality-enhanced digital learning systems for mobile devices

NASA Astrophysics Data System (ADS)

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

2013-03-01

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

ERIC Educational Resources Information Center

Sossou, Marie-Antoinette; Dubus, Nicole

2013-01-01

This paper examines a developing model for building an international social work placement that meets the needs of the host agency and community first. The paper addresses the challenges for social work departments to develop a strong learning environment while also keeping primary the needs of the host community and agency.
A Suggested Model for a Working Cyberschool.

ERIC Educational Resources Information Center

Javid, Mahnaz A.

2000-01-01

Suggests a model for a working cyberschool based on a case study of Kamiak Cyberschool (Washington), a technology-driven public high school. Topics include flexible hours; one-to-one interaction with teachers; a supportive school environment; use of computers, interactive media, and online resources; and self-paced, project-based learning.…
A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

PubMed Central

Hassani, S. A.; Oemisch, M.; Balcarras, M.; Westendorff, S.; Ardid, S.; van der Meer, M. A.; Tiesinga, P.; Womelsdorf, T.

2017-01-01

Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework. PMID:28091572
The Open Learning Object Model to Promote Open Educational Resources

ERIC Educational Resources Information Center

Fulantelli, Giovanni; Gentile, Manuel; Taibi, Davide; Allegra, Mario

2008-01-01

In this paper we present the results of research work, that forms part of the activities of the EU-funded project SLOOP: Sharing Learning Objects in an Open Perspective, aimed at encouraging the definition, development and management of Open Educational Resources based on the Learning Object paradigm (Wiley, 2000). We present a model of Open…
Teaching and Learning for Artists and Their Works of Art within the Market Model: A Discussion of Value

ERIC Educational Resources Information Center

Atanasovski, Daniel; Bender, Melinda; Fulwider, Miles; Stemkoski, Michael; Walker, Tricia

2013-01-01

This broad-based article discusses teaching and learning that connects artists and their creative art products with the capitalistic system within the market valuation model for a created product that is particularly associated with artists. This general discussion is transferable across teaching and learning disciplines involving the market…
From Tootsie Rolls to Composites: Assessing a Spectrum of Active Learning Activities in Engineering Mechanics

DTIC Science & Technology

2009-05-01

Appendix 9.1. Learning Styles & Pedagogical Theory Overview Educational theory plays a foundational role for the methodology and the development...of ALPs. We selected two methods to categorize student’s learning styles: (1) MBTI, (2) VARK, and five models of the learning process: (1) Kolb , (2... learning process which gives our work a more balanced foundation than may be possible if one bases their approach on one or two theories only, 2) our work
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…
Understanding well-being and learning of Nigerian nurses: a job demand control support model approach.

PubMed

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

2016-10-01

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

PubMed

Troussas, Christos; Virvou, Maria; Alepis, Efthimios

2013-01-01

This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called "Comulang" while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system's basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.
Understanding the essential elements of work-based learning and its relevance to everyday clinical practice.

PubMed

Williams, Caroline

2010-09-01

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

ERIC Educational Resources Information Center

Paterson, Judy; Sneddon, Jamie

2011-01-01

This article reports on the learning conversations between a mathematician and a mathematics educator as they worked together to change the delivery model of a third year discrete mathematics course from a traditional lecture mode to team-based learning (TBL). This change prompted the mathematician to create team tasks which increasingly focused…
Support for Simulation-Based Learning; The Effects of Model Progression and Assignments on Learning about Oscillatory Motion.

ERIC Educational Resources Information Center

Swaak, Janine; And Others

In this study, learners worked with a simulation of harmonic oscillation. Two supportive measures were introduced: model progression and assignments. In model progression, the model underlying the simulation is not offered in its full complexity from the start, but variables are gradually introduced. Assignments are small exercises that help the…

Learning from Experience. Empowerment or Incorporation?

ERIC Educational Resources Information Center

Fraser, Wilma

Based on a Making Experience Count (MEC) project, this book examines current trends in learning from experience. Chapter 1 discusses key theoretical elements that underpin work in the field of experiential learning and analyzes the contribution of the andragogic approach to adult learning. Chapter 2 offers an alternative model--gynagogy--and…
Big data learning and suggestions in modern apps

NASA Astrophysics Data System (ADS)

Sharma, G.; Nadesh, R. K.; ArivuSelvan, K.

2017-11-01

Among many other tasks involved for emergent location-based applications such as those involved in prescribing touring places and those focused on publicizing based on destination, destination prediction is vital. Dealing with destination prediction involves determining the probability of a location (destination) depending on historical trajectories. In this paper, a destination prediction based on probabilistic model (Machine Learning Model) feed-forward neural networks will be presented, which will work by making the observation of driver’s habits. Some individuals drive to same locations such as work involving same route every day of the working week. Here, streaming of real-time driving data will be sent through Kafka queue in apache storm for real-time processing and finally storing the data in MongoDB.
A Module for Adaptive Course Configuration and Assessment in Moodle

NASA Astrophysics Data System (ADS)

Limongelli, Carla; Sciarrone, Filippo; Temperini, Marco; Vaste, Giulia

Personalization and Adaptation are among the main challenges in the field of e-learning, where currently just few Learning Management Systems, mostly experimental ones, support such features. In this work we present an architecture that allows Moodle to interact with the Lecomps system, an adaptive learning system developed earlier by our research group, that has been working in a stand-alone modality so far. In particular, the Lecomps responsibilities are circumscribed to the sole production of personalized learning objects sequences and to the management of the student model, leaving to Moodle all the rest of the activities for course delivery. The Lecomps system supports the "dynamic" adaptation of learning objects sequences, basing on the student model, i.e., learner's Cognitive State and Learning Style. Basically, this work integrates two main Lecomps tasks into Moodle, to be directly managed by it: Authentication and Quizzes.
The use of team-based, guided inquiry learning to overcome educational disadvantages in learning human physiology: a structural equation model.

PubMed

Rathner, Joseph A; Byrne, Graeme

2014-09-01

The study of human bioscience is viewed as a crucial curriculum in allied health. Nevertheless, bioscience (and particularly physiology) is notoriously difficult for undergraduates, particularly academically disadvantaged students. So endemic are the high failure rates (particularly in nursing) that it has come to be known as "the human bioscience problem." In the present report, we describe the outcomes for individual success in studying first-year human physiology in a subject that emphasises team-based active learning as the major pedagogy for mastering subject learning outcomes. Structural equation modeling was used to develop a model of the impact team learning had on individual performance. Modeling was consistent with the idea that students with similar academic abilities (as determined by tertiary entrance rank) were advantaged (scored higher on individual assessment items) by working in strong teams (teams that scored higher in team-based assessments). Analysis of covariance revealed that students who studied the subject with active learning as the major mode of learning activities outperformed students who studied the subject using the traditional didactic teaching format (lectures and tutorials, P = 0.000). After adjustment for tertiary entrance rank (via analysis of covariance) on two individual tests (the final exam and a late-semester in-class test), individual student grades improved by 8% (95% confidence interval: 6-10%) and 12% (95% confidence interval: 10-14%) when students engaged in team-based active learning. These data quantitatively support the notion that weaker students working in strong teams can overcome their educational disadvantages. Copyright © 2014 The American Physiological Society.
The use of team-based, guided inquiry learning to overcome educational disadvantages in learning human physiology: a structural equation model

PubMed Central

Byrne, Graeme

2014-01-01

The study of human bioscience is viewed as a crucial curriculum in allied health. Nevertheless, bioscience (and particularly physiology) is notoriously difficult for undergraduates, particularly academically disadvantaged students. So endemic are the high failure rates (particularly in nursing) that it has come to be known as “the human bioscience problem.” In the present report, we describe the outcomes for individual success in studying first-year human physiology in a subject that emphasises team-based active learning as the major pedagogy for mastering subject learning outcomes. Structural equation modeling was used to develop a model of the impact team learning had on individual performance. Modeling was consistent with the idea that students with similar academic abilities (as determined by tertiary entrance rank) were advantaged (scored higher on individual assessment items) by working in strong teams (teams that scored higher in team-based assessments). Analysis of covariance revealed that students who studied the subject with active learning as the major mode of learning activities outperformed students who studied the subject using the traditional didactic teaching format (lectures and tutorials, P = 0.000). After adjustment for tertiary entrance rank (via analysis of covariance) on two individual tests (the final exam and a late-semester in-class test), individual student grades improved by 8% (95% confidence interval: 6–10%) and 12% (95% confidence interval: 10–14%) when students engaged in team-based active learning. These data quantitatively support the notion that weaker students working in strong teams can overcome their educational disadvantages. PMID:25179611
Example-Based Learning: Effects of Model Expertise in Relation to Student Expertise

ERIC Educational Resources Information Center

Boekhout, Paul; van Gog, Tamara; van de Wiel, Margje W. J.; Gerards-Last, Dorien; Geraets, Jacques

2010-01-01

Background: Worked examples are very effective for novice learners. They typically present a written-out ideal (didactical) solution for learners to study. Aims: This study used worked examples of patient history taking in physiotherapy that presented a "non"-didactical solution (i.e., based on actual performance). The effects of model expertise…
Work-Based Learning and Continuing Professional Development

ERIC Educational Resources Information Center

Sobiechowska, Paula; Maisch, Maire

2007-01-01

Purpose: The purpose of this paper is to provide an evaluation of the key features of a work-based, competency-led curriculum model of continuing professional development for social workers and to present a revised model, which addresses the issues that arise for learners pursuing continuing professional and academic development (CPD) within a…
Analysis of e-learning implementation readiness based on integrated elr model

NASA Astrophysics Data System (ADS)

Adiyarta, K.; Napitupulu, D.; Rahim, R.; Abdullah, D.; Setiawan, MI

2018-04-01

E-learning nowadays has become a requirement for institutions to support their learning activities. To adopt e-learning, an institution requires a large strategy and resources for optimal application. Unfortunately, not all institutions that have used e-learning got the desired results or expectations. This study aims to identify the extent of the level of readiness of e-learning implementation in institution X. The degree of institutional readiness will determine the success of future e-learning utilization. In addition, institutional readiness measurement are needed to evaluate the effectiveness of strategies in e-learning development. The research method used is survey with questionnaire designed based on integration of 8 best practice ELR (e-learning readiness) model. The results showed that from 13 factors of integrated ELR model being measured, there are 3 readiness factors included in the category of not ready and needs a lot of work. They are human resource (2.57), technology skill (2.38) and content factors (2.41). In general, e-learning implementation in institutions is in the category of not ready but needs some of work (3.27). Therefore, the institution should consider which factors or areas of ELR factors are considered still not ready and needs improvement in the future.
Discriminatively learning for representing local image features with quadruplet model

NASA Astrophysics Data System (ADS)

Zhang, Da-long; Zhao, Lei; Xu, Duan-qing; Lu, Dong-ming

2017-11-01

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network (CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.
Learning Disabilities: From Identification to Intervention

ERIC Educational Resources Information Center

Fletcher, Jack M.; Lyon, G. Reid; Fuchs, Lynn S.; Barnes, Marcia A.

2006-01-01

Evidence based and comprehensive, this important work offers a new approach to understanding and intervening with students with learning disabilities. The authors--leading experts in neuropsychology and special education--present a unique model of learning disabilities that integrates the cognitive, neural, genetic, and contextual factors…
Cooperative Learning through Team-Based Projects in the Biotechnology Industry †

PubMed Central

Luginbuhl, Sarah C.; Hamilton, Paul T.

2013-01-01

We have developed a cooperative-learning, case studies project model that has teams of students working with biotechnology professionals on company-specific problems. These semester-long, team-based projects can be used effectively to provide students with valuable skills in an industry environment and experience addressing real issues faced by biotechnology companies. Using peer-evaluations, we have seen improvement in students’ professional skills such as time-management, quality of work, and level of contribution over multiple semesters. This model of team-based, industry-sponsored projects could be implemented in other college and university courses/programs to promote professional skills and expose students to an industry setting. PMID:24358386
Cooperative Learning through Team-Based Projects in the Biotechnology Industry.

PubMed

Luginbuhl, Sarah C; Hamilton, Paul T

2013-01-01

We have developed a cooperative-learning, case studies project model that has teams of students working with biotechnology professionals on company-specific problems. These semester-long, team-based projects can be used effectively to provide students with valuable skills in an industry environment and experience addressing real issues faced by biotechnology companies. Using peer-evaluations, we have seen improvement in students' professional skills such as time-management, quality of work, and level of contribution over multiple semesters. This model of team-based, industry-sponsored projects could be implemented in other college and university courses/programs to promote professional skills and expose students to an industry setting.
Effectiveness of a Service Learning Model with Allied Health Assistant Students in Aged Care

ERIC Educational Resources Information Center

Zulch, Debbie; Saunders, Rosemary; Peters, Judith; Quinlivan, Julie

2016-01-01

This paper explores the impact of a student learning activity involving service learning. As part of a vocational course in the Academy of Health Sciences at a Western Australian TAFE (Technical and Further Education) institute, Allied Health Assistant (AHA) students participated in a service learning program focused on work-based learning in…
Work-Based Learning: Learning To Work; Working To Learn; Learning To Learn.

ERIC Educational Resources Information Center

Strumpf, Lori; Mains, Kristine

This document describes a work-based learning approach designed to integrate work and learning at the workplace and thereby help young people develop the skills required for changing workplaces. The following considerations in designing work-based programs are discussed: the trend toward high performance workplaces and changes in the way work is…
20 CFR 670.515 - What responsibilities do the center operators have in managing work-based learning?

Code of Federal Regulations, 2010 CFR

2010-04-01

... have in managing work-based learning? 670.515 Section 670.515 Employees' Benefits EMPLOYMENT AND... managing work-based learning? (a) The center operator must emphasize and implement work-based learning... arrangements with employers. Work-based learning must be under actual working conditions and must be designed...
20 CFR 670.515 - What responsibilities do the center operators have in managing work-based learning?

Code of Federal Regulations, 2011 CFR

2011-04-01

... have in managing work-based learning? 670.515 Section 670.515 Employees' Benefits EMPLOYMENT AND... managing work-based learning? (a) The center operator must emphasize and implement work-based learning... arrangements with employers. Work-based learning must be under actual working conditions and must be designed...
Activation and Binding in Verbal Working Memory: A Dual-Process Model for the Recognition of Nonwords

ERIC Educational Resources Information Center

Oberauer, Klauss; Lange, Elke B.

2009-01-01

The article presents a mathematical model of short-term recognition based on dual-process models and the three-component theory of working memory [Oberauer, K. (2002). Access to information in working memory: Exploring the focus of attention. "Journal of Experimental Psychology: Learning, Memory, and Cognition, 28", 411-421]. Familiarity arises…
Working Towards a Scalable Model of Problem-Based Learning Instruction in Undergraduate Engineering Education

ERIC Educational Resources Information Center

Mantri, Archana

2014-01-01

The intent of the study presented in this paper is to show that the model of problem-based learning (PBL) can be made scalable by designing curriculum around a set of open-ended problems (OEPs). The detailed statistical analysis of the data collected to measure the effects of traditional and PBL instructions for three courses in Electronics and…
A new model in teaching undergraduate research: A collaborative approach and learning cooperatives.

PubMed

O'Neal, Pamela V; McClellan, Lynx Carlton; Jarosinski, Judith M

2016-05-01

Forming new, innovative collaborative approaches and cooperative learning methods between universities and hospitals maximize learning for undergraduate nursing students in a research course and provide professional development for nurses on the unit. The purpose of this Collaborative Approach and Learning Cooperatives (CALC) Model is to foster working relations between faculty and hospital administrators, maximize small group learning of undergraduate nursing students, and promote onsite knowledge of evidence based care for unit nurses. A quality improvement study using the CALC Model was implemented in an undergraduate nursing research course at a southern university. Hospital administrators provided a list of clinical concerns based on national performance outcome measures. Undergraduate junior nursing student teams chose a clinical question, gathered evidence from the literature, synthesized results, demonstrated practice application, and developed practice recommendations. The student teams developed posters, which were evaluated by hospital administrators. The administrators selected several posters to display on hospital units for continuing education opportunity. This CALC Model is a systematic, calculated approach and an economically feasible plan to maximize personnel and financial resources to optimize collaboration and cooperative learning. Universities and hospital administrators, nurses, and students benefit from working together and learning from each other. Copyright © 2016 Elsevier Ltd. All rights reserved.
Direct care worker's perceptions of job satisfaction following implementation of work-based learning.

PubMed

Lopez, Cynthia; White, Diana L; Carder, Paula C

2014-02-01

The purpose of this study was to understand the impact of a work-based learning program on the work lives of Direct Care Workers (DCWs) at assisted living (AL) residences. The research questions were addressed using focus group data collected as part of a larger evaluation of a work-based learning (WBL) program called Jobs to Careers. The theoretical perspective of symbolic interactionism was used to frame the qualitative data analysis. Results indicated that the WBL program impacted DCWs' job satisfaction through the program curriculum and design and through three primary categories: relational aspects of work, worker identity, and finding time. This article presents a conceptual model for understanding how these categories are interrelated and the implications for WBL programs. Job satisfaction is an important topic that has been linked to quality of care and reduced turnover in long-term care settings.

Social Learning by Design: The Role of Social Media

ERIC Educational Resources Information Center

Brooks, Laura

2009-01-01

It is no secret that learning has a social context. As library media specialists work with students nearly every day, they take for granted their pedagogical roots in social learning theory based on the premise that students need modeling and observation to learn from one another. Information gathering becomes a key activity, and social…
A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

PubMed

Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

2018-05-01

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

NASA Astrophysics Data System (ADS)

Zhang, Wei; Li, Chuanhao; Peng, Gaoliang; Chen, Yuanhang; Zhang, Zhujun

2018-02-01

In recent years, intelligent fault diagnosis algorithms using machine learning technique have achieved much success. However, due to the fact that in real world industrial applications, the working load is changing all the time and noise from the working environment is inevitable, degradation of the performance of intelligent fault diagnosis methods is very serious. In this paper, a new model based on deep learning is proposed to address the problem. Our contributions of include: First, we proposed an end-to-end method that takes raw temporal signals as inputs and thus doesn't need any time consuming denoising preprocessing. The model can achieve pretty high accuracy under noisy environment. Second, the model does not rely on any domain adaptation algorithm or require information of the target domain. It can achieve high accuracy when working load is changed. To understand the proposed model, we will visualize the learned features, and try to analyze the reasons behind the high performance of the model.
Guided-Inquiry Experiments for Physical Chemistry: The POGIL-PCL Model

ERIC Educational Resources Information Center

Hunnicutt, Sally S.; Grushow, Alexander; Whitnell, Robert

2015-01-01

The POGIL-PCL project implements the principles of process-oriented, guided-inquiry learning (POGIL) in order to improve student learning in the physical chemistry laboratory (PCL) course. The inquiry-based physical chemistry experiments being developed emphasize modeling of chemical phenomena. In each experiment, students work through at least…
Predicting User Acceptance of Collaborative Technologies: An Extension of the Technology Acceptance Model for E-Learning

ERIC Educational Resources Information Center

Cheung, Ronnie; Vogel, Doug

2013-01-01

Collaborative technologies support group work in project-based environments. In this study, we enhance the technology acceptance model to explain the factors that influence the acceptance of Google Applications for collaborative learning. The enhanced model was empirically evaluated using survey data collected from 136 students enrolled in a…
Preceptors' perspectives of an integrated clinical learning model in a mental health environment.

PubMed

Boardman, Gayelene; Lawrence, Karen; Polacsek, Meg

2018-02-14

Supervised clinical practice is an essential component of undergraduate nursing students' learning and development. In the mental health setting, nursing students traditionally undertake four-week block placements. An integrated clinical learning model, where preceptors mentor students on an individual basis, has been used successfully in the clinical learning environment. This flexible model provides the opportunity for students to work across morning, afternoon, night and weekend shifts. There is a need to improve the evidence base for a flexible model for students undertaking a mental health placement. The aim of this study was to understand preceptors' experience of, and satisfaction with, a mental health integrated clinical learning model. Focus groups were used to elicit the views of preceptors from a mental health service. Findings highlight the advantages and disadvantages of an integrated clinical learning model in the mental health setting. Participants suggested that students may benefit from flexible work arrangements, a variety of experiences and a more realistic experience of working in a mental health service. However, they found it challenging to mentor and evaluate students under this model. Most also agreed that the model impeded students' ability to engage with consumers and develop rapport with staff. The findings indicate the need to develop a placement model that meets the unique needs of the mental health setting. © 2018 Australian College of Mental Health Nurses Inc.
Modeling Behavior of Students in E-Learning Courses on the Basis of Use Interactive Animations

ERIC Educational Resources Information Center

Magdin, Martin; Turcáni, Milan

2016-01-01

Authors in their contribution deal with modeling the behavior of user in e-learning course based on the use of interactive animations. Nowadays, E-learning courses form a standard part of educational process. However, it is not so easy to determine the way students work with study material, whether they make use of it in order to increase didactic…
A new computational account of cognitive control over reinforcement-based decision-making: Modeling of a probabilistic learning task.

PubMed

Zendehrouh, Sareh

2015-11-01

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

NASA Astrophysics Data System (ADS)

Jiang, Chiyu; Sun, Anzhu; Marcus, Philip

2017-11-01

We reduced the drag of a 2D airfoil by starting with a NACA-0012 airfoil and used deep learning methods. We created a database which consists of simulations of 2D external flow over randomly generated shapes. We then developed a machine learning framework for external flow field inference given input shapes. Past work which utilized machine learning in Computational Fluid Dynamics focused on estimations of specific flow parameters, but this work is novel in the inference of entire flow fields. We further showed that learned flow patterns are transferable to cases that share certain similarities. This study illustrates the prospects of deeper integration of data-based modeling into current CFD simulation frameworks for faster flow inference and more accurate flow modeling.
Work-Based Courses: Bringing College to the Production Line

ERIC Educational Resources Information Center

Kobes, Deborah; Girardi, Amy

2016-01-01

Work-based courses are an innovative way to bring college to the production line by using the job as a learning lab. This toolkit provides guidance to community college administrators and faculty who are interested in bringing a work-based course model to their college. It contains video content and teaching tips that introduce the six steps of…
Fostering the development of effective person-centered healthcare communication skills: an interprofessional shared learning model.

PubMed

Cavanaugh, James T; Konrad, Shelley Cohen

2012-01-01

To describe the implementation of an interprofessional shared learning model designed to promote the development of person-centered healthcare communication skills. Master of social work (MSW) and doctor of physical therapy (DPT) degree students. The model used evidence-based principles of effective healthcare communication and shared learning methods; it was aligned with student learning outcomes contained in MSW and DPT curricula. Students engaged in 3 learning sessions over 2 days. Sessions involved interactive reflective learning, simulated role-modeling with peer assessment, and context-specific practice of communication skills. The perspective of patients/clients was included in each learning activity. Activities were evaluated through narrative feedback. Students valued opportunities to learn directly from each other and from healthcare consumers. Important insights and directions for future interprofessional learning experiences were gleaned from model implementation. The interprofessional shared learning model shows promise as an effective method for developing person-centered communication skills.
Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning

PubMed Central

Schad, Daniel J.; Jünger, Elisabeth; Sebold, Miriam; Garbusow, Maria; Bernhardt, Nadine; Javadi, Amir-Homayoun; Zimmermann, Ulrich S.; Smolka, Michael N.; Heinz, Andreas; Rapp, Michael A.; Huys, Quentin J. M.

2014-01-01

Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation. PMID:25566131
Integrating Mathematical Modeling for Undergraduate Pre-Service Science Education Learning and Instruction in Middle School Classrooms

ERIC Educational Resources Information Center

Carrejo, David; Robertson, William H.

2011-01-01

Computer-based mathematical modeling in physics is a process of constructing models of concepts and the relationships between them in the scientific characteristics of work. In this manner, computer-based modeling integrates the interactions of natural phenomenon through the use of models, which provide structure for theories and a base for…
Rule-Based Category Learning in Children: The Role of Age and Executive Functioning

PubMed Central

Rabi, Rahel; Minda, John Paul

2014-01-01

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

PubMed Central

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

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

PubMed

Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

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

ERIC Educational Resources Information Center

Durrant, Philip; Schmitt, Norbert

2009-01-01

Usage-based models claim that first language learning is based on the frequency-based analysis of memorised phrases. It is not clear though, whether adult second language learning works in the same way. It has been claimed that non-native language lacks idiomatic formulas, suggesting that learners neglect phrases, focusing instead on orthographic…
The Moderating Role of Self-Regulated Learning in Job Characteristics and Attitudes towards Web-Based Continuing Learning in the Airlines Workplace

ERIC Educational Resources Information Center

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

2018-01-01

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

PubMed

Rucker, Sydney Y; Ozdogan, Zulfukar; Al Achkar, Morhaf

2017-01-01

Journal club (JC), as a pedagogical strategy, has long been used in graduate medical education (GME). As evidence-based medicine (EBM) becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice.
Flipped classroom model for learning evidence-based medicine

PubMed Central

Rucker, Sydney Y; Ozdogan, Zulfukar; Al Achkar, Morhaf

2017-01-01

Journal club (JC), as a pedagogical strategy, has long been used in graduate medical education (GME). As evidence-based medicine (EBM) becomes a mainstay in GME, traditional models of JC present a number of insufficiencies and call for novel models of instruction. A flipped classroom model appears to be an ideal strategy to meet the demands to connect evidence to practice while creating engaged, culturally competent, and technologically literate physicians. In this article, we describe a novel model of flipped classroom in JC. We present the flow of learning activities during the online and face-to-face instruction, and then we highlight specific considerations for implementing a flipped classroom model. We show that implementing a flipped classroom model to teach EBM in a residency program not only is possible but also may constitute improved learning opportunity for residents. Follow-up work is needed to evaluate the effectiveness of this model on both learning and clinical practice. PMID:28919831

Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans

PubMed Central

Bornstein, Aaron M.; Daw, Nathaniel D.

2013-01-01

How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward — such as when planning routes using a cognitive map or chess moves using predicted countermoves — and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to be recalled during choice formation. PMID:24339770
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…
Continuous Learning: Balancing Educational Excellence and Cultural Diversity for At-Risk. A Developing, Generalizing, Working Model.

ERIC Educational Resources Information Center

Demery, Marie

This proactive research and development model presents data of misfortune, reality, and hope for approximately 40 percent of American children labeled as "at-risk." The model was based on the premise that in spite of their past and an environment of failure, these children can learn successfully and continuously through the balancing of…
Modeling Peer Assessment as Agent Negotiation in a Computer Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Lai, K. Robert; Lan, Chung Hsien

2006-01-01

This work presents a novel method for modeling collaborative learning as multi-issue agent negotiation using fuzzy constraints. Agent negotiation is an iterative process, through which, the proposed method aggregates student marks to reduce personal bias. In the framework, students define individual fuzzy membership functions based on their…
Service Learning and Student Engagement: A Dual Language Book Project

ERIC Educational Resources Information Center

Roessingh, Hetty

2012-01-01

A model is proposed followed by a case study of collaborative project work between student teachers, teachers and English language learners in kindergarten and grade 1. As a model, service learning provides a framework for making explicit linkages between course-based, credit bearing academic content, the identified need of the community school,…
Improving Teaching and Learning: Three Models to Reshape Educational Practice

ERIC Educational Resources Information Center

Roberson, Sam

2014-01-01

The work of schools is teaching and learning. However, the current educational culture is dominated by three characteristics: (1) the mechanistic view of organization and its practice based on the assembly line model where students progress along a value added conveyor; (2) the predominance of the Essentialist philosophy of education, in which the…
XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

NASA Astrophysics Data System (ADS)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-08-01

XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.
Improving patient care through work-based learning.

PubMed

Chapman, Linda

To record post-registration community nurses' perceptions of the impact of work-based learning on the quality of patient care. Ten nurses were interviewed. Each interviewee, who had successfully completed work-based learning programmes, was asked to describe their impact on the quality of patient care. The participants valued work-based learning. Four themes emerged where work-based learning contributed to improving the quality of care: increased health promotion, increased access to services, increased patient choice and reduced risk of infection. The relevance of studies and distance learning materials were perceived to be the main aspects that influenced changes in practice. The study provides insight into how work-based learning helped staff develop practice. It highlights that time for learning and mentoring are paramount for changes in practice to occur through work-based learning. Further studies are required to establish the best structure and style of distance learning materials needed to meet the needs of post-registration community nurses.
Incremental Bayesian Category Learning From Natural Language.

PubMed

Frermann, Lea; Lapata, Mirella

2016-08-01

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., chair is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata .). Copyright © 2015 Cognitive Science Society, Inc.
Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning.

PubMed

Mei, Suyu

2012-10-07

Recent years have witnessed much progress in computational modeling for protein subcellular localization. However, there are far few computational models for predicting plant protein subcellular multi-localization. In this paper, we propose a multi-label multi-kernel transfer learning model for predicting multiple subcellular locations of plant proteins (MLMK-TLM). The method proposes a multi-label confusion matrix and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which we further extend our published work MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for plant protein subcellular multi-localization. By proper homolog knowledge transfer, MLMK-TLM is applicable to novel plant protein subcellular localization in multi-label learning scenario. The experiments on plant protein benchmark dataset show that MLMK-TLM outperforms the baseline model. Unlike the existing models, MLMK-TLM also reports its misleading tendency, which is important for comprehensive survey of model's multi-labeling performance. Copyright © 2012 Elsevier Ltd. All rights reserved.
Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.
Exploring Team-Based Learning at a State University

ERIC Educational Resources Information Center

Leisey, Monica; Mulcare, Dan; Comeford, Lorrie; Kudrimoti, Sanjay

2014-01-01

A small group of faculty at Salem State University representing the disciplines of Chemistry, Finance, Geography, Political Science, and Social Work implemented a Team-Based Learning (TBL) model in their courses to explore its efficacy for increasing student engagement. Surveys were used to collect pre- and post-data from students to determine the…
The Role of Digital Technologies in Numeracy Teaching and Learning

ERIC Educational Resources Information Center

Geiger, Vince; Goos, Merrilyn; Dole, Shelley

2015-01-01

This paper presents a model of numeracy that integrates the use of digital technologies among other elements of teaching and learning mathematics. Drawing on data from a school-based project, which includes records of classroom observations, semi-structured teacher interviews and artefacts such as student work samples, a classroom-based vignette…
20 CFR 670.515 - What responsibilities do the center operators have in managing work-based learning?

Code of Federal Regulations, 2012 CFR

2012-04-01

... have in managing work-based learning? 670.515 Section 670.515 Employees' Benefits EMPLOYMENT AND... operators have in managing work-based learning? (a) The center operator must emphasize and implement work... training, and through arrangements with employers. Work-based learning must be under actual working...
20 CFR 670.515 - What responsibilities do the center operators have in managing work-based learning?

Code of Federal Regulations, 2013 CFR

2013-04-01

... have in managing work-based learning? 670.515 Section 670.515 Employees' Benefits EMPLOYMENT AND... operators have in managing work-based learning? (a) The center operator must emphasize and implement work... training, and through arrangements with employers. Work-based learning must be under actual working...
20 CFR 670.515 - What responsibilities do the center operators have in managing work-based learning?

Code of Federal Regulations, 2014 CFR

2014-04-01

... have in managing work-based learning? 670.515 Section 670.515 Employees' Benefits EMPLOYMENT AND... operators have in managing work-based learning? (a) The center operator must emphasize and implement work... training, and through arrangements with employers. Work-based learning must be under actual working...
E-learning: Web-based education.

PubMed

Sajeva, Marco

2006-12-01

This review introduces state-of-the-art Web-based education and shows how the e-learning model can be applied to an anaesthesia department using Open Source solutions, as well as lifelong learning programs, which is happening in several European research projects. The definition of the term e-learning is still a work in progress due to the fact that technologies are evolving every day and it is difficult to improve teaching methodologies or to adapt traditional methods to a new or already existing educational model. The European Community is funding several research projects to define the new common market place for tomorrow's educational system; this is leading to new frontiers like virtual Erasmus inter-exchange programs based on e-learning. The first step when adapting a course to e-learning is to re-define the educational/learning model adopted: cooperative learning and tutoring are the two key concepts. This means that traditional lecture notes, books and exercises are no longer effective; teaching files must use rich multimedia content and have to be developed using the new media. This can lead to several pitfalls that can be avoided with an accurate design phase.
With you or against you: social orientation dependent learning signals guide actions made for others.

