Study of the Entrepreneurship in Universities as Learning Organization Based on Senge Model

ERIC Educational Resources Information Center

Nejad, Bahareh Azizi; Abbaszadeh, Mir Mohammad Seiied; Hassani, Mohammad; Bernousi, Iraj

2012-01-01

Learning organization and entrepreneurship are the most important issues that are focused on different themes in management. The purpose of present research was to study the relationship between learning organization elements and entrepreneurship among academic faculty members of the West Azarbaijan State Universities. The research method was…

Individual and Collective Reflection: How to Meet the Needs of Development in Teaching

ERIC Educational Resources Information Center

Nissila, Sade-Pirkko

2005-01-01

The following five core ideas explain how learning organizations function as wholes. The core ideas are central when school is examined as a learning organization. Personal mastery, mental models, team learning, shared visions and system thinking offer different angles to examine the organization. (1) Personal mastery. Without personal commitment,…

Informal learning processes in support of clinical service delivery in a service-oriented community pharmacy.

PubMed

Patterson, Brandon J; Bakken, Brianne K; Doucette, William R; Urmie, Julie M; McDonough, Randal P

The evolving health care system necessitates pharmacy organizations' adjustments by delivering new services and establishing inter-organizational relationships. One approach supporting pharmacy organizations in making changes may be informal learning by technicians, pharmacists, and pharmacy owners. Informal learning is characterized by a four-step cycle including intent to learn, action, feedback, and reflection. This framework helps explain individual and organizational factors that influence learning processes within an organization as well as the individual and organizational outcomes of those learning processes. A case study of an Iowa independent community pharmacy with years of experience in offering patient care services was made. Nine semi-structured interviews with pharmacy personnel revealed initial evidence in support of the informal learning model in practice. Future research could investigate more fully the informal learning model in delivery of patient care services in community pharmacies. Copyright © 2016 Elsevier Inc. All rights reserved.

The health care learning organization.

PubMed

Hult, G T; Lukas, B A; Hult, A M

1996-01-01

To many health care executives, emphasis on marketing strategy has become a means of survival in the threatening new environment of cost attainment, intense competition, and prospective payment. This paper develops a positive model of the health care organization based on organizational learning theory and the concept of the health care offering. It is proposed that the typical health care organization represents the prototype of the learning organization. Thus, commitment to a shared vision is proposed to be an integral part of the health care organization and its diagnosis, treatment, and delivery of the health care offering, which is based on the exchange relationship, including its communicative environment. Based on the model, strategic marketing implications are discussed.

Sound Foundations: Organic Approaches to Learning Notation in Beginning Band

ERIC Educational Resources Information Center

West, Chad

2016-01-01

By starting with a foundation of sound before sight, we can help our students learn notation organically in a way that honors the natural process. This article describes five organic approaches to learning notation in beginning band: (1) iconic notation, (2) point and play, (3) student lead-sheet, (4) modeling, and (5) kid dictation. While…

The Construction and Development of Indicators of Learning Organization at Higher Educational Institutions Emphasizing Graduate Production and Social Development

ERIC Educational Resources Information Center

Hanrin, Chanwit; Sri-Amphai, Pissamai; Ruangmontri, Karn; Namwan, Tharinthorn

2011-01-01

The Purposes of this research were to construct and develop indicators of learning organization at higher educational institutions emphasize graduate production and social development, and to test the congruence of the structural model of the indicators of learning organization at higher educational institutions emphasizing graduate production and…

MODeLeR: A Virtual Constructivist Learning Environment and Methodology for Object-Oriented Design

ERIC Educational Resources Information Center

Coffey, John W.; Koonce, Robert

2008-01-01

This article contains a description of the organization and method of use of an active learning environment named MODeLeR, (Multimedia Object Design Learning Resource), a tool designed to facilitate the learning of concepts pertaining to object modeling with the Unified Modeling Language (UML). MODeLeR was created to provide an authentic,…

The Knowledge Building Paradigm: A Model of Learning for Net Generation Students

ERIC Educational Resources Information Center

Philip, Donald

2005-01-01

In this article Donald Philip describes Knowledge Building, a pedagogy based on the way research organizations function. The global economy, Philip argues, is driving a shift from older, industrial models to the model of the business as a learning organization. The cognitive patterns of today's Net Generation students, formed by lifetime exposure…

A Performance Enhanced Interactive Learning Workshop Model as a Supplement for Organic Chemistry Instruction

ERIC Educational Resources Information Center

Phillips, Karen E. S.; Grose-Fifer, Jilliam

2011-01-01

In this study, the authors describe a Performance Enhanced Interactive Learning (PEIL) workshop model as a supplement for organic chemistry instruction. This workshop model differs from many others in that it includes public presentations by students and other whole-class-discussion components that have not been thoroughly investigated in the…

An Introduction to Topic Modeling as an Unsupervised Machine Learning Way to Organize Text Information

ERIC Educational Resources Information Center

Snyder, Robin M.

2015-01-01

The field of topic modeling has become increasingly important over the past few years. Topic modeling is an unsupervised machine learning way to organize text (or image or DNA, etc.) information such that related pieces of text can be identified. This paper/session will present/discuss the current state of topic modeling, why it is important, and…

Learning Strategies for Police Organization--Modeling Organizational Learning Perquisites.

ERIC Educational Resources Information Center

Luoma, Markku; Nokelainen, Petri; Ruohotie, Pekka

The factors contributing to organizational learning in police units in Finland and elsewhere were examined to find strategies to improve the prerequisites of learning and compare linear and nonlinear methods of modeling organizational learning prerequisites. A questionnaire was used to collect data from the 281 staff members of five police…

A Cross-Cultural Analysis of the Effectiveness of the Learning Organization Model in School Contexts

ERIC Educational Resources Information Center

Alavi, Seyyed Babak; McCormick, John

2004-01-01

It has been argued that some management theories and models may not be universal and are based on some cultural assumptions. It is argued in this paper that the effectiveness of applying the Learning Organization (LO) model in school contexts across different countries may be associated with cultural differences such as individualism,…

Stacked Multilayer Self-Organizing Map for Background Modeling.

PubMed

Zhao, Zhenjie; Zhang, Xuebo; Fang, Yongchun

2015-09-01

In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

The "Organization" as an Interdisciplinary Learning Zone: Using a Strategic Game to Integrate Learning about Supply Chain Management and Advertising

ERIC Educational Resources Information Center

Arora, Anshu Saxena

2012-01-01

Purpose: The research study seeks to explore the relationship among strategic gaming, the learning organization model and approach, and transfer of learning as key success strategies for improved individual and organizational performance and sustainable competitive advantage. This research aims to identify and elaborate on the strategic…

Measuring the learning capacity of organisations: development and factor analysis of the Questionnaire for Learning Organizations.

PubMed

Oudejans, S C C; Schippers, G M; Schramade, M H; Koeter, M W J; van den Brink, W

2011-04-01

To investigate internal consistency and factor structure of a questionnaire measuring learning capacity based on Senge's theory of the five disciplines of a learning organisation: Personal Mastery, Mental Models, Shared Vision, Team Learning, and Systems Thinking. Cross-sectional study. Substance-abuse treatment centres (SATCs) in The Netherlands. A total of 293 SATC employees from outpatient and inpatient treatment departments, financial and human resources departments. Psychometric properties of the Questionnaire for Learning Organizations (QLO), including factor structure, internal consistency, and interscale correlations. A five-factor model representing the five disciplines of Senge showed good fit. The scales for Personal Mastery, Shared Vision and Team Learning had good internal consistency, but the scales for Systems Thinking and Mental Models had low internal consistency. The proposed five-factor structure was confirmed in the QLO, which makes it a promising instrument to assess learning capacity in teams. The Systems Thinking and the Mental Models scales have to be revised. Future research should be aimed at testing criterion and discriminatory validity.

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

PubMed Central

Mender, Bedeho M. W.; Stringer, Simon M.

2013-01-01

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization. PMID:24349064

An Inquiry into the Foundations of Organizational Learning and the Learning Organization

ERIC Educational Resources Information Center

Jensen, Jorgen A.; Rasmussen, Ole E.

2004-01-01

People's mental models are viewed as being significant in achieving organizational outcomes, on the assumption that mental models influence people's acts. A fundamental issue in the area of organizational learning, then, is the relation between mental models, learning and performance. We contend that a fruitful line of work is to study persons as…

Learning Organization Perceptions in Elementary Education in Terms of Teachers and the Effect of Learning Organization on Organizational Commitment

ERIC Educational Resources Information Center

Erdem, Mustafa; Ucar, Ibrahim Halil

2013-01-01

In this study, it was tried to determine to what degree the learning organization predicted organizational commitment according to primary school teachers' perceptions. Descriptive survey model was used in this study and 429 teachers were chosen among 2387 teachers who worked in primary schools in Van in 2010-2011 education years and were included…

Learning Organization and Innovative Behavior: The Mediating Effect of Work Engagement

ERIC Educational Resources Information Center

Park, Yu Kyoung; Song, Ji Hoon; Yoon, Seung Won; Kim, Jungwoo

2014-01-01

Purpose: The purpose of this study is to investigate the mediating effect of work engagement on the relationship between learning organization and innovative behavior. Design/methodology/approach: This study used surveys as a data collection tool and implemented structural equation modeling for empirically testing the proposed research model.…

Action Learning for Strategic Innovation in Mature Organizations: Key Cognitive, Design and Contextual Considerations

ERIC Educational Resources Information Center

Kuhn, Jeffrey S.; Marsick, Victoria J.

2005-01-01

This article lays out a model of action learning for catalyzing strategic innovation in mature organizations that are faced with a new competitive playing field. Central to this model is the development of a set of sophisticated cognitive capabilities--sensemaking, strategic thinking, critical thinking, divergent thinking, conceptual capacity and…

The MIKS (Member Integrated Knowledge System) Model: A Visualization of the Individual Organizational Member's Role When a Knowledge Management System Is Utilized in the Learning Organization

ERIC Educational Resources Information Center

Grobmeier, Cynthia

2007-01-01

Relating knowledge management (KM) case studies in various organizational contexts to existing theoretical constructs of learning organizations, a new model, the MIKS (Member Integrated Knowledge System) Model is proposed to include the role of the individual in the process. Their degree of motivation as well as communication and learning…

Hospitals as learning organizations: fostering innovation through interactive learning.

PubMed

Dias, Casimiro; Escoval, Ana

2015-01-01

The article aims to provide an analytical understanding of hospitals as "learning organizations." It further analyzes the development of learning organizations as a way to enhance innovation and performance in the hospital sector. The article pulls together primary data on organizational flexibility, innovation, and performance from 95 administrators from hospital boards in Portugal, collected through a survey, interviews with hospital's boards, and a nominal group technique with a panel of experts on health systems. Results show that a combination of several organizational traits of the learning organization enhances its capacity for innovation development. The logistic model presented reveals that hospitals classified as "advanced learning organizations" have 5 times more chance of developing innovation than "basic learning organizations." Empirical findings further pointed out incentives, standards, and measurement requirements as key elements for integration of service delivery systems and expansion of the current capacity for structured and real-time learning in the hospital sector. The major implication arising from this study is that policy needs to combine instruments that promote innovation opportunities and incentives, with instruments stimulating the further development of the core components of learning organizations. Such a combination of policy instruments has the potential to ensure a wide external cooperation through a learning infrastructure.

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

[Scale of organizational learning in schools. Organizational learning is a key element for the development of organizations].

PubMed

López, Verónica; Ahumada, Luis; Olivares, Rodrigo; González, Alvaro

2012-05-01

Organizational learning is a key element for the development of organizations. School organizations are not exempt from this challenge and they currently face a highly dynamic and demanding context of education policies that emphasize the school's ability to learn. Thus, research on organizational learning in educational contexts requires valid instruments that are sensitive to the specifics of schools as organizations. In this study, we adapted and validated a scale of organizational learning in a sample of 119 Chilean municipal schools (N= 1,545). The results suggest a structural model made up of three factors: culture of learning, strategic clarity, and group learning. These factors predicted dimensions of educational achievement, as measured through the National Assessment System of Educational Achievement (SNED). Results are discussed in view of the literature on school improvement.

The organization of an autonomous learning system

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1988-01-01

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined.

Validation of Senge's Learning Organization Model with Teachers of Vocational High Schools at the Seoul Megalopolis

ERIC Educational Resources Information Center

Park, Joo Ho

2008-01-01

This study measured and applied Senge's (1990) fifth discipline model of learning organizations in a culturally distinct population, namely teachers in 17 vocational high schools located in the Seoul megalopolis. The participants were 976 full-time vocational and academic teachers in public trade/industry-technical and business high schools in the…

Brain Research: Implications for Learning.

ERIC Educational Resources Information Center

Soares, Louise M.; Soares, Anthony T.

Brain research has illuminated several areas of the learning process: (1) learning as association; (2) learning as reinforcement; (3) learning as perception; (4) learning as imitation; (5) learning as organization; (6) learning as individual style; and (7) learning as brain activity. The classic conditioning model developed by Pavlov advanced…

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

PubMed

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-09-15

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. xinghua@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Organizational Teaching and Learning--A (Re)View from Educational Science

ERIC Educational Resources Information Center

Lahn, Leif Christian

2016-01-01

Purpose: The purpose of this paper is to critically examine the dominance of the participation metaphor for learning in the literature on learning organizations and to propose a working model of the teaching organization with conceptual input from educational science and the sociology of professions. Design/methodology/approach: The paper combines…

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

NASA Astrophysics Data System (ADS)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

A strategic systems perspective of organizational learning theory: models for a case study at the Jet Propulsion Laboratory

NASA Technical Reports Server (NTRS)

Neece, O.

2000-01-01

Organizational learning is an umbrella term that covers a variety of topics including; learning curves, productivity, organizational memory, organizational forgetting, knowledge transfer, knowledge sharing and knowledge creation. This treatise will review some of these theories in concert with a model of how organizations learn.

Research on Model of Student Engagement in Online Learning

ERIC Educational Resources Information Center

Peng, Wang

2017-01-01

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

The MVP Model as an Organizing Framework for Neuroscience Findings Related to Learning

ERIC Educational Resources Information Center

Zakrajsek, Todd M.

2017-01-01

This chapter describes the ways in which the MVP model relates to recent research on neuroscience and learning, and demonstrates how those relationships may be used to better understand physiological impacts on motivation, and to facilitate improved learning.

A Guide to the Literature on Learning Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Friedland, Peter (Technical Monitor)

1994-01-01

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Are All Hands-On Activities Equally Effective? Effect of Using Plastic Models, Organ Dissections, and Virtual Dissections on Student Learning and Perceptions

ERIC Educational Resources Information Center

Lombardi, Sara A.; Hicks, Reimi E.; Thompson, Katerina V.; Marbach-Ad, Gili

2014-01-01

This study investigated the impact of three commonly used cardiovascular model-assisted activities on student learning and student attitudes and perspectives about science. College students enrolled in a Human Anatomy and Physiology course were randomly assigned to one of three experimental groups (organ dissections, virtual dissections, or…

Accurate Segmentation of CT Male Pelvic Organs via Regression-based Deformable Models and Multi-task Random Forests

PubMed Central

Gao, Yaozong; Shao, Yeqin; Lian, Jun; Wang, Andrew Z.; Chen, Ronald C.

2016-01-01

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation. PMID:26800531

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

Cohorts, Communities of Inquiry, and Course Delivery Methods: UTC Best Practices in Learning--The Hybrid Learning Community Model

ERIC Educational Resources Information Center

Rausch, David W.; Crawford, Elizabeth K.

2012-01-01

From the early 1990s to present, the practice of cohort-based learning has been on the rise in colleges, universities, organizations, and even some K-12 programs across the nation. This type of learning model uses the power of the interpersonal relationships to enhance the learning process and provide additional support to the cohort members as…

Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization

ERIC Educational Resources Information Center

Rastegarmoghadam, Mahin; Ziarati, Koorush

2017-01-01

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…

Lifelong learning of human actions with deep neural network self-organization.

PubMed

Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

2017-12-01

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Communities of clinical practice: the social organization of clinical learning.

PubMed

Egan, Tony; Jaye, Chrystal

2009-01-01

The social organization of clinical learning is under-theorized in the sociological literature on the social organization of health care. Professional scopes of practice and jurisdictions are formally defined by professional principles and standards and reflected in legislation; however, these are mediated through the day-to-day clinical activities of social groupings of clinical teams. The activities of health service providers typically occur within communities of clinical practice. These are also major sites for clinical curriculum delivery, where clinical students learn not only clinical skills but also how to be health professionals. In this article, we apply Wenger's model of social learning within organizations to curriculum delivery within a health service setting. Here, social participation is the basis of learning. We suggest that it offers a powerful framework for recognizing and explaining paradox and incongruence in clinical teaching and learning, and also for recognizing opportunities, and devising means, to add value to students' learning experiences.

The 1974 AVCR Young Scholar Paper: An Open-System Model of Learning

ERIC Educational Resources Information Center

Winn, William

1975-01-01

Rejecting the cybernetic model of the learner, the author offers an open-system model based on von Bertalanffy's equation for growth of the living organism. The model produces four learning curves, not just the logarithmic curve produced by the successive approximations of the cybernetic model. (Editor)

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

Mack, Michael L.; Love, Bradley C.; Preston, Alison R.

2016-01-01

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal. PMID:27803320

Perry's Scheme of Intellectual and Epistemological Development as a Framework for Describing Student Difficulties in Learning Organic Chemistry

ERIC Educational Resources Information Center

Grove, Nathaniel P.; Bretz, Stacey Lowery

2010-01-01

We have investigated student difficulties with the learning of organic chemistry. Using Perry's Model of Intellectual Development as a framework revealed that organic chemistry students who function as dualistic thinkers struggle with the complexity of the subject matter. Understanding substitution/elimination reactions and multi-step syntheses is…

Children and adolescents' performance on a medium-length/nonsemantic word-list test.

PubMed

Flores-Lázaro, Julio César; Salgado Soruco, María Alejandra; Stepanov, Igor I

2017-01-01

Word-list learning tasks are among the most important and frequently used tests for declarative memory evaluation. For example, the California Verbal Learning Test-Children's Version (CVLT-C) and Rey Auditory Verbal Learning Test provide important information about different cognitive-neuropsychological processes. However, the impact of test length (i.e., number of words) and semantic organization (i.e., type of words) on children's and adolescents' memory performance remains to be clarified, especially during this developmental stage. To explore whether a medium-length non-semantically organized test can produce the typical curvilinear performance that semantically organized tests produce, reflecting executive control, we studied and compared the cognitive performance of normal children and adolescents by utilizing mathematical modeling. The model is based on the first-order system transfer function and has been successfully applied to learning curves for the CVLT-C (15 words, semantically organized paradigm). Results indicate that learning nine semantically unrelated words produces typical curvilinear (executive function) performance in children and younger adolescents and that performance could be effectively analyzed with the mathematical model. This indicates that the exponential increase (curvilinear performance) of correctly learned words does not solely depend on semantic and/or length features. This type of test controls semantic and length effects and may represent complementary tools for executive function evaluation in clinical populations in which semantic and/or length processing are affected.