PubMed

Christopoulos, George I; King-Casas, Brooks

2015-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests that neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying 'other-value' learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual's cooperative or competitive orientation toward others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. Published by Elsevier Inc.
With you or against you: Social orientation dependent learning signals guide actions made for others

PubMed Central

Christopoulos, George I.; King-Casas, Brooks

2014-01-01

In social environments, it is crucial that decision-makers take account of the impact of their actions not only for oneself, but also on other social agents. Previous work has identified neural signals in the striatum encoding value-based prediction errors for outcomes to oneself; also, recent work suggests neural activity in prefrontal cortex may similarly encode value-based prediction errors related to outcomes to others. However, prior work also indicates that social valuations are not isomorphic, with social value orientations of decision-makers ranging on a cooperative to competitive continuum; this variation has not been examined within social learning environments. Here, we combine a computational model of learning with functional neuroimaging to examine how individual differences in orientation impact neural mechanisms underlying ‘other-value’ learning. Across four experimental conditions, reinforcement learning signals for other-value were identified in medial prefrontal cortex, and were distinct from self-value learning signals identified in striatum. Critically, the magnitude and direction of the other-value learning signal depended strongly on an individual’s cooperative or competitive orientation towards others. These data indicate that social decisions are guided by a social orientation-dependent learning system that is computationally similar but anatomically distinct from self-value learning. The sensitivity of the medial prefrontal learning signal to social preferences suggests a mechanism linking such preferences to biases in social actions and highlights the importance of incorporating heterogeneous social predispositions in neurocomputational models of social behavior. PMID:25224998
Agent Based Modeling of Collaboration and Work Practices Onboard the International Space Station

NASA Technical Reports Server (NTRS)

Acquisti, Alessandro; Sierhuis, Maarten; Clancey, William J.; Bradshaw, Jeffrey M.; Shaffo, Mike (Technical Monitor)

2002-01-01

The International Space Station is one the most complex projects ever, with numerous interdependent constraints affecting productivity and crew safety. This requires planning years before crew expeditions, and the use of sophisticated scheduling tools. Human work practices, however, are difficult to study and represent within traditional planning tools. We present an agent-based model and simulation of the activities and work practices of astronauts onboard the ISS based on an agent-oriented approach. The model represents 'a day in the life' of the ISS crew and is developed in Brahms, an agent-oriented, activity-based language used to model knowledge in situated action and learning in human activities.

Contemporary Issues in Group Learning in Undergraduate Science Classrooms: A Perspective from Student Engagement.

PubMed

Hodges, Linda C

2018-06-01

As the use of collaborative-learning methods such as group work in science, technology, engineering, and mathematics classes has grown, so has the research into factors impacting effectiveness, the kinds of learning engendered, and demographic differences in student response. Generalizing across the range of this research is complicated by the diversity of group-learning approaches used. In this overview, I discuss theories of how group-work formats support or hinder learning based on the ICAP (interactive, constructive, active, passive) framework of student engagement. I then use this model to analyze current issues in group learning, such as the nature of student discourse during group work, the role of group learning in making our classrooms inclusive, and how classroom spaces factor into group learning. I identify key gaps for further research and propose implications from this research for teaching practice. This analysis helps identify essential, effective, and efficient features of group learning, thus providing faculty with constructive guidelines to support their work and affirm their efforts.
Instance-Based Ontology Matching for Open and Distance Learning Materials

ERIC Educational Resources Information Center

Cerón-Figueroa, Sergio; López-Yáñez, Itzamá; Villuendas-Rey, Yenny; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Yáñez-Márquez, Cornelio

2017-01-01

The present work describes an original associative model of pattern classification and its application to align different ontologies containing Learning Objects (LOs), which are in turn related to Open and Distance Learning (ODL) educative content. The problem of aligning ontologies is known as Ontology Matching Problem (OMP), whose solution is…
Verbal Knowledge, Working Memory, and Processing Speed as Predictors of Verbal Learning in Older Adults

ERIC Educational Resources Information Center

Rast, Philippe

2011-01-01

The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables--speed of information processing, verbal knowledge, working…
Gold-standard evaluation of a folksonomy-based ontology learning model

NASA Astrophysics Data System (ADS)

Djuana, E.

2018-03-01

Folksonomy, as one result of collaborative tagging process, has been acknowledged for its potential in improving categorization and searching of web resources. However, folksonomy contains ambiguities such as synonymy and polysemy as well as different abstractions or generality problem. To maximize its potential, some methods for associating tags of folksonomy with semantics and structural relationships have been proposed such as using ontology learning method. This paper evaluates our previous work in ontology learning according to gold-standard evaluation approach in comparison to a notable state-of-the-art work and several baselines. The results show that our method is comparable to the state-of the art work which further validate our approach as has been previously validated using task-based evaluation approach.
Pedagogy of the logic model: teaching undergraduates to work together to change their communities.

PubMed

Zimmerman, Lindsey; Kamal, Zohra; Kim, Hannah

2013-01-01

Undergraduate community psychology courses can empower students to address challenging problems in their local communities. Creating a logic model is an experiential way to learn course concepts by "doing." Throughout the semester, students work with peers to define a problem, develop an intervention, and plan an evaluation focused on an issue of concern to them. This report provides an overview of how to organize a community psychology course around the creation of a logic model in order for students to develop this applied skill. Two undergraduate student authors report on their experience with the logic model assignment, describing the community problem they chose to address, what they learned from the assignment, what they found challenging, and what they are doing now in their communities based on what they learned.
Designing a Web-Based Science Learning Environment for Model-Based Collaborative Inquiry

NASA Astrophysics Data System (ADS)

Sun, Daner; Looi, Chee-Kit

2013-02-01

The paper traces a research process in the design and development of a science learning environment called WiMVT (web-based inquirer with modeling and visualization technology). The WiMVT system is designed to help secondary school students build a sophisticated understanding of scientific conceptions, and the science inquiry process, as well as develop critical learning skills through model-based collaborative inquiry approach. It is intended to support collaborative inquiry, real-time social interaction, progressive modeling, and to provide multiple sources of scaffolding for students. We first discuss the theoretical underpinnings for synthesizing the WiMVT design framework, introduce the components and features of the system, and describe the proposed work flow of WiMVT instruction. We also elucidate our research approach that supports the development of the system. Finally, the findings of a pilot study are briefly presented to demonstrate of the potential for learning efficacy of the WiMVT implementation in science learning. Implications are drawn on how to improve the existing system, refine teaching strategies and provide feedback to researchers, designers and teachers. This pilot study informs designers like us on how to narrow the gap between the learning environment's intended design and its actual usage in the classroom.
Model-based hierarchical reinforcement learning and human action control

PubMed Central

Botvinick, Matthew; Weinstein, Ari

2014-01-01

Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour. PMID:25267822
Transforming Primary Care Practice and Education: Lessons From 6 Academic Learning Collaboratives.

PubMed

Koch, Ursula; Bitton, Asaf; Landon, Bruce E; Phillips, Russell S

Adoption of new primary care models has been slow in academic teaching practices. We describe a common framework that academic learning collaboratives are using to transform primary care practice based on our analysis of 6 collaboratives nationally. We show that the work of the collaboratives could be divided into 3 phases and provide detail on the phases of work and a road map for those who seek to emulate this work. We found that learning collaboratives foster transformation, even in complex academic practices, but need specific support adapted to their unique challenges.
An Example-Based Brain MRI Simulation Framework.

PubMed

He, Qing; Roy, Snehashis; Jog, Amod; Pham, Dzung L

2015-02-21

The simulation of magnetic resonance (MR) images plays an important role in the validation of image analysis algorithms such as image segmentation, due to lack of sufficient ground truth in real MR images. Previous work on MRI simulation has focused on explicitly modeling the MR image formation process. However, because of the overwhelming complexity of MR acquisition these simulations must involve simplifications and approximations that can result in visually unrealistic simulated images. In this work, we describe an example-based simulation framework, which uses an "atlas" consisting of an MR image and its anatomical models derived from the hard segmentation. The relationships between the MR image intensities and its anatomical models are learned using a patch-based regression that implicitly models the physics of the MR image formation. Given the anatomical models of a new brain, a new MR image can be simulated using the learned regression. This approach has been extended to also simulate intensity inhomogeneity artifacts based on the statistical model of training data. Results show that the example based MRI simulation method is capable of simulating different image contrasts and is robust to different choices of atlas. The simulated images resemble real MR images more than simulations produced by a physics-based model.
Implementation of project based learning on the Prakerin subject of vocational high school students of the building engineering to enhance employment skill readiness of graduates in the construction services field

NASA Astrophysics Data System (ADS)

Sugandi, Machmud

2017-09-01

Implementation of the Prakerin subject in the field of Building Engineering study program in vocational high school (VHS) are facing many issues associated to non-compliance unit of work in the industry and the expected competencies in learning at school. Project Based Learning (PBL) is an appropriate model learning used for Prakerin subject to increase student competence as the extension of the Prakerin implementation in the construction industry services. Assignments based on the selected project during their practical industry work were given to be completed by student. VHS students in particular field of Building Engineering study program who has been completed Prakerin subject will have a better job readiness, and therefore they will have an understanding on the knowledge, skills, and attitudes and good vision on the construction project in accordance with their experience during Prakerin work in the industry.
Leveraging Intermediaries to Expand Work-Based Learning. Connecting the Classroom to Careers: The State's Role in Work-Based Learning

ERIC Educational Resources Information Center

Advance CTE: State Leaders Connecting Learning to Work, 2016

2016-01-01

Work-based learning is an educational strategy that offers students an opportunity to reinforce and deepen their classroom learning, explore future career fields and demonstrate their skills in an authentic setting. Managing work-based learning requires layers of coordination, which is typically done by an individual or organizational…
Example-based learning: comparing the effects of additionally providing three different integrative learning activities on physiotherapy intervention knowledge.

PubMed

Dyer, Joseph-Omer; Hudon, Anne; Montpetit-Tourangeau, Katherine; Charlin, Bernard; Mamede, Sílvia; van Gog, Tamara

2015-03-07

Example-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of problem-solving skills more than studying worked examples alone. Completion examples are worked examples in which some of the solution steps remain unsolved for learners to complete. Providing learners engaged in example-based learning with self-explanation prompts has been shown to foster increased meaningful learning compared to providing no self-explanation prompts. Concept mapping and concept map study are other instructional activities known to promote meaningful learning. This study compares the effects of self-explaining, completing a concept map and studying a concept map on conceptual knowledge and problem-solving skills among novice learners engaged in example-based learning. Ninety-one physiotherapy students were randomized into three conditions. They performed a pre-test and a post-test to evaluate their gains in conceptual knowledge and problem-solving skills (transfer performance) in intervention selection. They studied three pairs of worked/completion examples in a digital learning environment. Worked examples consisted of a written reasoning process for selecting an optimal physiotherapy intervention for a patient. The completion examples were partially worked out, with the last few problem-solving steps left blank for students to complete. The students then had to engage in additional self-explanation, concept map completion or model concept map study in order to synthesize and deepen their knowledge of the key concepts and problem-solving steps. Pre-test performance did not differ among conditions. Post-test conceptual knowledge was higher (P < .001) in the concept map study condition (68.8 ± 21.8%) compared to the concept map completion (52.8 ± 17.0%) and self-explanation (52.2 ± 21.7%) conditions. Post-test problem-solving performance was higher (P < .05) in the self-explanation (63.2 ± 16.0%) condition compared to the concept map study (53.3 ± 16.4%) and concept map completion (51.0 ± 13.6%) conditions. Students in the self-explanation condition also invested less mental effort in the post-test. Studying model concept maps led to greater conceptual knowledge, whereas self-explanation led to higher transfer performance. Self-explanation and concept map study can be combined with worked example and completion example strategies to foster intervention selection.
Environmental Learning Centers: A Template.

ERIC Educational Resources Information Center

Vozick, Eric

1999-01-01

Provides a working model, or template, for community-based environmental learning centers (ELCs). The template presents a philosophy as well as a plan for staff and administration operations, educational programming, and financial support. The template also addresses "green" construction and maintenance of buildings and grounds and…
Analysis of a Physics Teacher's Pedagogical "Micro-Actions" That Support 17-Year-Olds' Learning of Free Body Diagrams via a Modelling Approach

ERIC Educational Resources Information Center

Tay, Su Lynn; Yeo, Jennifer

2018-01-01

Great teaching is characterised by the specific actions a teacher takes in the classroom to bring about learning. In the context of model-based teaching (MBT), teachers' difficulty in working with students' models that are not scientifically consistent is troubling. To address this problem, the aim of this study is to identify the pedagogical…
Evolution of a Model for Socio-Scientific Issue Teaching and Learning

ERIC Educational Resources Information Center

Sadler, Troy D.; Foulk, Jaimie A.; Friedrichsen, Patricia J.

2017-01-01

Socio-scientific teaching and learning (SSI-TL) has been suggested as an effective approach for supporting meaningful learning in school contexts; however, limited tools exist to support the work of designing and implementing this approach. In this paper, we draw from a series of four design based research projects that have produced SSI…
A Learning Design Ontology Based on the IMS Specification

ERIC Educational Resources Information Center

Amorim, Ricardo R.; Lama, Manuel; Sanchez, Eduardo; Riera, Adolfo; Vila, Xose A.

2006-01-01

In this paper, we present an ontology to represent the semantics of the IMS Learning Design (IMS LD) specification, a meta-language used to describe the main elements of the learning design process. The motivation of this work relies on the expressiveness limitations found on the current XML-Schema implementation of the IMS LD conceptual model. To…
A learning curve-based method to implement multifunctional work teams in the Brazilian footwear sector.

PubMed

Guimarães, L B de M; Anzanello, M J; Renner, J S

2012-05-01

This paper presents a method for implementing multifunctional work teams in a footwear company that followed the Taylor/Ford system for decades. The suggested framework first applies a Learning Curve (LC) modeling to assess whether rotation between tasks of different complexities affects workers' learning rate and performance. Next, the Macroergonomic Work Analysis (MA) method (Guimarães, 1999, 2009) introduces multifunctional principles in work teams towards workers' training and resources improvement. When applied to a pilot line consisting of 100 workers, the intervention-reduced work related accidents in 80%, absenteeism in 45.65%, and eliminated work related musculoskeletal disorders (WMSD), medical consultations, and turnover. Further, the output rate of the multifunctional team increased average 3% compared to the production rate of the regular lines following the Taylor/Ford system (with the same shoe model being manufactured), while the rework and spoilage rates were reduced 85% and 69%, respectively. Copyright © 2011 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Discovering and Articulating What Is Not yet Known: Using Action Learning and Grounded Theory as a Knowledge Management Strategy

ERIC Educational Resources Information Center

Pauleen, David J.; Corbitt, Brian; Yoong, Pak

2007-01-01

Purpose: To provide a conceptual model for the discovery and articulation of emergent organizational knowledge, particularly knowledge that develops when people work with new technologies. Design/methodology/approach: The model is based on two widely accepted research methods--action learning and grounded theory--and is illustrated using a case…
Towards an Object-Oriented Model for the Design and Development of Learning Objects

ERIC Educational Resources Information Center

Chrysostomou, Chrysostomos; Papadopoulos, George

2008-01-01

This work introduces the concept of an Object-Oriented Learning Object (OOLO) that is developed in a manner similar to the one that software objects are developed through Object-Oriented Software Engineering (OO SWE) techniques. In order to make the application of the OOLO feasible and efficient, an OOLO model needs to be developed based on…
A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.
Measuring Work-Based Learning for Continuous Improvement. Connecting the Classroom to Careers: The State's Role in Work-Based Learning

ERIC Educational Resources Information Center

Advance CTE: State Leaders Connecting Learning to Work, 2016

2016-01-01

Work-based learning provides a continuum of activities--from career exploration and job shadowing to internships and apprenticeships--that help students develop technical and professional skills in an authentic work environment. While many work-based learning programs are designed and operated at the local level, several states have begun building…
Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism.

PubMed

Cheng, Zhenbo; Deng, Zhidong; Hu, Xiaolin; Zhang, Bo; Yang, Tianming

2015-12-01

The brain often has to make decisions based on information stored in working memory, but the neural circuitry underlying working memory is not fully understood. Many theoretical efforts have been focused on modeling the persistent delay period activity in the prefrontal areas that is believed to represent working memory. Recent experiments reveal that the delay period activity in the prefrontal cortex is neither static nor homogeneous as previously assumed. Models based on reservoir networks have been proposed to model such a dynamical activity pattern. The connections between neurons within a reservoir are random and do not require explicit tuning. Information storage does not depend on the stable states of the network. However, it is not clear how the encoded information can be retrieved for decision making with a biologically realistic algorithm. We therefore built a reservoir-based neural network to model the neuronal responses of the prefrontal cortex in a somatosensory delayed discrimination task. We first illustrate that the neurons in the reservoir exhibit a heterogeneous and dynamical delay period activity observed in previous experiments. Then we show that a cluster population circuit decodes the information from the reservoir with a winner-take-all mechanism and contributes to the decision making. Finally, we show that the model achieves a good performance rapidly by shaping only the readout with reinforcement learning. Our model reproduces important features of previous behavior and neurophysiology data. We illustrate for the first time how task-specific information stored in a reservoir network can be retrieved with a biologically plausible reinforcement learning training scheme. Copyright © 2015 the American Physiological Society.
Work-Based Learning: A Resource Guide for Change. Test Draft.

ERIC Educational Resources Information Center

Hudson River Center for Program Development, Glenmont, NY.

This resource guide is intended to provide New York schools, business/industry, and others with resources to develop work-based learning strategies and components. Section 1 examines the scope, foundation, categories, and operation of work-based learning. Section 2 presents detailed information about the following forms of work-based learning:…
Service Learning in Social Work Education: The State of Knowledge, Pedagogical Practicalities, and Practice Conundrums

ERIC Educational Resources Information Center

Lemieux, Catherine M.; Allen, Priscilla D.

2007-01-01

This article reviews research-based knowledge about service learning in social work education. Student learning outcomes common to both service learning and social work education are examined, and the research-based literature on service learning in social work is analyzed. Service-learning practice issues in social work education are described:…
Using Model-Based Reasoning for Autonomous Instrument Operation - Lessons Learned From IMAGE/LENA

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

ERIC Educational Resources Information Center

Darabi, Aubteen; Nelson, David W.; Meeker, Richard; Liang, Xinya; Boulware, Wilma

2010-01-01

In a diagnostic problem solving operation of a computer-simulated chemical plant, chemical engineering students were randomly assigned to two groups: one studying product-oriented worked examples, the other practicing conventional problem solving. Effects of these instructional strategies on the progression of learners' mental models were examined…
Small-Group Learning in an Upper-Level University Biology Class Enhances Academic Performance and Student Attitudes Toward Group Work

PubMed Central

Nakonechny, Joanne; Cragg, Jacquelyn J.; Ramer, Matt S.

2010-01-01

To improve science learning, science educators' teaching tools need to address two major criteria: teaching practice should mirror our current understanding of the learning process; and science teaching should reflect scientific practice. We designed a small-group learning (SGL) model for a fourth year university neurobiology course using these criteria and studied student achievement and attitude in five course sections encompassing the transition from individual work-based to SGL course design. All students completed daily quizzes/assignments involving analysis of scientific data and the development of scientific models. Students in individual work-based (Individualistic) sections usually worked independently on these assignments, whereas SGL students completed assignments in permanent groups of six. SGL students had significantly higher final exam grades than Individualistic students. The transition to the SGL model was marked by a notable increase in 10th percentile exam grade (Individualistic: 47.5%; Initial SGL: 60%; Refined SGL: 65%), suggesting SGL enhanced achievement among the least prepared students. We also studied student achievement on paired quizzes: quizzes were first completed individually and submitted, and then completed as a group and submitted. The group quiz grade was higher than the individual quiz grade of the highest achiever in each group over the term. All students – even term high achievers –could benefit from the SGL environment. Additionally, entrance and exit surveys demonstrated student attitudes toward SGL were more positive at the end of the Refined SGL course. We assert that SGL is uniquely-positioned to promote effective learning in the science classroom. PMID:21209910
Small-group learning in an upper-level university biology class enhances academic performance and student attitudes toward group work.

PubMed

Gaudet, Andrew D; Ramer, Leanne M; Nakonechny, Joanne; Cragg, Jacquelyn J; Ramer, Matt S

2010-12-29

To improve science learning, science educators' teaching tools need to address two major criteria: teaching practice should mirror our current understanding of the learning process; and science teaching should reflect scientific practice. We designed a small-group learning (SGL) model for a fourth year university neurobiology course using these criteria and studied student achievement and attitude in five course sections encompassing the transition from individual work-based to SGL course design. All students completed daily quizzes/assignments involving analysis of scientific data and the development of scientific models. Students in individual work-based (Individualistic) sections usually worked independently on these assignments, whereas SGL students completed assignments in permanent groups of six. SGL students had significantly higher final exam grades than Individualistic students. The transition to the SGL model was marked by a notable increase in 10th percentile exam grade (Individualistic: 47.5%; Initial SGL: 60%; Refined SGL: 65%), suggesting SGL enhanced achievement among the least prepared students. We also studied student achievement on paired quizzes: quizzes were first completed individually and submitted, and then completed as a group and submitted. The group quiz grade was higher than the individual quiz grade of the highest achiever in each group over the term. All students--even term high achievers--could benefit from the SGL environment. Additionally, entrance and exit surveys demonstrated student attitudes toward SGL were more positive at the end of the Refined SGL course. We assert that SGL is uniquely-positioned to promote effective learning in the science classroom.
Defining and Assessing Learning: Exploring Competency-Based Initiatives. Report of the National Postsecondary Education Cooperative Working Group on Competency-Based Initiatives in Postsecondary Education. Brochure [and] Report.

ERIC Educational Resources Information Center

Jones, Elizabeth A.; Voorhees, Richard A.

This document includes a 3-page brochure describing the main report and the main report. The report explores competency-based models in postsecondary institutions and other learning environments. It is intended primarily as a guide for postsecondary educators who are interested in establishing such efforts at their institutions. The following…
Nebraska Work Based Learning Manual. Planning and Implementation Guides for Educators, Employers, Policymakers, and Parents.

ERIC Educational Resources Information Center

Nebraska State Dept. of Education, Lincoln.

This manual contains a series of 10 detailed guides for school practitioners who are beginning to create work-based learning programs at their schools. Work-Based Learning Overview defines the different elements of work-based learning and describes the roles of program participants. Program Planning Guide offers suggestions about how to plan…
A reinforcement learning model of joy, distress, hope and fear

NASA Astrophysics Data System (ADS)

Broekens, Joost; Jacobs, Elmer; Jonker, Catholijn M.

2015-07-01

In this paper we computationally study the relation between adaptive behaviour and emotion. Using the reinforcement learning framework, we propose that learned state utility, ?, models fear (negative) and hope (positive) based on the fact that both signals are about anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error signal. We present agent-based simulation experiments that show that this model replicates psychological and behavioural dynamics of emotion. This work distinguishes itself by assessing the dynamics of emotion in an adaptive agent framework - coupling it to the literature on habituation, development, extinction and hope theory. Our results support the idea that the function of emotion is to provide a complex feedback signal for an organism to adapt its behaviour. Our work is relevant for understanding the relation between emotion and adaptation in animals, as well as for human-robot interaction, in particular how emotional signals can be used to communicate between adaptive agents and humans.
Multi-issue Agent Negotiation Based on Fairness

NASA Astrophysics Data System (ADS)

Zuo, Baohe; Zheng, Sue; Wu, Hong

Agent-based e-commerce service has become a hotspot now. How to make the agent negotiation process quickly and high-efficiently is the main research direction of this area. In the multi-issue model, MAUT(Multi-attribute Utility Theory) or its derived theory usually consider little about the fairness of both negotiators. This work presents a general model of agent negotiation which considered the satisfaction of both negotiators via autonomous learning. The model can evaluate offers from the opponent agent based on the satisfaction degree, learn online to get the opponent's knowledge from interactive instances of history and negotiation of this time, make concessions dynamically based on fair object. Through building the optimal negotiation model, the bilateral negotiation achieved a higher efficiency and fairer deal.
The Effect of Teaching Model ‘Learning Cycles 5E’ toward Students’ Achievement in Learning Mathematic at X Years Class SMA Negeri 1 Banuhampu 2013/2014 Academic Year

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

PubMed

Collins, Anne G E; Ciullo, Brittany; Frank, Michael J; Badre, David

2017-04-19

Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning. SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors. Copyright © 2017 the authors 0270-6474/17/374332-11$15.00/0.
Tying knots: an activity theory analysis of student learning goals in clinical education.

PubMed

Larsen, Douglas P; Wesevich, Austin; Lichtenfeld, Jana; Artino, Antony R; Brydges, Ryan; Varpio, Lara

2017-07-01

Learning goal programmes are often created to help students develop self-regulated learning skills; however, these programmes do not necessarily consider the social contexts surrounding learning goals or how they fit into daily educational practice. We investigated a high-frequency learning goal programme in which students generated and shared weekly learning goals with their clinical teams in core Year 3 clerkships. Our study explores: (i) how learning goals were incorporated into the clinical work, and (ii) the factors that influenced the use of students' learning goals in work-based learning. We conducted semi-structured interviews with 14 students and 14 supervisors (attending physicians and residents) sampled from all participating core clerkships. Interviews were coded for emerging themes. Using cultural historical activity theory and knotworking as theoretical lenses, we developed a model of the factors that influenced students' learning goal usage in a work-based learning context. Students and supervisors often faced the challenge of reconciling contradictions that arose when the desired outcomes of student skill development, grading and patient care were not aligned. Learning goals could function as tools for developing new ways of acting that overcame those contradictions by facilitating collaborative effort between students and their supervisors. However, for new collaborations to take place, both students and supervisors had to engage with the goals, and the necessary patients needed to be present. When any one part of the system did not converge around the learning goals, the impact of the learning goals programme was limited. Learning goals are potentially powerful tools to mediate interactions between students, supervisors and patients, and to reconcile contradictions in work-based learning environments. Learning goals provide a means to develop not only learners, but also learning systems. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.
Intuitive Physics: Current Research and Controversies.

PubMed

Kubricht, James R; Holyoak, Keith J; Lu, Hongjing

2017-10-01

Early research in the field of intuitive physics provided extensive evidence that humans succumb to common misconceptions and biases when predicting, judging, and explaining activity in the physical world. Recent work has demonstrated that, across a diverse range of situations, some biases can be explained by the application of normative physical principles to noisy perceptual inputs. However, it remains unclear how knowledge of physical principles is learned, represented, and applied to novel situations. In this review we discuss theoretical advances from heuristic models to knowledge-based, probabilistic simulation models, as well as recent deep-learning models. We also consider how recent work may be reconciled with earlier findings that favored heuristic models. Copyright © 2017 Elsevier Ltd. All rights reserved.
Utah Work-Based Learning Manual.

ERIC Educational Resources Information Center

Utah State Office of Education, Salt Lake City.

This document presents materials to assist Utah school personnel who are initiating, implementing, or improving work-based learning opportunities for students. The document presents detailed guidelines for creating and maintaining work-based learning systems in schools and resource materials for improving existing work-based opportunities. Formal…
Temperature based Restricted Boltzmann Machines

NASA Astrophysics Data System (ADS)

Li, Guoqi; Deng, Lei; Xu, Yi; Wen, Changyun; Wang, Wei; Pei, Jing; Shi, Luping

2016-01-01

Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.
Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

NASA Astrophysics Data System (ADS)

Kyo, Koki

Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

Can Work-Based Learning Add to the Research Inventory of Higher Education? The Case of Collaborative Research

ERIC Educational Resources Information Center

Portwood, Derek

2007-01-01

Work-based learning's preoccupation with developing award-bearing programmes has affected the scope and style of work-based research. While offering development opportunities for work-based research, the emphasis of work-based learning programmes on the individual learner has curtailed the use of collaborative research. This article explores how…
Statistically Modeling Individual Students' Learning over Successive Collaborative Practice Opportunities

ERIC Educational Resources Information Center

Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol

2017-01-01

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…
A rational model of function learning.

PubMed

Lucas, Christopher G; Griffiths, Thomas L; Williams, Joseph J; Kalish, Michael L

2015-10-01

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.
Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991
Being Online Peer Supported: Experiences from a Work-Based Learning Programme

ERIC Educational Resources Information Center

Altinay Aksal, Fahriye; Altinay, Zehra; De Rossi, Gazivalerio; Isman, Aytekin

2012-01-01

Problem Statement: Work-based learning programmes have become an increasingly popular way of fulfilling the desire for life-long learning; multi-dimensional work-based learning modes have recently played a large role in both personal and institutional development. The peculiarity of this innovative way of learning derives from the fact that…
Design Learning of Teaching Factory in Mechanical Engineering

NASA Astrophysics Data System (ADS)

Putra, R. C.; Kusumah, I. H.; Komaro, M.; Rahayu, Y.; Asfiyanur, E. P.

2018-02-01

The industrial world that is the target of the process and learning outcomes of vocational high school (SMK) has its own character and nuance. Therefore, vocational education institutions in the learning process should be able to make the appropriate learning approach and in accordance with the industrial world. One approach to learning that is based on production and learning in the world of work is by industry-based learning or known as Teaching Factory, where in this model apply learning that involves direct students in goods or service activities are expected to have the quality so it is worth selling and accepted by consumers. The method used is descriptive approach. The purpose of this research is to get the design of the teaching factory based on the competency requirements of the graduates of the spouse industry, especially in the engineering department. The results of this study is expected to be one of the choice of model factory teaching in the field of machinery engineering in accordance with the products and competencies of the graduates that the industry needs.
Andragogical Modeling and the Success of the "EMPACTS" project-based learning model in the STEM disciplines: A decade of growth and learner success in the 2Y College Learning Environment.

NASA Astrophysics Data System (ADS)

Phillips, C. D.; Thomason, R.; Galloway, M.; Sorey, N.; Stidham, L.; Torgerson, M.

2014-12-01

EMPACTS (Educationally Managed Projects Advancing Curriculum, Technology/Teamwork and Service) is a project-based, adult learning modelthat is designed to enhance learning of course content through real-world application and problem solving self directed and collaborative learning use of technology service to the community EMPACTS students are self-directed in their learning, often working in teams to develop, implement, report and present final project results. EMPACTS faculty use community based projects to increase deeper learning of course content through "real-world" service experiences. Learners develop personal and interpersonal work and communication skills as they plan, execute and complete project goals together. Technology is used as a tool to solve problems and to publish the products of their learning experiences. Courses across a broad STEM curriculum integrate the EMPACTS project experience into the overall learning outcomes as part of the learning college mission of preparing 2Y graduates for future academic and/or workforce success. Since the program began in 2005, there have been over 200 completed projects/year. Student driven successes have led to the establishment of an EMPACTS Technology Corp, which is funded through scholarship and allows EMPACTS learners the opportunity to serve and learn from one another as "peer instructors." Engineering and 3D graphic design teams have written technology proposals and received funding for 3D printing replication projects, which have benefited the college as a whole through grant opportunities tied to these small scale successes. EMPACTS students engage in a variety of outreachprojects with area schools as they share the successes and joys of self directed, inquiry, project based learning. The EMPACTS Program has successfully trained faculty and students in the implementation of the model and conduct semester to semester and once a year workshops for college and K-12 faculty, who are interested in enhancing the learning experience and retention of course content through meaningful, engaging, character building projects. Learner Project successes are celebrated and archived within the framework of the EMPACTS Student Project website. http://faculty.nwacc.edu/EAST_original/Spring2014/Spring2014index.htm
Research in the Real World: Improving Adult Learners Web Search and Evaluation Skills through Motivational Design and Problem-Based Learning

ERIC Educational Resources Information Center

Roberts, Lindsay

2017-01-01

How can we better engage adult learners during information literacy sessions? How do we increase students' perception of the relevance and importance of information literacy skills for academic work and life in the real world? To explore these questions, the ARCS Model of Motivational Design and Problem-Based Learning were used to develop…
Towards a Framework to Improve the Quality of Teaching and Learning: Consciousness and Validation in Computer Engineering Science, UCT

ERIC Educational Resources Information Center

Lévano, Marcos; Albornoz, Andrea

2016-01-01

This paper aims to propose a framework to improve the quality in teaching and learning in order to develop good practices to train professionals in the career of computer engineering science. To demonstrate the progress and achievements, our work is based on two principles for the formation of professionals, one based on the model of learning…
Teacher as the Instrument of Evaluation: A Descriptive Case Study of Connoisseurship and Educational Criticism's Role in Project-Based Learning, Technology Arts Classroom

ERIC Educational Resources Information Center

Castro, Edward Anthony

2010-01-01

Purpose: The study's purpose was to determine the degree to which connoisseurship and educational criticism exists in student work evaluation; to identify master teachers' methods utilizing verbal feedback; and to determine the degree that project-based learning (PBL) principles serve as a "model of practice" for selected PBL technology…
Predictors of Processing-Based Task Performance in Bilingual and Monolingual Children

PubMed Central

Buac, Milijana; Gross, Megan; Kaushanskaya, Margarita

2016-01-01

In the present study we examined performance of bilingual Spanish-English-speaking and monolingual English-speaking school-age children on a range of processing-based measures within the framework of Baddeley’s working memory model. The processing-based measures included measures of short-term memory, measures of working memory, and a novel word-learning task. Results revealed that monolinguals outperformed bilinguals on the short-term memory tasks but not the working memory and novel word-learning tasks. Further, children’s vocabulary skills and socioeconomic status (SES) were more predictive of processing-based task performance in the bilingual group than the monolingual group. Together, these findings indicate that processing-based tasks that engage verbal working memory rather than short-term memory may be better-suited for diagnostic purposes with bilingual children. However, even verbal working memory measures are sensitive to bilingual children’s language-specific knowledge and demographic characteristics, and therefore may have limited clinical utility. PMID:27179914
Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.
An exploration of the relationship between academic and experiential learning approaches in vocational education.