Global assessment of soil organic carbon stocks and spatial distribution of histosols: the Machine Learning approach

NASA Astrophysics Data System (ADS)

Hengl, Tomislav

2016-04-01

Preliminary results of predicting distribution of soil organic soils (Histosols) and soil organic carbon stock (in tonnes per ha) using global compilations of soil profiles (about 150,000 points) and covariates at 250 m spatial resolution (about 150 covariates; mainly MODIS seasonal land products, SRTM DEM derivatives, climatic images, lithological and land cover and landform maps) are presented. We focus on using a data-driven approach i.e. Machine Learning techniques that often require no knowledge about the distribution of the target variable or knowledge about the possible relationships. Other advantages of using machine learning are (DOI: 10.1371/journal.pone.0125814): All rules required to produce outputs are formalized. The whole procedure is documented (the statistical model and associated computer script), enabling reproducible research. Predicted surfaces can make use of various information sources and can be optimized relative to all available quantitative point and covariate data. There is more flexibility in terms of the spatial extent, resolution and support of requested maps. Automated mapping is also more cost-effective: once the system is operational, maintenance and production of updates are an order of magnitude faster and cheaper. Consequently, prediction maps can be updated and improved at shorter and shorter time intervals. Some disadvantages of automated soil mapping based on Machine Learning are: Models are data-driven and any serious blunders or artifacts in the input data can propagate to order-of-magnitude larger errors than in the case of expert-based systems. Fitting machine learning models is at the order of magnitude computationally more demanding. Computing effort can be even tens of thousands higher than if e.g. linear geostatistics is used. Many machine learning models are fairly complex often abstract and any interpretation of such models is not trivial and require special multidimensional / multivariable plotting and data mining tools. Results of model fitting using the R packages nnet, randomForest and the h2o software (machine learning functions) show that significant models can be fitted for soil classes, bulk density (R-square 0.76), soil organic carbon (R-square 0.62) and coarse fragments (R-square 0.59). Consequently, we were able to estimate soil organic carbon stock for majority of the land mask (excluding permanent ice) and detect patches of landscape containing mainly organic soils (peat and similar). Our results confirm that hotspots of soil organic carbon in Tropics are peatlands in Indonesia, north of Peru, west Amazon and Congo river basin. Majority of world soil organic carbon stock is likely in the Northern latitudes (tundra and taiga of the north). Distribution of histosols seems to be mainly controlled by climatic conditions (especially temperature regime and water vapor) and hydrologic position in the landscape. Predicted distributions of organic soils (probability of occurrence) and total soil organic carbon stock at resolutions of 1 km and 250 m are available via the SoilGrids.org project homepage.

Developing Learning Environments: Challenges for Theory, Research and Practice.

ERIC Educational Resources Information Center

Iles, Paul

1994-01-01

Key challenges in development of learning organizations, promotion of learning culture, enhancement of learning processes, and development of learning communities are appropriateness of current models for interdisciplinary teams; whether valuing diversity enhances effectiveness; how global human resource development affects domestic; and what…

The Influence of Pintrich's Self-Regulated Learning Model on Elementary Teacher Candidates in a Life Science Course

ERIC Educational Resources Information Center

Çetin, Baris

2017-01-01

The purpose of this research was to determine whether the use of activities based on Pintrich's self-regulated learning model effect the self-regulated learning perceptions of elementary teacher candidates within a Life Science course. The research was organized in accordance with the quasi-experimental designs model. This study was conducted…

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations.

PubMed

Grossberg, Stephen; Pilly, Praveen K

2014-02-05

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

Learning Leaders for Learning Schools

ERIC Educational Resources Information Center

Brown, Frederick; Psencik, Kay

2017-01-01

Principals who pay attention to their own learning serve as models for others. What principals do every day, how they view and value student and educator learning, how they organize their staff into learning communities, and the designs they support for those teams to learn make a significant difference in the learning of those they serve. In this…

Habituation in non-neural organisms: evidence from slime moulds.

PubMed

Boisseau, Romain P; Vogel, David; Dussutour, Audrey

2016-04-27

Learning, defined as a change in behaviour evoked by experience, has hitherto been investigated almost exclusively in multicellular neural organisms. Evidence for learning in non-neural multicellular organisms is scant, and only a few unequivocal reports of learning have been described in single-celled organisms. Here we demonstrate habituation, an unmistakable form of learning, in the non-neural organism Physarum polycephalum In our experiment, using chemotaxis as the behavioural output and quinine or caffeine as the stimulus, we showed that P. polycephalum learnt to ignore quinine or caffeine when the stimuli were repeated, but responded again when the stimulus was withheld for a certain time. Our results meet the principle criteria that have been used to demonstrate habituation: responsiveness decline and spontaneous recovery. To distinguish habituation from sensory adaptation or motor fatigue, we also show stimulus specificity. Our results point to the diversity of organisms lacking neurons, which likely display a hitherto unrecognized capacity for learning, and suggest that slime moulds may be an ideal model system in which to investigate fundamental mechanisms underlying learning processes. Besides, documenting learning in non-neural organisms such as slime moulds is centrally important to a comprehensive, phylogenetic understanding of when and where in the tree of life the earliest manifestations of learning evolved. © 2016 The Author(s).

Habituation in non-neural organisms: evidence from slime moulds

PubMed Central

Boisseau, Romain P.; Vogel, David; Dussutour, Audrey

2016-01-01

Learning, defined as a change in behaviour evoked by experience, has hitherto been investigated almost exclusively in multicellular neural organisms. Evidence for learning in non-neural multicellular organisms is scant, and only a few unequivocal reports of learning have been described in single-celled organisms. Here we demonstrate habituation, an unmistakable form of learning, in the non-neural organism Physarum polycephalum. In our experiment, using chemotaxis as the behavioural output and quinine or caffeine as the stimulus, we showed that P. polycephalum learnt to ignore quinine or caffeine when the stimuli were repeated, but responded again when the stimulus was withheld for a certain time. Our results meet the principle criteria that have been used to demonstrate habituation: responsiveness decline and spontaneous recovery. To distinguish habituation from sensory adaptation or motor fatigue, we also show stimulus specificity. Our results point to the diversity of organisms lacking neurons, which likely display a hitherto unrecognized capacity for learning, and suggest that slime moulds may be an ideal model system in which to investigate fundamental mechanisms underlying learning processes. Besides, documenting learning in non-neural organisms such as slime moulds is centrally important to a comprehensive, phylogenetic understanding of when and where in the tree of life the earliest manifestations of learning evolved. PMID:27122563

Validating and Optimizing the Effects of Model Progression in Simulation-Based Inquiry Learning

ERIC Educational Resources Information Center

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

2012-01-01

Model progression denotes the organization of the inquiry learning process in successive phases of increasing complexity. This study investigated the effectiveness of model progression in general, and explored the added value of either broadening or narrowing students' possibilities to change model progression phases. Results showed that…

Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

PubMed Central

Badre, David

2012-01-01

Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with more anterior regions having increasingly abstract representations. How does this organization support hierarchical cognitive control and the rapid discovery of abstract action rules? We present computational models at different levels of description. A neural circuit model simulates interacting corticostriatal circuits organized hierarchically. In each circuit, the basal ganglia gate frontal actions, with some striatal units gating the inputs to PFC and others gating the outputs to influence response selection. Learning at all of these levels is accomplished via dopaminergic reward prediction error signals in each corticostriatal circuit. This functionality allows the system to exhibit conditional if–then hypothesis testing and to learn rapidly in environments with hierarchical structure. We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence of choices and rewards. This model yields accurate probabilistic estimates about which hypotheses are attended by manipulating attentional states in the generative neural model and recovering them with the MoE model. This 2-pronged modeling approach leads to multiple quantitative predictions that are tested with functional magnetic resonance imaging in the companion paper. PMID:21693490

Integrating cortico-limbic-basal ganglia architectures for learning model-based and model-free navigation strategies

PubMed Central

Khamassi, Mehdi; Humphries, Mark D.

2012-01-01

Behavior in spatial navigation is often organized into map-based (place-driven) vs. map-free (cue-driven) strategies; behavior in operant conditioning research is often organized into goal-directed vs. habitual strategies. Here we attempt to unify the two. We review one powerful theory for distinct forms of learning during instrumental conditioning, namely model-based (maintaining a representation of the world) and model-free (reacting to immediate stimuli) learning algorithms. We extend these lines of argument to propose an alternative taxonomy for spatial navigation, showing how various previously identified strategies can be distinguished as “model-based” or “model-free” depending on the usage of information and not on the type of information (e.g., cue vs. place). We argue that identifying “model-free” learning with dorsolateral striatum and “model-based” learning with dorsomedial striatum could reconcile numerous conflicting results in the spatial navigation literature. From this perspective, we further propose that the ventral striatum plays key roles in the model-building process. We propose that the core of the ventral striatum is positioned to learn the probability of action selection for every transition between states of the world. We further review suggestions that the ventral striatal core and shell are positioned to act as “critics” contributing to the computation of a reward prediction error for model-free and model-based systems, respectively. PMID:23205006

Reconstructing spatial organizations of chromosomes through manifold learning

PubMed Central

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

2018-01-01

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

Reconstructing spatial organizations of chromosomes through manifold learning.

PubMed

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

2018-05-04

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

Optimizing learning in healthcare: how Island Health is evolving to learn at the speed of change.