PubMed

de Jong, Jan A Stavenga; Wierstra, Ronny F A; Hermanussen, José

2006-03-01

Research on individual learning approaches (or learning styles) is split in two traditions, one of which is biased towards academic learning, and the other towards learning from direct experience. In the reported study, the two traditions are linked by investigating the relationships between school-based (academic) and work-based (experiential) learning approaches of students in vocational education programs. Participants were 899 students of a Dutch school for secondary vocational education; 758 provided data on school-based learning, and 407 provided data on work-based learning, resulting in an overlap of 266 students from whom data were obtained on learning in both settings. Learning approaches in school and work settings were measured with questionnaires. Using factor analysis and cluster analysis, items and students were grouped, both with respect to school- and work-based learning. The study identified two academic learning dimensions (constructive learning and reproductive learning), and three experiential learning dimensions (analysis, initiative, and immersion). Construction and analysis were correlated positively, and reproduction and initiative negatively. Cluster analysis resulted in the identification of three school-based learning orientations and three work-based learning orientations. The relation between the two types of learning orientations, expressed in Cramér's V, appeared to be weak. It is concluded that learning approaches are relatively context specific, which implies that neither theoretical tradition can claim general applicability.
A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language.

PubMed

Golosio, Bruno; Cangelosi, Angelo; Gamotina, Olesya; Masala, Giovanni Luca

2015-01-01

Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.
Scaffolding Learning by Modelling: The Effects of Partially Worked-out Models

ERIC Educational Resources Information Center

Mulder, Yvonne G.; Bollen, Lars; de Jong, Ton; Lazonder, Ard W.

2016-01-01

Creating executable computer models is a potentially powerful approach to science learning. Learning by modelling is also challenging because students can easily get overwhelmed by the inherent complexities of the task. This study investigated whether offering partially worked-out models can facilitate students' modelling practices and promote…
Toward an Optimal Pedagogy for Teamwork.

PubMed

Earnest, Mark A; Williams, Jason; Aagaard, Eva M

2017-10-01

Teamwork and collaboration are increasingly listed as core competencies for undergraduate health professions education. Despite the clear mandate for teamwork training, the optimal method for providing that training is much less certain. In this Perspective, the authors propose a three-level classification of pedagogical approaches to teamwork training based on the presence of two key learning factors: interdependent work and explicit training in teamwork. In this classification framework, level 1-minimal team learning-is where learners work in small groups but neither of the key learning factors is present. Level 2-implicit team learning-engages learners in interdependent learning activities but does not include an explicit focus on teamwork. Level 3-explicit team learning-creates environments where teams work interdependently toward common goals and are given explicit instruction and practice in teamwork. The authors provide examples that demonstrate each level. They then propose that the third level of team learning, explicit team learning, represents a best practice approach in teaching teamwork, highlighting their experience with an explicit team learning course at the University of Colorado Anschutz Medical Campus. Finally, they discuss several challenges to implementing explicit team-learning-based curricula: the lack of a common teamwork model on which to anchor such a curriculum; the question of whether the knowledge, skills, and attitudes acquired during training would be transferable to the authentic clinical environment; and effectively evaluating the impact of explicit team learning.
Learning clinically useful information from images: Past, present and future.

PubMed

Rueckert, Daniel; Glocker, Ben; Kainz, Bernhard

2016-10-01

Over the last decade, research in medical imaging has made significant progress in addressing challenging tasks such as image registration and image segmentation. In particular, the use of model-based approaches has been key in numerous, successful advances in methodology. The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones. More recently, medical imaging has moved away from hand-crafted, and often explicitly designed models towards data-driven, implicit models that are constructed using machine learning techniques. This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation. As more and more imaging data is becoming available, e.g., from large population studies, this trend is likely to continue and accelerate. At the same time new developments in machine learning, e.g., deep learning, as well as significant improvements in computing power, e.g., parallelisation on graphics hardware, offer new potential for data-driven, semantic and intelligent medical imaging. This article outlines the work of the BioMedIA group in this area and highlights some of the challenges and opportunities for future work. Copyright © 2016 Elsevier B.V. All rights reserved.
Work-Based Learning and Work-Integrated Learning: Fostering Engagement with Employers

ERIC Educational Resources Information Center

Atkinson, Georgina

2016-01-01

Work-based learning and the inclusion of the world of work into tertiary students' learning lie at the heart of the Australian vocational education and training (VET) system. Traditionally this has been through apprenticeships and traineeships, which have a strong focus on "on-the-job" training, but also through "work-oriented"…
A manifold learning approach to data-driven computational materials and processes

NASA Astrophysics Data System (ADS)

Ibañez, Ruben; Abisset-Chavanne, Emmanuelle; Aguado, Jose Vicente; Gonzalez, David; Cueto, Elias; Duval, Jean Louis; Chinesta, Francisco

2017-10-01

Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy, …), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ universal laws while minimizing the need of explicit, often phenomenological, models. They are based on manifold learning methodologies.
Towards a Pedagogy of Work-Based Learning: Perceptions of Work-Based Learning in Foundation Degrees

ERIC Educational Resources Information Center

Burke, Linda; Marks-Maran, Diane J.; Ooms, Ann; Webb, Marion; Cooper, Denise

2009-01-01

One of the features of foundation degrees (FDs) is the incorporation of work-based learning (WBL) into the curriculum. WBL is seen as an important part of vocational programmes and is described by Foundation Degrees Forward (FDF) as a potentially radical approach to connecting work with learning. The Quality Assurance Agency (QAA), in its…

Economy, Work, and Education: Critical Connections. Routledge Advances in Management and Business Studies

ERIC Educational Resources Information Center

Casey, Catherine

2011-01-01

"Economy, Work and Education: Critical Connections" addresses effects of neoliberal capitalism in particular regard to work and education. The book elaborates key aspects and problems of generalized policy models of knowledge-based economies and learning societies in contexts of liberalized firm action, accelerated competitiveness and labor market…
Work Based Learning in Intercultural Settings: A Model in Practice

ERIC Educational Resources Information Center

Leeming, David Elvis; Mora, Maria Dolores Iglesias

2016-01-01

The Intercultural Business Communication at the University of Central Lancashire offers a taught module with a work placement that exists within a multicultural context as part of an MA in Intercultural Business Communication. As part of this process, students must work towards completing two practical assessments, a project presented in a report…
Pedagogical Strategies for Work-Based Learning. IEE Working Paper No. 12.

ERIC Educational Resources Information Center

Hughes, Katherine L.; Moore, David Thornton

Fourteen school-to-work programs characterized by strong work-based learning components and solid employer involvement were examined in a 3-year study to identify pedagogical factors associated with successful work-based learning programs. The main data collection activities were as follows: site visits to the 15 programs to interview faculty,…
Towards tailored teaching: using participatory action research to enhance the learning experience of Longitudinal Integrated Clerkship students in a South African rural district hospital.

PubMed

von Pressentin, Klaus B; Waggie, Firdouza; Conradie, Hoffie

2016-03-08

The introduction of Stellenbosch University's Longitudinal Integrated Clerkship (LIC) model as part of the undergraduate medical curriculum offers a unique and exciting training model to develop generalist doctors for the changing South African health landscape. At one of these LIC sites, the need for an improvement of the local learning experience became evident. This paper explores how to identify and implement a tailored teaching and learning intervention to improve workplace-based learning for LIC students. A participatory action research approach was used in a co-operative inquiry group (ten participants), consisting of the students, clinician educators and researchers, who met over a period of 5 months. Through a cyclical process of action and reflection this group identified a teaching intervention. The results demonstrate the gaps and challenges identified when implementing a LIC model of medical education. A structured learning programme for the final 6 weeks of the students' placement at the district hospital was designed by the co-operative inquiry group as an agreed intervention. The post-intervention group reflection highlighted a need to create a structured programme in the spirit of local collaboration and learning across disciplines. The results also enhance our understanding of both students and clinician educators' perceptions of this new model of workplace-based training. This paper provides practical strategies to enhance teaching and learning in a new educational context. These strategies illuminate three paradigm shifts: (1) from the traditional medical education approach towards a transformative learning approach advocated for the 21(st) century health professional; (2) from the teaching hospital context to the district hospital context; and (3) from block-based teaching towards a longitudinal integrated learning model. A programme based on balancing structured and tailored learning activities is recommended in order to address the local learning needs of students in the LIC model. We recommend that action learning sets should be developed at these LIC sites, where the relevant aspects of work-place based learning are negotiated.
Applying Learning Design to Work-Based Learning

ERIC Educational Resources Information Center

Miao, Yongwu; Hoppe, Heinz Ulrich

2011-01-01

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

PubMed

Phillips, Sue

This article examines some of the issues encountered in helping to develop and facilitate work-based learning (WBL) in clinical areas from the author's perspective of APEL/WBL co-ordinator. The advantages of work-based learning to both organisations and practitioners are discussed, together with possible drawbacks. The article concludes by identifying the positive aspects, including that of practice development, but suggests caution in attempting to use work-based learning in all circumstances.
Example-Based Learning: Effects of Different Types of Examples on Student Performance, Cognitive Load and Self-Efficacy in a Statistical Learning Task

ERIC Educational Resources Information Center

Huang, Xiaoxia

2017-01-01

Previous research has indicated the disconnect between example-based research focusing on worked examples (WEs) and that focusing on modeling examples. The purpose of this study was to examine and compare the effect of four different types of examples from the two separate lines of research, including standard WEs, erroneous WEs, expert (masterly)…
Taking Aim at the Cognitive Side of Learning in Sensorimotor Adaptation Tasks.

PubMed

McDougle, Samuel D; Ivry, Richard B; Taylor, Jordan A

2016-07-01

Sensorimotor adaptation tasks have been used to characterize processes responsible for calibrating the mapping between desired outcomes and motor commands. Research has focused on how this form of error-based learning takes place in an implicit and automatic manner. However, recent work has revealed the operation of multiple learning processes, even in this simple form of learning. This review focuses on the contribution of cognitive strategies and heuristics to sensorimotor learning, and how these processes enable humans to rapidly explore and evaluate novel solutions to enable flexible, goal-oriented behavior. This new work points to limitations in current computational models, and how these must be updated to describe the conjoint impact of multiple processes in sensorimotor learning. Copyright © 2016 Elsevier Ltd. All rights reserved.
The use of CORE model by metacognitive skill approach in developing characters junior high school students

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

PubMed Central

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions. PMID:26089862
Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

PubMed

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

2015-01-01

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.
Preparing skilled labor in industry through production-based curriculum approach in vocational high school

NASA Astrophysics Data System (ADS)

Yoto

2017-09-01

Vocational high school (Sekolah Menengah Kejuruan / SMK) aims to prepare mid-level skilled labors to work in the industry and are able to create self-employment opportunities. For those reasons, the curriculum in SMK should be based on meeting the needs of the industries and is able to prepare learners to master the competence in accordance with the skills program of their choice. Production based curriculum is the curriculum which the learning process is designed together with the production process or using production process as a learning medium. This approach with the primary intention to introduce students with the real working environment and not merely simulations. In the production-based curriculum implementation model, students are directly involved in the industry through the implementation of industrial working practices, do work on production units in school, and do practical work in school by doing the job as done in the industry by using industry standards machines.
Multimodal Modeling Activities with Special Needs Students in an Informal Learning Context: Vygotsky Revisited

ERIC Educational Resources Information Center

Kim, Mi Song

2017-01-01

In light of the challenges facing science educators and special education teachers in Singapore, this study entails design-based research to develop participatory learning environments. Drawing upon Vygotskian perspectives, this case study was situated in an informal workshop around the theme of "day and night" working for Special Needs…
Observational Learning on Industry Work Practices toward Job Readiness

ERIC Educational Resources Information Center

Rojuli, Subkhan; Rahayu, Agus; Disman

2017-01-01

This research aims to find out the influence of observational learning on job readiness based on some indicators and variables. This is a quantitative research in which Structural Equation Modeling (SEM) was used. The research method is survey. The participants of this research are the Grade XII students of Accountancy Department of State…
Competencies in Geriatric Nursing: Empirical Evidence from a Computer-Based Large-Scale Assessment Calibration Study

ERIC Educational Resources Information Center

Kaspar, Roman; Döring, Ottmar; Wittmann, Eveline; Hartig, Johannes; Weyland, Ulrike; Nauerth, Annette; Möllers, Michaela; Rechenbach, Simone; Simon, Julia; Worofka, Iberé

2016-01-01

Valid and reliable standardized assessment of nursing competencies is needed to monitor the quality of vocational education and training (VET) in nursing and evaluate learning outcomes for care work trainees with increasingly heterogeneous learning backgrounds. To date, however, the modeling of professional competencies has not yet evolved into…
Neonatal nurses' perceptions of a work-based learning approach.

PubMed

Stanley, Helen; Simmons, Susanne

2011-09-01

To examine how senior neonatal nurses perceive their experience of a continuing professional development module on their practice. A qualitative approach was used. Focus group interviews were held with five senior neonatal nurses at band 6 and 7. Discussions were taped and transcribed verbatim and field notes captured non-verbal communication. Four themes emerged: work-based learning as a new way of learning; barriers to learning at work; professional role development; and complexities of evaluating such learning. Work-based learning emerged as an active form of learning that can develop personal and professional skills required in the neonatal workforce.
Reconceptualizing Working Memory in Educational Research

ERIC Educational Resources Information Center

Fenesi, Barbara; Sana, Faria; Kim, Joseph A.; Shore, David I.

2015-01-01

In recent years, research from cognitive science has provided a solid theoretical framework to develop evidence-based interventions in education. In particular, research into reading, writing, language, mathematics and multimedia learning has been guided by the application of Baddeley's multicomponent model of working memory. However, an…
Lessons learned from recent geomagnetic disturbance model validation activities

NASA Astrophysics Data System (ADS)

Pulkkinen, A. A.; Welling, D. T.

2017-12-01

Due to concerns pertaining to geomagnetically induced current impact on ground-based infrastructure, there has been significantly elevated interest in applying models for local geomagnetic disturbance or "delta-B" predictions. Correspondingly there has been elevated need for testing the quality of the delta-B predictions generated by the modern empirical and physics-based models. To address this need, community-wide activities were launched under the GEM Challenge framework and one culmination of the activities was the validation and selection of models that were transitioned into operations at NOAA SWPC. The community-wide delta-B action is continued under the CCMC-facilitated International Forum for Space Weather Capabilities Assessment and its "Ground Magnetic Perturbations: dBdt, delta-B, GICs, FACs" working group. The new delta-B working group builds on the past experiences and expands the collaborations to cover the entire international space weather community. In this paper, we discuss the key lessons learned from the past delta-B validation exercises and lay out the path forward for building on those experience under the new delta-B working group.
[Learning about social determinants of health through chronicles, using a virtual learning environment].

PubMed

Restrepo-Palacio, Sonia; Amaya-Guio, Jairo

2016-01-01

To describe the contributions of a pedagogical strategy based on the construction of chronicles, using a Virtual Learning Environment for training medical students from Universidad de La Sabana on social determinants of health. Descriptive study with a qualitative approach. Design and implementation of a Virtual Learning Environment based on the ADDIE instructional model. A Virtual Learning Environment was implemented with an instructional design based on the five phases of the ADDIE model, on the grounds of meaningful learning and social constructivism, and through the narration of chronicles or life stories as a pedagogical strategy. During the course, the structural determinants and intermediaries were addressed, and nine chronicles were produced by working groups made up of four or five students, who demonstrated meaningful learning from real life stories, presented a coherent sequence, and kept a thread; 82% of these students incorporated in their contents most of the social determinants of health, emphasizing on the concepts of equity or inequity, equality or inequality, justice or injustice and social cohesion. A Virtual Learning Environment, based on an appropriate instructional design, allows to facilitate learning of social determinants of health through a constructivist pedagogical approach by analyzing chronicles or life stories created by ninth-semester students of medicine from Universidad de La Sabana.
Learning the Task Management Space of an Aircraft Approach Model

NASA Technical Reports Server (NTRS)

Krall, Joseph; Menzies, Tim; Davies, Misty

2014-01-01

Validating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.

Work-Based Learning: A New Higher Education?

ERIC Educational Resources Information Center

Boud, David, Ed.; Solomon, Nicky, Ed.

This three-part book contains 16 chapters exploring work-based learning from a theoretical and case-study perspective in the United Kingdom. Part 1, Framing Work-based Learning, contains the following four chapters: "New Practices for New Times" (David Boud, Nicky Solomon, and Colin Symes); "Repositioning Universities and Work"…
Washington State Guide to Planning, Implementing and Improving Work-based Learning. A Guide for Educators at All Levels.

ERIC Educational Resources Information Center

Highline Community Coll., Des Moines, WA.

This guide, which is intended primarily for school and college personnel interested in initiating or improving work-based learning, examines the development and implementation of work-based education programs in Washington. The following topics are discussed: the rationale for work-based learning (legislative and educational change information,…
Employer Involvement in Work-Based Learning Programs.

ERIC Educational Resources Information Center

Bailey, Thomas; Hughes, Katherine

A 3-year research project focused on whether sufficient numbers of employers could be recruited to create a national school-to-work system with a substantial work-based learning component as called for by the 1994 School-to-Work Opportunities Act. Research methods were as follows: case studies of 12 work-based learning programs at 9 sites located…
Using enterprise architecture to analyse how organisational structure impact motivation and learning

NASA Astrophysics Data System (ADS)

Närman, Pia; Johnson, Pontus; Gingnell, Liv

2016-06-01

When technology, environment, or strategies change, organisations need to adjust their structures accordingly. These structural changes do not always enhance the organisational performance as intended partly because organisational developers do not understand the consequences of structural changes in performance. This article presents a model-based analysis framework for quantitative analysis of the effect of organisational structure on organisation performance in terms of employee motivation and learning. The model is based on Mintzberg's work on organisational structure. The quantitative analysis is formalised using the Object Constraint Language (OCL) and the Unified Modelling Language (UML) and implemented in an enterprise architecture tool.
The Bologna Process Implementation and its Consequent Changes in the Teaching/Learning Model—the Industrial Management and Engineering Degree Case

NASA Astrophysics Data System (ADS)

Luísa Soares, Ana; Costa, Elga; Ferreira, Luís Pinto

2009-11-01

The present paper aims to present a Project included in a diversified programme and consequent implementation of a new Teaching/Learning model adapted to the Industrial Management and Engineering Degree (IMED) of the Management and Industrial Studies School (O'Porto Polytechnic Institute). Owning particular and specific characteristics, this model is based on the graduates' professional profile as well as on the work market dynamics, placing the student in the centre of the Learning Process, in opposition to the `teacher centred' method (as conceived by the Bologna Treat). Diverse in the approach, the model includes differentiating factors when compared to the project based traditional model. Through the development and conception of practical Interdisciplinary Projects, centring knowledges and techniques from the different Industrial Management and Engineering areas, we seek a new way of implementing the `Project Led Education' (PLE) bases, according to the Active Learning paradigm. This teaching/learning model aims to contribute to the Industrial Management and Engineering graduates' formation focused on a high level of performance and professional rectitude, to induce students' enthusiasm and motivation for acquiring scientific and technical knowledge, as well as to satisfy the diverse interest groups' expectations and promote the regional development.
Experiences of nursing home staff using the targeted interdisciplinary model for evaluation and treatment of neuropsychiatric symptoms (TIME) - a qualitative study.

PubMed

Lichtwarck, Bjørn; Myhre, Janne; Goyal, Alka R; Rokstad, Anne Marie Mork; Selbaek, Geir; Kirkevold, Øyvind; Bergh, Sverre

2018-04-19

Neuropsychiatric symptoms (NPS) in dementia pose great challenges for residents and staff in nursing homes. The Targeted Interdisciplinary Model for Evaluation and Treatment of Neuropsychiatric Symptoms (TIME) has recently in a randomized controlled trial demonstrated reductions in NPS. We explored the participating staff's experiences with the model and how it meets the challenges when dealing with the complexity of NPS. Three to six months after the end of the intervention, we interviewed 32 of the caregivers, leaders, and physicians participating in the trial, in five focus groups. We used thematic content analysis. The analysis yielded two main themes: (1) a systematic reflection method enhanced learning at work; (2) the structure of the approach helped staff to cope with NPS in residents with dementia. TIME shifts the way of learning for the staff from a traditional to a more innovative and reflection-based learning through a process of learning how to learn at work. The staff's experienced increased coping in their approach to complex problems. Our results emphasise the importance of a structured and biopsychosocial approach to NPS in clinical practice. Future research should explore models for integrating situated learning in daily routines in nursing homes.
Teachers' Temporary Support and Worked-Out Examples as Elements of Scaffolding in Mathematical Modeling

ERIC Educational Resources Information Center

Tropper, Natalie; Leiss, Dominik; Hänze, Martin

2015-01-01

Empirical findings show that students have manifold difficulties when dealing with mathematical modeling problems. Accordingly, approaches for supporting students in modeling-based learning environments have to be investigated. In the research presented here, we adopted a scaffolding perspective on teaching modeling with the aim of both providing…
Theories and Frameworks for Online Education: Seeking an Integrated Model

ERIC Educational Resources Information Center

Picciano, Anthony G.

2017-01-01

This article examines theoretical frameworks and models that focus on the pedagogical aspects of online education. After a review of learning theory as applied to online education, a proposal for an integrated "Multimodal Model for Online Education" is provided based on pedagogical purpose. The model attempts to integrate the work of…
Prognostic Physiology: Modeling Patient Severity in Intensive Care Units Using Radial Domain Folding

PubMed Central

Joshi, Rohit; Szolovits, Peter

2012-01-01

Real-time scalable predictive algorithms that can mine big health data as the care is happening can become the new “medical tests” in critical care. This work describes a new unsupervised learning approach, radial domain folding, to scale and summarize the enormous amount of data collected and to visualize the degradations or improvements in multiple organ systems in real time. Our proposed system is based on learning multi-layer lower dimensional abstractions from routinely generated patient data in modern Intensive Care Units (ICUs), and is dramatically different from most of the current work being done in ICU data mining that rely on building supervised predictive models using commonly measured clinical observations. We demonstrate that our system discovers abstract patient states that summarize a patient’s physiology. Further, we show that a logistic regression model trained exclusively on our learned layer outperforms a customized SAPS II score on the mortality prediction task. PMID:23304406
Machine Learning-based discovery of closures for reduced models of dynamical systems

NASA Astrophysics Data System (ADS)

Pan, Shaowu; Duraisamy, Karthik

2017-11-01

Despite the successful application of machine learning (ML) in fields such as image processing and speech recognition, only a few attempts has been made toward employing ML to represent the dynamics of complex physical systems. Previous attempts mostly focus on parameter calibration or data-driven augmentation of existing models. In this work we present a ML framework to discover closure terms in reduced models of dynamical systems and provide insights into potential problems associated with data-driven modeling. Based on exact closure models for linear system, we propose a general linear closure framework from viewpoint of optimization. The framework is based on trapezoidal approximation of convolution term. Hyperparameters that need to be determined include temporal length of memory effect, number of sampling points, and dimensions of hidden states. To circumvent the explicit specification of memory effect, a general framework inspired from neural networks is also proposed. We conduct both a priori and posteriori evaluations of the resulting model on a number of non-linear dynamical systems. This work was supported in part by AFOSR under the project ``LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.
Towards a Person-Centered Model of Instruction: Can an Emphasis on the Personal Enhance Instruction in Cyberspace?.

ERIC Educational Resources Information Center

Miller, Christopher T.; Mazur, Joan M.

A person-centered model of instruction has been developed for use in designing instruction in virtual, Web-based environments. This model, based on the work of Carl Rogers, attempts to address several issues raised in the literature regarding: (1) the changing role of instructors and students; (2) the broadening of the notion of learning outcomes;…
Evolution of learning strategies in temporally and spatially variable environments: A review of theory

PubMed Central

Aoki, Kenichi; Feldman, Marcus W.

2013-01-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change – coevolutionary, two-timescale, and information decay – are compared and shown to sometimes yield contradictory results. The so-called Rogers’ paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers’ paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. PMID:24211681
Evolution of learning strategies in temporally and spatially variable environments: a review of theory.

PubMed

Aoki, Kenichi; Feldman, Marcus W

2014-02-01

The theoretical literature from 1985 to the present on the evolution of learning strategies in variable environments is reviewed, with the focus on deterministic dynamical models that are amenable to local stability analysis, and on deterministic models yielding evolutionarily stable strategies. Individual learning, unbiased and biased social learning, mixed learning, and learning schedules are considered. A rapidly changing environment or frequent migration in a spatially heterogeneous environment favors individual learning over unbiased social learning. However, results are not so straightforward in the context of learning schedules or when biases in social learning are introduced. The three major methods of modeling temporal environmental change--coevolutionary, two-timescale, and information decay--are compared and shown to sometimes yield contradictory results. The so-called Rogers' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies, but is often eliminated when the other methods are used. Moreover, Rogers' paradox is not observed for the mixed learning strategies and learning schedules that we review. We believe that further theoretical work is necessary on learning schedules and biased social learning, based on models that are logically consistent and empirically pertinent. Copyright © 2013 Elsevier Inc. All rights reserved.
Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

NASA Astrophysics Data System (ADS)

Vaccaro, James M.; Yaworsky, Paul S.

1999-06-01

Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.
Pedagogy of Work-Based Learning: The Role of the Learning Group

ERIC Educational Resources Information Center

Siebert, Sabina; Mills, Vince; Tuff, Caroline

2009-01-01

Purpose: The aim of this paper is to evaluate the role of learning from participation in a group of work-based learners. Design/methodology/approach: This study relies on qualitative data obtained from a survey of perspectives of students on two work-based learning programmes. A group of 16 undergraduate and seven postgraduate students…
Illinois Work-Based Learning Programs: Worksite Mentor Knowledge and Training

ERIC Educational Resources Information Center

Chadd, Julie; Anderson, Marcia A.

2005-01-01

Teacher-coordinators and worksite mentors of high school work-based learning programs throughout Illinois were the subjects of this study which described worksite mentors' knowledge of teaching work skills to students participating in work-based learning programs and the nature of the training provided to these worksite mentors. There were no…
Work-Based Learning, Identity and Organisational Culture

ERIC Educational Resources Information Center

Ahlgren, Linda; Tett, Lyn

2010-01-01

This paper discusses the ways in which employers view the contribution of work-based learning, how participating learners' experience the provision offered to them and how far work-based programmes can contribute to changing the discourse about learning from one of deficit to one of strengths. It draws on two complementary studies of work based…
Inequality and Opportunity in Work-Based Learning

ERIC Educational Resources Information Center

Reilly, Michael Chavez

2014-01-01

Work-based learning in college--in the form of internships, cooperative education programs, and apprenticeships--sit at the crossroads of education and employment. It can play a crucial role in shaping a student's transition from school to work. This study explores the extent to which college students participate in work-based learning and the…
How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

PubMed Central

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Smolka, Michael N.; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities. PMID:28642696
How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

PubMed

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M; Zimmermann, Ulrich S; Schlagenhauf, Florian; Smolka, Michael N; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.

History matching through dynamic decision-making

PubMed Central

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

2017-01-01

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

ERIC Educational Resources Information Center

McRae, Norah; Johnston, Nancy

2016-01-01

Building on the work completed in BC that resulted in the development of a WIL Matrix for comparing and contrasting various forms of WIL with the Canadian co-op model, this paper proposes a Global Work-Integrated Learning Framework that allows for the comparison of a variety of models of work-integrated learning found in the international…
Toward a 3-P Model of Workplace Learning: A Literature Review

ERIC Educational Resources Information Center

Tynjala, Paivi

2013-01-01

The interest in research focusing on learning taking place at work, through work and for work has considerably increased over the past two decades. The purpose of the paper is to review and structure this wide and diverse research field. A tentative holistic model--the 3-P model of workplace learning--is presented, in relation to which the…
Stochastic Hybrid Systems Modeling and Middleware-enabled DDDAS for Next-generation US Air Force Systems

DTIC Science & Technology

2017-03-30

experimental evaluations for hosting DDDAS-like applications in public cloud infrastructures . Finally, we report on ongoing work towards using the DDDAS...developed and their experimental evaluations for hosting DDDAS-like applications in public cloud infrastructures . Finally, we report on ongoing work towards...Dynamic resource management, model learning, simulation-based optimizations, cloud infrastructures for DDDAS applications. I. INTRODUCTION Critical cyber
EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes

NASA Astrophysics Data System (ADS)

Wagh, Aditi; Wilensky, Uri

2018-04-01

Extensive research has shown that one of the benefits of programming to learn about scientific phenomena is that it facilitates learning about mechanisms underlying the phenomenon. However, using programming activities in classrooms is associated with costs such as requiring additional time to learn to program or students needing prior experience with programming. This paper presents a class of programming environments that we call quickstart: Environments with a negligible threshold for entry into programming and a modest ceiling. We posit that such environments can provide benefits of programming for learning without incurring associated costs for novice programmers. To make this claim, we present a design-based research study conducted to compare programming models of evolutionary processes with a quickstart toolkit with exploring pre-built models of the same processes. The study was conducted in six seventh grade science classes in two schools. Students in the programming condition used EvoBuild, a quickstart toolkit for programming agent-based models of evolutionary processes, to build their NetLogo models. Students in the exploration condition used pre-built NetLogo models. We demonstrate that although students came from a range of academic backgrounds without prior programming experience, and all students spent the same number of class periods on the activities including the time students took to learn programming in this environment, EvoBuild students showed greater learning about evolutionary mechanisms. We discuss the implications of this work for design research on programming environments in K-12 science education.
Developing an Online Learning Media Using Smartphone for the Electrical Machinery Course

ERIC Educational Resources Information Center

Muchlas

2018-01-01

This research is aimed to prepare a desktop-based learning media that can be used to support an online lab activities using android smartphones in Electrical Machinery Course at the Department of Electrical Engineering for the undergraduate level. This work uses a conceptual development model which integrates some sub systems of internet…
Students' Task Interpretation and Conceptual Understanding in an Electronics Laboratory

ERIC Educational Resources Information Center

Rivera-Reyes, Presentacion; Lawanto, Oenardi; Pate, Michael L.

2017-01-01

Task interpretation is a critical first step for students in the process of self-regulated learning, and a key determinant when they set goals in their learning and select strategies in assigned work. This paper focuses on the explicit and implicit aspects of task interpretation based on Hadwin's model. Laboratory activities improve students'…
Assessing the Viability of Team Learning with Remedial Students in a Lecture Based Japanese Higher Education Culture

ERIC Educational Resources Information Center

Duncan, Brent

2013-01-01

Despite a culture with cooperation as a core value, (Nagao, Takashi, & Okuda, 2011) Japanese higher education generally uses rigid lecture-test teaching models that neither support nor condone small-group learning methods in the classroom. As a result, Japanese college students usually work outside the classroom to develop the collaborative…
Proposals for Improving Assessment Systems in Higher Education: An Approach from the Model "Working with People"

ERIC Educational Resources Information Center

de los Ríos-Carmenado, I.; Sastre-Merino, Susana; Fernández Jiménez, Consuelo; Núñez del Río, Mª Cristina; Reyes Pozo, Encarnación; García Arjona, Noemi

2016-01-01

The European Higher Education Area (EHEA) represents a challenge to university teachers to adapt their assessment systems, directing them towards continuous assessment. The integration of competence-based learning as an educational benchmark has also led to a perspective more focused on student and with complex learning situations closer to…
Third Year Report: Evaluation of the Artful Learning Program. CRESST Report 760

ERIC Educational Resources Information Center

Griffin, Noelle C.; Miyoshi, Judy N.