PubMed

Gottfredson, Conrad; Stroud, Carol; Jackson, Mary; Stevenson, R Lynn; Archer, Jana

2014-01-01

Healthcare organizations are challenged with constrained resources and increasing service demands by an aging population with complex care needs. Exponential growth in competency requirements also challenges staff's ability to provide quality patient care. How can a healthcare organization support its staff to learn "at or above the speed of change" while continuing to provide the quality patient care? Island Health is addressing this challenge by transforming its traditional education model into an innovative, evidence-based learning and performance support approach. Implementation of the methodology is yielding several lessons learned, both for the internal Learning and Performance Support team, and for what it takes to bring a new way of doing business into an organization. A key result is that this approach is enabling the organization to be more responsive in helping staff gain and maintain competencies.

Trans-species learning of cellular signaling systems with bimodal deep belief networks

PubMed Central

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-01-01

Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25995230

Workplace Learning and Higher Education in Finland: Reflections on Current Practice

ERIC Educational Resources Information Center

Virolainen, Maarit

2007-01-01

Purpose: The purpose of this article is to describe the organization of workplace learning in Finnish polytechnics, the models that have been developed for this purpose, and the challenges presented. Design/methodology/approach: First, the models for embedding workplace learning in the curriculum are described and analysed. Second, the conflicting…

Teaching and Learning in Higher Education.

ERIC Educational Resources Information Center

Dart, Barry; Boulton-Lewis, Gillian

The 11 chapters in this book, each contributed by a different author, are organized around the "3P model" of learning at the college level developed by John Biggs, which allows teachers to monitor and modify their teaching in light of students' learning. The 3P model includes presage (student and situational variables), process (how…

Use a Building Learning Center Enrichment Program to Meet Needs of Gifted/Talented.

ERIC Educational Resources Information Center

Schurr, Sandra

The paper describes the Learning Center Enrichment Program for elementary school gifted and talented children. The nomenclature associated with the program model (learning center, enrichment, and management system) is defined; and it is explained that the program is organized according to the enrichment triad model advocated by J. Renzulli because…

Community Based Learning and Civic Engagement: Informal Learning among Adult Volunteers in Community Organizations

ERIC Educational Resources Information Center

Mundel, Karsten; Schugurensky, Daniel

2008-01-01

Many iterations of community based learning employ models, such as consciousness raising groups, cultural circles, and participatory action research. In all of them, learning is a deliberate part of an explicit educational activity. This article explores another realm of community learning: the informal learning that results from volunteering in…

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

PubMed

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.

Contribution of organizational strategy to verbal learning and memory in adults with attention-deficit/hyperactivity disorder.

PubMed

Roth, Robert M; Wishart, Heather A; Flashman, Laura A; Riordan, Henry J; Huey, Leighton; Saykin, Andrew J

2004-01-01

Statistical mediation modeling was used to test the hypothesis that poor use of a semantic organizational strategy contributes to verbal learning and memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD). Comparison of 28 adults with ADHD and 34 healthy controls revealed lower performance by the ADHD group on tests of verbal learning and memory, sustained attention, and use of semantic organization during encoding. Mediation modeling indicated that state anxiety, but not semantic organization, significantly contributed to the prediction of both learning and delayed recall in the ADHD group. The pattern of findings suggests that decreased verbal learning and memory in adult ADHD is due in part to situational anxiety and not to poor use of organizational strategies during encoding. ((c) 2004 APA, all rights reserved)

Fostering Learning Opportunities through Employee Participation amid Organizational Change

ERIC Educational Resources Information Center

Valleala, Ulla Maija; Herranen, Sanna; Collin, Kaija; Paloniemi, Susanna

2015-01-01

Health care organizations are facing rapid changes, frequently involving modification of existing procedures. The case study reported here examined change processes and learning in a health care organization. The organizational change in question occurred in the emergency clinic of a Finnish central hospital where a new action model for…

The Learning Organization Model across Vocational and Academic Teacher Groups

ERIC Educational Resources Information Center

Park, Joo Ho; Rojewski, Jay W.

2006-01-01

Multiple-group confirmatory factor analysis was used to investigate factorial invariance between vocational and academic teacher groups on a measure of the learning organization concept. Participants were 488 full-time teachers of public trade industry-technical and business schools located within Seoul, South Korea. Statistically significant…

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

ERIC Educational Resources Information Center

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

2011-01-01

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

Factors associated with Spanish older people's membership in political organizations: the role of active aging activities.

PubMed

Serrat, Rodrigo; Villar, Feliciano; Celdrán, Montserrat

2015-09-01

This study explores older people's membership in political organizations by using data from the Survey on older people 2010, carried out by Spain's National Institute for older people and social services. The objectives were to describe the extent of this kind of participation among Spaniards aged 65 and over, and to analyze the factors that are associated with it. Results show that only slightly less than 7 % of the sample belonged to a political organization. To analyze the factors related to this membership, a set of models of multivariate analyses were run, including socioeconomic resources and participation in other types of active aging activity (participation in leisure, learning, and productive activities). Educational level, leisure activities, learning activities, and only volunteering in the case of productive activities were found to be associated with membership in political organizations. Results provide partial support for the socioeconomic resources model and suggest that engagement in leisure activities, learning activities, and volunteering might have an enhancing effect on membership in political organizations.

Learning while (re)configuring: Business model innovation processes in established firms.

PubMed

Berends, Hans; Smits, Armand; Reymen, Isabelle; Podoynitsyna, Ksenia

2016-08-01

This study addresses the question of how established organizations develop new business models over time, using a process research approach to trace how four business model innovation trajectories unfold. With organizational learning as analytical lens, we discern two process patterns: "drifting" starts with an emphasis on experiential learning and shifts later to cognitive search; "leaping," in contrast, starts with an emphasis on cognitive search and shifts later to experiential learning. Both drifting and leaping can result in radical business model innovations, while their occurrence depends on whether a new business model takes off from an existing model and when it goes into operation. We discuss the implications of these findings for theory on business models and organizational learning.

Learning while (re)configuring: Business model innovation processes in established firms

PubMed Central

Berends, Hans; Smits, Armand; Reymen, Isabelle; Podoynitsyna, Ksenia

2016-01-01

This study addresses the question of how established organizations develop new business models over time, using a process research approach to trace how four business model innovation trajectories unfold. With organizational learning as analytical lens, we discern two process patterns: “drifting” starts with an emphasis on experiential learning and shifts later to cognitive search; “leaping,” in contrast, starts with an emphasis on cognitive search and shifts later to experiential learning. Both drifting and leaping can result in radical business model innovations, while their occurrence depends on whether a new business model takes off from an existing model and when it goes into operation. We discuss the implications of these findings for theory on business models and organizational learning. PMID:28596704

Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex.

PubMed

Mhatre, Himanshu; Gorchetchnikov, Anatoli; Grossberg, Stephen

2012-02-01

Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general mathematical property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. Copyright © 2010 Wiley Periodicals, Inc.

Globally Sustainable Management: A Dynamic Model of IHRM Learning and Control

ERIC Educational Resources Information Center

Takeda, Margaret B.; Helms, Marilyn M.

2010-01-01

Purpose: After a thorough literature review on multinational learning, it is apparent organizations "learn" when they capitalize on expatriate management, a "learning strategy" (international work teams, employee involvement and other human resource policies), technology transfer and political environment and cross-cultural adaptation. This…

The Impact of Knowledge Conversion Processes on Implementing a Learning Organization Strategy

ERIC Educational Resources Information Center

Al-adaileh, Raid Moh'd; Dahou, Khadra; Hacini, Ishaq

2012-01-01

Purpose: The purpose of this research is to explore the influence of the knowledge conversion processes (KCP) on the success of a learning organization (LO) strategy implementation. Design/methodology/approach: Using a case study approach, the research model examines the impact of the KCP including socialization, externalization, combination and…

The Construct of the Learning Organization: Dimensions, Measurement, and Validation

ERIC Educational Resources Information Center

Yang, Baiyin; Watkins, Karen E.; Marsick, Victoria J.

2004-01-01

This research describes efforts to develop and validate a multidimensional measure of the learning organization. An instrument was developed based on a critical review of both the conceptualization and practice of this construct. Supporting validity evidence for the instrument was obtained from several sources, including best model-data fit among…

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.

Sleep, offline processing, and vocal learning

PubMed Central

Margoliash, Daniel; Schmidt, Marc F

2009-01-01

The study of song learning and the neural song system has provided an important comparative model system for the study of speech and language acquisition. We describe some recent advances in the bird song system, focusing on the role of offline processing including sleep in processing sensory information and in guiding developmental song learning. These observations motivate a new model of the organization and role of the sensory memories in vocal learning. PMID:19906416

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

PubMed

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

Challenges of information security incident learning: An industrial case study in a Chinese healthcare organization.

PubMed

He, Ying; Johnson, Chris

2017-12-01

Security incidents can have negative impacts on healthcare organizations, and the security of medical records has become a primary concern of the public. However, previous studies showed that organizations had not effectively learned lessons from security incidents. Incident learning as an essential activity in the "follow-up" phase of security incident response lifecycle has long been addressed but not given enough attention. This paper conducted a case study in a healthcare organization in China to explore their current obstacles in the practice of incident learning. We interviewed both IT professionals and healthcare professionals. The results showed that the organization did not have a structured way to gather and redistribute incident knowledge. Incident response was ineffective in cycling incident knowledge back to inform security management. Incident reporting to multiple stakeholders faced a great challenge. In response to this case study, we suggest the security assurance modeling framework to address those obstacles.