2009-01-01

The National Center for Research on Evaluation, Standards, and Student Testing (CRESST) at University of California, Los Angeles (UCLA) was contracted to undertake a three-year external evaluation of the Artful Learning program, an arts-based school improvement model developed from the work and philosophy of the late composer Leonard Bernstein.…
Consequences of Team Charter Quality: Teamwork Mental Model Similarity and Team Viability in Engineering Design Student Teams

ERIC Educational Resources Information Center

Conway Hughston, Veronica

2014-01-01

Since 1996 ABET has mandated that undergraduate engineering degree granting institutions focus on learning outcomes such as professional skills (i.e. solving unstructured problems and working in teams). As a result, engineering curricula were restructured to include team based learning--including team charters. Team charters were diffused into…
The Development and Application of Distance Learning Courses on the Internet.

ERIC Educational Resources Information Center

Fuks, Hugo; Gerosa, Marco Aurelio; Lucena, Carlos Jose Pereira de

2002-01-01

Presents the methodology, results, and difficulties encountered in the development and delivery of a course through the Internet at a university in Rio de Janeiro. Provides a model for group work, including group discussions; and shows how a Web-based environment can be used to provide support and to facilitate cooperative learning. (Author/LRW)
A proposal on teaching methodology: cooperative learning by peer tutoring based on the case method

NASA Astrophysics Data System (ADS)

Pozo, Antonio M.; Durbán, Juan J.; Salas, Carlos; del Mar Lázaro, M.

2014-07-01

The European Higher Education Area (EHEA) proposes substantial changes in the teaching-learning model, moving from a model based mainly on the activity of teachers to a model in which the true protagonist is the student. This new framework requires that students develop new abilities and acquire specific skills. This also implies that the teacher should incorporate new methodologies in class. In this work, we present a proposal on teaching methodology based on cooperative learning and peer tutoring by case study. A noteworthy aspect of the case-study method is that it presents situations that can occur in real life. Therefore, students can acquire certain skills that will be useful in their future professional practice. An innovative aspect in the teaching methodology that we propose is to form work groups consisting of students from different levels in the same major. In our case, the teaching of four subjects would be involved: one subject of the 4th year, one subject of the 3rd year, and two subjects of the 2nd year of the Degree in Optics and Optometry of the University of Granada, Spain. Each work group would consist of a professor and a student of the 4th year, a professor and a student of the 3rd year, and two professors and two students of the 2nd year. Each work group would have a tutoring process from each professor for the corresponding student, and a 4th-year student providing peer tutoring for the students of the 2nd and 3rd year.
A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

NASA Astrophysics Data System (ADS)

Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

2017-11-01

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.
Linking College and Work: Exemplary Policies and Practices of Two-Year College Work-Based Learning Programs.

ERIC Educational Resources Information Center

Bragg, Debra D.; Hamm, Russell E.

Based on an initial phase of a study conducted in 1993 that surveyed 1,200 two-year colleges to describe the scope and character of work-based learning programs already in existence, phase 2 sought a more in-depth understanding of selected exemplary work-based learning programs. Ten programs in eight two-year colleges were identified for further…
Work-Based Learning: Good News, Bad News and Hope. Research Brief.

ERIC Educational Resources Information Center

Bottoms, Gene; Presson, Alice

The effects of work-based learning on student achievement were examined by analyzing data from the 1996 High Schools That Work (HSTW) assessment. The comparison focused on the experiences of 12th-graders in structured work-based learning programs and 12th-graders with after-school jobs. A larger percentage of students earning school credit for…
Solving problems with group work in problem-based learning: hold on to the philosophy.

PubMed

Dolmans, D H; Wolfhagen, I H; van der Vleuten, C P; Wijnen, W H

2001-09-01

Problem-based learning (PBL) has gained a foothold within many schools in higher education as a response to the problems faced within traditional education. Working with PBL tutorial groups is assumed to have positive effects on student learning. Several studies provide empirical evidence that PBL stimulates cognitive effects and leads to restructuring of knowledge and enhanced intrinsic interest in the subject matter. However, staff members do not always experience the positive effects of group work which they had hoped for. When confronted with problems in group work, such as students who only maintain an appearance of being actively involved and students who let others do the work, teachers all too often implement solutions which can be characterized as teacher- directed rather than student-directed. Teachers tend to choose solutions which are familiar from their own experience during professional training, i.e. using the teacher-directed model. These solutions are not effective in improving group work and the negative experiences persist. It is argued that teachers should hold on to the underlying educational philosophy when solving problems arising from group work in PBL, by choosing actions which are consistent with the student-directed view of education in PBL.
Better Categorizing Misconceptions Using a Contemporary Cognitive Science Lens

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

ERIC Educational Resources Information Center

Evans, Carl; Richardson, Mark

2018-01-01

Models of accrediting work-based learning are now commonplace in universities. The purpose of this viewpoint article is to highlight an opportunity for universities not only to accredit students' part-time work against the degree award but also to extend the process into schools by accrediting the part-time work undertaken by year 12 and 13…
Learning in Smaller Companies. Final Report.

ERIC Educational Resources Information Center

Seagraves, Liz; Osborne, Mike; Neal, Peter; Dockrell, Richard; Hartshorn, Christina; Boyd, Alison

The Learning in Smaller Companies (LISC) project was undertaken to develop links between academic institutions and work-based learning in Scotland. The University of Stirling worked with Falkirk College and Clackmannan College to create a number of work-based learning schemes for employers in small and medium-sized enterprises. The programs were…

SISL and SIRL: Two knowledge dissemination models with leader nodes on cooperative learning networks

NASA Astrophysics Data System (ADS)

Li, Jingjing; Zhang, Yumei; Man, Jiayu; Zhou, Yun; Wu, Xiaojun

2017-02-01

Cooperative learning is one of the most effective teaching methods, which has been widely used. Students' mutual contact forms a cooperative learning network in this process. Our previous research demonstrated that the cooperative learning network has complex characteristics. This study aims to investigating the dynamic spreading process of the knowledge in the cooperative learning network and the inspiration of leaders in this process. To this end, complex network transmission dynamics theory is utilized to construct the knowledge dissemination model of a cooperative learning network. Based on the existing epidemic models, we propose a new susceptible-infected-susceptible-leader (SISL) model that considers both students' forgetting and leaders' inspiration, and a susceptible-infected-removed-leader (SIRL) model that considers students' interest in spreading and leaders' inspiration. The spreading threshold λcand its impact factors are analyzed. Then, numerical simulation and analysis are delivered to reveal the dynamic transmission mechanism of knowledge and leaders' role. This work is of great significance to cooperative learning theory and teaching practice. It also enriches the theory of complex network transmission dynamics.
Work-based learning: challenges and opportunities.

PubMed

Gallagher, Ann; Holland, Lesley

This article discusses some of the challenges and opportunities arising from the development and implementation of an innovative work-based open and distance learning programme available exclusively to healthcare assistants working in general health and mental health practice. The programme is based on a partnership between the sponsoring organisation and the Open University. The focus is on the development of standards of proficiency, service user involvement, partnership working, skills development and the pedagogic implications of a work-based learning format.
Mathematics Literacy on Problem Based Learning with Indonesian Realistic Mathematics Education Approach Assisted E-Learning Edmodo

NASA Astrophysics Data System (ADS)

Wardono; Waluya, S. B.; Mariani, Scolastika; Candra D, S.

2016-02-01

This study aims to find out that there are differences in mathematical literacy ability in content Change and Relationship class VII Junior High School 19, Semarang by Problem Based Learning (PBL) model with an Indonesian Realistic Mathematics Education (called Pendidikan Matematika Realistik Indonesia or PMRI in Indonesia) approach assisted Elearning Edmodo, PBL with a PMRI approach, and expository; to know whether the group of students with learning PBL models with PMRI approach and assisted E-learning Edmodo can improve mathematics literacy; to know that the quality of learning PBL models with a PMRI approach assisted E-learning Edmodo has a good category; to describe the difficulties of students in working the problems of mathematical literacy ability oriented PISA. This research is a mixed methods study. The population was seventh grade students of Junior High School 19, Semarang Indonesia. Sample selection is done by random sampling so that the selected experimental class 1, class 2 and the control experiment. Data collected by the methods of documentation, tests and interviews. From the results of this study showed average mathematics literacy ability of students in the group PBL models with a PMRI approach assisted E-learning Edmodo better than average mathematics literacy ability of students in the group PBL models with a PMRI approach and better than average mathematics literacy ability of students in the expository models; Mathematics literacy ability in the class using the PBL model with a PMRI approach assisted E-learning Edmodo have increased and the improvement of mathematics literacy ability is higher than the improvement of mathematics literacy ability of class that uses the model of PBL learning with PMRI approach and is higher than the improvement of mathematics literacy ability of class that uses the expository models; The quality of learning using PBL models with a PMRI approach assisted E-learning Edmodo have very good category.
A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.
Epistemological Beliefs and Knowledge Sharing in Work Teams: A New Model and Research Questions

ERIC Educational Resources Information Center

Weinberg, Frankie J.

2015-01-01

Purpose: The purpose of this paper is to present a knowledge-sharing model that explains individual members' motivation to share knowledge (knowledge donation and knowledge collection). Design/methodology/approach: The model is based on social-constructivist theories of epistemological beliefs, learning and distributed cognition, and is organized…
Work, Train, Win: Work-Based Learning Design and Management for Productivity Gains. OECD Education Working Papers, No. 135

ERIC Educational Resources Information Center

Kis, Viktoria

2016-01-01

Realising the potential of work-based learning schemes as a driver of productivity requires careful design and support. The length of work-based learning schemes should be adapted to the profile of productivity gains. A scheme that is too long for a given skill set might be unattractive for learners and waste public resources, but a scheme that is…
Work-Based Learning and Social Justice: "Learning to Labour" and the New Vocationalism in England

ERIC Educational Resources Information Center

Avis, James

2004-01-01

The article explores work-based learning in the context of current changes taking place in vocational education and training in England. It seeks to locate these within an understanding of the economy and the way in which work-based knowledge is construed. The article analyses these issues, drawing upon a literature that examines the work-based…
UPMC's blueprint for BuILDing a high-value health care system.

PubMed

Keyser, Donna; Kogan, Jane; McGowan, Marion; Peele, Pamela; Holder, Diane; Shrank, William

2018-03-30

National-level demonstration projects and real-world studies continue to inform health care transformation efforts and catalyze implementation of value-based service delivery and payment models, though evidence generation and diffusion of learnings often occurs at a relatively slow pace. Rapid-cycle learning models, however, can help individual organizations to more quickly adapt health care innovations to meet the challenges and demands of a rapidly changing health care landscape. Integrated delivery and financing systems (IDFSs) offer a unique platform for rapid-cycle learning and innovation. Since both the provider and payer benefit from delivering care that enhances the patient experience, improves quality, and reduces cost, incentives are aligned to experiment with value-based models, enhance learning about what works and why, and contribute to solutions that can accelerate transformation. In this article, we describe how the UPMC Insurance Services Division, as part of a large IDFS, uses its Business, Innovation, Learning, and Dissemination (BuILD) model to prioritize, design, test, and refine health care innovations and accelerate learning. We provide examples of how the BuILD model offers an approach for quickly assessing the impact and value of health care transformation efforts. Lessons learned through the BuILD process will offer insights and guidance for a wide range of stakeholders whether an IDFS or independent payer-provider collaborators. Copyright © 2018 Elsevier Inc. All rights reserved.
Bankruptcy prediction based on financial ratios using Jordan Recurrent Neural Networks: a case study in Polish companies

NASA Astrophysics Data System (ADS)

Hardinata, Lingga; Warsito, Budi; Suparti

2018-05-01

Complexity of bankruptcy causes the accurate models of bankruptcy prediction difficult to be achieved. Various prediction models have been developed to improve the accuracy of bankruptcy predictions. Machine learning has been widely used to predict because of its adaptive capabilities. Artificial Neural Networks (ANN) is one of machine learning which proved able to complete inference tasks such as prediction and classification especially in data mining. In this paper, we propose the implementation of Jordan Recurrent Neural Networks (JRNN) to classify and predict corporate bankruptcy based on financial ratios. Feedback interconnection in JRNN enable to make the network keep important information well allowing the network to work more effectively. The result analysis showed that JRNN works very well in bankruptcy prediction with average success rate of 81.3785%.
Junior Army Officer Retention Intentions: A Path Analytic Model

DTIC Science & Technology

1991-07-01

theoretically useful only if they explain behavior that cannot be predicted within traditional expectancy and equity based motivational models. Scholl (1981), in...argue that long-tenured employees need to justify their behavioral commitment to the organization. They do this by developing more positive attitudes...good" to "very poor" scale to rate opportunities for intrinsic work satisfaction (learn/ develop skills, do interesting work, exercise initiative) in
The Greek Challenge in Work-Based Learning

ERIC Educational Resources Information Center

Taousanidis, Nikolaos I.; Antoniadou, Myrofora A.

2008-01-01

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

PubMed

Kemp, Philip; Gilding, Moorene; Seewooruttun, Khooseal; Walsh, Hannah

2016-09-14

Background With a rise in the number of unqualified staff providing health and social care, and reports raising concerns about the quality of care provided, there is a need to address the learning needs of clinical support workers. This article describes a qualitative evaluation of a service improvement project that involved a work-based learning approach for clinical support workers on mental health inpatient wards. Aim To investigate and identify insights in relation to the content and process of learning using a work-based learning approach for clinical support workers. Method This was a qualitative evaluation of a service improvement project involving 25 clinical support workers at the seven mental health inpatient units in South London and Maudsley NHS Foundation Trust. Three clinical skills tutors were appointed to develop, implement and evaluate the work-based learning approach. Four sources of data were used to evaluate this approach, including reflective journals, qualitative responses to questionnaires, three focus groups involving the clinical support workers and a group interview involving the clinical skills tutors. Data were analysed using thematic analysis. Findings The work-based learning approach was highly valued by the clinical support workers and enhanced learning in practice. Face-to-face learning in practice helped the clinical support workers to develop practice skills and reflective learning skills. Insights relating to the role of clinical support workers were also identified, including the benefits of face-to-face supervision in practice, particularly in relation to the interpersonal aspects of care. Conclusion A work-based learning approach has the potential to enhance care delivery by meeting the learning needs of clinical support workers and enabling them to apply learning to practice. Care providers should consider how the work-based learning approach can be used on a systematic, organisation-wide basis in the context of budgetary restrictions.
The contribution of temporary storage and executive processes to category learning.

PubMed

Wang, Tengfei; Ren, Xuezhu; Schweizer, Karl

2015-09-01

Three distinctly different working memory processes, temporary storage, mental shifting and inhibition, were proposed to account for individual differences in category learning. A sample of 213 participants completed a classic category learning task and two working memory tasks that were experimentally manipulated for tapping specific working memory processes. Fixed-links models were used to decompose data of the category learning task into two independent components representing basic performance and improvement in performance in category learning. Processes of working memory were also represented by fixed-links models. In a next step the three working memory processes were linked to components of category learning. Results from modeling analyses indicated that temporary storage had a significant effect on basic performance and shifting had a moderate effect on improvement in performance. In contrast, inhibition showed no effect on any component of the category learning task. These results suggest that temporary storage and the shifting process play different roles in the course of acquiring new categories. Copyright © 2015 Elsevier B.V. All rights reserved.
A Neural Circuit Model of Flexible Sensori-motor Mapping: Learning and Forgetting on Multiple Timescales

PubMed Central

Fusi, Stefano; Asaad, Wael F.; Miller, Earl K.; Wang, Xiao-Jing

2007-01-01

Summary Volitional behavior relies on the brain’s ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically-based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuo-motor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well established sensorimotor associations. PMID:17442251
A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales.

PubMed

Fusi, Stefano; Asaad, Wael F; Miller, Earl K; Wang, Xiao-Jing

2007-04-19

Volitional behavior relies on the brain's ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuomotor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well-established sensorimotor associations.
20 CFR 670.520 - Are students permitted to hold jobs other than work-based learning opportunities?

Code of Federal Regulations, 2012 CFR

2012-04-01

... 20 Employees' Benefits 4 2012-04-01 2012-04-01 false Are students permitted to hold jobs other than work-based learning opportunities? 670.520 Section 670.520 Employees' Benefits EMPLOYMENT AND... than work-based learning opportunities? Yes, a center operator may authorize a student to participate...
20 CFR 670.520 - Are students permitted to hold jobs other than work-based learning opportunities?

Code of Federal Regulations, 2013 CFR

2013-04-01

... 20 Employees' Benefits 4 2013-04-01 2013-04-01 false Are students permitted to hold jobs other than work-based learning opportunities? 670.520 Section 670.520 Employees' Benefits EMPLOYMENT AND... than work-based learning opportunities? Yes, a center operator may authorize a student to participate...
20 CFR 670.520 - Are students permitted to hold jobs other than work-based learning opportunities?

Code of Federal Regulations, 2014 CFR

2014-04-01

... 20 Employees' Benefits 4 2014-04-01 2014-04-01 false Are students permitted to hold jobs other than work-based learning opportunities? 670.520 Section 670.520 Employees' Benefits EMPLOYMENT AND... than work-based learning opportunities? Yes, a center operator may authorize a student to participate...
Statewide Work-Based Learning Intermediary Network: Fiscal Year 2014 Report

ERIC Educational Resources Information Center

Iowa Department of Education, 2014

2014-01-01

The Statewide Work-based Learning Intermediary Network Fiscal Year 2014 Report summarizes fiscal year 2014 (FY14) work-based learning activities of the 15 regional intermediary networks. This report includes activities which occurred between October 1, 2013, to June 30, 2014. It is notable that some intermediary regional networks have been in…
Florida's Work-Based Learning and Child Labor Law. Resource Guide.

ERIC Educational Resources Information Center

Florida State Univ., Tallahassee. School-to-Work Clearinghouse.

This guide was developed to address issues related to work-based learning experiences at an employer's worksite and to explain when and how federal and state (Florida) labor laws and minimum wage provisions apply. It includes the following documents: "Definitions of Terms--Work Based Learning" (Institute for Workforce Competitiveness);…

An Open IMS-Based User Modelling Approach for Developing Adaptive Learning Management Systems

ERIC Educational Resources Information Center

Boticario, Jesus G.; Santos, Olga C.

2007-01-01

Adaptive LMS have not yet reached the eLearning marketplace due to methodological, technological and management open issues. At aDeNu group, we have been working on two key challenges for the last five years in related research projects. Firstly, develop the general framework and a running architecture to support the adaptive life cycle (i.e.,…
Suggestopedia Based Storytelling Teaching Model for Primary Students in Salatiga

ERIC Educational Resources Information Center

Sunardi; Waluyo, Herman J.; Suudi, Astini; Wardani, Nugraheni Eko

2018-01-01

Teaching and learning speaking skills should be able to engage students in a creative process. Students have to be able to speak in front of the class, create a dialogue, tell a story, and produce the language creatively. The teaching and learning of the speaking skill focusing on story telling ability can work well when supported by the…
Knowledge Ecology for Conceptual Growth: Teachers as Active Agents in Developing a Pluriliteracies Approach to Teaching for Learning (PTL)

ERIC Educational Resources Information Center

Coyle, Do; Halbach, Ana; Meyer, Oliver; Schuck, Kevin

2018-01-01

This article explores how a group of educators and researchers enacted an inclusive process of conceptual growth involving teachers and teacher educators as active agents, knowledge builders and meaning-makers in the development of a Pluriliteracies approach to Teaching for Learning (PTL). The evolution of a working model based on five emergent…
Behavioral and neural properties of social reinforcement learning

PubMed Central

Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Libby, Victoria; Glover, Gary; Voss, Henning U.; Ballon, Douglas J.; Casey, BJ

2011-01-01

Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based upon work in non-human primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging (fMRI). Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis - social preferences, response latencies and modeling neural responses – are consistent with reinforcement learning theory and non-human primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one’s peers in altering subsequent behavior. PMID:21917787
From the Reality of Work to Grounded Work-Based Learning in German Vocational Education and Training: Background, Concept and Tools

ERIC Educational Resources Information Center

Gessler, Michael; Howe, Falk

2015-01-01

The "Riga Conclusions" of the European Ministries of Education of 22 June 2015 for the orientation of vocational education and training in Europe are promoting work-based learning as one of five "medium-term deliverables" for the next five years. But: How should and can work-based teaching and learning be designed? Our approach…
Brain drain? An examination of stereotype threat effects during training on knowledge acquisition and organizational effectiveness.

PubMed

Grand, James A

2017-02-01

Stereotype threat describes a situation in which individuals are faced with the risk of upholding a negative stereotype about their subgroup based on their actions. Empirical work in this area has primarily examined the impact of negative stereotypes on performance for threatened individuals. However, this body of research seldom acknowledges that performance is a function of learning-which may also be impaired by pervasive group stereotypes. This study presents evidence from a 3-day self-guided training program demonstrating that stereotype threat impairs acquisition of cognitive learning outcomes for females facing a negative group stereotype. Using hierarchical Bayesian modeling, results revealed that stereotyped females demonstrated poorer declarative knowledge acquisition, spent less time reflecting on learning activities, and developed less efficiently organized knowledge structures compared with females in a control condition. Findings from a Bayesian mediation model also suggested that despite stereotyped individuals "working harder" to perform well, their underachievement was largely attributable to failures in learning to "work smarter." Building upon these empirical results, a computational model and computer simulation is also presented to demonstrate the practical significance of stereotype-induced impairments to learning on the development of an organization's human capital resources and capabilities. The simulation results show that even the presence of small effects of stereotype threat during learning/training have the potential to exert a significant negative impact on an organization's performance potential. Implications for future research and practice examining stereotype threat during learning are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Cesar Chavez--Kindergarten Model Curriculum and Resources.

ERIC Educational Resources Information Center

California State Dept. of Education, Sacramento.

In this California state curriculum model for kindergarten, "Learning and Working Now and Long Ago," students study the life, work, and philosophy of Cesar Chavez. The students learn that being a good citizen involves acting in certain ways. They have the opportunity to learn about the work people do to grow food, to harvest the crops,…
Metrological traceability in education: A practical online system for measuring and managing middle school mathematics instruction

NASA Astrophysics Data System (ADS)

Torres Irribarra, D.; Freund, R.; Fisher, W.; Wilson, M.

2015-02-01

Computer-based, online assessments modelled, designed, and evaluated for adaptively administered invariant measurement are uniquely suited to defining and maintaining traceability to standardized units in education. An assessment of this kind is embedded in the Assessing Data Modeling and Statistical Reasoning (ADM) middle school mathematics curriculum. Diagnostic information about middle school students' learning of statistics and modeling is provided via computer-based formative assessments for seven constructs that comprise a learning progression for statistics and modeling from late elementary through the middle school grades. The seven constructs are: Data Display, Meta-Representational Competence, Conceptions of Statistics, Chance, Modeling Variability, Theory of Measurement, and Informal Inference. The end product is a web-delivered system built with Ruby on Rails for use by curriculum development teams working with classroom teachers in designing, developing, and delivering formative assessments. The online accessible system allows teachers to accurately diagnose students' unique comprehension and learning needs in a common language of real-time assessment, logging, analysis, feedback, and reporting.
Fostering Positive Youth Development through Work-Based Learning: The Cristo Rey Model

ERIC Educational Resources Information Center

Bempechat, Janine; Kenny, Maureen; Blustein, David L.; Seltzer, Joanne

2014-01-01

This chapter presents findings of a three-year longitudinal study of academic motivation and school engagement among low-income high school students enrolled in a corporate work-study program. Our findings demonstrate ways in which the workplace functioned for students as a conduit of emotional resources, offering instrumental support from caring…
Roles of Working Memory Performance and Instructional Strategy in Complex Cognitive Task Performance

ERIC Educational Resources Information Center

Cevik, V.; Altun, A.

2016-01-01

This study aims to investigate how working memory (WM) performances and instructional strategy choices affect learners' complex cognitive task performance in online environments. Three different e-learning environments were designed based on Merrill's (2006a) model of instructional strategies. The lack of experimental research on his framework is…
Development of a Work-Based Learning Model for Youth with Disabilities from the Perspective of Employers

ERIC Educational Resources Information Center

Sametz, Rebecca R.

2017-01-01

For youth with disabilities, transitioning from school to work and adult life often means overcoming multiple social, academic, and environmental constraints that may present as roadblocks to meeting society's expectations of 'successful transition' (Lehman, Clark, Bullis, Rinkin, & Castellanos, 2002). According to the United States Department…
Enhancing Social Work Education through Team-Based Learning

ERIC Educational Resources Information Center

Gillespie, Judy

2012-01-01

Group learning strategies are used extensively in social work education, despite the challenges and negative outcomes regularly experienced by students and faculty. Building on principles of cooperative learning, team-based learning offers a more structured approach that maximizes the benefits of cooperative learning while also offering…
Is that really my movement? - Students' experiences of a video-supported interactive learning model for movement awareness.

PubMed

Backåberg, Sofia; Gummesson, Christina; Brunt, David; Rask, Mikael

2015-01-01

Healthcare staff and students have a great risk of developing musculoskeletal symptoms. One cause of this is heavy load related work activities such as manual handling, in which the quality of individual work technique may play a major role. Preventive interventions and well-defined educational strategies to support movement awareness and long-lasting movement changes need to be developed. The aim of the present study was to explore nursing students' experiences of a newly developed interactive learning model for movement awareness. The learning model, which is based on a life-world perspective with focus on interpersonal interaction, has been used with 11 undergraduate students from the second and final year. Each student participated in three individual video sessions with a facilitator. Two individual interviews were carried out with each student during the learning process and one interview 12-18 months after the last session. The interviews were audio-recorded and transcribed verbatim, and a phenomenological hermeneutic method inspired by Paul Ricoeur and described by Lindseth and Norberg was used to interpret the interviews and diary notes. The interpretation resulted in three key themes and nine subthemes. The key themes were; "Obtaining better preconditions for bodily awareness," "Experiencing changes in one's own movement," and "Experiencing challenges in the learning process." The interactive learning model entails a powerful and challenging experience that develops movement awareness. The experience of meaningfulness and usefulness emerges increasingly and alternates with a feeling of discomfort. The learning model may contribute to the body of knowledge of well-defined educational strategies in movement awareness and learning in, for example, preventive interventions and ergonomic education. It may also be valuable in other practical learning situations where movement awareness is required.
Learning to recognize objects on the fly: a neurally based dynamic field approach.

PubMed

Faubel, Christian; Schöner, Gregor

2008-05-01

Autonomous robots interacting with human users need to build and continuously update scene representations. This entails the problem of rapidly learning to recognize new objects under user guidance. Based on analogies with human visual working memory, we propose a dynamical field architecture, in which localized peaks of activation represent objects over a small number of simple feature dimensions. Learning consists of laying down memory traces of such peaks. We implement the dynamical field model on a service robot and demonstrate how it learns 30 objects from a very small number of views (about 5 per object are sufficient). We also illustrate how properties of feature binding emerge from this framework.
Teaching Foreign Trade in English through the Modalities Based on Competences and Using Moodle (Enseñanza del régimen de comercio exterior a través de las modalidades centradas en el desarrollo de competencias y con el uso de Moodle)

ERIC Educational Resources Information Center

Correa Díaz, Ana María

2012-01-01

With the new approach to guide the learning process of students with a model based on the development of competences, and in comparison with the traditional lecture-based learning, it is necessary to start working with the teaching modalities that help to achieve this objective. With that in mind, the aim of the study reported in this article was…
Semi-supervised Learning for Phenotyping Tasks.

PubMed

Dligach, Dmitriy; Miller, Timothy; Savova, Guergana K

2015-01-01

Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review.
Using self-paced, `flipped' teaching to promote deep learning in an Earth Sciences programming course

NASA Astrophysics Data System (ADS)

Kalnins, L. M.

2015-12-01

Over the last year we implemented a complete restructuring of a second year Matlab-based course on numerical modelling of Earth processes, with changes aimed at 1) strengthening students' independence as programmers, 2) addressing student concerns about support in developing coding skills, and 3) improving key modelling skills such as choosing boundary conditions. To address this, we designed a mastery-based approach where students progress through a series of small programming projects at their own pace. As part of this, all lectures are `flipped' into short videos, allowing all contact hours to be spent on programming. The projects themselves are structured based on a `bottlenecks to learning' approach, explicitly separating out the steps of learning new commands and code structures, creating a conceptual and mathematical model of the problem, and development of more generic programmings skills such as debugging before asking the students to combine all of the above to build a numerical model of an Earth Sciences problem. Compared with the previous, traditionally taught cohort, student questionnaires show a strong improvement in overall satisfaction. Free text responses show a focus on learning for understanding, and that students particularly valued the encouragement to slow down and work towards understanding when they encountered a difficult topic, rather than being pressured by a set timetable to move on. Quantitatively, exam performance improved on key conceptual questions, such as boundary conditions and discretisation, and overall achievement also rose, with 25% of students achieving an `A+' standard of work. Many of the final projects also demonstrated programming and modelling skills that had not been directly taught, ranging from use of new commands to extension of techniques taught in 1D to the 2D case: strong confirmation of the independent skills we aimed to foster with this new approach.
Learning with the Arts: What Opportunities Are There for Work-Related Adult Learning?

ERIC Educational Resources Information Center

Manning, Claire; Verenikina, Irina; Brown, Ian

2010-01-01

What can arts-based learning offer to adult, work-related education? A study was undertaken that explored the benefits of learning with the arts for professional development of an adult learner in Australia. The individual experiences of nine adults who participated in arts-based workshops to build work-related skills were examined using the…
Work-Based Learning for Adolescents with Learning Disabilities: Creating a Context for Success

ERIC Educational Resources Information Center

Versnel, Joan; Hutchinson, Nancy L.; Munby, Hugh; Chin, Peter

2008-01-01

This paper describes cases of two adolescents with learning disabilities working in automotive repair businesses as part of a work-based education program. Neither adolescent was judged to have been successful by the workplace supervisors. The frameworks chosen for analyzing these cases draw upon recent work in self-determination, workplace…
Work-based learning in health care organisations experienced by nursing staff: A systematic review of qualitative studies.

PubMed

Nevalainen, Marja; Lunkka, Nina; Suhonen, Marjo

2018-03-01

The aim of this review is to systematically summarise qualitative evidence about work-based learning in health care organisations as experienced by nursing staff. Work-based learning is understood as informal learning that occurs inside the work community in the interaction between employees. Studies for this review were searched for in the CINAHL, PubMed, Scopus and ABI Inform ProQuest databases for the period 2000-2015. Nine original studies met the inclusion criteria. After the critical appraisal by two researchers, all nine studies were selected for the review. The findings of the original studies were aggregated, and four statements were prepared, to be utilised in clinical work and decision-making. The statements concerned the following issues: (1) the culture of the work community; (2) the physical structures, spaces and duties of the work unit; (3) management; and (4) interpersonal relations. Understanding the nurses' experiences of work-based learning and factors behind these experiences provides an opportunity to influence the challenges of learning in the demanding context of health care organisations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sinte Gleska University Reclaims Land from Loneliness.

ERIC Educational Resources Information Center

Crazy Bull, Cheryl

2000-01-01

Sinte Gleska University's (SGU) model for community development includes transformation of an old boarding school site, community-based collaborations in gardening and nutrition, and a bison restoration project. Tribal members learn to work with the land in harmony with tribal stewardship models as well as Western land use and agricultural…
Teaching, Learning and Evaluation Techniques in the Engineering Courses.

ERIC Educational Resources Information Center

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

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

ERIC Educational Resources Information Center

Nastasi, Bonnie K.; Varjas, Kristen; Sarkar, Sreeroopa; Jayasena, Asoka

1998-01-01

Describes application of participatory model for creating school-based mental health services in a developing country. Describes process of identifying individual and cultural factors relevant to mental health. Discusses importance of formative research and collaboration with stakeholders to ensure cultural specificity of interventions, and the…
Model-based Utility Functions

NASA Astrophysics Data System (ADS)

Hibbard, Bill

2012-05-01

Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.
Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.
Model-Based Learning of Local Image Features for Unsupervised Texture Segmentation

NASA Astrophysics Data System (ADS)

Kiechle, Martin; Storath, Martin; Weinmann, Andreas; Kleinsteuber, Martin

2018-04-01

Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods. The manual selection of features or designing new ones can be a tedious task. Therefore, it is desirable to automatically adapt the features to a certain image or class of images. Typically, this requires a large set of training images with similar textures and ground truth segmentation. In this work, we propose a framework to learn features for texture segmentation when no such training data is available. The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model. This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set. Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation. We note that the features can be learned from a small set of images, from a single image, or even from image patches. The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.
Perception Evolution Network Based on Cognition Deepening Model--Adapting to the Emergence of New Sensory Receptor.

PubMed

Xing, Youlu; Shen, Furao; Zhao, Jinxi

2016-03-01

The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. In the experiment, artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.
Use of Inverse Reinforcement Learning for Identity Prediction

NASA Technical Reports Server (NTRS)

Hayes, Roy; Bao, Jonathan; Beling, Peter; Horowitz, Barry

2011-01-01

We adopt Markov Decision Processes (MDP) to model sequential decision problems, which have the characteristic that the current decision made by a human decision maker has an uncertain impact on future opportunity. We hypothesize that the individuality of decision makers can be modeled as differences in the reward function under a common MDP model. A machine learning technique, Inverse Reinforcement Learning (IRL), was used to learn an individual's reward function based on limited observation of his or her decision choices. This work serves as an initial investigation for using IRL to analyze decision making, conducted through a human experiment in a cyber shopping environment. Specifically, the ability to determine the demographic identity of users is conducted through prediction analysis and supervised learning. The results show that IRL can be used to correctly identify participants, at a rate of 68% for gender and 66% for one of three college major categories.
A Collaborative Team Teaching Model for a MSW Capstone Course.

PubMed

Moore, Rebecca M; Darby, Kathleen H; Blake, Michelle E

2016-01-01

This exploratory study was embedded in a formative process for the purposes of improving content delivery to an evidence-based practice class, and improving students' performance on a comprehensive exam. A learning and teaching model was utilized by faculty from a three-university collaborative graduate social work program to examine the extent to which course texts and assignments explicitly supported the process, application, and evaluation of evidence-based practices. The model was grounded in a collaborative culture, allowing each faculty to share their collective skills and knowledge across a range of practice settings as they revised the course curriculum. As a result, faculty found they had created a unique community that allowed a wider context for learning and professional development that translated into the classroom. Students enrolled in the revised course across all three universities showed improvement on the comprehensive exam. When faculty themselves invest in collaborative learning and teaching, students benefit.
Conversations about curriculum change: mathematical thinking and team-based learning in a discrete mathematics course

NASA Astrophysics Data System (ADS)

Paterson, Judy; Sneddon, Jamie

2011-10-01

This article reports on the learning conversations between a mathematician and a mathematics educator as they worked together to change the delivery model of a third year discrete mathematics course from a traditional lecture mode to team-based learning (TBL). This change prompted the mathematician to create team tasks which increasingly focused on what he calls the 'unspoken curriculum': mathematical thinking. We consider the ways in which the TBL model promoted and enabled this in the light of literature on mathematical thinking, sense-making and behaviours, and strongly suggest that this approach warrants more attention from the mathematics teaching community. We also discuss shifts in the mathematician's thinking about task construction as he refined the tasks to encourage students to think and behave like mathematicians.
Using texts in science education: cognitive processes and knowledge representation.

PubMed

van den Broek, Paul

2010-04-23

Texts form a powerful tool in teaching concepts and principles in science. How do readers extract information from a text, and what are the limitations in this process? Central to comprehension of and learning from a text is the construction of a coherent mental representation that integrates the textual information and relevant background knowledge. This representation engenders learning if it expands the reader's existing knowledge base or if it corrects misconceptions in this knowledge base. The Landscape Model captures the reading process and the influences of reader characteristics (such as working-memory capacity, reading goal, prior knowledge, and inferential skills) and text characteristics (such as content/structure of presented information, processing demands, and textual cues). The model suggests factors that can optimize--or jeopardize--learning science from text.
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.
The Challenges of Work-Based Learning in the Changing Context of the European Higher Education Area

ERIC Educational Resources Information Center

Schmidt, Reinhard; Gibbs, Paul

2009-01-01

This article discusses the key features of the common European framework for work-based learning (WBL) of the "Developing European Work Based Learning Approaches and Methods" (DEWBLAM) project (2003-2006). It examines the context of recent European initiatives and comments on the potential implications for policy, practice and theory,…
The Use of Work-Based Learning Pedagogical Perspectives to Inform Flexible Practice within Higher Education

ERIC Educational Resources Information Center

Nottingham, Paula

2016-01-01

The renewed emphasis on developing flexible learning practices in higher education (HE) underscores the importance of understanding pedagogies for students who are based in the workplace or undertake significant work-related elements of study. This paper draws on research that explores how work-based learning (WBL) pedagogy operates in UK HE using…
Modeling the Water Balloon Slingshot

NASA Astrophysics Data System (ADS)

Bousquet, Benjamin D.; Figura, Charles C.

2013-01-01

In the introductory physics courses at Wartburg College, we have been working to create a lab experience focused on the scientific process itself rather than verification of physical laws presented in the classroom or textbook. To this end, we have developed a number of open-ended modeling exercises suitable for a variety of learning environments, from non-science major classes to algebra-based and calculus-based introductory physics classes.
Development of concept-based physiology lessons for biomedical engineering undergraduate students.

PubMed

Nelson, Regina K; Chesler, Naomi C; Strang, Kevin T

2013-06-01

Physiology is a core requirement in the undergraduate biomedical engineering curriculum. In one or two introductory physiology courses, engineering students must learn physiology sufficiently to support learning in their subsequent engineering courses and careers. As preparation for future learning, physiology instruction centered on concepts may help engineering students to further develop their physiology and biomedical engineering knowledge. Following the Backward Design instructional model, a series of seven concept-based lessons was developed for undergraduate engineering students. These online lessons were created as prerequisite physiology training to prepare students to engage in a collaborative engineering challenge activity. This work is presented as an example of how to convert standard, organ system-based physiology content into concept-based content lessons.
Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.
Comparative Cooperative Education: Evaluating Thai Models on Work-Integrated Learning, Using the German Duale Hochschule Baden-Wuerttemberg Model as a Benchmark

ERIC Educational Resources Information Center

Reinhard, Karin; Pogrzeba, Anna

2016-01-01

The role of industry in the higher education system is becoming more prevalent, as universities integrate a practical element into their curricula. However, the level of development of cooperative education and work-integrated learning varies from country to country. In Germany, cooperative education and work-integrated learning has a long…
Learning from lives together: medical and social work students' experiences of learning from people with disabilities in the community.

PubMed

Anderson, E S; Smith, R; Thorpe, L N

2010-05-01

The study aims to evaluate an interprofessional community-based learning event, focussing on disability. The learning opportunity was based on the Leicester Model of Interprofessional Education, organised around the experiences and perceptions of service users and their carers. Programme participants were drawn from medicine and social work education in Leicester, UK, bringing together diverse traditions in the care of people with disabilities. Small student groups (3-4 students) worked from one of the eight community rehabilitation hospitals through a programme of contact with people with disabilities in hospital, at home or in other community settings. The evaluation, in March 2005, used a mixed methods approach, incorporating questionnaire surveys, focus group interviews with students and feedback from service users. Responses were collated and analysed using quantitative and qualitative measures. Fifty social work and 100 medical students completed the first combined delivery of the module. The findings indicated that the merging of social work and medical perspectives appear to create some tensions, although overall the student experience was found to be beneficial. Service users (16 responses) valued the process. They were not concerned at the prospect of meeting a number of students at home or elsewhere and were pleased to think of themselves as educators. Problems and obstacles still anticipated include changing the mindset of clinicians and practising social workers to enable them to support students from each other's disciplines in practice learning. The generally positive outcomes highlight that disability focussed joint learning offers a meaningful platform for interprofessional education in a practice environment.
Cognitive control predicts use of model-based reinforcement learning.

PubMed

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

2015-02-01

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

'Ain't nothin' like the real thing'. Motivation and study processes on a work-based project course in information systems design.

PubMed

Helle, Laura; Tynjälä, Päivi; Olkinuora, Erkki; Lonka, Kirsti

2007-06-01

Advocates of the project method claim that project-based learning inspires student learning. However, it has been claimed that project-based learning environments demand quite a bit of self-regulation on the part of the learner. Consequently, it was tested whether students scoring low in self-regulation of learning experienced 'friction', an incompatibility between student self-regulation and the demands posed by the learning environment. This would be manifest in cognitive processing and motivation. The target group consisted of 58 mainly third-year Finnish university students taking a mandatory project course in information systems design. During the project course, student teams completed a commissioned assignment. The study also included a matched nonequivalent comparison group composed of computer science students attending study programmes without a project-based component. Data were gathered by means of a questionnaire administered at the beginning and end of the project course and it was analysed by between-groups repeated measures ANOVA. In addition, the students on the course were interviewed. Results suggest that the work-based project model in question may indeed have a substantial motivational impact, interestingly benefitting especially those students who scored low in self-regulation. It is argued that we tend to view learning environments too simplistically. In particular, a basic distinction should be made between individual and collaborative learning contexts, since peer scaffolding, group grading and choice of group roles may explain why students scoring low in self-regulation of learning did not encounter friction as expected.
Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.
Organizational learning capability and organizational citizenship behaviour in the health sector: examining the role of happiness at work from a gender perspective.

PubMed

Salas-Vallina, Andrés; Alegre, Joaquin; Fernández, Rafael

2017-04-01

Both researchers and managers are interested in finding the factors that raise organizational citizenship behaviour (OCB), particularly in the health sector. In this complex context, characterized by a high workload, it becomes essential that physicians voluntarily contribute beyond their official job description. Our research aims to evidence the working conditions that promote OCB, considering the role of organizational learning capability through happiness at work. Our research was based on a sample of 167 allergists at Spanish public hospitals, and by means of structural equation models, interesting results were found. We offer to hospital managers both a tool and an explanation for the fostering of OCB. Physicians that progress through learning, under positive attitudes at work, may indeed behave more civically, going beyond their job description. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Out of the Comfort Zone: Enhancing Work-Based Learning about Employability through Student Reflection on Work Placements

ERIC Educational Resources Information Center

Eden, Sally

2014-01-01

This paper examines the work-based learning about employability reported by 26 undergraduate Geography and Environmental Management students on part-time, unpaid work placements. The students' "reflective essays" emphasized their learning more in terms of emotional challenges than in terms of skills, as being pushed out of their…
A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.
Systems-Oriented Workplace Learning Experiences for Early Learners: Three Models.

PubMed

O'Brien, Bridget C; Bachhuber, Melissa R; Teherani, Arianne; Iker, Theresa M; Batt, Joanne; O'Sullivan, Patricia S

2017-05-01

Early workplace learning experiences may be effective for learning systems-based practice. This study explores systems-oriented workplace learning experiences (SOWLEs) for early learners to suggest a framework for their development. The authors used a two-phase qualitative case study design. In Phase 1 (spring 2014), they prepared case write-ups based on transcribed interviews from 10 SOWLE leaders at the authors' institution and, through comparative analysis of cases, identified three SOWLE models. In Phase 2 (summer 2014), studying seven 8-week SOWLE pilots, the authors used interview and observational data collected from the seven participating medical students, two pharmacy students, and site leaders to construct case write-ups of each pilot and to verify and elaborate the models. In Model 1, students performed specific patient care activities that addressed a system gap. Some site leaders helped students connect the activities to larger systems problems and potential improvements. In Model 2, students participated in predetermined systems improvement (SI) projects, gaining experience in the improvement process. Site leaders had experience in SI and often had significant roles in the projects. In Model 3, students worked with key stakeholders to develop a project and conduct a small test of change. They experienced most elements of an improvement cycle. Site leaders often had experience with SI and knew how to guide and support students' learning. Each model could offer systems-oriented learning opportunities provided that key elements are in place including site leaders facile in SI concepts and able to guide students in SOWLE activities.
Guided discovery learning in geometry learning

NASA Astrophysics Data System (ADS)

Khasanah, V. N.; Usodo, B.; Subanti, S.

2018-03-01

Geometry is a part of the mathematics that must be learned in school. The purpose of this research was to determine the effect of Guided Discovery Learning (GDL) toward geometry learning achievement. This research had conducted at junior high school in Sukoharjo on academic years 2016/2017. Data collection was done based on student’s work test and documentation. Hypothesis testing used two ways analysis of variance (ANOVA) with unequal cells. The results of this research that GDL gave positive effect towards mathematics learning achievement. GDL gave better mathematics learning achievement than direct learning. There was no difference of mathematics learning achievement between male and female. There was no an interaction between sex differences and learning models toward student’s mathematics learning achievement. GDL can be used to improve students’ mathematics learning achievement in geometry.
Work-based learning as a means of developing and assessing nursing competence.

PubMed

Flanagan, J; Baldwin, S; Clarke, D

2000-05-01

Work-Based Learning is the bringing together of self-knowledge, expertise at work and formal knowledge. It takes a structured and learner-managed approach to maximizing opportunities for learning and professional development in the workplace. The development and assessment of nursing competence can be facilitated through Work-Based Learning, although this may require pedagogic and structural changes within nurse education. There are a number of conditions which must accompany effective participative learning, and these are discussed in the paper in relation to examples of nursing programmes. This method of learning and assessment has potential to bridge the gap between theory and practice, and as such it can only be achieved through commitment and partnership between the individual practitioner, clinical services and universities.
A Comparative Case Analysis of the Innovation in Five STEM Schools Based on Zhao's Framework for 21st Century World Class Learners

ERIC Educational Resources Information Center

Griffin, Patricia A.

2015-01-01

STEM Schools purport to prepare students to learn and work in the 21st Century by providing students with innovative learning experiences through the interdisciplinary integration of science, technology, engineering, and math (Tsupros, 2009). Advocates of STEM and innovative school models argue that the traditional school system does not and…
Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features

PubMed Central

Ho, King Chung; Speier, William; El-Saden, Suzie; Arnold, Corey W.

2017-01-01

Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient’s treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden representations from the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional imaging features. Finally, we discuss a strategy to visualize the learned features from the proposed deep learning model. The cross-validation results show that our best classifier achieved an area under the curve of 0.68, which improves significantly over current clinical methods (0.58), demonstrating the potential benefit of using advanced machine learning methods in TSS classification. PMID:29854156
Competition-Based Learning: A Model for the Integration of Competitions with Project-Based Learning Using Open Source LMS

ERIC Educational Resources Information Center

Issa, Ghassan; Hussain, Shakir M.; Al-Bahadili, Hussein

2014-01-01

In an effort to enhance the learning process in higher education, a new model for Competition-Based Learning (CBL) is presented. The new model utilizes two well-known learning models, namely, the Project-Based Learning (PBL) and competitions. The new model is also applied in a networked environment with emphasis on collective learning as well as…
Student Use of Academic Knowledge and Skills in Work-Based Learning

ERIC Educational Resources Information Center

Hawley, Joshua D.; Marks, Helen M.

2006-01-01

Using data from in a large Mid-western district, this study analyses the use of academic skills in work-based learning. The primary question asked in this study has to do with the impact of participating in work-based learning on the use of academic skills. Four sets of academic skills were measured using surveys (language arts, math, science, and…
Assessing the Value: Work-Based Learning Placements for Post-Graduate Human Resource Development Students?

ERIC Educational Resources Information Center

Morse, Shona M.

2006-01-01

Purpose: The objective of the pilot study reported on here was to identify some of the more elusive "costs and benefits" of work-based learning (WBL) placements. This was addressed by exploring the views and experience of a small number of human resource development (HRD) professionals who currently offer supervised work-based learning placements…
Construction and validation of a distance learning module on premedication antisepsis for nursing professionals.

PubMed

Pereira, Barbara Juliana da Costa; Mendes, Isabel Amélia Costa; Beatriz Maria, Jorge; Mazzo, Alessandra

2013-11-01

The aim of this descriptive study, carried out at a public university, was to design, develop, and validate a distance learning module on intramuscular premedication antisepsis. The content was introduced in the Modular Object-Oriented Dynamic Learning Environment, based on the Systematic Model for Web-Based Training projects. Ten nurses and information technologists at work consented to participate, in compliance with ethical guidelines, and answered a questionnaire to validate the Virtual Learning Environment. The educational aspects of the environment interface were mostly evaluated as "excellent," whereas the assessment of didactic resources indicated interactivity difficulties. It is concluded that distance learning is an important tool for the teaching of premedication antisepsis. To ensure its effectiveness, appropriate methods and interactive devices must be used.
A Research on the Generative Learning Model Supported by Context-Based Learning

ERIC Educational Resources Information Center

Ulusoy, Fatma Merve; Onen, Aysem Seda

2014-01-01

This study is based on the generative learning model which involves context-based learning. Using the generative learning model, we taught the topic of Halogens. This topic is covered in the grade 10 chemistry curriculum using activities which are designed in accordance with the generative learning model supported by context-based learning. The…
A Comparison of Reasoning Processes in a Collaborative Modelling Environment: Learning about genetics problems using virtual chat

NASA Astrophysics Data System (ADS)

Pata, Kai; Sarapuu, Tago

2006-09-01

This study investigated the possible activation of different types of model-based reasoning processes in two learning settings, and the influence of various terms of reasoning on the learners’ problem representation development. Changes in 53 students’ problem representations about genetic issue were analysed while they worked with different modelling tools in a synchronous network-based environment. The discussion log-files were used for the “microgenetic” analysis of reasoning types. For studying the stages of students’ problem representation development, individual pre-essays and post-essays and their utterances during two reasoning phases were used. An approach for mapping problem representations was developed. Characterizing the elements of mental models and their reasoning level enabled the description of five hierarchical categories of problem representations. Learning in exploratory and experimental settings was registered as the shift towards more complex stages of problem representations in genetics. The effect of different types of reasoning could be observed as the divergent development of problem representations within hierarchical categories.
Developing a Collaborative Model of Industry Feedback for Work Placement of Business Students

ERIC Educational Resources Information Center

Richardson, Joan; Jackling, Beverley; Henschke, Kathy; Tempone, Irene

2013-01-01

Work-integrated learning (WIL) is a signature feature of study in many higher education institutions. In business degrees, industry feedback is recognized as an integral part of the assessment of WIL, yet the role played by industry in appraising student performance in the workplace has not been clearly defined. Based on interviews with industry…
Neural Mechanisms Supporting Acquired Phasic Dopamine Responses in Learning: An Integrative Synthesis

PubMed Central

Hazy, Thomas E.; Frank, Michael J.; O’Reilly, Randall C.

2010-01-01

What biological mechanisms underlie the reward-predictive firing properties of midbrain dopaminergic neurons, and how do they relate to the complex constellation of empirical findings understood as Pavlovian and instrumental conditioning? We previously presented PVLV, a biologically-inspired Pavlovian learning algorithm accounting for DA activity in terms of two interrelated systems: a primary value (PV) system, which governs how DA cells respond to a US (reward) and; a learned value (LV) system, which governs how DA cells respond to a CS. Here, we provide a more extensive review of the biological mechanisms supporting phasic DA firing and their relation to the spate of Pavlovian conditioning phenomena and their sensitivity to focal brain lesions. We further extend the model by incorporating a new NV (novelty value) component reflecting the ability of novel stimuli to trigger phasic DA firing, providing “novelty bonuses” which encourages exploratory working memory updating and in turn speeds learning in trace conditioning and other working memory-dependent paradigms. The evolving PVLV model builds upon insights developed in many earlier computational models, especially reinforcement learning models based on the ideas of Sutton and Barto, biological models, and the psychological model developed by Savastano and Miller. The PVLV framework synthesizes these various approaches, overcoming important shortcomings of each by providing a coherent and specific mapping to much of the relevant empirical data at both the micro- and macro-levels, and examines their relevance for higher order cognitive functions. PMID:19944716
Predicting coronary artery disease using different artificial neural network models.

PubMed

Colak, M Cengiz; Colak, Cemil; Kocatürk, Hasan; Sağiroğlu, Seref; Barutçu, Irfan

2008-08-01

Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.
The Impact of Role Modeling on Proteges' Personal Learning and Work-to-Family Enrichment

ERIC Educational Resources Information Center

Kwan, Ho Kwong; Mao, Yina; Zhang, Haina

2010-01-01

The present study investigates the impact of role modeling as perceived by proteges on their personal learning (i.e., relational job learning and personal skill development) and work-to-family enrichment (WFE). Results from a two-wave field survey of 173 proteges in the People's Republic of China indicate that role modeling positively affects…

Learning to Work during Work Placement: Negotiating Access to Work and Participation through "Origination" and Establishing a "Legitimate Presence"

ERIC Educational Resources Information Center

Pang, Priscilla

2015-01-01

Research on work-based learning has produced much insight into how newcomers to work roles acquire the skills and knowledge required in their work. Overwhelmingly, studies have shown that learning takes place through participation in work activities which provides opportunities for learning. But participation can be problematic when workers and…
Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations. PMID:23166483
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.
Learning-based stochastic object models for characterizing anatomical variations

NASA Astrophysics Data System (ADS)

Dolly, Steven R.; Lou, Yang; Anastasio, Mark A.; Li, Hua

2018-03-01

It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.
Mathematical modelling in engineering: an alternative way to teach Linear Algebra

NASA Astrophysics Data System (ADS)

Domínguez-García, S.; García-Planas, M. I.; Taberna, J.

2016-10-01

Technological advances require that basic science courses for engineering, including Linear Algebra, emphasize the development of mathematical strengths associated with modelling and interpretation of results, which are not limited only to calculus abilities. Based on this consideration, we have proposed a project-based learning, giving a dynamic classroom approach in which students modelled real-world problems and turn gain a deeper knowledge of the Linear Algebra subject. Considering that most students are digital natives, we use the e-portfolio as a tool of communication between students and teachers, besides being a good place making the work visible. In this article, we present an overview of the design and implementation of a project-based learning for a Linear Algebra course taught during the 2014-2015 at the 'ETSEIB'of Universitat Politècnica de Catalunya (UPC).
Action-learning collaboratives as a platform for community-based participatory research to advance obesity prevention.

PubMed

Bazos, Dorothy A; Schifferdecker, Karen E; Fedrizzi, Rudolph; Hoebeke, Jaime; Ruggles, Laural; Goldsberry, Yvonne

2013-01-01

Although process elements that define community-based participatory research (CBPR) are well articulated and provide guidance for bringing together researchers and communities, additional models to implement CBPR are needed. One potential model for implementing and monitoring CBPR is Action Learning Collaboratives (ALCs); short term, team-based learning processes that are grounded in quality improvement. Since 2010, the Prevention Research Center at Dartmouth (PRCD) has used ALCs with three communities as a platform to design, implement and evaluate CBPR. The first ALC provided an opportunity for academia and community leadership to strengthen their relationships and knowledge of respective assets through design and evaluation of community-based QI projects. Building on this work, we jointly designed and are implementing a second ALC, a cross-community research project focused on obesity prevention in vulnerable populations. An enhanced community capacity now exists to support CBPR activities with a high degree of sophistication and decreased reliance on external facilitation.
Removing Legal Barriers around Work-Based Learning. Connecting the Classroom to Careers: The State's Role in Work-Based Learning

ERIC Educational Resources Information Center

Advance CTE: State Leaders Connecting Learning to Work, 2016

2016-01-01

This report, the second installment in Advance CTE's "Connecting the Classroom to Careers," series, explores an issue that is often a stumbling block for K-12 work-based learning--ensuring these experiences are safe and legal for students. This report features New Jersey, Kentucky and California and their approaches to dismantling…
Higher Education Institutions and Work-Based Learning in the UK: Employer Engagement within a Tripartite Relationship

ERIC Educational Resources Information Center

Basit, Tehmina N.; Eardley, Alan; Borup, Rosemary; Shah, Hanifa; Slack, Kim; Hughes, Amanda

2015-01-01

Higher education institutions (HEIs) in the UK are increasingly engaging in work-based learning. The tripartite relationship between the HEI, the employer and the employee is viewed to be of great significance in work-based learning, not only in the initial stages of procurement of a contract, but also in designing and delivering the programme to…
Clinical supervisors' perspectives on delivering work integrated learning: a survey study.

PubMed

Mather, Carey A; McKay, Angela; Allen, Penny

2015-04-01

Previous research has indicated a disconnect between academic nursing programmes and workplace learning environments. Nurse supervisors and clinical practitioners have reported inadequate information and training on how to support students of nursing to learn in the clinical setting. This study aimed to investigate the level of confidence that clinical supervisors have in relation to specific components of supporting student learning in the work place. Survey of clinical nurse supervisors. Simulation-based clinical reasoning workshops. Sixty participants: fifty nine registered nurses, including nurse managers and clinical nurse educators, and one allied health professional. Survey using Likert scales and free-text questions. The findings indicated that clinicians were confident in sharing their knowledge and experience with students and making them feel welcome in the work place, they were less confident about what were the significant learnings in relation to students' academic programme. Registered nurses supervising students were experienced clinicians with many role responsibilities, which were perceived as barriers to the role of clinical supervisor. Participants reported that they would like tools to assist them with developing links to the academic programme. They considered that these tools would support student learning and remediation in the work place. This study found that the abilities of supervisors to support student learning is an identified gap impacting on work integrated learning. The results indicated the need for a professional development workshop, to enable clinical supervisors to move beyond promoting a supervision model, towards a theoretical framework for assisting and guiding students to learn. Addressing this deficit will improve growth and change in student learning in the work place. Copyright © 2015 Elsevier Ltd. All rights reserved.
A Biologically Inspired Computational Model of Basal Ganglia in Action Selection.

PubMed

Baston, Chiara; Ursino, Mauro

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.
Precursor model and preschool science learning about shadows formation

NASA Astrophysics Data System (ADS)

Delserieys, Alice; Jégou, Corinne; Boilevin, Jean-Marie; Ravanis, Konstantinos

2018-04-01

This work is based on the idea that young children benefit from early introduction of scientific concepts. Few researches describe didactical strategies focusing on physics understanding for young children and analyse their effectiveness in standard classroom environments.
Work-based learning: supporting advanced perioperative practice.

PubMed

Quick, Julie

2010-07-01

The arrival of work-based learning awards in professional education offers an alternative route for healthcare professionals looking to undertake post-registration education. The unique way that work-based learning integrates individual learning needs with that of role requirements makes the award an ideal choice for the advanced perioperative practitioner (APP) who wishes to combine academic study with professional development. As an experienced and professionally qualified practitioner (Thatcher 2003) the APP will have an accumulation of knowledge, skills and experience that may go unrecognised in alternative awards. The term APP refers to a nurse, ODP or allied healthcare professional who undertakes a role that challenges the traditional boundaries of care within the perioperative environment (Radford 2004), such as that of a surgical care practitioner (SCP). Here Julie Quick, a SCP, examines the changes within post-registration education and in particular describes why work-based learning awards may be an appropriate choice for practitioners working at a higher level of practice.
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.
Clinical learning experiences of nursing students using an innovative clinical partnership model: A non-randomized controlled trial.

PubMed

Chan, Aileen W K; Tang, Fiona W K; Choi, Kai Chow; Liu, Ting; Taylor-Piliae, Ruth E

2018-06-05

Clinical practicum is a major learning component for pre-registration nursing students. Various clinical practicum models have been used to facilitate students' clinical learning experiences, employing both university-based and hospital-based clinical teachers. Considering the strengths and limitations of these clinical practicum models, along with nursing workforce shortages, we developed and tested an innovative clinical partnership model (CPM) in Hong Kong. To evaluate an innovative CPM among nursing students actual and preferred clinical learning environment, compared with a conventional facilitation model (CFM). A non-randomized controlled trial examining students' clinical experiences, comparing the CPM (supervised by hospital clinical teacher) with the CFM (supervised by university clinical teacher). One university in Hong Kong. Pre-registration nursing students (N = 331), including bachelor of nursing (n = 246 year three-BN) and masters-entry nursing (n = 85 year one-MNSP). Students were assigned to either the CPM (n = 48 BN plus n = 85 MNSP students) or the CFM (n = 198 BN students) for their clinical practice experiences in an acute medical-surgical ward. Clinical teachers supervised between 6 and 8 students at a time, during these clinical practicums (duration = 4-6 weeks). At the end of the clinical practicum, students were invited to complete the Clinical Learning Environment Inventory (CLEI). Analysis of covariance was used to compare groups; adjusted for age, gender and prior work experience. A total of 259 students (mean age = 22 years, 76% female, 81% prior work experience) completed the CLEI (78% response rate). Students had higher scores on preferred versus actual experiences, in all domains of the CLEI. CPM student experiences indicated a higher preferred task orientation (p = 0.004), while CFM student experiences indicated a higher actual (p < 0.001) and preferred individualization (p = 0.005). No significant differences were noted in the other domains. The CPM draws on the strengths of existing clinical learning models and provides complementary methods to facilitate clinical learning for pre-registration nursing students. Additional studies examining this CPM with longer duration of clinical practicum are recommended. Copyright © 2018 Elsevier Ltd. All rights reserved.
Reflections on Distributive Leadership for Work-Based Mobile Learning of Canadian Registered Nurses

ERIC Educational Resources Information Center

Fahlman, Dorothy

2017-01-01

The ubiquity, flexibility, and accessibility of mobile devices can transform how registered nurses in Canada learn beyond the confines of traditional education/training boundaries in their work settings. Many Canadian registered nurses have actively embraced mobile technologies for their work-based learning to meet their competency requirements…
Work-Based Learning through Civic Engagement

ERIC Educational Resources Information Center

McKoy, Deborah L.; Stern, David; Bierbaum, Ariel H.

2011-01-01

Work-based learning (WBL), an important part of the 1990s "School to Work" movement, is a core component of the Linked Learning strategy which is now shaping efforts to improve secondary education in California and around the nation in cities such as Detroit, New York, and Philadelphia. WBL can include not only classic internships and…
Reflection in Work-Based Learning: Self-Regulation or Self-Liberation?

ERIC Educational Resources Information Center

Siebert, Sabina; Walsh, Anita

2013-01-01

This article considers the role of reflective practice in work-based learning in higher education. The benefits of using reflection for learning at work have been widely recognised and the pedagogy to support reflection is now established. However, the use of reflective practice has been subjected to considerable critique, and many of the…
Fostering science literacy, environmental stewardship, and collaboration: Assessing a garden-based approach to teaching life science

NASA Astrophysics Data System (ADS)

Fisher-Maltese, Carley B.

Recently, schools nationwide have expressed a renewed interest in school gardens (California School Garden Network, 2010), viewing them as innovative educational tools. Most of the scant studies on these settings investigate the health/nutritional impacts, environmental attitudes, or emotional dispositions of students. However, few studies examine the science learning potential of a school garden from an informal learning perspective. Those studies that do examine learning emphasize individual learning of traditional school content (math, science, etc.) (Blaire, 2009; Dirks & Orvis, 2005; Klemmer, Waliczek & Zajicek, 2005a & b; Smith & Mostenbocker, 2005). My study sought to demonstrate the value of school garden learning through a focus on measures of learning typically associated with traditional learning environments, as well as informal learning environments. Grounded in situated, experiential, and contextual model of learning theories, the purpose of this case study was to examine the impacts of a school garden program at a K-3 elementary school. Results from pre/post tests, pre/post surveys, interviews, recorded student conversations, and student work reveal a number of affordances, including science learning, cross-curricular lessons in an authentic setting, a sense of school community, and positive shifts in attitude toward nature and working collaboratively with other students. I also analyzed this garden-based unit as a type curriculum reform in one school in an effort to explore issues of implementing effective practices in schools. Facilitators and barriers to implementing a garden-based science curriculum at a K-3 elementary school are discussed. Participants reported a number of implementation processes necessary for success: leadership, vision, and material, human, and social resources. However, in spite of facilitators, teachers reported barriers to implementing the garden-based curriculum, specifically lack of time and content knowledge.
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.
Acceptability of the flipped classroom approach for in-house teaching in emergency medicine.