Capstone Teaching Models: Combining Simulation, Analytical Intuitive Learning Processes, History and Effectiveness

ERIC Educational Resources Information Center

Reid, Maurice; Brown, Steve; Tabibzadeh, Kambiz

2012-01-01

For the past decade teaching models have been changing, reflecting the dynamics, complexities, and uncertainties of today's organizations. The traditional and the more current active models of learning have disadvantages. Simulation provides a platform to combine the best aspects of both types of teaching practices. This research explores the…

Learning of Cross-Sectional Anatomy Using Clay Models

ERIC Educational Resources Information Center

Oh, Chang-Seok; Kim, Ji-Young; Choe, Yeon Hyeon

2009-01-01

We incorporated clay modeling into gross anatomy and neuro-anatomy courses to help students understand cross-sectional anatomy. By making clay models, cutting them and comparing cut surfaces to CT and MR images, students learned how cross-sectional two-dimensional images were created from three-dimensional structure of human organs. Most students…

Fostering Organizational Performance: The Role of Learning and Intrapreneurship

ERIC Educational Resources Information Center

Molina, Carlos; Callahan, Jamie L.

2009-01-01

Purpose: The purpose of this paper is to explore the connections between individual learning, intrapreneurship, and organizational learning to create an alternative model of how learning facilitates performance in organizations. Design/methodology/approach: This is a conceptual paper selecting targeted scholarly works that provide support for the…

A Collaborative Model for Ubiquitous Learning Environments

ERIC Educational Resources Information Center

Barbosa, Jorge; Barbosa, Debora; Rabello, Solon

2016-01-01

Use of mobile devices and widespread adoption of wireless networks have enabled the emergence of Ubiquitous Computing. Application of this technology to improving education strategies gave rise to Ubiquitous e-Learning, also known as Ubiquitous Learning. There are several approaches to organizing ubiquitous learning environments, but most of them…

The Relationship of Learning and Performance Diagnosis at Different System Levels.

ERIC Educational Resources Information Center

Lubega, Khalid

2003-01-01

Examines learning and performance diagnosis, separately and in relation to each other, as they function in organization systems; explains the relationship between learning and performance diagnosis at the individual, process, and organizational levels using a three-level performance model; and discusses types of learning, including nonlearning,…

Sustaining Change in a Learning Organization

ERIC Educational Resources Information Center

Steenekamp, K.; Botha, G.; Moloi, K. C.

2012-01-01

This article looks at how the application of the concept of a "learning organisation" can be used at a specific organisation in South Africa to change the work performance of its employees. We do this by exploring different theories, models and definitions of organisational learning, learning organisation, organisational knowledge and…

Learning to learn causal models.

PubMed

Kemp, Charles; Goodman, Noah D; Tenenbaum, Joshua B

2010-09-01

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. Copyright © 2010 Cognitive Science Society, Inc.

Some Thoughts on "Using Learning Progressions to Design Vertical Scales That Support Coherent Inferences about Student Growth"

ERIC Educational Resources Information Center

Kingston, Neal M.; Broaddus, Angela; Lao, Hongling

2015-01-01

Briggs and Peck (2015) have written a thought-provoking article on the use of learning progressions in the design of vertical scales that support inferences about student growth. Organized learning models, including learning trajectories, learning progressions, and learning maps have been the subject of research for many years, but more recently…

An Investigation on the Correlation of Learner Styles and Learning Objects Characteristics in a Proposed Learning Objects Management Model (LOMM)

ERIC Educational Resources Information Center

Wanapu, Supachanun; Fung, Chun Che; Kerdprasop, Nittaya; Chamnongsri, Nisachol; Niwattanakul, Suphakit

2016-01-01

The issues of accessibility, management, storage and organization of Learning Objects (LOs) in education systems are a high priority of the Thai Government. Incorporating personalized learning or learning styles in a learning object management system to improve the accessibility of LOs has been addressed continuously in the Thai education system.…

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…

Profile of students’ learning styles in Sorogan-Bandongan organic chemistry lecture

NASA Astrophysics Data System (ADS)

Rinaningsih; Kadarohman, A.; Firman, H.; Sutoyo

2018-05-01

Individual-based independent curriculum as one of target of national education of Indonesia in XXI century can be achieved with the implementation of Sorogan-Bandongan model. This kind of learning model highly facilitates students in understanding various concepts with their own, respective learning styles. This research aims to perceive the effectiveness of Sorogan-Bandongan in increasing the mastery of concept in various learning styles. The samples of this research are students majoring in chemistry amounted to 31 students. Using pre-test and post-test instrument, data are analyzed in descriptive-qualitative method. Based on the result of the data analysis, it is found that 16% of students have mathematical/logical learning style, 22.6% naturalist, 9.7% visual/spatial, 13% kinesthetic, 6% linguistic, 13% intrapersonal, 9.7% interpersonal, and 10% musical. After the implementation of Sorogan-Bandongan model in the Organic Chemistry lectures, improvement of classical learning outcomes as 11,07 is obtained. Six out of eight learning styles of students experienced increase in mastery of concept, where 7 students have the naturalist learning style, 4 students experienced decrease in mastery of concept while 1 student is stagnant (0); meanwhile, 2 out of 4 students that have the interpersonal learning style experienced decrease in mastery of concept.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Reflections on experiential learning in evaluation capacity building with a community organization, Dancing With Parkinson's.

PubMed

Nakaima, April; Sridharan, Sanjeev

2017-05-08

This paper discusses what was learned about evaluation capacity building with community organizations who deliver services to individuals with neurological disorders. Evaluation specialists engaged by the Ontario Brain Institute Evaluation Support Program were paired with community organizations, such as Dancing With Parkinson's. Some of the learning included: relationship building is key for this model of capacity building; community organizations often have had negative experiences with evaluation and the idea that evaluations can be friendly tools in implementing meaningful programs is one key mechanism by which such an initiative can work; community organizations often need evaluation most to be able to demonstrate their value; a strength of this initiative was that the focus was not just on creating products but mostly on developing a learning process in which capacities would remain; evaluation tools and skills that organizations found useful were developing a theory of change and the concept of heterogeneous mechanisms (informed by a realist evaluation lens). Copyright © 2017. Published by Elsevier Ltd.

Needles in the Haystack: Finding Content Worth Preparing for Workplace Learning with the KEP Model

ERIC Educational Resources Information Center

Thalmann, Stefan; Maier, Ronald

2017-01-01

Knowledge transfer between employees is a primary concern in organizations. Employees create or acquire content that partially represents knowledge. These knowledge elements are specific to the context in and for which they are created and rarely address the learning needs of other employees in other work situations. Organizations therefore need…

Model I & II Organizations: Examining Organizational Learning in Institutions Participating in the Academy for the Assessment of Student Learning

ERIC Educational Resources Information Center

Haywood, Antwione Maurice

2012-01-01

The Academy was an assessment enhancement program created by the HLC to help institutions strengthen and improve the assessment of student learning. Using a multiple case study approach, this study applies Argyis and Schon's (1976) Theory of Action to explore the espoused values and existence of Model I and II behavior characteristics. Argyis…

Tandem internal models execute motor learning in the cerebellum.

PubMed

Honda, Takeru; Nagao, Soichi; Hashimoto, Yuji; Ishikawa, Kinya; Yokota, Takanori; Mizusawa, Hidehiro; Ito, Masao

2018-06-25

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition. Copyright © 2018 the Author(s). Published by PNAS.

Integrating Learning and Performance.

ERIC Educational Resources Information Center

1998

This document contains four papers from a symposium on integrating learning and performance in human resource development (HRD). "Action Imperatives that Impact Knowledge Performance and Financial Performance in the Learning Organization: An Exploratory Model" (Gary L. Selden, Karen E. Watkins, Thomas Valentine, Victoria J. Marsick)…

Semantic Coherence Facilitates Distributional Learning.

PubMed

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

2017-04-01

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

Effectiveness of Instructional Design Model (Isman - 2011) in Developing the Planning Teaching Skills of Teachers College Students' at King Saud University

ERIC Educational Resources Information Center

Isman, Aytekin; Abanmy, Fahad AbdulAziz; Hussein, Hisham Barakat; Al Saadany, Mohammed Abdurrahman

2012-01-01

The new instructional design model (Isman - 2011) aims at planing, developing, implementing, evaluating, and organizing full learning activities effectively to ensure competent performance by students. The theoretical foundation of this model comes from behaviorism, cognitivism and constructivism views. And it's based on active learning. During…

On Learning: Metrics Based Systems for Countering Asymmetric Threats

DTIC Science & Technology

2006-05-25

of self- education . While this alone cannot guarantee organizational learning , no organization can learn without a spirit of individual learning ...held views on globalization and the impact of Information Age technology . The emerging environment conceptually links to the learning model as part...On Learning : Metrics Based Systems for Countering Asymmetric Threats A Monograph by MAJ Rafael Lopez U.S. Army School of Advanced Military

Adults as Learners. Increasing Participation and Facilitating Learning.