PubMed

Tan, Eunicia; Brainard, Andrew; Larkin, Gregory L

2015-10-01

To evaluate the relative acceptability of the flipped classroom approach compared with traditional didactics for in-house teaching in emergency medicine. Our department changed its learning model from a 'standard' lecture-based model to a 'flipped classroom' model. The 'flipped classroom' included provided pre-session learning objectives and resources before each 2 h weekly session. In-session activities emphasised active learning strategies and knowledge application. Feedback was sought from all medical staff regarding the acceptability of the new approach using an online anonymous cross-sectional qualitative survey. Feedback was received from 49/57 (86%) medical staff. Ninety-eight per cent (48/49) of respondents preferred the flipped classroom over the traditional approach. Aspects of the flipped classroom learners liked most included case-based discussion, interaction with peers, application of knowledge, self-directed learning and small-group learning. Barriers to pre-session learning include work commitments, 'life', perceived lack of time, family commitments, exam preparation and high volume of learning materials. Reported motivational factors promoting pre-session learning include formal assessment, participation requirements, more time, less material, more clinical relevance and/or more interesting material. Case studies and 'hands-on' activities were perceived to be the most useful in-session activities. The flipped classroom shows promise as an acceptable approach to in-house emergency medicine teaching. © 2015 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Work-based learning in health care environments.

PubMed

Spouse, J

2001-03-01

In reviewing contemporary literature and theories about work-based learning, this paper explores recent trends promoting life-long learning. In the process the paper reviews and discusses some implications of implementing recent policies and fostering le arning in health care practice settings. Recent Government policies designed to provide quality health care services and to improve staffing levels in the nursing workforce, have emphasized the importance of life-long learning whilst learning-on-the-job and the need to recognize and credit experiential learning. Such calls include negotiation of personal development plans tailored to individual educational need and context-sensitive learning activities. To be implemented effectively, this policy cann ot be seen as a cheap option but requires considerable financial resourcing for preparation of staff and the conduct of such activities. Successful work-based learning requires investment in staff at all levels as well as changes to staffing structures in organizations and trusts; changes designed to free people up to work and learn collaboratively. Creating an organizational environment where learning is prized depends upon a climate of trust; a climate where investigation and speculation are fostered and where time is protected for engaging in discussions about practice. Such a change may be radical for many health care organizations and may require a review of current policies and practices ensuring that they include education at all levels. The nature of such education also requires reconceptualizing. In the past, learning in practice settings was seen as formal lecturing or demonstration, and relied upon behaviourist principles of learning. Contemporary thinking suggests effective learning in work-settings is multi-faceted and draws on previously acquired formal knowledge, contextualizes it and moulds it according to situations at hand. Thinking about work-based learning in this way raises questions about how such learning can be supported and facilitated.
Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

PubMed

Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

2017-01-25

The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.
Team Resilience Training in the Workplace: E-Learning Adaptation, Measurement Model, and Two Pilot Studies

PubMed Central

Neeper, Michael; Linde, Brittany D; Lucas, Gale M; Simone, Lindsay

2018-01-01

Background The majority of resilience interventions focus on the individual. Workplace resilience is a growing field of research. Given the ever-increasing interconnectedness in businesses, teamwork is a guarantee. There is also growing recognition that resilience functions at the team level. Objective The objective of our work was to address three shortcomings in the study of workplace resilience interventions: lack of interventions focusing on group-level or team resilience, the need for brief interventions, and the need for more theoretical precision in intervention studies. Methods The authors took an established evidence-based program (Team Resilience) and modified it based on these needs. A working model for brief intervention evaluation distinguishes outcomes that are proximal (perceptions that the program improved resilience) and distal (dispositional resilience). A total of 7 hypotheses tested the model and program efficacy. Results Two samples (n=118 and n=181) of engineering firms received the Web-based training and provided immediate reactions in a posttest-only design. The second sample also included a control condition (n=201). The findings support the model and program efficacy. For example, workplace resilience was greater in the intervention group than in the control group. Other findings suggest social dissemination effects, equal outcomes for employees at different stress levels, and greater benefit for females. Conclusions This preliminary research provides evidence for the capabilities of e-learning modules to effectively promote workplace resilience and a working model of team resilience. PMID:29720362
PlayPhysics: An Emotional Games Learning Environment for Teaching Physics

NASA Astrophysics Data System (ADS)

Muñoz, Karla; Kevitt, Paul Mc; Lunney, Tom; Noguez, Julieta; Neri, Luis

To ensure learning, game-based learning environments must incorporate assessment mechanisms, e.g. Intelligent Tutoring Systems (ITSs). ITSs are focused on recognising and influencing the learner's emotional or motivational states. This research focuses on designing and implementing an affective student model for intelligent gaming, which reasons about the learner's emotional state from cognitive and motivational variables using observable behaviour. A Probabilistic Relational Models (PRMs) approach is employed to derive Dynamic Bayesian Networks (DBNs). The model uses the Control-Value theory of 'achievement emotions' as a basis. A preliminary test was conducted to recognise the students' prospective-outcome emotions with results presented and discussed. PlayPhysics is an emotional games learning environment for teaching Physics. Once the affective student model proves effective it will be incorporated into PlayPhysics' architecture. The design, evaluation and postevaluation of PlayPhysics are also discussed. Future work will focus on evaluating the affective student model with a larger population of students, and on providing affective feedback.
Examining Educational Climate Change Technology: How Group Inquiry Work with Realistic Scientific Technology Alters Classroom Learning

NASA Astrophysics Data System (ADS)

Bush, Drew; Sieber, Renee; Seiler, Gale; Chandler, Mark

2018-04-01

This study with 79 students in Montreal, Quebec, compared the educational use of a National Aeronautics and Space Administration (NASA) global climate model (GCM) to climate education technologies developed for classroom use that included simpler interfaces and processes. The goal was to show how differing climate education technologies succeed and fail at getting students to evolve in their understanding of anthropogenic global climate change (AGCC). Many available climate education technologies aim to convey key AGCC concepts or Earth systems processes; the educational GCM used here aims to teach students the methods and processes of global climate modeling. We hypothesized that challenges to learning about AGCC make authentic technology-enabled inquiry important in developing accurate understandings of not just the issue but how scientists research it. The goal was to determine if student learning trajectories differed between the comparison and treatment groups based on whether each climate education technology allowed authentic scientific research. We trace learning trajectories using pre/post exams, practice quizzes, and written student reflections. To examine the reasons for differing learning trajectories, we discuss student pre/post questionnaires, student exit interviews, and 535 min of recorded classroom video. Students who worked with a GCM demonstrated learning trajectories with larger gains, higher levels of engagement, and a better idea of how climate scientists conduct research. Students who worked with simpler climate education technologies scored lower in the course because of lower levels of engagement with inquiry processes that were perceived to not actually resemble the work of climate scientists.
Learning and Language: Supporting Group Work so Group Work Supports Learning

ERIC Educational Resources Information Center

Mylett, Terri; Gluck, Russell

2005-01-01

This paper reports on developments in teaching and learning for first year employment relations students at the University of Wollongong based on creating conditions of learning informed by Vygotsky's "zone of proximal development" theory. Essentially, this meant emphasising collaborative learning (group work) in the lecture theatre and…
Strategies for Achieving Scale within Communities of Practice Aimed at Pedagogical Reform in Higher Education

ERIC Educational Resources Information Center

Kezar, Adrianna; Gehrke, Sean

2017-01-01

A community of practice (CoP) is a group of people who share a concern or a passion for something they do and learn how to do it as they interact regularly. Higher education leaders are attracted to CoPs because they are a peer-based model of learning with colleagues, which works well for professionals such as faculty. These authors identified…
Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction.

PubMed

Al-Jarrah, Omar Y; Yoo, Paul D; Taha, Kamal; Muhaidat, Sami; Shami, Abdallah; Zaki, Nazar

2015-01-01

Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.
Working Memory, Motivation, and Teacher-Initiated Learning

NASA Astrophysics Data System (ADS)

Brooks, David W.; Shell, Duane F.

2006-03-01

Working memory is where we "think" as we learn. A notion that emerges as a synthesis from several threads in the research literatures of cognition, motivation, and connectionism is that motivation in learning is the process whereby working memory resource allocation is instigated and sustained. This paper reviews much literature on motivation and working memory, and concludes that the apparent novelty of the proposal offered to describe motivation in terms of working memory results from the apparent lack of cross-channel exchange among these research traditions. The relation between working memory and motivation is explored in the context of the interactive compensatory model of learning (ICML) in which learning is considered to result from the interaction of ability, motivation, and prior learning. The ICML is recast in light of the revised definition of motivation offered here. This paper goes on to suggest ways in which a range of teaching and learning issues and activities may be reconceptualized in the context of a model emphasizing a learner's working memory that makes use of chunks of previously acquired knowledge.
Implementing the Next Generation Science Standards: How Instructional Coaches Mediate Standards-Based Educational Reform to Teacher Practice

NASA Astrophysics Data System (ADS)

Laxton, Katherine E.

This dissertation takes a close look at how district-level instructional coaches support teachers in learning to shifting their instructional practice, related to the Next Generation Science Standards. This dissertation aims to address how re-structuring professional development to a job-embedded coaching model supports individual teacher learning of new reform-related instructional practice. Implementing the NGSS is a problem of supporting professional learning in a way that will enable educators to make fundamental changes to their teaching practice. However, there are few examples in the literature that explain how coaches interact with teachers to improve teacher learning of reform-related instructional practice. There are also few examples in the literature that specifically address how supporting teachers with extended professional learning opportunities, aligned with high-leverage practices, tools and curriculum, impacts how teachers make sense of new standards-based educational reforms and what manifests in classroom instruction. This dissertation proposes four conceptual categories of sense-making that influence how instructional coaches interpret the nature of reform, their roles and in instructional improvement and how to work with teachers. It is important to understand how coaches interpret reform because their interpretations may have unintended consequences related to privileging certain views about instruction, or establishing priorities for how to work with teachers. In this dissertation, we found that re-structuring professional development to a job-embedded coaching model supported teachers in learning new reform-related instructional practice. However, individual teacher interpretations of reform emerged and seemed to be linked to how instructional coaches supported teacher learning.
National Advisory Commission on Work-Based Learning. Minutes of Full Commission Meeting (5th, Washington, District of Columbia, April 7, 1992).

ERIC Educational Resources Information Center

National Commission on Work-Based Learning (DOL), Washington, DC.

This document contains minutes of the fifth meeting of the National Advisory Commission on Work-Based Learning. The meeting's stated objective was to produce a set of specific action steps for the U.S. Department of Labor to promote work-based learning. A summary of introductory remarks (Jack MacAllister) is followed by a description of the…
A Teacher-Scientist Partnership as a Vehicle to Incorporate Climate Data in Secondary Science Curriculum

NASA Astrophysics Data System (ADS)

Hatheway, B.

2013-12-01

After three years of running a climate science professional development program for secondary teachers, project staff from UCAR and UNC-Greeley have learned the benefits of ample time for interaction between teachers and scientists, informal educators, and their peers. This program gave us the opportunity to develop and refine strategies that leverage teacher-scientist partnerships to improve teachers' ability to teach climate change. First, we prepared both teachers and scientists to work together. Each cohort of teachers took an online course that emphasized climate change content and pedagogy and built a learning community. Scientists were recruited based on their enthusiasm for working with teachers and coached to present materials in an accessible way. Second, the teachers and scientists collaborated during a four-week summer workshop at UCAR. During the workshop, teachers met with a wide range of climate and atmospheric scientists to learn about research, selected a specific scientist's research they would like to adapt for their classrooms, and developed and refined activities based on that research. The program includes strong mentoring from a team of science educators, structured peer feedback, and ample opportunity to interact with the scientists by asking questions, accessing data, or checking resources. This new model of professional development fosters teacher-scientist partnerships. By the end of the four-week workshop, the teachers have built customized activities based on the cutting-edge research being conducted by participating scientists, developed plans to implement and assess those activities, and further strengthened the learning-community that they will rely on for support during the following academic year. This session will provide information about how this model, which differs from the more common model of engaging teachers in research under the direction of scientists, was successful and accomplished positive outcomes for both the teachers and scientists who participated. Lessons learned that will improve this model will also be discussed.
Enhancing a Multi-body Mechanism with Learning-Aided Cues in an Augmented Reality Environment

NASA Astrophysics Data System (ADS)

Singh Sidhu, Manjit

2013-06-01

Augmented Reality (AR) is a potential area of research for education, covering issues such as tracking and calibration, and realistic rendering of virtual objects. The ability to augment real world with virtual information has opened the possibility of using AR technology in areas such as education and training as well. In the domain of Computer Aided Learning (CAL), researchers have long been looking into enhancing the effectiveness of the teaching and learning process by providing cues that could assist learners to better comprehend the materials presented. Although a number of works were done looking into the effectiveness of learning-aided cues, but none has really addressed this issue for AR-based learning solutions. This paper discusses the design and model of an AR based software that uses visual cues to enhance the learning process and the outcome perception results of the cues.
[Innovative educational technology in the teaching of propaedeutic of internal diseases].

PubMed

Тusupbekova, К; Bakirova, R; Nursultanova, S

2015-03-01

This article presents analysis of the results of inculcation of innovative learning technologies in teaching on propaedeutic of internal diseases which is first clinical discipline faced by medical students of the University. Credit-modular training included integration of propaedeutic of internal diseases with basic disciplines of the third year (the normal anatomy, physiology, pathological anatomy, histology, pathophysiology, visual diagnostics and pharmacology). There are 8 models on following systems: respiratory, cardiovascular, musculoskeletal, endocrine, urogenital, hematopoietic, nervous, digestive. The innovative implementation of learning technologies (Problem-based learning, clinical cases, team-oriented teaching, lectures, symposium lectures, discussions, role plays, etc.) and knowledge control (mini-clinical examination, objective structured clinical exam, comprehensive testing) help students to acquire clinical skills, team working and skills of researching work.
Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation.

PubMed

Song, Jingkuan; Gao, Lianli; Nie, Feiping; Shen, Heng Tao; Yan, Yan; Sebe, Nicu

2016-11-01

In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods.
Interprofessional Education and Practice Guide No. 6: Developing practice-based interprofessional learning using a short placement model.

PubMed

Anderson, Elizabeth Susan; Ford, Jenny; Kinnair, Daniel James

2016-07-01

Offering undergraduate and post-qualified learners opportunities to take part in, and reflect on, the nature of interprofessional working when in practice remains an important goal for interprofessional educators. There are a plethora of opportunities within hospital and community care for learners to actively participate in health and social care delivery where collaborative practice prevails. However, it remains challenging to know how to establish and sustain meaningful interprofessional practice-based learning. This is because profession-specific teaching is prioritised and many teams are under-resourced, leaving little time for additional teaching activities. In some instances, practitioners lack the knowledge concerning how to design meaningful interprofessional learning and often feel unprepared for this teaching because of limited interprofessional faculty development. Others are simply unaware of the presence of the different students within their practice area. This guide offers key lessons developed over many years for setting up practice-based interprofessional education. The learning model has been adapted and adopted in different settings and countries and offers a method for engaging clinical front-line practitioners in learning with, and from learners who can help support and in some instances advance care delivery.
What Teacher Education Students Learn about Collaboration from Problem-Based Learning

ERIC Educational Resources Information Center

Murray-Harvey, Rosalind; Pourshafie, Tahereh; Reyes, Wilma Santos

2013-01-01

Group work, an essential component of learning and teaching in problem-based learning (PBL), is compromised if students' experiences of PBL are colored by dissatisfaction with the process or outcomes. For the potential benefits of PBL to be realized PBL group work must be genuinely collaborative to address students' personal and professional…
Assessment in Work-Based Learning: Investigating a Pedagogical Approach to Enhance Student Learning

ERIC Educational Resources Information Center

Brodie, Pandy; Irving, Kate

2007-01-01

Work-based learning (WBL) is undertaken in a wide variety of higher education contexts and is increasingly viewed as a valuable, and increasingly essential, component of both the undergraduate and postgraduate student learning experience. However, the development of rigorous pedagogies to underpin WBL and its assessment is still embryonic. This…
Assessing the Unassessable: Making Learning Visible in Undergraduates' Experiences of Scientific Research

ERIC Educational Resources Information Center

Wilson, Anna; Howitt, Susan; Higgins, Denise

2016-01-01

We suggest that academics involved in the provision of research experiences to undergraduate science students may benefit by reconceptualising these experiences as work-based learning. In particular, drawing on the widespread use of reflective practice in work-based learning allows for a more effective focus on process-related learning. We…
Career Immersion. School-to-Work Outreach Project 1997 Exemplary Model/Practice/Strategy.

ERIC Educational Resources Information Center

Minnesota Univ., Minneapolis. Inst. on Community Integration.

The Career Immersion program, which has been conducted since 1989-90 at Silver Spring Elementary School in Milwaukee, Wisconsin, introduces the world of work to students at an early age. The program's primary activities involve hands-on, school-based learning in which all students participate. Students between the ages of 3 and 13 participate in 2…

Computer-based learning of spelling skills in children with and without dyslexia.

PubMed

Kast, Monika; Baschera, Gian-Marco; Gross, Markus; Jäncke, Lutz; Meyer, Martin

2011-12-01

Our spelling training software recodes words into multisensory representations comprising visual and auditory codes. These codes represent information about letters and syllables of a word. An enhanced version, developed for this study, contains an additional phonological code and an improved word selection controller relying on a phoneme-based student model. We investigated the spelling behavior of children by means of learning curves based on log-file data of the previous and the enhanced software version. First, we compared the learning progress of children with dyslexia working either with the previous software (n = 28) or the adapted version (n = 37). Second, we investigated the spelling behavior of children with dyslexia (n = 37) and matched children without dyslexia (n = 25). To gain deeper insight into which factors are relevant for acquiring spelling skills, we analyzed the influence of cognitive abilities, such as attention functions and verbal memory skills, on the learning behavior. All investigations of the learning process are based on learning curve analyses of the collected log-file data. The results evidenced that those children with dyslexia benefit significantly from the additional phonological cue and the corresponding phoneme-based student model. Actually, children with dyslexia improve their spelling skills to the same extent as children without dyslexia and were able to memorize phoneme to grapheme correspondence when given the correct support and adequate training. In addition, children with low attention functions benefit from the structured learning environment. Generally, our data showed that memory sources are supportive cognitive functions for acquiring spelling skills and for using the information cues of a multi-modal learning environment.
Conceptual Commitments of the LIDA Model of Cognition

NASA Astrophysics Data System (ADS)

Franklin, Stan; Strain, Steve; McCall, Ryan; Baars, Bernard

2013-06-01

Significant debate on fundamental issues remains in the subfields of cognitive science, including perception, memory, attention, action selection, learning, and others. Psychology, neuroscience, and artificial intelligence each contribute alternative and sometimes conflicting perspectives on the supervening problem of artificial general intelligence (AGI). Current efforts toward a broad-based, systems-level model of minds cannot await theoretical convergence in each of the relevant subfields. Such work therefore requires the formulation of tentative hypotheses, based on current knowledge, that serve to connect cognitive functions into a theoretical framework for the study of the mind. We term such hypotheses "conceptual commitments" and describe the hypotheses underlying one such model, the Learning Intelligent Distribution Agent (LIDA) Model. Our intention is to initiate a discussion among AGI researchers about which conceptual commitments are essential, or particularly useful, toward creating AGI agents.
Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

NASA Astrophysics Data System (ADS)

Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

2018-03-01

Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.
A general-purpose machine learning framework for predicting properties of inorganic materials

DOE PAGES

Ward, Logan; Agrawal, Ankit; Choudhary, Alok; ...

2016-08-26

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method formore » partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.« less
A general-purpose machine learning framework for predicting properties of inorganic materials

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

Ward, Logan; Agrawal, Ankit; Choudhary, Alok

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method formore » partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.« less
A Physics-Based Deep Learning Approach to Shadow Invariant Representations of Hyperspectral Images.

PubMed

Windrim, Lloyd; Ramakrishnan, Rishi; Melkumyan, Arman; Murphy, Richard J

2018-02-01

This paper proposes the Relit Spectral Angle-Stacked Autoencoder, a novel unsupervised feature learning approach for mapping pixel reflectances to illumination invariant encodings. This work extends the Spectral Angle-Stacked Autoencoder so that it can learn a shadow-invariant mapping. The method is inspired by a deep learning technique, Denoising Autoencoders, with the incorporation of a physics-based model for illumination such that the algorithm learns a shadow invariant mapping without the need for any labelled training data, additional sensors, a priori knowledge of the scene or the assumption of Planckian illumination. The method is evaluated using datasets captured from several different cameras, with experiments to demonstrate the illumination invariance of the features and how they can be used practically to improve the performance of high-level perception algorithms that operate on images acquired outdoors.
Simulations with Elaborated Worked Example Modeling: Beneficial Effects on Schema Acquisition

ERIC Educational Resources Information Center

Meier, Debra K.; Reinhard, Karl J.; Carter, David O.; Brooks, David W.

2008-01-01

Worked examples have been effective in enhancing learning outcomes, especially with novice learners. Most of this research has been conducted in laboratory settings. This study examined the impact of embedding elaborated worked example modeling in a computer simulation practice activity on learning achievement among 39 undergraduate students…
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.
An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

PubMed Central

Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

2015-01-01

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age. PMID:26402795
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.
An evaluation of the role of the clinical education facilitator.

PubMed

McCormack, Brendan; Slater, Paul

2006-02-01

The objective of the study was to identify whether clinical education facilitators made a difference to the learning experiences of nurses in a large teaching hospital. Strategies for enabling continuous professional development are well established in health care organizations as key components of approaches to lifelong learning. The benefits of continuous professional development include the maintenance of high standards of care, the improvement and development of services, ensuring the competency of all nursing staff and guaranteeing the accountability of nurses for their actions. The role of clinical education facilitator is relatively new and little evaluation of this role has been undertaken. This study highlights important issues to be considered in developing a 'learning culture' in a hospital organization, through the adoption of such roles as clinical education facilitators. Whilst the roles have had an important function in the active coordination of learning activities in the hospital, there is little evidence of the role directly impacting on the learning culture of clinical settings. Learning mechanisms have been identified. The results of this evaluation can be subjected to further testing through ongoing evaluation of the outcomes arising from the learning mechanisms in place. Given the emphasis on work-based learning and continuing professional development in health care, then this ongoing evaluation can yield important information about future directions for nurse education. The study highlights the importance of supported learning in the workplace. However, more importantly, it identifies the need for a culture of professional practice to be developed in order to sustain learning in practice. Classroom-based learning alone, cannot create a culture of development in nursing and there is thus a need for models of work-based learning to be integrated into practice environments.
Integration of health and social care: a case of learning and knowledge management.

PubMed

Williams, Paul M

2012-09-01

This paper considers integration of health and social care as an exercise in learning and knowledge management (KM). Integration assembles diverse actors and organisations in a collective effort to design and deliver new service models underpinned by multidisciplinary working and generic practice. Learning and KM are integral to this process. A critical review of the literature is undertaken to identify theoretical insights and models in this field, albeit grounded mainly in a private sector context. The findings from a research study involving two integrated services are then used to explore the role of, and approach to, learning and KM. This case study research was qualitative in nature and involved an interrogation of relevant documentary material, together with 25 in-depth interviews with a cross-section of strategic managers and professionals undertaken between March and May 2011. The evidence emerging indicated no planned strategies for learning and KM, but rather, interventions and mechanisms at different levels to support integration processes. These included formal activities, particularly around training and appraisal, but also informal ones within communities of practice and networking. Although structural enablers such as a co-location of facilities and joint appointments were important, the value of trust and inter-personal relationships was highlighted especially for tacit knowledge exchange. The infrastructure for learning and KM was constructed around a collaborative culture characterised by a coherent strategic framework; clarity of purpose based on new models of service; a collaborative leadership approach that was facilitative and distributed; and, a focus on team working to exploit the potential of multidisciplinary practice, generic working and integrated management. The discussion and conclusion use Nonaka's knowledge conversation model to reflect on the research findings, to comment on the absence of an explicit approach to learning and KM, and to develop a template to assist policy-makers with the design of planned strategies. © 2012 Blackwell Publishing Ltd.
Guided Learning at Work.

ERIC Educational Resources Information Center

Billett, Stephen

2000-01-01

Guided learning (questioning, diagrams/analogies, modeling, coaching) was studied through critical incident interviews in five workplaces. Participation in everyday work activities was the most effective contributor to workplace learning. Organizational readiness and the efficacy of guided learning in resolving novel tasks were also important. (SK)
Working Memory Functioning in Children with Learning Disorders and Specific Language Impairment

ERIC Educational Resources Information Center

Schuchardt, Kirsten; Bockmann, Ann-Katrin; Bornemann, Galina; Maehler, Claudia

2013-01-01

Purpose: On the basis of Baddeley's working memory model (1986), we examined working memory functioning in children with learning disorders with and without specific language impairment (SLI). We pursued the question whether children with learning disorders exhibit similar working memory deficits as children with additional SLI. Method: In…
The Integrative Principle: Higher Education and Work-Based Learning in the UK.

ERIC Educational Resources Information Center

Saunders, Murray

1995-01-01

The United Kingdom's developing policy to integrate higher education curriculum and the demands of the workplace is explored, particularly in the context of undergraduate and graduate programs based on learning within the workplace. The policy itself, the relationship between work-based learning and conventional college instruction, and validation…
20 CFR 670.520 - Are students permitted to hold jobs other than work-based learning opportunities?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Are students permitted to hold jobs other than work-based learning opportunities? 670.520 Section 670.520 Employees' Benefits EMPLOYMENT AND...-based learning opportunities? Yes, a center operator may authorize a student to participate in gainful...
20 CFR 670.520 - Are students permitted to hold jobs other than work-based learning opportunities?

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 3 2011-04-01 2011-04-01 false Are students permitted to hold jobs other than work-based learning opportunities? 670.520 Section 670.520 Employees' Benefits EMPLOYMENT AND...-based learning opportunities? Yes, a center operator may authorize a student to participate in gainful...
Learning for sustainability among faith-based organizations in Kenya.

PubMed

Moyer, Joanne M; Sinclair, A John; Diduck, Alan P

2014-08-01

The complex and unpredictable contexts in which environmental and development work take place require an adaptable, learning approach. Faith-based organizations (FBOs) play a significant role in sustainability work around the world, and provide a unique setting in which to study learning. This paper explores individual learning for sustainability within two FBOs engaged in sustainability work in Kenya. Learning outcomes covered a broad range of areas, including the sustainability framework, environment/conservation, skills, community work, interpersonal engagement, and personal and faith development. These outcomes were acquired through embodied experience and activity, facilitation by the workplace, interpersonal interaction, personal reflection, and Bible study and worship. Grounded categories were compared to learning domains and processes described by Mezirow's transformative learning theory. The findings indicate that for learning in the sustainability field, instrumental learning and embodied learning processes are particularly important, and consequently they require greater attention in the theory when applied in this field.
Learning for Sustainability Among Faith-Based Organizations in Kenya

NASA Astrophysics Data System (ADS)

Moyer, Joanne M.; Sinclair, A. John; Diduck, Alan P.

2014-08-01

The complex and unpredictable contexts in which environmental and development work take place require an adaptable, learning approach. Faith-based organizations (FBOs) play a significant role in sustainability work around the world, and provide a unique setting in which to study learning. This paper explores individual learning for sustainability within two FBOs engaged in sustainability work in Kenya. Learning outcomes covered a broad range of areas, including the sustainability framework, environment/conservation, skills, community work, interpersonal engagement, and personal and faith development. These outcomes were acquired through embodied experience and activity, facilitation by the workplace, interpersonal interaction, personal reflection, and Bible study and worship. Grounded categories were compared to learning domains and processes described by Mezirow's transformative learning theory. The findings indicate that for learning in the sustainability field, instrumental learning and embodied learning processes are particularly important, and consequently they require greater attention in the theory when applied in this field.
Learning in robotic manipulation: The role of dimensionality reduction in policy search methods. Comment on "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands" by Marco Santello et al.

NASA Astrophysics Data System (ADS)

Ficuciello, Fanny; Siciliano, Bruno

2016-07-01

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

The Pedagogical Variation Model (PVM) for Work-Based Training in Virtual Classrooms: Evaluation at Kuwait University

ERIC Educational Resources Information Center

Rogers, Maria Susy; Aldhafeeri, Fayiz Mensher

2015-01-01

A collaborative research initiative was undertaken to evaluate the pedagogical variation model (PVM) for online learning and teaching at Kuwait University. Outcomes from sample populations of students--both postgraduates and undergraduates--from the Faculty of Education were analyzed for comparison. As predicted in the PVM, the findings indicate…
Design Approaches to Support Preservice Teachers in Scientific Modeling

NASA Astrophysics Data System (ADS)

Kenyon, Lisa; Davis, Elizabeth A.; Hug, Barbara

2011-02-01

Engaging children in scientific practices is hard for beginning teachers. One such scientific practice with which beginning teachers may have limited experience is scientific modeling. We have iteratively designed preservice teacher learning experiences and materials intended to help teachers achieve learning goals associated with scientific modeling. Our work has taken place across multiple years at three university sites, with preservice teachers focused on early childhood, elementary, and middle school teaching. Based on results from our empirical studies supporting these design decisions, we discuss design features of our modeling instruction in each iteration. Our results suggest some successes in supporting preservice teachers in engaging students in modeling practice. We propose design principles that can guide science teacher educators in incorporating modeling in teacher education.
Operator Performance Measures for Assessing Voice Communication Effectiveness

DTIC Science & Technology

1989-07-01

performance and work- load assessment techniques have been based.I Broadbent (1958) described a limited capacity filter model of human information...INFORMATION PROCESSING 20 3.1.1. Auditory Attention 20 3.1.2. Auditory Memory 24 3.2. MODELS OF INFORMATION PROCESSING 24 3.2.1. Capacity Theories 25...Learning 0 Attention * Language Specialization • Decision Making• Problem Solving Auditory Information Processing Models of Processing Ooemtor
The Development of Work-Based Learning as Part of Post-Qualifying Education: A Case Study from the School of Nursing, University of Salford, UK

ERIC Educational Resources Information Center

Owens, Denise; Rutherford, June

2007-01-01

This paper describes the conception and progress to date of the development of work-based learning as part of post-qualifying education in the School of Nursing at the University of Salford. There is a changing culture in the university in relation to the delivery of programmes via non-traditional routes. In this context, work-based learning is a…
But I Know that Already: Rhetoric or Reality the Accreditation of Prior Experiential Learning in the Context of Work-Based Learning

ERIC Educational Resources Information Center

Scott, Ian

2010-01-01

Within the context of work-based learning, this article reviews the available evidence that supports the assumptions behind, and the claims made for the practice of accrediting prior experiential learning. Many of the claims made for accreditation of prior experiential learning (APEL) were found not to have been substantiated and some of the…
Human semi-supervised learning.

PubMed

Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.
Anomaly detection for medical images based on a one-class classification

NASA Astrophysics Data System (ADS)

Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

2018-02-01

Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.
Higher Education Quality and Work-Based Learning: Two Concepts Not yet Fully Integrated

ERIC Educational Resources Information Center

Gibbs, Paul; Armsby, Pauline

2010-01-01

This short paper recognises the growth in emphasis in work-based learning as Europe moves forward on economically driven life-long learning. We support such a move and point to issues which still need to be resolved.
High School Physical Education Students and Experiential Learning in the Community: A Classroom Assignment

ERIC Educational Resources Information Center

Tapps, Tyler; Passmore, Tim; Lindenmeier, Donna; Kensinger, Weston

2014-01-01

The experiential learning model for students working with community groups was developed for specific experiential learning experiences involving 40 hours of actual experience for high school physical education students working with groups in the community. This article discusses the development and specific segments of the model, as well as how…
Implementation of a project-based engineering school: increasing student motivation and relevant learning

NASA Astrophysics Data System (ADS)

Terrón-López, María-José; García-García, María-José; Velasco-Quintana, Paloma-Julia; Ocampo, Jared; Vigil Montaño, María-Reyes; Gaya-López, María-Cruz

2017-11-01

The School of Engineering at Universidad Europea de Madrid (UEM) implemented, starting in the 2012-2013 period, a unified academic model based on project-based learning as the methodology used throughout the entire School. This model expects that every year, in each grade, all the students should participate in a capstone project integrating the contents and competencies of several courses. This paper presents the academic context under which this experience has been implemented, and a summary of the work done to design and implement the Project-Based Engineering School at the UEM. The steps followed, the structure used, some sample projects, as well as the difficulties and benefits of implementing the programme are discussed in this paper. The results are encouraging as students are more motivated and the initial set objectives were accomplished.
Cognitive Control Predicts Use of Model-Based Reinforcement-Learning

PubMed Central

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

2015-01-01

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

NASA Astrophysics Data System (ADS)

Wang, Jin

2018-06-01

Different from the traditional method which recognize the state of substation switches based on the running rules of electrical power system, this work proposes a novel convolutional neuron network-based state recognition approach of substation switches. Inspired by the theory of transfer learning, we first establish a convolutional neuron network model trained on the large-scale image set ILSVRC2012, then the restricted Boltzmann machine is employed to replace the full connected layer of the convolutional neuron network and trained on our small image dataset of 110kV substation switches to get a stronger model. Experiments conducted on our image dataset of 110kV substation switches show that, the proposed approach can be applicable to the substation to reduce the running cost and implement the real unattended operation.
Using Multiple Ways to Investigate Cognitive Load Theory in the Context of Physics Instruction

NASA Astrophysics Data System (ADS)

Zu, Tianlong

Cognitive load theory (CLT) (Sweller 1988, 1998, 2010) provides us a guiding framework for designing instructional materials. CLT differentiates three subtypes of cognitive load: intrinsic, extraneous, and germane cognitive load. The three cognitive loads are theorized based on the number of simultaneously processed elements in working memory. Intrinsic cognitive load depends upon the number of interacting elements in the instructional material that are related to the learning objective. Extraneous cognitive load is the mental resources allocated to processing unnecessary information which does not contribute to learning as caused by non- optimal instructional procedure. It is determined by the number of interacting elements which are not related to learning goal. Both intrinsic and extraneous load vary according to prior knowledge of learners. Germane cognitive load is indirectly related to interacting elements. It represents the cognitive resources deployed for processing intrinsic load, chunking information and constructing and automating schema. Germane cognitive load is related to level of motivation of the learner. Given this triarchic model of cognitive load and their different roles in learning activities, different learning outcomes can be expected depending upon the characteristics of the educational materials, learner characteristics, and instructional setting. In three experiments, we investigated cognitive load theory following different approaches. Given the triarchic nature of cognitive load construct, it is critical to find non- intrusive ways to measure cognitive load. In study one, we replicated and extended a previous landmark study to investigate the use of eye movements related metrics to measure the three kinds of cognitive load independently. We also collected working memory capacity of students using a cognitive operation-span task. Two of the three types of cognitive load (intrinsic and extraneous) were directly manipulated, and the third type of cognitive load (germane) was indirectly ascertained. We found that different eye-movement based parameters were most sensitive to different types of cognitive load. These results indicate that it is possible to monitor the three kinds of cognitive load separately using eye movement parameters. We also compared the up-to-date cognitive load theory model with an alternative model using a multi-level model analysis and we found that Sweller's (2010) up-to-date model is supported by our data. In educational settings, active learning based methodologies such as peer instruction have been shown to be effective in facilitating students' conceptual understanding. In study two, we discussed the effect of peer interaction on conceptual test performance of students from a cognitive load perspective. Based on the literature, a self-reported cognitive load survey was developed to measure each type of cognitive load. We found that a certain level of prior knowledge is necessary for peer interaction to work and that peer interaction is effective mainly through significantly decreasing the intrinsic load experienced by students, even though it may increase the extraneous load. In study three, we compared the effect of guided instruction in the form of worked examples using narrated-animated video solutions and semi-guided instruction using visual cues on students' performance, shift of visual attention during transfer, and extraneous cognitive load during learning. We found that multimedia video solutions can be more effective in promoting transfer performance of learners than visual cues. We also found evidence that guided instruction in the form of multimedia video solutions can decrease extraneous cognitive load of students during learning, more so than semi-guided instruction using visual cues.
What works in genomics education: outcomes of an evidenced-based instructional model for community-based physicians.

PubMed

Reed, E Kate; Johansen Taber, Katherine A; Ingram Nissen, Therese; Schott, Suzanna; Dowling, Lynn O; O'Leary, James C; Scott, Joan A

2016-07-01

Education of practicing health professionals is likely to be one factor that will speed appropriate integration of genomics into routine clinical practice. Yet many health professionals, including physicians, find it difficult to keep up with the rapid pace of clinical genomic advances and are often uncomfortable using genomic information in practice. Having identified the genomics educational needs of physicians in a Silicon Valley-area community hospital, we developed, implemented, and evaluated an educational course entitled Medicine's Future: Genomics for Practicing Doctors. The course structure and approach were based on best practices in adult learning, including interactivity, case-based learning, skill-focused objectives, and sequential monthly modules. Approximately 20-30 physicians attended each module. They demonstrated significant gains in genomics knowledge and confidence in practice skills that were sustained throughout and following the course. Six months following the course, the majority of participants reported that they had changed their practice to incorporate skills learned during the course. We believe the adult-learning principles underlying the development and delivery of Medicine's Future were responsible for participants' outcomes. These principles form a model for the development and delivery of other genomics educational programs for health professionals.Genet Med 18 7, 737-745.
Linking School-Based and Work-Based Learning: The Implications of LaGuardia's Co-op Seminars for School-to-Work Programs.

ERIC Educational Resources Information Center

Grubb, W. Norton; Badway, Norena

The cooperative education program at LaGuardia Community College, New York, incorporates seminars that integrate school- and work-based learning. In the seminars, students examine issues related to work in general, the organizations in which they are placed, and the ways in which their academic preparation is applied at the work site. The…
New jobs old roles - working for prevention in a whole-system model of health and social care for older people.

PubMed

Smith, Naomi; Barnes, Marian

2013-01-01

The Partnerships for Older People Projects programme provided government funding for local and health authorities to pilot prevention and intervention services in partnership with the voluntary sector and older people between 2006 and 2009. This local evaluation of a pilot in southern England undertaken between 2007 and 2009 used a Theory of Change approach to gathering and reflecting on data with different groups involved in the delivery of this whole-system based model of prevention. The model was delivered in the same way in seven social services locality areas within a large county authority. The method of data gathering enabled structured reflection on the implementation, development and projected outcomes of the model and a consideration of the key learning of working in a whole-system way with partners and stakeholders. The whole-system model, although complex and challenging to implement, was considered overall to have been a success and provided significant learning for partners and stakeholders on the challenges and benefits of working across professional and sectoral boundaries. New posts were created as part of the model. Two of these, recruited to and managed by voluntary sector partners, were identified as 'new jobs', but echoed 'old roles' within community and voluntary sector based health and social care. The authors reflect on the parallels of these roles with previously existing roles and ways of working and reflect on how the whole-system approach of this particular pilot enabled these new jobs to develop in particularly appropriate and successful ways. © 2012 Blackwell Publishing Ltd.
A model of teaching and learning in the operating theatre.

PubMed

Lyon, Patricia

2004-12-01

This paper extends the work of an earlier publication by the same author which reported the findings of a case study designed to investigate how medical students learn and are taught in the operating theatre. The earlier paper was descriptive in nature, examining the challenges students face as learners in theatres. These were conceptualised around 3 key domains: the challenge posed by the physical environment; the challenge of the educational task, and the challenge of managing and negotiating a role as a participant in the professional workplace of theatres. This paper focuses exclusively on the third domain. It presents an interpretive model of teaching and learning in the operating theatre, drawing largely on conceptual frameworks developed within the literature on learning in work-based settings. A multimethod strategy included observation in theatres, interviews with students and surgeons, and a student survey. The themes that characterised the case were identified and the relationships among these themes were explored, leading to the development of the model. Symbolic interactionism provided the underlying theoretical framework. In any particular theatre session, the way in which learning evolves or is obstructed for any student, and the shape that teaching takes, depends on the interpretations that the student and the surgeon make in 'sizing up' the teaching and learning environment. How surgeons and students interpret and respond to each others' behaviour, style, attitude and even demeanour, has consequences for the way teaching and learning develop. The concepts of legitimacy and trust underpin these interpretations and are central to understanding the processes of teaching and learning in this setting.
New Blended Learning Strategy Based on Flipped-Learning for Vocational Work-Linked Training

ERIC Educational Resources Information Center

El Hajji, Mohamed; El Bouzaidi, Rachid Drissi; Douzi, Hassan; Khouya, El Hassane

2016-01-01

In a changing Moroccan educational landscape, Addressing teaching development needs is becoming a major issue in vocational work-linked Training. In this context, this paper present a new blended learning strategy based on the flipped classroom, using a social learning platform as support. Through this strategy, we increased the time dedicated to…
A Practice-Based Approach to Student Reflection in the Workplace during a Work-Integrated Learning Placement

ERIC Educational Resources Information Center

Sykes, Christopher; Dean, Bonnie Amelia

2013-01-01

In the Work-Integrated Learning (WIL) curriculum, reflection on workplace activities is widely used to support student learning. Recent critiques have demonstrated the limitations of current approaches to support students' reflective learning of workplace practices. By employing a practice-based approach, we seek to refocus WIL reflection on…
Raising Retention and Achievement in Work-Based Learning

ERIC Educational Resources Information Center

Smith, Vikki

2003-01-01

Work based learning has been subject to some negative press in recent times. Particular emphasis has been given to poor inspection reports within the sector. The Raising Quality and Achievement programme of the Learning and Skills Development Agency, funded by the Learning and Skills Council as part of a grant for a programme of research and…

Improving Initial Assessment in Work-Based Learning.

ERIC Educational Resources Information Center

Green, Muriel

This document, which is designed to assist managers, trainers, or assessors in work-based provision across the United Kingdom, shares the experiences of five work-based learning providers that sought to improve their initial assessment processes. Section 1 explains the purpose of initial assessment and presents guidelines for evaluating intake…
Understanding Work-Related Learning: The Case of ICT Workers

ERIC Educational Resources Information Center

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

2012-01-01

Purpose: A central issue in the field of workplace learning is how work-related learning can be stimulated so that a powerful learning work environment is created. This paper seeks to further enlarge understanding on this issue. Based on the demand-control-support the aim is to investigate the influence of job-characteristics on the work-related…
Twelve tips for a successful interprofessional team-based high-fidelity simulation education session

PubMed Central

Bould, M. Dylan; Layat Burn, Carine; Reeves, Scott

2014-01-01

Simulation-based education allows experiential learning without risk to patients. Interprofessional education aims to provide opportunities to different professions for learning how to work effectively together. Interprofessional simulation-based education presents many challenges, including the logistics of setting up the session and providing effective feedback to participants with different backgrounds and mental models. This paper aims to provide educators with a series of practical and pedagogical tips for designing, implementing, assessing, and evaluating a successful interprofessional team-based simulation session. The paper is organized in the sequence that an educator might use in developing an interprofessional simulation-based education session. Collectively, this paper provides guidance from determining interprofessional learning objectives and curricular design to program evaluation. With a better understanding of the concepts and pedagogical methods underlying interprofessional education and simulation, educators will be able to create conditions for a unique educational experience where individuals learn with and from other specialties and professions in a controlled, safe environment. PMID:25023765
Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.
The trials and tribulations of a practitioner-researcher: challenges and lessons learned through testing a feminist-cognitive-relational social work model of practice.

PubMed

Dombo, Eileen A; Bass, Ami P

2014-01-01

In practice with adult women who survived childhood sexual abuse, the field of social work currently lacks an evidence-based intervention. The current interventions, from the 1990s, come primarily from psychologists. The hypothesis that the Feminist-Cognitive-Relational Social Work Model and Intervention will be more effective in decreasing cognitive distortions, and increasing intimacy and relational health when compared to the standard agency intervention was tested in a quasi-experimental study. The challenges in carrying out the study in small, non-profit organizations are explored to highlight the difficulties in developing evidence-based interventions. Changes to implementation that resulted from the research findings are discussed.
Effects of Learning Support in Simulation-Based Physics Learning

ERIC Educational Resources Information Center

Chang, Kuo-En; Chen, Yu-Lung; Lin, He-Yan; Sung, Yao-Ting

2008-01-01

This paper describes the effects of learning support on simulation-based learning in three learning models: experiment prompting, a hypothesis menu, and step guidance. A simulation learning system was implemented based on these three models, and the differences between simulation-based learning and traditional laboratory learning were explored in…
Work-based learning experiences help students with disabilities transition to careers: a case study of University of Washington projects.

PubMed

Bellman, Scott; Burgstahler, Sheryl; Ladner, Richard

2014-01-01

This case study describes evidence-based practices employed by a collection of University of Washington projects that engage high school and postsecondary students with disabilities in work-based learning experiences such as industry and research internships, career development activities, job shadows, field trips, and mock interviews. The purpose of the article is two-fold. First, authors share best practices with others who wish to increase the participation of students with disabilities in work-based learning and thereby contribute to their academic and career success. The article discusses methods used to recruit students, employers and mentors, match students with specific opportunities, and prepare students for success. Second, authors share outcomes from studies regarding participation in these work-based learning opportunities, which include increased employment success, motivation to work toward a career, knowledge about careers and the workplace, job-related skills, ability to work with supervisors and coworkers, skills in self-advocating for accommodations, and perceived career options.
A remote sensing computer-assisted learning tool developed using the unified modeling language

NASA Astrophysics Data System (ADS)

Friedrich, J.; Karslioglu, M. O.

The goal of this work has been to create an easy-to-use and simple-to-make learning tool for remote sensing at an introductory level. Many students struggle to comprehend what seems to be a very basic knowledge of digital images, image processing and image arithmetic, for example. Because professional programs are generally too complex and overwhelming for beginners and often not tailored to the specific needs of a course regarding functionality, a computer-assisted learning (CAL) program was developed based on the unified modeling language (UML), the present standard for object-oriented (OO) system development. A major advantage of this approach is an easier transition from modeling to coding of such an application, if modern UML tools are being used. After introducing the constructed UML model, its implementation is briefly described followed by a series of learning exercises. They illustrate how the resulting CAL tool supports students taking an introductory course in remote sensing at the author's institution.
Audiovisual facilitation of clinical knowledge: a paradigm for dispersed student education based on Paivio's Dual Coding Theory.

PubMed

Hartland, William; Biddle, Chuck; Fallacaro, Michael

2008-06-01

This article explores the application of Paivio's Dual Coding Theory (DCT) as a scientifically sound rationale for the effects of multimedia learning in programs of nurse anesthesia. We explore and highlight this theory as a practical infrastructure for programs that work with dispersed students (ie, distance education models). Exploring the work of Paivio and others, we are engaged in an ongoing outcome study using audiovisual teaching interventions (SBVTIs) that we have applied to a range of healthcare providers in a quasiexperimental model. The early results of that study are reported in this article. In addition, we have observed powerful and sustained learning in a wide range of healthcare providers with our SBVTIs and suggest that this is likely explained by DCT.
Selective attention to philopatric models causes directed social learning in wild vervet monkeys.

PubMed

van de Waal, Erica; Renevey, Nathalie; Favre, Camille Monique; Bshary, Redouan

2010-07-22

Human behaviour is often based on social learning, a mechanism that has been documented also in a variety of other vertebrates. However, social learning as a means of problem-solving may be optimal only under specific conditions, and both theoretical work and laboratory experiments highlight the importance of a potential model's identity. Here we present the results from a social learning experiment on six wild vervet monkey groups, where models were either a dominant female or a dominant male. We presented 'artificial fruit' boxes that had doors on opposite, differently coloured ends for access to food. One option was blocked during the demonstration phase, creating consistent demonstrations of one possible solution. Following demonstrations we found a significantly higher participation rate and same-door manipulation in groups with female models compared to groups with male models. These differences appeared to be owing to selective attention of bystanders to female model behaviour rather than owing to female tolerance. Our results demonstrate the favoured role of dominant females as a source for 'directed' social learning in a species with female philopatry. Our findings imply that migration does not necessarily lead to an exchange of socially acquired information within populations, potentially causing highly localized traditions.
[Multifamily therapy in children with learning disabilities].

PubMed

Retzlaff, Rüdiger; Brazil, Susanne; Goll-Kopka, Andrea

2008-01-01

Multifamily therapy is an evidence-based method used in the treatment and prevention of severe psychiatric disorders, behavioral problems and physical illnesses in children, adolescents and adults. For preventive family-oriented work with children with learning disorders there is a lack of therapeutic models. This article presents results from an innovative pilot project--multiple family groups for families with a learning disabled child of primary school age (six to eleven years old). Based on a systemic approach, this resource-oriented program integrates creative, activity-based interventions and group therapy techniques and conveys a comprehensive understanding of the challenges associated with learning disorders. Because of the pilot character of the study and the small sample size, the results have to be interpreted with care. The results do however clearly support the wider implementation and evaluation of the program in child guidance clinics, social-pediatric centers, as well as child and adolescent clinics and schools.
A processing architecture for associative short-term memory in electronic noses

NASA Astrophysics Data System (ADS)

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

2006-11-01

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

NASA Astrophysics Data System (ADS)

Warnakulasooriya, Rasil

Model-based learning have been emphasized by many researchers. Furthermore, many theories have been put forward by researchers on how students reason. However, how the theories of reasoning are manifested within the context of electricity and magnetism and how to implement a model-based learning environment within such a context has not been the object of research. In this dissertation, we address the above two concerns. We probe students' reasoning, through a model-based diagnostic instrument. The instrument consists of a set of related multiple-choice questions that can be categorized as belonging to the same conceptual domain. The contextual features of a set are also kept to a minimum. We find that students' responses are tied to the models they have constructed or construct on the spot when faced with novel situations. We find that the concepts such as electric fields and electric potentials exist as mere "definitions" and do not contribute to forming a set of working models, and as such the need for the use of such concepts cannot be easily recognized. We also find that students function within a set of procedural rules. Whether these rules are extended directly from familiar situations through analogies or lead to constructing a set of new rules is constrained by the underlying models and the context of the questions. Models also either exist or are constructed in ways that lead students to overlook the common sense reality of physical phenomena. We also find that the way questions are perceived and interpreted are dependent on the underlying models and that different models exist without conflicting with each other. Based on the above findings, we argue that students' reasoning is context specific and is sensitive to the way the learning has taken place. Thus, we suggest a recontexualization process as a specific model-based learning environment to help students learn electricity and magnetism. The step-by-step guidance through a series of such related questions would then elucidate the context within which concepts are introduced, the limitations of particular representations and the ontological demands required by the subject.
Teaching Research to MSW Students: Effectiveness of the Team-Based Learning Pedagogy

ERIC Educational Resources Information Center

Macke, Caroline; Tapp, Karen

2012-01-01

Social work students often have been labeled as research-reluctant. Consequently, it is important to identify effective teaching strategies. One innovative strategy is team-based learning. The effectiveness of team-based learning has not yet been evaluated with a social work research class. As a result, the current study compared the effectiveness…
Teaching and learning techniques in secondary school science education using a techno-science context of industrial technological problems

NASA Astrophysics Data System (ADS)

McKenzie, Neil Llewellyn

In support of the title of this thesis, an historical review of the literature and methods of delivering practical work in science in secondary schools was carried out. From the work of others, a personal model of classroom scientific investigations was developed and formulated in terms which could be tested in schools. The main emphasis was to see (a) whether the educational value of the model for practical investigations set in a context of integrated science and technology ('techno-science') could be defended theoretically and demonstrated by intervention methods; and (b) how closely such a model could comply with the requirements set out m the Orders for Science in the National Curriculum. The conclusions may be summarised as: (i) The first case study established the value of work experience which was curriculum-based on 'techno-science'. (ii) The second case study suggested that: a) based solely on the information from the questionnaire, the differences in teaching and learning styles measured before and following intervention do not yield a consistent pattern; b) other information, such as the statistical evidence from the analysis of trends in the number of students opting to study science at A-Level and the achievements of the test group in GCSE examinations and AT1 (experimental and investigative science) skills, pointed to the success of the predictions based on the hypothesis.
Probability density function learning by unsupervised neurons.

PubMed

Fiori, S

2001-10-01

In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.
Understanding partnership practice in primary health as pedagogic work: what can Vygotsky's theory of learning offer?

PubMed

Hopwood, Nick

2015-01-01

Primary health policy in Australia has followed international trends in promoting models of care based on partnership between professionals and health service users. This reform agenda has significant practice implications, and has been widely adopted in areas of primary health that involve supporting families with children. Existing research shows that achieving partnership in practice is associated with three specific challenges: uncertainty regarding the role of professional expertise, tension between immediate needs and longer-term capacity development in families, and the need for challenge while maintaining relationships based on trust. Recently, pedagogic or learning-focussed elements of partnership practice have been identified, but there have been no systematic attempts to link theories of learning with the practices and challenges of primary health-care professionals working with families in a pedagogic role. This paper explores key concepts of Vygotsky's theory of learning (including mediation, the zone of proximal development, internalisation, and double stimulation), showing how pedagogic concepts can provide a bridge between the policy rhetoric of partnership and primary health practice. The use of this theory to address the three key challenges is explicitly discussed.
Making Work-Based Learning Work

ERIC Educational Resources Information Center

Cahill, Charlotte

2016-01-01

Americans seeking employment often face a conundrum: relevant work experience is a prerequisite for many jobs, but it is difficult to gain the required experience without being in the workplace. Work-based learning--activities that occur in workplaces through which youth and adults gain the knowledge, skills, and experience needed for entry or…
Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

NASA Astrophysics Data System (ADS)

Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

2018-03-01

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.
A systematic review on US-based community health navigator (CHN) interventions for cancer screening promotion--comparing community- versus clinic-based navigator models.

PubMed

Hou, Su-I; Roberson, Kiersten

2015-03-01

This study synthesized lessons learned from US-based community and clinic health navigator (CHN) interventions on cancer screening promotion to identify characteristics of models and approaches for addressing cancer disparities. The combination terms "cancer screening" and "community health workers or navigators" or "patient navigators" were used in searching Medline, CINAHL, and PsycInfo. A total of 27 articles published during January 2005∼April 2014 were included. Two CHN models were identified: community-based (15 studies) and clinic/hospital-based (12 studies). While both models used the term "navigators," most community-based programs referred them as community health workers/navigators/advisors, whereas clinic-based programs often called them patient navigators. Most community-based CHN interventions targeted specific racial/ethnic minority or rural groups, while clinic-based programs mostly targeted urban low income or mixed ethnic groups. Most community-based CHN programs outreached members from community networks, while clinic-based programs commonly worked with pre-identified in-service clients. Overall, regardless model type, CHNs had similar roles and responsibilities, and interventions demonstrated effective outcomes. Our review identified characteristics of CHN interventions with attention to different settings. Lessons learned have implication on the dissemination and implementation of CHN interventions for cancer screening promotion across setting and target groups.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

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

ERIC Educational Resources Information Center

Fahlman, Dorothy

2016-01-01

This paper reflects on work-based mobile learning in the Canadian healthcare system for registered nurses' ongoing skills development and continuing professional development. It calls on distributed leadership to address the organizational contextual factors for making this mode of learning sustainable. [For the full proceedings, see ED571335.
An overview of game-based learning in building services engineering education

NASA Astrophysics Data System (ADS)

Alanne, Kari

2016-03-01

To ensure proper competence development and short graduation times for engineering students, it is essential that the study motivation is encouraged by new learning methods. In game-based learning, the learner's engagement is increased and learning is made meaningful by applying game-like features such as competition and rewarding through virtual promotions or achievement badges. In this paper, the state of the art of game-based learning in building services engineering education at university level is reviewed and discussed. A systematic literature review indicates that educational games have been reported in the field of related disciplines, such as mechanical and civil engineering. The development of system-level educational games that realistically simulate work life in building services engineering is still in its infancy. Novel rewarding practices and more comprehensive approaches entailing the state-of-the-art information tools such as building information modelling, geographic information systems, building management systems and augmented reality are needed in the future.
School to Work Fact Sheets: Making School to Work Opportunities Happen for Youth with Disabilities.

ERIC Educational Resources Information Center

Horne, Richard L.; Thuli, Kelli J.

These six fact sheets are designed to communicate strategies for serving all youth, especially youth with disabilities, in school to work programs: (1) "Overview of the School-to-Work Opportunities Act" briefly describes this 1994 federal law and the three components of school-to-work programs: school-based learning, work-based learning,…
Making It Work: Embedding a Supported Employment Approach in Vocational Education and Training for People with Learning Difficulties

ERIC Educational Resources Information Center

Jacobsen, Yola

2010-01-01

The "Making it Work" book is for practitioners and managers working with people with learning difficulties in FE colleges, adult and community learning services, work-based learning and voluntary sector organisations. This book was produced as a result of a two-year project funded by the Department of Health. It provides information…
Scientific realism and wishful thinking in soil hydrology

NASA Astrophysics Data System (ADS)

Flühler, H.

2009-04-01

In our field we often learn - or could have learned - more from failures than from successes provided we had postulated testable hypotheses to be accepted or rejected. In soil hydrology, hypotheses are testable if independent information quantifying the pertinent system features is at hand. This view on how to operate is an idealized concept of how we could or should have worked. In reality, the path to success is more tortuous and we usually progress differently obeying to other professional musts. Although we missed some shortcuts over the past few decades, we definitely made significant progress in understanding vadose zone progresses, but we could have advanced our system understanding faster by more rigorously questioning the fundamental assumptions. I will try to illustrate the tortuous path of learning and identify some causes of the slowed-down learning curve. In the pioneering phase of vadose zone research many models have been mapped in our minds and implemented on our computers. Many of them are now well established, powerful and represent the state-of-the-art even when they do not work. Some of them are based on erroneous or misleading concepts. Even when based on adequate concepts they might have been applied in the wrong context or inadequate models may have lead to apparent success. I address this process of collective learning with the intention that we spend more time and efforts to find the right question instead of improving tools, which are questionably suitable for solving the main problems.
Organizing Project-Based Learning in Work Contexts: A Cross-Cultural Cross Analysis of Data from Two Projects

ERIC Educational Resources Information Center

Poell, Rob F.; Yorks, Lyle; Marsick, Victoria J.

2009-01-01

The authors describe research aimed at developing a more comprehensive framework for project-based learning in work contexts. This grows out of a cross-cultural reanalysis of data from two previous studies using two different frameworks: actor-centered learning network theory and a critical pragmatist lens on action reflection learning. Findings…
The Effectiveness of Project-Based Learning on Pupils with Learning Difficulties Regarding Academic Performance, Group Work and Motivation

ERIC Educational Resources Information Center

Filippatou, Diamanto; Kaldi, Stavroula

2010-01-01

This study focuses upon the effectiveness of project-based learning on primary school pupils with learning difficulties regarding their academic performance and attitudes towards self efficacy, task value, group work and teaching methods applied. The present study is a part of a larger one that included six Greek fourth-grade primary school…
Connecting the Classroom to Careers: A Comprehensive Guide to the State's Role in Work-Based Learning

ERIC Educational Resources Information Center

Advance CTE: State Leaders Connecting Learning to Work, 2016

2016-01-01

As state education agencies turn their focus to preparing students for both college and careers, work-based learning is becoming an increasingly popular strategy for students to reinforce and deepen their classroom learning, explore future career fields and demonstrate their skills in an authentic setting. While much of the hard work to identify,…
Web-based Interactive Landform Simulation Model - Grand Canyon

NASA Astrophysics Data System (ADS)

Luo, W.; Pelletier, J. D.; Duffin, K.; Ormand, C. J.; Hung, W.; Iverson, E. A.; Shernoff, D.; Zhai, X.; Chowdary, A.

2013-12-01

Earth science educators need interactive tools to engage and enable students to better understand how Earth systems work over geologic time scales. The evolution of landforms is ripe for interactive, inquiry-based learning exercises because landforms exist all around us. The Web-based Interactive Landform Simulation Model - Grand Canyon (WILSIM-GC, http://serc.carleton.edu/landform/) is a continuation and upgrade of the simple cellular automata (CA) rule-based model (WILSIM-CA, http://www.niu.edu/landform/) that can be accessed from anywhere with an Internet connection. Major improvements in WILSIM-GC include adopting a physically based model and the latest Java technology. The physically based model is incorporated to illustrate the fluvial processes involved in land-sculpting pertaining to the development and evolution of one of the most famous landforms on Earth: the Grand Canyon. It is hoped that this focus on a famous and specific landscape will attract greater student interest and provide opportunities for students to learn not only how different processes interact to form the landform we observe today, but also how models and data are used together to enhance our understanding of the processes involved. The latest development in Java technology (such as Java OpenGL for access to ubiquitous fast graphics hardware, Trusted Applet for file input and output, and multithreaded ability to take advantage of modern multi-core CPUs) are incorporated into building WILSIM-GC and active, standards-aligned curricula materials guided by educational psychology theory on science learning will be developed to accompany the model. This project is funded NSF-TUES program.
Evidence in the learning organization

PubMed Central

Crites, Gerald E; McNamara, Megan C; Akl, Elie A; Richardson, W Scott; Umscheid, Craig A; Nishikawa, James

2009-01-01

Background Organizational leaders in business and medicine have been experiencing a similar dilemma: how to ensure that their organizational members are adopting work innovations in a timely fashion. Organizational leaders in healthcare have attempted to resolve this dilemma by offering specific solutions, such as evidence-based medicine (EBM), but organizations are still not systematically adopting evidence-based practice innovations as rapidly as expected by policy-makers (the knowing-doing gap problem). Some business leaders have adopted a systems-based perspective, called the learning organization (LO), to address a similar dilemma. Three years ago, the Society of General Internal Medicine's Evidence-based Medicine Task Force began an inquiry to integrate the EBM and LO concepts into one model to address the knowing-doing gap problem. Methods During the model development process, the authors searched several databases for relevant LO frameworks and their related concepts by using a broad search strategy. To identify the key LO frameworks and consolidate them into one model, the authors used consensus-based decision-making and a narrative thematic synthesis guided by several qualitative criteria. The authors subjected the model to external, independent review and improved upon its design with this feedback. Results The authors found seven LO frameworks particularly relevant to evidence-based practice innovations in organizations. The authors describe their interpretations of these frameworks for healthcare organizations, the process they used to integrate the LO frameworks with EBM principles, and the resulting Evidence in the Learning Organization (ELO) model. They also provide a health organization scenario to illustrate ELO concepts in application. Conclusion The authors intend, by sharing the LO frameworks and the ELO model, to help organizations identify their capacities to learn and share knowledge about evidence-based practice innovations. The ELO model will need further validation and improvement through its use in organizational settings and applied health services research. PMID:19323819
WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

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

Gao, Y; Lian, J; Chen, R

Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around themore » location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially support by a grant from NCI 1R01CA140413.« less
Project-Based Learning as a Contributing Factor to Graduates' Work Readiness

ERIC Educational Resources Information Center

Jollands, Margaret; Jolly, Lesley; Molyneaux, Tom

2012-01-01

This paper explores what work readiness means for two cohorts of graduate engineers, one from a traditional curriculum, the second from a largely project-based curriculum. Professional bodies and employers have defined a set of attributes for engineering graduates so that graduates will be "work ready". Problem-based learning (PBL) is…
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)
A Single-Display Groupware Collaborative Language Laboratory

ERIC Educational Resources Information Center

Calderón, Juan Felipe; Nussbaum, Miguel; Carmach, Ignacio; Díaz, Juan Jaime; Villalta, Marco

2016-01-01

Language learning tools have evolved to take into consideration new teaching models of collaboration and communication. While second language acquisition tasks have been taken online, the traditional language laboratory has remained unchanged. By continuing to follow its original configuration based on individual work, the language laboratory…
Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning.

PubMed

Du, Tianchuan; Liao, Li; Wu, Cathy H; Sun, Bilin

2016-11-01

Protein-protein interactions play essential roles in many biological processes. Acquiring knowledge of the residue-residue contact information of two interacting proteins is not only helpful in annotating functions for proteins, but also critical for structure-based drug design. The prediction of the protein residue-residue contact matrix of the interfacial regions is challenging. In this work, we introduced deep learning techniques (specifically, stacked autoencoders) to build deep neural network models to tackled the residue-residue contact prediction problem. In tandem with interaction profile Hidden Markov Models, which was used first to extract Fisher score features from protein sequences, stacked autoencoders were deployed to extract and learn hidden abstract features. The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%. We showed that the stacked autoencoders could extract novel features, which can be utilized by deep neural networks and other classifiers to enhance learning, out of the Fisher score features. It is further shown that deep neural networks have significant advantages over SVM in making use of the newly extracted features. Copyright © 2016. Published by Elsevier Inc.
Supporting Scientific Experimentation and Reasoning in Young Elementary School Students

NASA Astrophysics Data System (ADS)

Varma, Keisha

2014-06-01

Researchers from multiple perspectives have shown that young students can engage in the scientific reasoning involved in science experimentation. However, there is little research on how well these young students learn in inquiry-based learning environments that focus on using scientific experimentation strategies to learn new scientific information. This work investigates young children's science concept learning via inquiry-based instruction on the thermodynamics system in a developmentally appropriate, technology-supported learning environment. First- and third-grade students participate in three sets of guided experimentation activities that involve using handheld computers to measure change in temperature given different types of insulation materials. Findings from pre- and post-comparisons show that students at both grade levels are able to learn about the thermodynamics system through engaging in the guided experiment activities. The instruction groups outperformed the control groups on multiple measures of thermodynamics knowledge, and the older children outperform the younger children. Knowledge gains are discussed in the context of mental models of the thermodynamics system that include the individual concepts mentioned above and the relationships between them. This work suggests that young students can benefit from science instruction centered on experimentation activities. It shows the benefits of presenting complex scientific information authentic contexts and the importance of providing the necessary scaffolding for meaningful scientific inquiry and experimentation.
Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

PubMed

El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher

2018-01-01

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.
Team Resilience Training in the Workplace: E-Learning Adaptation, Measurement Model, and Two Pilot Studies.