ERIC Educational Resources Information Center

Cross, K. Patricia

The literature on adult learners is reviewed, and two models of adult learning are developed. Demographic, social, and technological trends that stimulate the increasing demand for learning opportunities are examined, and the views of those who see dangers in new pressures on adults to participate in organized learning activities are considered.…

The Conceptual Mechanism for Viable Organizational Learning Based on Complex System Theory and the Viable System Model

ERIC Educational Resources Information Center

Sung, Dia; You, Yeongmahn; Song, Ji Hoon

2008-01-01

The purpose of this research is to explore the possibility of viable learning organizations based on identifying viable organizational learning mechanisms. Two theoretical foundations, complex system theory and viable system theory, have been integrated to provide the rationale for building the sustainable organizational learning mechanism. The…

Global How?--Linking Practice to Theory: A Competency Model for Training Global Learning Facilitators

ERIC Educational Resources Information Center

Büker, Gundula; Schell-Straub, Sigrid

2017-01-01

Global learning facilitators from civil society organizations (CSOs) design and enrich educational processes in formal and non-formal educational settings. They need to be empowered through adequate training opportunities in global learning (GL) contexts. The project Facilitating Global Learning--Key Competences from Members of European CSOs (FGL)…

Lifting off the Ground to Return Anew: Mediated Praxis, Transformative Learning, and Social Design Experiments

ERIC Educational Resources Information Center

Gutierrez, Kris D.; Vossoughi, Shirin

2010-01-01

This article examines a praxis model of teacher education and advances a new method for engaging novice teachers in reflective practice and robust teacher learning. Social design experiments--cultural historical formations designed to promote transformative learning for adults and children--are organized around expansive notions of learning and…

PACALL: Supporting Language Learning Using SenseCam

ERIC Educational Resources Information Center

Hou, Bin; Ogata, Hiroaki; Kunita, Toma; Li, Mengmeng; Uosaki, Noriko

2013-01-01

The authors' research defines a ubiquitous learning log (ULLO) as a digital record of what a learner has learned in the daily life using ubiquitous technologies. In their previous works, the authors proposed a model named LORE (Log--Organize--Recall--Evaluate) to describe the learning process of ULLO and developed a system named SCROLL to…

A Bayesian generative model for learning semantic hierarchies

PubMed Central

Mittelman, Roni; Sun, Min; Kuipers, Benjamin; Savarese, Silvio

2014-01-01

Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical structure of categories and concepts, has been shown to provide a key prior which allows for optimal predictions. The hierarchical organization of various domains and concepts has been subject to extensive research, and led to the development of the WordNet domains hierarchy (Fellbaum, 1998), which was also used to organize the images in the ImageNet (Deng et al., 2009) dataset, in which the category count approaches the human capacity. Still, for the human visual system, the form of the hierarchy must be discovered with minimal use of supervision or innate knowledge. In this work, we propose a new Bayesian generative model for learning such domain hierarchies, based on semantic input. Our model is motivated by the super-subordinate organization of domain labels and concepts that characterizes WordNet, and accounts for several important challenges: maintaining context information when progressing deeper into the hierarchy, learning a coherent semantic concept for each node, and modeling uncertainty in the perception process. PMID:24904452

Alchemical and structural distribution based representation for universal quantum machine learning

NASA Astrophysics Data System (ADS)

Faber, Felix A.; Christensen, Anders S.; Huang, Bing; von Lilienfeld, O. Anatole

2018-06-01

We introduce a representation of any atom in any chemical environment for the automatized generation of universal kernel ridge regression-based quantum machine learning (QML) models of electronic properties, trained throughout chemical compound space. The representation is based on Gaussian distribution functions, scaled by power laws and explicitly accounting for structural as well as elemental degrees of freedom. The elemental components help us to lower the QML model's learning curve, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training. This point is demonstrated for the prediction of covalent binding in single, double, and triple bonds among main-group elements as well as for atomization energies in organic molecules. We present numerical evidence that resulting QML energy models, after training on a few thousand random training instances, reach chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands of structurally and compositionally diverse organic molecules, non-covalently bonded protein side-chains, (H2O)40-clusters, and crystalline solids. Learning curves for QML models also indicate competitive predictive power for various other electronic ground state properties of organic molecules, calculated with hybrid density functional theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero point vibrational energy, dipole moment, and highest vibrational fundamental frequency.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Topological self-organization and prediction learning support both action and lexical chains in the brain.

PubMed

Chersi, Fabian; Ferro, Marcello; Pezzulo, Giovanni; Pirrelli, Vito

2014-07-01

A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall. Copyright © 2014 Cognitive Science Society, Inc.

Computer-Mediated Assessment of Higher-Order Thinking Development

ERIC Educational Resources Information Center

Tilchin, Oleg; Raiyn, Jamal

2015-01-01

Solving complicated problems in a contemporary knowledge-based society requires higher-order thinking (HOT). The most productive way to encourage development of HOT in students is through use of the Problem-based Learning (PBL) model. This model organizes learning by solving corresponding problems relative to study courses. Students are directed…

Achieving Quality in e-Learning through Relational Coordination

ERIC Educational Resources Information Center

Margalina, Vasilica Maria; De-Pablos-Heredero, Carmen; Montes-Botella, Jose Luis

2017-01-01

In this research, the relational coordination model has been applied to prove learners' and instructors' high levels of satisfaction in e-learning. According to the model, organizations can obtain better results in terms of satisfaction by providing shared knowledge, shared goals and mutual respect mechanisms, supported by a frequent, timely and…

Machine-learning-assisted materials discovery using failed experiments

NASA Astrophysics Data System (ADS)

Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.; Falk, Casey; Wenny, Malia B.; Mollo, Aurelio; Zeller, Matthias; Friedler, Sorelle A.; Schrier, Joshua; Norquist, Alexander J.

2016-05-01

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on ‘dark’ reactions—failed or unsuccessful hydrothermal syntheses—collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.

Computational modeling of neural plasticity for self-organization of neural networks.

PubMed

Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

How does Learning Impact Development in Infancy? The Case of Perceptual Organization

PubMed Central

Bhatt, Ramesh S.; Quinn, Paul C.

2011-01-01

Pattern perception and organization are critical functions of the visual cognition system. Many organizational processes are available early in life, such that infants as young 3 months of age are able to readily utilize a variety of cues to organize visual patterns. However, other processes are not readily evident in young infants, and their development involves perceptual learning. We describe a theoretical framework that addresses perceptual learning in infancy and the manner in which it affects visual organization and development. It identifies five kinds of experiences that induce learning, and suggests that they work via attentional and unitization mechanisms to modify visual organization. In addition, the framework proposes that this kind of learning is abstract, domain general, functional at different ages in a qualitatively similar manner, and has a long-term impact on development through a memory reactivation process. Although most models of development assume that experience is fundamental to development, very little is actually known about the process by which experience affects development. The proposed framework is an attempt to account for this process in the domain of perception. PMID:21572570

Learning under Conditions of Hierarchy and Discipline: The Case of the German Army, 1939-1940

ERIC Educational Resources Information Center

Visser, Max

2008-01-01

To survive in and adapt to dynamic, turbulent, and complex environments, organizations need to engage in learning. This truism is particularly relevant for army organizations in times of war and armed conflict. In this article a case of army operations during World War II is analyzed on the basis of Ortenblad's integrated model of the learning…

Hands-On! Living in the Biosphere: Production, Pattern, Population, and Diversity. Developing Active Learning Module on the Human Dimensions of Global Change.

ERIC Educational Resources Information Center

Brown, Dwight

Biogeography examines questions of organism inventory and pattern, organisms' interactions with the environment, and the processes that create and change inventory, pattern, and interactions. This learning module uses time series maps and simple simulation models to illustrate how human actions alter biological productivity patterns at local and…

STEM based learning to facilitate middle school students’ conceptual change, creativity and collaboration in organization of living system topic

NASA Astrophysics Data System (ADS)

Rustaman, N. Y.; Afianti, E.; Maryati, S.

2018-05-01

A study using one group pre-post-test experimental design on Life organization system topic was carried out to investigate student’s tendency in learning abstract concept, their creativity and collaboration in designing and producing cell models through STEM-based learning. A number of seventh grade students in Cianjur district were involved as research subjects (n=34). Data were collected using two tier test for tracing changes in student conception before and after the application of STEM-based learning, and rubrics in creativity design (adopted from Torrance) and product on cell models (individually, in group), and rubric for self-assessment and observed skills on collaboration adapted from Marzano’s for life-long learning. Later the data obtained were analyzed qualitatively by interpreting the tendency of data presented in matrix sorted by gender. Research findings showed that the percentage of student’s scientific concept mastery is moderate in general. Their creativity in making a cell model design varied in category (expressing, emergent, excellent, not yet evident). Student’s collaboration varied from excellent, fair, good, less once, to less category in designing cell model. It was found that STEM based learning can facilitate students conceptual change, creativity and collaboration.

Learning organizations, internal marketing, and organizational commitment in hospitals.

PubMed

Tsai, Yafang

2014-04-04

Knowledge capital is becoming more important to healthcare establishments, especially for hospitals that are facing changing societal and industrial patterns. Hospital staff must engage in a process of continual learning to improve their healthcare skills and provide a superior service to their patients. Internal marketing helps hospital administrators to improve the quality of service provided by nursing staff to their patients and allows hospitals to build a learning culture and enhance the organizational commitment of its nursing staff. Our empirical study provides nursing managers with a tool to allow them to initiate a change in the attitudes of nurses towards work, by constructing a new 'learning organization' and using effective internal marketing. A cross-sectional design was employed. Two hundred questionnaires were distributed to nurses working in either a medical centre or a regional hospital in Taichung City, Taiwan, and 114 valid questionnaires were returned (response rate: 57%). The entire process of distribution and returns was completed between 1 October and 31 October 2009. Hypothesis testing was conducted using structural equation modelling. A significant positive correlation was found between the existence of a 'learning organization', internal marketing, and organizational commitment. Internal marketing was a mediator between creating a learning organization and organizational commitment. Nursing managers may be able to apply the creation of a learning organization to strategies that can strengthen employee organizational commitment. Further, when promoting the creation of a learning organization, managers can coordinate their internal marketing practices to enhance the organizational commitment of nurses.

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

NASA Astrophysics Data System (ADS)

Avianti, R.; Suyatno; Sugiarto, B.

2018-04-01

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

Lessons Learned About Organic Aerosol Formation in the Southeast U.S. Using Observations and Modeling

EPA Science Inventory

Isoprene emitted by vegetation is an important precursor of secondary organic aerosol (SOA). In this work, modeling of isoprene SOA via heterogeneous uptake is explored and compared to observations from the Southern Oxidant and Aerosol Study (SOAS).

Three-Dimensional Printing of a Scalable Molecular Model and Orbital Kit for Organic Chemistry Teaching and Learning

ERIC Educational Resources Information Center

Penny, Matthew R.; Cao, Zi Jing; Patel, Bhaven; dos Santos, Bruno Sil; Asquith, Christopher R. M.; Szulc, Blanka R.; Rao, Zenobia X.; Muwaffak, Zaid; Malkinson, John P.; Hilton, Stephen T.

2017-01-01

Three-dimensional (3D) chemical models are a well-established learning tool used to enhance the understanding of chemical structures by converting two-dimensional paper or screen outputs into realistic three-dimensional objects. While commercial atom model kits are readily available, there is a surprising lack of large molecular and orbital models…

R&D Organizations As Learning Systems

ERIC Educational Resources Information Center

Carlsson, Barbara; And Others

1976-01-01

Describes how the authors developed an understanding of the research and development process at the Proctor and Gamble Company by applying David Kolb's Learning Model for Individuals to the organizational learning process. (Available from Alfred P. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;…

Imitation learning based on an intrinsic motivation mechanism for efficient coding

PubMed Central

Triesch, Jochen

2013-01-01

A hypothesis regarding the development of imitation learning is presented that is rooted in intrinsic motivations. It is derived from a recently proposed form of intrinsically motivated learning (IML) for efficient coding in active perception, wherein an agent learns to perform actions with its sense organs to facilitate efficient encoding of the sensory data. To this end, actions of the sense organs that improve the encoding of the sensory data trigger an internally generated reinforcement signal. Here it is argued that the same IML mechanism might also support the development of imitation when general actions beyond those of the sense organs are considered: The learner first observes a tutor performing a behavior and learns a model of the the behavior's sensory consequences. The learner then acts itself and receives an internally generated reinforcement signal reflecting how well the sensory consequences of its own behavior are encoded by the sensory model. Actions that are more similar to those of the tutor will lead to sensory signals that are easier to encode and produce a higher reinforcement signal. Through this, the learner's behavior is progressively tuned to make the sensory consequences of its actions match the learned sensory model. I discuss this mechanism in the context of human language acquisition and bird song learning where similar ideas have been proposed. The suggested mechanism also offers an account for the development of mirror neurons and makes a number of predictions. Overall, it establishes a connection between principles of efficient coding, intrinsic motivations and imitation. PMID:24204350

Learning and memory in zebrafish (Danio rerio).

PubMed

Gerlai, R

2016-01-01

Learning and memory are defining features of our own species inherently important to our daily lives and to who we are. Without our memories we cease to exist as a person. Without our ability to learn individuals and collectively our society would cease to function. Diseases of the mind still remain incurable. The interest in understanding of the mechanisms of learning and memory is thus well founded. Given the complexity of such mechanisms, concerted efforts have been made to study them under controlled laboratory conditions, ie, with laboratory model organisms. The zebrafish, although new in this field, is one such model organism. The rapidly developing forward- and reverse genetic methods designed for the zebrafish and the increasing use of pharmacological tools along with numerous neurobiology techniques make this species perhaps the best model for the analysis of the mechanisms of complex central nervous system characteristics. The fact that it is an evolutionarily ancient and simpler vertebrate, but at the same time it possesses numerous conserved features across multiple levels of biological organization makes this species an excellent tool for the analysis of the mechanisms of learning and memory. The bottleneck lies in our understanding of its cognitive and mnemonic features, the topic of this chapter. The current paper builds on a chapter published in the previous edition and continues to focus on associative learning, but now it extends the discussion to other forms of learning and to recent discoveries on memory-related features and findings obtained both in adults and larval zebrafish. Copyright © 2016 Elsevier Inc. All rights reserved.

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).

Designing Connected Learning: Emerging Learning Systems in a Craft Teacher Education Course

ERIC Educational Resources Information Center

Vartiaien, Henrikka; Pöllänen, Sinikka; Liljeström, Anu; Vanninen, Petteri; Enkenberg, Jorma

2016-01-01

This socioculturally informed study aims to apply learning by collaborative designing (LCD) as an instructional model for the creation and studying of new kinds of connected learning systems in teacher education. A case study was organized at the University of Eastern Finland in the context of an information and communication technology (ICT)…

In Support of Coaching Models of Management and Leadership: A Comparative Study of Empirically Derived Managerial Coaching/Facilitating Learning Behaviors

ERIC Educational Resources Information Center

Hamlin, Bob; Ellinger, Andrea D.; Beattie, Rona S.

2004-01-01

The concept of managers assuming developmental roles such as coaches and learning facilitators has gained considerable attention in recent years as organizations seek to leverage learning by creating infrastructures that foster employee learning and development. Despite the increased focus on coaching, the literature base remains atheoretical.…

Learning and Teaching Styles in Management Education: Identifying, Analyzing, and Facilitating

ERIC Educational Resources Information Center

Provitera, Michael J.; Esendal, Esin

2008-01-01

Drawing on the learning theory of the Felder-Silverman model (2002), and the work of A.F. Grasha, this paper provides a brief review of teaching and learning styles used in management education. Professors, like students, demonstrate a number of learning styles and a professor has some responsibility to organize and present a course to satisfy…

Culture as a Tool: Facilitating Knowledge Construction in the Context of a Learning Community

ERIC Educational Resources Information Center

Chang, Bo

2010-01-01

Knowledge construction is regarded as an effective learning model in practice. When more and more learning communities are organized to promote knowledge construction, it is necessary to know how to use different tools to support knowledge construction in the learning community context. In the literature, few researchers discuss how to construct…

Deep Interactive Learning with Sharkzor

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

None

Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user’s mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the applications algorithmic underpinnings. Methods of interaction within Sharkzor’s user interface and user experience support three primary user tasks: triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. Aftermore » interacting with the images and groups, deep learning helps to automate the user’s interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.« less

The Development of Inquiry Learning Materials to Complete Content Life System Organization in Junior High School Students

NASA Astrophysics Data System (ADS)

Mayasari, F.; Raharjo; Supardi, Z. A. I.

2018-01-01

This research aims to develop the material eligibility to complete the inquiry learning of student in the material organization system of junior high school students. Learning materials developed include syllabi, lesson plans, students’ textbook, worksheets, and learning achievement test. This research is the developmental research which employ Dick and Carey model to develop learning material. The experiment was done in Junior High School 4 Lamongan regency using One Group Pretest-Posttest Design. The data collection used validation, observation, achievement test, questionnaire administration, and documentation. Data analysis techniques used quantitative and qualitative descriptive.The results showed that the developed learning material was valid and can be used. Learning activity accomplished with good category, where student activities were observed. The aspects of attitudes were observed during the learning process are honest, responsible, and confident. Student learning achievement gained an average of 81, 85 in complete category, with N-Gain 0, 75 for a high category. The activities and student response to learning was very well categorized. Based on the results, this researcher concluded that the device classified as feasible of inquiry-based learning (valid, practical, and effective) system used on the material organization of junior high school students.

Spontaneous neuronal activity as a self-organized critical phenomenon

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

Students' Individual Schematization Pathways--Empirical Reconstructions for the Case of Part-of-Part Determination for Fractions

ERIC Educational Resources Information Center

Glade, Matthias; Prediger, Susanne

2017-01-01

According to the design principle of progressive schematization, learning trajectories towards procedural rules can be organized as independent discoveries when the learning arrangement invites the students first to develop models for mathematical concepts and model-based informal strategies; then to explore the strategies and to discover pattern…

Layers of Critical Engagement: Exploring the Intersections of Leadership, Critical Theory, and Learning

ERIC Educational Resources Information Center

Patterson, Shawna M.

2013-01-01

In this article, the author provides a model that juxtaposes leadership, critical theory, and learning to address the needs of educators, the organization, and students. This model provides educators with a foundational approach to nurture students' critical consciousness through self-awareness and to actualize transformational change within their…

Effects of Leadership Style on Team Learning

ERIC Educational Resources Information Center

Bucic, Tania; Robinson, Linda; Ramburuth, Prem

2010-01-01

Purpose: This paper seeks to explore the effect of leadership style of a team leader on team-member learning in organizations, to conceptually extend an initial model of leadership and to empirically examine the new model of ambidextrous leadership in a team context. Design/methodology/approach: Qualitative research utilizing the case study method…

Implementation of Cooperative Learning Model in Preschool

ERIC Educational Resources Information Center

Akçay, Nilüfer Okur

2016-01-01

In this study, the effectivity of jigsaw method, one of the cooperative learning models, on teaching the concepts related to sense organs and their functions to four-five year-old children in nursery class was analyzed. The study is in the semi-experimental design consisting of experimental and control groups and pretest and posttest. The sample…

Organizational Learning and Large-Scale Change: Adoption of Electronic Medical Records

ERIC Educational Resources Information Center

Chavis, Virginia D.

2010-01-01

Despite implementation of electronic medical record (EMR) systems in the United States and other countries, there is no organizational development model that addresses medical professionals' attitudes toward technology adoption in a learning organization. The purpose of this study was to assess whether a model would change those attitudes toward…

The Personal Learning Environment and the Human Condition: From Theory to Teaching Practice

ERIC Educational Resources Information Center

Johnson, Mark; Liber, Oleg

2008-01-01

We present the Personal Learning Environment (PLE) as a practical intervention concerning the organization of technology in education. We explain this by proposing a cybernetic model of the "Personal Learner" using Beer's Viable System Model (VSM). Using the VSM, we identify different regulatory mechanisms that maintain viability for learners, and…

Cognitive Modeling of Learning Abilities: A Status Report of LAMP.