PubMed

Bennett, Joel B; Neeper, Michael; Linde, Brittany D; Lucas, Gale M; Simone, Lindsay

2018-05-02

The majority of resilience interventions focus on the individual. Workplace resilience is a growing field of research. Given the ever-increasing interconnectedness in businesses, teamwork is a guarantee. There is also growing recognition that resilience functions at the team level. The objective of our work was to address three shortcomings in the study of workplace resilience interventions: lack of interventions focusing on group-level or team resilience, the need for brief interventions, and the need for more theoretical precision in intervention studies. The authors took an established evidence-based program (Team Resilience) and modified it based on these needs. A working model for brief intervention evaluation distinguishes outcomes that are proximal (perceptions that the program improved resilience) and distal (dispositional resilience). A total of 7 hypotheses tested the model and program efficacy. Two samples (n=118 and n=181) of engineering firms received the Web-based training and provided immediate reactions in a posttest-only design. The second sample also included a control condition (n=201). The findings support the model and program efficacy. For example, workplace resilience was greater in the intervention group than in the control group. Other findings suggest social dissemination effects, equal outcomes for employees at different stress levels, and greater benefit for females. This preliminary research provides evidence for the capabilities of e-learning modules to effectively promote workplace resilience and a working model of team resilience. ©Joel B Bennett, Michael Neeper, Brittany D Linde, Gale M Lucas, Lindsay Simone. Originally published in JMIR Mental Health (http://mental.jmir.org), 02.05.2018.
The involvement of model-based but not model-free learning signals during observational reward learning in the absence of choice.

PubMed

Dunne, Simon; D'Souza, Arun; O'Doherty, John P

2016-06-01

A major open question is whether computational strategies thought to be used during experiential learning, specifically model-based and model-free reinforcement learning, also support observational learning. Furthermore, the question of how observational learning occurs when observers must learn about the value of options from observing outcomes in the absence of choice has not been addressed. In the present study we used a multi-armed bandit task that encouraged human participants to employ both experiential and observational learning while they underwent functional magnetic resonance imaging (fMRI). We found evidence for the presence of model-based learning signals during both observational and experiential learning in the intraparietal sulcus. However, unlike during experiential learning, model-free learning signals in the ventral striatum were not detectable during this form of observational learning. These results provide insight into the flexibility of the model-based learning system, implicating this system in learning during observation as well as from direct experience, and further suggest that the model-free reinforcement learning system may be less flexible with regard to its involvement in observational learning. Copyright © 2016 the American Physiological Society.

A Biologically Inspired Computational Model of Basal Ganglia in Action Selection

PubMed Central

Baston, Chiara

2015-01-01

The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments. PMID:26640481
From sMOOC to tMOOC, Learning towards Professional Transference: ECO European Project

ERIC Educational Resources Information Center

Osuna-Acedo, Sara; Marta-Lazo, Carmen; Frau-Meigs, Divina

2018-01-01

The evolution of MOOCs in the last decade has been constant and dynamic. The first cMOOC and xMOOC models eventually evolved into different post-MOOC modalities, such as sMOOC, which conjugates interaction among students with a participation model based on social networks. This work is focused on carrying out a systematic review of the…
ComPLuS Model: A New Insight in Pupils' Collaborative Talk, Actions and Balance during a Computer-Mediated Music Task

ERIC Educational Resources Information Center

Nikolaidou, Georgia N.

2012-01-01

This exploratory work describes and analyses the collaborative interactions that emerge during computer-based music composition in the primary school. The study draws on socio-cultural theories of learning, originated within Vygotsky's theoretical context, and proposes a new model, namely Computer-mediated Praxis and Logos under Synergy (ComPLuS).…
Studio Mathematics: The Epistemology and Practice of Design Pedagogy as a Model for Mathematics Learning. WCER Working Paper No. 2005-3

ERIC Educational Resources Information Center

Shaffer, David Williamson

2005-01-01

This paper examines how middle school students developed understanding of transformational geometry through design activities in Escher's World, a computationally rich design experiment explicitly modeled on an architectural design studio. Escher's World was based on the theory of pedagogical praxis (Shaffer, 2004a), which suggests that preserving…
Science Education Using a Computer Model-Virtual Puget Sound

NASA Astrophysics Data System (ADS)

Fruland, R.; Winn, W.; Oppenheimer, P.; Stahr, F.; Sarason, C.

2002-12-01

We created an interactive learning environment based on an oceanographic computer model of Puget Sound-Virtual Puget Sound (VPS)-as an alternative to traditional teaching methods. Students immersed in this navigable 3-D virtual environment observed tidal movements and salinity changes, and performed tracer and buoyancy experiments. Scientific concepts were embedded in a goal-based scenario to locate a new sewage outfall in Puget Sound. Traditional science teaching methods focus on distilled representations of agreed-upon knowledge removed from real-world context and scientific debate. Our strategy leverages students' natural interest in their environment, provides meaningful context and engages students in scientific debate and knowledge creation. Results show that VPS provides a powerful learning environment, but highlights the need for research on how to most effectively represent concepts and organize interactions to support scientific inquiry and understanding. Research is also needed to ensure that new technologies and visualizations do not foster misconceptions, including the impression that the model represents reality rather than being a useful tool. In this presentation we review results from prior work with VPS and outline new work for a modeling partnership recently formed with funding from the National Ocean Partnership Program (NOPP).
Goal-directed decision making as probabilistic inference: A computational framework and potential neural correlates

PubMed Central

Solway, A.; Botvinick, M.

2013-01-01

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory addressing habit formation, centering on temporal-difference learning mechanisms. Less progress has been made toward formalizing the processes involved in goal-directed decision making. We draw on recent work in cognitive neuroscience, animal conditioning, cognitive and developmental psychology and machine learning, to outline a new theory of goal-directed decision making. Our basic proposal is that the brain, within an identifiable network of cortical and subcortical structures, implements a probabilistic generative model of reward, and that goal-directed decision making is effected through Bayesian inversion of this model. We present a set of simulations implementing the account, which address benchmark behavioral and neuroscientific findings, and which give rise to a set of testable predictions. We also discuss the relationship between the proposed framework and other models of decision making, including recent models of perceptual choice, to which our theory bears a direct connection. PMID:22229491
Workplace learning through peer groups in medical school clerkships.

PubMed

Chou, Calvin L; Teherani, Arianne; Masters, Dylan E; Vener, Margo; Wamsley, Maria; Poncelet, Ann

2014-01-01

Purpose When medical students move from the classroom into clinical practice environments, their roles and learning challenges shift dramatically from a formal curricular approach to a workplace learning model. Continuity among peers during clinical clerkships may play an important role in this different mode of learning. We explored students' perceptions about how they achieved workplace learning in the context of intentionally formed or ad hoc peer groups. Method We invited students in clerkship program models with continuity (CMCs) and in traditional block clerkships (BCs) to complete a survey about peer relationships with open-ended questions based on a workplace learning framework, including themes of workplace-based relationships, the nature of work practices, and selection of tasks and activities. We conducted qualitative content analysis to characterize students' experiences. Results In both BCs and CMCs, peer groups provided rich resources, including anticipatory guidance about clinical expectations of students, best practices in interacting with patients and supervisors, helpful advice in transitioning between rotations, and information about implicit rules of clerkships. Students also used each other as benchmarks for gauging strengths and deficits in their own knowledge and skills. Conclusions Students achieve many aspects of workplace learning in clerkships through formal or informal workplace-based peer groups. In these groups, peers provide accessible, real-time, and relevant resources to help each other navigate transitions, clarify roles and tasks, manage interpersonal challenges, and decrease isolation. Medical schools can support effective workplace learning for medical students by incorporating continuity with peers in the main clinical clerkship year.
Workplace learning through peer groups in medical school clerkships.

PubMed

Chou, Calvin L; Teherani, Arianne; Masters, Dylan E; Vener, Margo; Wamsley, Maria; Poncelet, Ann

2014-01-01

When medical students move from the classroom into clinical practice environments, their roles and learning challenges shift dramatically from a formal curricular approach to a workplace learning model. Continuity among peers during clinical clerkships may play an important role in this different mode of learning. We explored students' perceptions about how they achieved workplace learning in the context of intentionally formed or ad hoc peer groups. We invited students in clerkship program models with continuity (CMCs) and in traditional block clerkships (BCs) to complete a survey about peer relationships with open-ended questions based on a workplace learning framework, including themes of workplace-based relationships, the nature of work practices, and selection of tasks and activities. We conducted qualitative content analysis to characterize students' experiences. In both BCs and CMCs, peer groups provided rich resources, including anticipatory guidance about clinical expectations of students, best practices in interacting with patients and supervisors, helpful advice in transitioning between rotations, and information about implicit rules of clerkships. Students also used each other as benchmarks for gauging strengths and deficits in their own knowledge and skills. Students achieve many aspects of workplace learning in clerkships through formal or informal workplace-based peer groups. In these groups, peers provide accessible, real-time, and relevant resources to help each other navigate transitions, clarify roles and tasks, manage interpersonal challenges, and decrease isolation. Medical schools can support effective workplace learning for medical students by incorporating continuity with peers in the main clinical clerkship year.
Tracing fifth-grade students' epistemologies in modeling through their participation in a model-based curriculum unit

NASA Astrophysics Data System (ADS)

Baek, Hamin

In the past decade, there has been a growing interest in scientific practices as a reform focus in K--12 science education of the United States. In this context, scientific practices refer to practices that have family resemblance to scientists' professional practices and simultaneously are pedagogically accessible and useful to students. In this study, I propose development of students' epistemic agency as an overarching goal for this practice-based approach to science learning. In particular, I argue that students' epistemologies, one dimension of epistemic agency, should be developed as a result of participating in practice-based science learning. The research within this dissertation focuses on studying the practice of scientific modeling. There is a body of prior studies on students' epistemological understandings about models and modeling. None have examined how students' epistemologies about modeling changes over time and why they change the way they do. This research aims to contribute to this body of work by investigating how three elementary students' epistemologies as deployed in their modeling practice, or, their epistemologies in modeling (EIMs) changed over time as a class of 5th-grade students (N=24) and their teacher, Mrs. M, and an intern teacher, Ms. H, enacted a model-based curriculum unit about evaporation and condensation and ways in which some of the curriculum events influenced the changes of their EIMs. To achieve these goals, I conducted a microgenetic analysis of the three focus students' EIMs from the models, utterances, and notes they made in nine modeling activities as part of their curriculum enactment, and analyzed ideas about modeling from some modeling-related curriculum events that preceded each modeling activity using a coding scheme I developed based on prior analytical frameworks and the data. Analysis indicates that the students attended to three main model features such as communicative features (e.g., labels, sentences, key, colors), microscopic/theoretical entities (e.g., water particles), and empirical data (e.g., percentage humidity) with varying epistemic ideas about modeling throughout the unit. The students began with nascent epistemic ideas that emphasize clarity and including many details, but as they gained more experience with modeling, they developed more advanced epistemic ideas related to providing a scientific explanation (mechanism) and making a model accurate and persuasive. The curriculum materials, teachers' instructions and scaffolding, and students' interactions played important roles in the development of the focus students' EIMs. These findings provide some insights into elementary students' epistemologies about modeling that can contribute to learning progression research for scientific modeling. First, this study suggests that we need to attend to epistemic ideas that elementary students have in common as a result of sharing a fairly homogeneous historically established sociocultural world in developing a learning progression for modeling. Second, by showing an intermediary state that elementary students had as they developed their epistemologies about modeling, this study provides an insight into a trajectory or mechanism of how students' epistemologies about modeling become increasingly sophisticated. I hope that this work contributes to the large effort to help students become more active and capable epistemic agents by learning science from engaging in scientific practices both for their present science learning and for their future life they will live as citizens in societies that will be increasingly populated with complicated, controversial socioscientific issues.
Inter-institutional Development of a Poster-Based Cancer Biology Learning Tool

PubMed Central

Andraos-Selim, Cecile; Modzelewski, Ruth A.; Steinman, Richard A.

2010-01-01

There is a paucity of African-American Cancer researchers. To help address this, an educational collaboration was developed between a Comprehensive Cancer Center and a distant undergraduate biology department at a minority institution that sought to teach students introductory cancer biology while modeling research culture. A student-centered active learning curriculum was established that incorporated scientific poster presentations and simulated research exercises to foster learning of cancer biology. Students successfully mined primary literature for supportive data to test cancer-related hypotheses. Student feedback indicated that the poster project substantially enhanced depth of understanding of cancer biology and laid the groundwork for subsequent laboratory work. This inter-institutional collaboration modeled the research process while conveying facts and concepts about cancer. PMID:20237886
A broadband multimedia TeleLearning system

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

Wang, Ruiping; Karmouch, A.

1996-12-31

In this paper we discuss a broadband multimedia TeleLearning system under development in the Multimedia Information Research Laboratory at the University of Ottawa. The system aims at providing a seamless environment for TeleLearning using the latest telecommunication and multimedia information processing technology. It basically consists of a media production center, a courseware author site, a courseware database, a courseware user site, and an on-line facilitator site. All these components are distributed over an ATM network and work together to offer a multimedia interactive courseware service. An MHEG-based model is exploited in designing the system architecture to achieve the real-time, interactive,more » and reusable information interchange through heterogeneous platforms. The system architecture, courseware processing strategies, courseware document models are presented.« less
Factors Promoting Vocational Students' Learning at Work: Study on Student Experiences

ERIC Educational Resources Information Center

Virtanen, Anne; Tynjälä, Päivi; Eteläpelto, Anneli

2014-01-01

In order to promote effective pedagogical practices for students' work-based learning, we need to understand better how students' learning at work can be supported. This paper examines the factors explaining students' workplace learning (WPL) outcomes, addressing three aspects: (1) student-related individual factors, (2) social and…
The implementation of multiple intelligences based teaching model to improve mathematical problem solving ability for student of junior high school

NASA Astrophysics Data System (ADS)

Fasni, Nurli; Fatimah, Siti; Yulanda, Syerli

2017-05-01

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

NASA Astrophysics Data System (ADS)

Gidena, Asay; Gebeyehu, Desta

2017-11-01

The purpose of this study was to investigate the effectiveness of the advance organiser model (AOM) on students' academic achievement in learning work and energy. The design of the study was quasi-experimental pretest-posttest nonequivalent control groups. The total population of the study was 139 students of three sections in Endabaguna preparatory school in Tigray Region, Ethiopia. Two sections with equivalent means on the pretest were taken to participate in the study purposely and one section assigned as the experimental group and the other section assigned as the control group randomly. The experimental group was taught using the lesson plan based on the AOM, and the control group was taught using the lesson plan based on the conventional teaching method. Pretest and posttest were administered before and after the treatment, respectively. Independent sample t-test was used to analyse the data at the probability level of 0.05. The findings of the study showed that the AOM was more effective than the conventional teaching method with effect size of 0.49. This model was also effective to teach male and female students and objectives namely understanding and application. However, both methods were equally important to teach work and energy under the objective knowledge level.
Accredited work-based learning: an approach for collaboration between higher education and practice.

PubMed

Chalmers, H; Swallow, V M; Miller, J

2001-11-01

This article discusses the experience of creating a programme of accredited work based learning (AWBL) for emergency nurse practitioners (ENPs) who work in an Accident and Emergency (A&E) Department in the North East of England. The initiative highlighted the challenges of collaboration with purchasers of education and with professional colleagues, other than nurses. Accredited work-based learning was seen to be an appropriate means of supporting ENP role development. Some of the drivers of the development were: the need for a rapid response to wide ranging changes in the health service; the need to ensure rigour in the quality of both education and health care; to enable participants to engage in role development with appropriate skills, confidence and competence; and to ensure that the learning programme had parity in its design with conventional university based learning. The aim was to collaborate in the creation of learning which was relevant to the Trust's drive to improve patient care which accommodated the nurses' common and individual learning needs and offered academically recognized learning opportunities in tune with the post-Dearing ethos in higher education. This aim was reached and included a great deal of learning on the part of the collaborating partners. Copyright 2001 Harcourt Publishers Ltd.
Developing a Five-Stage Model of Learning in "Second Life"

ERIC Educational Resources Information Center

Salmon, Gilly; Nie, Ming; Edirisingha, Palitha

2010-01-01

Background: In the 1990s, Salmon developed a five-stage model for enabling and scaffolding remote groups to work and learn together using asynchronous bulletin boards. The model has informed online learning and development practice across different levels and education for online and blended learning. Purpose: This paper reports our testing of the…
Cognitive changes in conjunctive rule-based category learning: An ERP approach.

PubMed

Rabi, Rahel; Joanisse, Marc F; Zhu, Tianshu; Minda, John Paul

2018-06-25

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule ("easy" stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule ("difficult" stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.
Learning To Compete.

ERIC Educational Resources Information Center

National Advisory Commission on Work-Based Learning (DOL), Washington, DC.

The National Advisory Commission on Work-Based Learning worked to identify practical steps that the Labor Department could take to help increase the skill levels of the U.S. work force and expand work-based training. The findings gained from a series of roundtables and further studies were synthesized into a set of recommendations in five major…
[Developing team reflexivity as a learning and working tool for medical teams].

PubMed

Riskin, Arieh; Bamberger, Peter

2014-01-01

Team reflexivity is a collective activity in which team members review their previous work, and develop ideas on how to modify their work behavior in order to achieve better future results. It is an important learning tool and a key factor in explaining the varying effectiveness of teams. Team reflexivity encompasses both self-awareness and agency, and includes three main activities: reflection, planning, and adaptation. The model of briefing-debriefing cycles promotes team reflexivity. Its key elements include: Pre-action briefing--setting objectives, roles, and strategies the mission, as well as proposing adaptations based on what was previously learnt from similar procedures; Post-action debriefing--reflecting on the procedure performed and reviewing the extent to which objectives were met, and what can be learnt for future tasks. Given the widespread attention to team-based work systems and organizational learning, efforts should be made toward ntroducing team reflexivity in health administration systems. Implementation could be difficult because most teams in hospitals are short-lived action teams formed for a particular event, with limited time and opportunity to consciously reflect upon their actions. But it is precisely in these contexts that reflexive processes have the most to offer instead of the natural impulsive collective logics. Team reflexivity suggests a potential solution to the major problems of iatorgenesis--avoidable medical errors, as it forces all team members to participate in a reflexive process together. Briefing-debriefing technology was studied mainly in surgical teams and was shown to enhance team-based learning and to improve quality-related outcomes and safety.
Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Developing Engineering and Science Process Skills Using Design Software in an Elementary Education

NASA Astrophysics Data System (ADS)

Fusco, Christopher

This paper examines the development of process skills through an engineering design approach to instruction in an elementary lesson that combines Science, Technology, Engineering, and Math (STEM). The study took place with 25 fifth graders in a public, suburban school district. Students worked in groups of five to design and construct model bridges based on research involving bridge building design software. The assessment was framed around individual student success as well as overall group processing skills. These skills were assessed through an engineering design packet rubric (student work), student surveys of learning gains, observation field notes, and pre- and post-assessment data. The results indicate that students can successfully utilize design software to inform constructions of model bridges, develop science process skills through problem based learning, and understand academic concepts through a design project. The final result of this study shows that design engineering is effective for developing cooperative learning skills. The study suggests that an engineering program offered as an elective or as part of the mandatory curriculum could be beneficial for developing students' critical thinking, inter- and intra-personal skills, along with an increased their understanding and awareness for scientific phenomena. In conclusion, combining a design approach to instruction with STEM can increase efficiency in these areas, generate meaningful learning, and influence student attitudes throughout their education.
MO-DE-304-01: The Abt Study of Medical Physicist Work Values for Radiation Oncology Physics Services: Round IV

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

Mills, M.

The Abt study of medical physicist work values for radiation oncology physics services, Round IV is completed. It supersedes the Abt III study of 2008. The 2015 Abt study measured qualified medical physicist (QMP) work associated with routine radiation oncology procedures as well as some special procedures. As before, a work model was created to allow the medical physicist to defend QMP work based on both routine and special procedures service mix. The work model can be used to develop a cost justification report for setting charges for radiation oncology physics services. The Abt study Round IV was designed tomore » empower the medical physicist to negotiate a service or employment contract with providers based on measured national QMP workforce and staffing data. For a variety of reasons, the diagnostic imaging contingent of AAPM has had a more difficult time trying estimate workforce requirements than their therapy counterparts. Over the past several years, the Diagnostic Work and Workforce Study Subcommittee (DWWSS) has collected survey data from AAPM members, but the data have been very difficult to interpret. The DWWSS has reached out to include more AAPM volunteers to create a more full and accurate representation of actual clinical practice models on the subcommittee. Though much work remains, through hours of discussion and brainstorming, the DWWSS has somewhat of a clear path forward. This talk will provide attendees with an update on the efforts of the subcommittee. Learning Objectives: Understand the new information documented in the Abt studies. Understand how to use the Abt studies to justify medical physicist staffing. Learn relevant historical information on imaging physicist workforce. Understand the process of the DWWSS in 2014. Understand the intended path forward for the DWWSS.« less
Why Are There Developmental Stages in Language Learning? A Developmental Robotics Model of Language Development.

PubMed

Morse, Anthony F; Cangelosi, Angelo

2017-02-01

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to "switch" between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills. Copyright © 2016 Cognitive Science Society, Inc.
From professional development for science teachers to student learning in science

NASA Astrophysics Data System (ADS)

Tinoca, Luis Fonseca

This study investigates the effects of professional development for science teachers on student learning. It is usually expected that professional development programs positively impact student learning, however this dimension is not commonly incorporated in the programs evaluation. It is simply assumed that students will be indirectly impacted through their participating teachers in the work with their students. Two main research questions are addressed: (1) Are professional development programs effective in enhancing student learning in science? (2) What are the characteristics of the most and least effective programs? To answer these questions a meta-analysis of 37 professional development programs reporting their impact on student learning was performed. Program characteristics have been defined according to the categories defined by Loucks-Horsley et al (1998), the National Science Education Standards (NRC, 1996), as well as new categories developed by us analyzing other variables such as the programs length. A significant impact of professional development for science teachers on student learning has been found in the form of an overall correlation effect size of r = 0.22 (p < 0.001). Moreover, a Fixed Effects Model was used to differentiate between the impacts of the different characteristics of professional development programs for science teachers. In particular, programs emphasizing work on curriculum development, replacement, or implementation, scientific inquiry, pedagogical content knowledge, lasting over 6 month and with a total duration of at least 100 hours have been identified as having a larger impact on student learning. To enhance the findings vignettes have been developed based on the attained effect sizes describing possible professional development programs. Recommendations for present and future professional development programs are made based on what works best in order to maximize their impact on student learning.
Long-term Engagement in Authentic Research with NASA (LEARN): Lessons Learned from an Innovative Model for Teacher Research Experiences

NASA Astrophysics Data System (ADS)

Pippin, M. R.; Kollmeyer, R.; Joseph, J.; Yang, M. M.; Omar, A. H.; Harte, T.; Taylor, J.; Lewis, P. M.; Weisman, A.; Hyater-Adams, S.

2013-12-01

The NASA LEARN Project is an innovative program that provides long-term immersion in the practice of atmospheric science for middle and high school in-service teachers. Working alongside NASA scientists and using authentic NASA Science Mission Directorate (SMD) Research and Analysis (R&A) related and mission-based research data, teachers develop individual research topics of interest during two weeks in the summer while on-site at NASA Langley. With continued, intensive mentoring and guidance of NASA scientists, the teachers further develop their research throughout the academic year through virtual group meetings and data team meetings mirroring scientific collaborations. At the end of the first year, the LEARN teachers present scientific posters. During summer 2013, Cohort 1 (7 teachers) presented posters at an open session and discussed their research topics with Cohort 2 (6 teachers) and science and educator personnel at Langley. The LEARN experience has had such an impact that 6 teachers from Cohort 1 have elected to continue a second year of research working alongside Cohort 2 and LEARN scientists. In addition, Cohort 1 teachers have brought their LEARN experiences back to their classrooms in a variety of ways. The LEARN project evaluation has provided insights into the outcomes of this research experience for teachers and particularly effective program elements. In particular, the LEARN evaluation has focused on how an extended research experience for teachers spanning a full year influences teacher views of science and classroom integration of scientific principles. Early findings indicate that teachers' perceptions of the scientific enterprise have changed, and that LEARN provided substantial resources to help them take real-world research to their students. Teachers also valued the teamwork and cohort approach. In addition, the LEARN evaluation focuses on the experiences of scientists involved in the LEARN program and how their experiences working with teachers have changed their ability to communicate the results of research to the public. During this presentation, we will share the LEARN model, findings from the evaluation, and our lessons learned in providing meaningful scientific research experiences for teachers. Our model incorporates intensive support and mentoring, ongoing virtual and face-to-face check-ins, and flexibility to meet teachers' needs on their research projects and in their classrooms.
Assessing competencies: an evaluation of ASTD's Certified Professional in Learning and Performance (CPLP) designation.

PubMed

Kwon, Seolim; Wadholm, Robert R; Carmody, Laurie E

2014-06-01

The American Society of Training and Development's (ASTD) Certified Professional in Learning and Performance (CPLP) program is purported to be based on the ASTD's competency model, a model which outlines foundational competencies, roles, and areas of expertise in the field of training and performance improvement. This study seeks to uncover the relationship between the competency model and the CPLP knowledge exam questions and work product submissions (two of the major instruments used to test for competency of CPLP applicants). A mixed qualitative-quantitative approach is used to identify themes, quantify relationships, and assess questions and guidelines. Multiple raters independently analyzed the data and identified key themes, and Fleiss' Kappa coefficient was used in measuring inter-rater agreement. The study concludes that several discrepancies exist between the competency model and the knowledge exam and work product submission guidelines. Recommendations are given for possible improvement of the CPLP program. Copyright © 2014 Elsevier Ltd. All rights reserved.
Practice-based learning and improvement.

PubMed

Moore, Donald E; Pennington, Floyd C

2003-01-01

Workplace learning is becoming increasingly important in all fields. While workplace learning in medicine, also called practice-based learning and improvement (PBLI) is not new, understanding how it works and how it fits with an individual physician's continuing professional development is new. In this article, we describe seven issues associated with PBLI and then pose questions for reflections, as continuing medical education (CME) planners consider working with PBLI.
Project-based learning as a contributing factor to graduates' work readiness

NASA Astrophysics Data System (ADS)

Jollands, Margaret; Jolly, Lesley; Molyneaux, Tom

2012-05-01

This paper explores what work readiness means for two cohorts of graduate engineers, one from a traditional curriculum, the second from a largely project-based curriculum. Professional bodies and employers have defined a set of attributes for engineering graduates so that graduates will be 'work ready'. Problem-based learning (PBL) is claimed to be a suitable approach to develop such skills. The graduates were interviewed some months after starting work, along with their managers. All the graduates recognised the benefits of taking PBL subjects as well as vacation work, with success in communication attributed more to PBL. Both cohorts had similar learning outcomes, high skill levels in project management, problem solving, communication skills, research and sustainability. A skills gap in ethics was identified for both cohorts of graduates and their managers. Further work is planned to link skill development with undergraduate learning experience.
Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

PubMed Central

Goldstein, Benjamin A.; Navar, Ann Marie; Carter, Rickey E.

2017-01-01

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. PMID:27436868
Three Forms of the Knowledge Economy: Learning, Creativity and Openness

ERIC Educational Resources Information Center

Peters, Michael A.

2010-01-01

This paper outlines and reviews three forms and associated discourses of the "knowledge economy": the "learning economy", based on the work of Bengt-Ake Lundvall; the "creative economy" based on the work of Charles Landry, John Howkins and Richard Florida; and the "open knowledge economy" based on the work of Yochai Benkler and others. Arguably,…
Work-Based Learning Opportunities for High School Students

ERIC Educational Resources Information Center

Alfeld, Corinne; Charner, Ivan; Johnson, Lisa; Watts, Eric

2013-01-01

This report describes the Year 5 work of the National Research Center for Career and Technical Education's (NRCCTE) Technical Assistance (TA) Academy. In 2011-2012, the TA plan carried out by FHI 360 on behalf of the NRCCTE focused on developing a conceptual base for work-based learning (WBL), a strategy that helps students apply academic and…
Experimental Learning Enhancing Improvisation Skills

ERIC Educational Resources Information Center

Pereira Christopoulos, Tania; Wilner, Adriana; Trindade Bestetti, Maria Luisa

2016-01-01

Purpose: This study aims to present improvisation training and experimentation as an alternative method to deal with unexpected events in which structured processes do not seem to work. Design/Methodology/Approach: Based on the literature of sensemaking and improvisation, the study designs a framework and process model of experimental learning…
The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

PubMed

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).
Interaction with Machine Improvisation

NASA Astrophysics Data System (ADS)

Assayag, Gerard; Bloch, George; Cont, Arshia; Dubnov, Shlomo

We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.
Work-Based Learning: Finding a New Niche.

ERIC Educational Resources Information Center

Johnston, George H.

2001-01-01

Describes how a new manufacturing technology program at one college, seeking to use work-based learning extensively, has addressed success factors identified in a national study of exemplary programs. (Contains 10 references.) (AUTH/NB)
Applying a Conceptual Model in Sport Sector Work- Integrated Learning Contexts

ERIC Educational Resources Information Center

Agnew, Deborah; Pill, Shane; Orrell, Janice

2017-01-01

This paper applies a conceptual model for work-integrated learning (WIL) in a multidisciplinary sports degree program. Two examples of WIL in sport will be used to illustrate how the conceptual WIL model is being operationalized. The implications for practice are that curriculum design must recognize a highly flexible approach to the nature of…
Evaluating Experiential Learning in the Business Context: Contributions to Group-Based and Cross-Functional Working

ERIC Educational Resources Information Center

Piercy, Niall

2013-01-01

The use of experiential learning techniques has become popular in business education. Experiential learning approaches offer major benefits for teaching contemporary management practices such as cross-functional and team-based working. However, there remains relatively little empirical data on the success of experiential pedagogies in supporting…
Undergraduate Social Work Students' Perceptions of a Team-Based Learning Approach to Exploring Adult Development

ERIC Educational Resources Information Center

Watkins, Karen; Forge, Nicholas; Lewinson, Terri; Garner, Brittany; Carter, Larance D.; Greenwald, Lindsay

2018-01-01

Social work educators are challenged to adopt innovative instructional methods and pedagogies to prepare students to meet the contemporary needs of diverse client populations. A team-based learning (TBL) approach is a pedagogical strategy that utilizes cooperative and collaborative learning principles to inspire academic, professional, and…
Utilizing Technology for Professional Learning in the Dissemination of Evidence-Based Practices to Paraprofessionals Working in Public Education

ERIC Educational Resources Information Center

Wright, Patricia I.; Prescott, Rosalind

2018-01-01

Although paraprofessionals are pivotal for the educational success of learners with autism, limited professional learning opportunities are provided resulting in inadequate application of evidence-based practices in their work. In this participatory action research study, thirty-six paraprofessionals participated in professional learning utilizing…
Life History Methodologies: An Investigation into Work-Based Learning Experiences of Community Education Workers

ERIC Educational Resources Information Center

Issler, Sally; Nixon, David

2007-01-01

This article focuses on an investigation into the learning journeys undertaken by managers of a community education project in an area of urban deprivation. A constructivist interpretation of life history narrative revealed the positive effects of community workers' heavy dependence on experiential work-based learning, which resulted in the…

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