ERIC Educational Resources Information Center

Kyllonen, Patrick C.; Christal, Raymond E.

Research activities underway as part of the Air Force's Learning Abilities Measurement Program (LAMP) are described. A major objective of the program is to devise new models of the nature and organization of human abilities, that could be applied to improve personnel selection and classification systems. The activities of the project have been…

The Role of the Family Context in the Development of Emotion Regulation

ERIC Educational Resources Information Center

Morris, Amanda Sheffield; Silk, Jennifer S.; Steinberg, Laurence; Myers, Sonya S.; Robinson, Lara Rachel

2007-01-01

This article reviews current literature examining associations between components of the family context and children and adolescents' emotion regulation (ER). The review is organized around a tripartite model of familial influence. Firstly, it is posited that children learn about ER through observational learning, modeling and social referencing.…

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…

Teaching and Learning Cycles in a Constructivist Approach to Instruction

ERIC Educational Resources Information Center

Singer, Florence Mihaela; Moscovici, Hedy

2008-01-01

This study attempts to analyze and synthesize the knowledge collected in the area of conceptual models used in teaching and learning during inquiry-based projects, and to propose a new frame for organizing the classroom interactions within a constructivist approach. The IMSTRA model consists in three general phases: Immersion, Structuring,…

Self-organization of head-centered visual responses under ecological training conditions.

PubMed

Mender, Bedeho M W; Stringer, Simon M

2014-01-01

We have studied the development of head-centered visual responses in an unsupervised self-organizing neural network model which was trained under ecological training conditions. Four independent spatio-temporal characteristics of the training stimuli were explored to investigate the feasibility of the self-organization under more ecological conditions. First, the number of head-centered visual training locations was varied over a broad range. Model performance improved as the number of training locations approached the continuous sampling of head-centered space. Second, the model depended on periods of time where visual targets remained stationary in head-centered space while it performed saccades around the scene, and the severity of this constraint was explored by introducing increasing levels of random eye movement and stimulus dynamics. Model performance was robust over a range of randomization. Third, the model was trained on visual scenes where multiple simultaneous targets where always visible. Model self-organization was successful, despite never being exposed to a visual target in isolation. Fourth, the duration of fixations during training were made stochastic. With suitable changes to the learning rule, it self-organized successfully. These findings suggest that the fundamental learning mechanism upon which the model rests is robust to the many forms of stimulus variability under ecological training conditions.

Collective (Team) Learning Process Models: A Conceptual Review

ERIC Educational Resources Information Center

Knapp, Randall

2010-01-01

Teams have become a key resource for learning and accomplishing work in organizations. The development of collective learning in specific contexts is not well understood, yet has become critical to organizational success. The purpose of this conceptual review is to inform human resource development (HRD) practice about specific team behaviors and…

Implementation and Deployment of the IMS Learning Design Specification

ERIC Educational Resources Information Center

Paquette, Gilbert; Marino, Olga; De la Teja, Ileana; Lundgren­-Cayrol, Karin; Lonard, Michel; Contamines, Julien

2005-01-01

Knowledge management in organizations, the learning objects paradigm, the advent of a new web generation, and the "Semantic Web" are major actual trends that reveal a potential for a renewed distance learning pedagogy. First and foremost is the use of educational modelling languages and instructional engineering methods to help decide…

Robert's Rules for Optimal Learning: Model Development, Field Testing, Implications!

ERIC Educational Resources Information Center

McGinty, Robert L.

The value of accelerated learning techniques developed by the national organization for Suggestive Accelerated Learning Techniques (SALT) was tested in a study using Administrative Policy students taking the capstone course in the Eastern Washington University School of Business. Educators have linked the brain and how it functions to various…

Management Strategies for Promoting Teacher Collective Learning

ERIC Educational Resources Information Center

Cheng, Eric C. K.

2011-01-01

This paper aims to validate a theoretical model for developing teacher collective learning by using a quasi-experimental design, and explores the management strategies that would provide a school administrator practical steps to effectively promote collective learning in the school organization. Twenty aided secondary schools in Hong Kong were…

An Analysis of the Effects of Varying Levels of Implementation of Disciplines Associated with Learning Organizations and Student Achievement at California Schools with Similar Demographic Characteristics

ERIC Educational Resources Information Center

Frisby, Sandra

2012-01-01

Purpose: The purpose of this study was to describe, measure, compare, and contrast the perceptions of elementary teachers and principals regarding the degree to which the schools in which they are employed have implemented learning organizations conforming to Senge's (1990) 5 disciplines: mental models, personal mastery, shared vision, team…

Universities as Inclusive Learning Organizations for Women?: Considering the Role of Women in Faculty and Leadership Roles in Academe

ERIC Educational Resources Information Center

Gouthro, Patricia; Taber, Nancy; Brazil, Amanda

2018-01-01

Purpose: The purpose of this paper is to explore the concept of the learning organization, first discussed by Senge (1990), to determine if it can work as a model in the higher education sector. Design/methodology/approach: Using a critical feminist framework, this paper assesses the possibilities and challenges of viewing universities as…

Data Mining and Machine Learning Models for Predicting Drug Likeness and their Disease or Organ Category

NASA Astrophysics Data System (ADS)

Yosipof, Abraham; Guedes, Rita C.; García-Sosa, Alfonso T.

2018-05-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neuronal network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

PubMed

Yosipof, Abraham; Guedes, Rita C; García-Sosa, Alfonso T

2018-01-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Reporting intellectual capital in health care organizations: specifics, lessons learned, and future research perspectives.

PubMed

Veltri, Stefania; Bronzetti, Giovanni; Sicoli, Graziella

2011-01-01

This article analyzes the concept of intellectual capital (IC) in the health sector sphere by studying the case of a major nonprofit research organization in this sector, which has for some time been publishing IC reports. In the last few years, health care organizations have been the object of great attention in the implementation and transfer of managerial models and tools; however, there is still a lack of attention paid to the strategic management of IC as a fundamental resource for supporting and enhancing performance improvement dynamics. The main aim of this article is to examine the IC reporting model used by the Center of Molecular Medicine (CMM), a Swedish health organization which is an outstanding benchmark in reporting its IC. We also consider the specifics of IC reporting for health organizations, the lessons learned by analyzing CMM's IC reporting, and future perspectives for research.

Effects of Role Division, Interaction, and Shared Mental Model on Team Performance in Project-Based Learning Environment

ERIC Educational Resources Information Center

Jo, Il-Hyun

2011-01-01

The purpose of this study was to investigate the cognitive mechanism of project-based learning teams of college students on the basis of the Shared Mental Model (SMM) theory. The study participants were 237 female college students in Korea organized into 51 project teams. To test the study hypotheses, a structural equation modeling was employed.…

From Doing to Learning: Changed Focus during a Pre-School Learning Study Project on Organic Decomposition

ERIC Educational Resources Information Center

Ljung-Djärf, Agneta; Magnusson, Andreas; Peterson, Sam

2014-01-01

We explored the use of the learning study (LS) model in developing Swedish pre-school science learning. This was done by analysing a 3-cycle LS project implemented to help a group of pre-school teachers (n?=?5) understand their science educational practice, by collaboratively and systematically challenging it. Data consisted of video recordings of…

Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition

NASA Astrophysics Data System (ADS)

Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku

2015-03-01

This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.

RM-SORN: a reward-modulated self-organizing recurrent neural network.

PubMed

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Are all hands-on activities equally effective? Effect of using plastic models, organ dissections, and virtual dissections on student learning and perceptions.

PubMed

Lombardi, Sara A; Hicks, Reimi E; Thompson, Katerina V; Marbach-Ad, Gili

2014-03-01

This study investigated the impact of three commonly used cardiovascular model-assisted activities on student learning and student attitudes and perspectives about science. College students enrolled in a Human Anatomy and Physiology course were randomly assigned to one of three experimental groups (organ dissections, virtual dissections, or plastic models). Each group received a 15-min lecture followed by a 45-min activity with one of the treatments. Immediately after the lesson and then 2 mo later, students were tested on anatomy and physiology knowledge and completed an attitude survey. Students who used plastic models achieved significantly higher overall scores on both the initial and followup exams than students who performed organ or virtual dissections. On the initial exam, students in the plastic model and organ dissection treatments scored higher on anatomy questions than students who performed virtual dissections. Students in the plastic model group scored higher than students who performed organ dissections on physiology questions. On the followup exam, when asked anatomy questions, students in the plastic model group scored higher than dissection students and virtual dissection students. On attitude surveys, organ dissections had higher perceived value and were requested for inclusion in curricula twice as often as any other activity. Students who performed organ dissections were more likely than the other treatment groups to agree with the statement that "science is fun," suggesting that organ dissections may promote positive attitudes toward science. The findings of this study provide evidence for the importance of multiple types of hands-on activities in anatomy laboratory courses.

Problem Solving Model for Science Learning

NASA Astrophysics Data System (ADS)

Alberida, H.; Lufri; Festiyed; Barlian, E.

2018-04-01

This research aims to develop problem solving model for science learning in junior high school. The learning model was developed using the ADDIE model. An analysis phase includes curriculum analysis, analysis of students of SMP Kota Padang, analysis of SMP science teachers, learning analysis, as well as the literature review. The design phase includes product planning a science-learning problem-solving model, which consists of syntax, reaction principle, social system, support system, instructional impact and support. Implementation of problem-solving model in science learning to improve students' science process skills. The development stage consists of three steps: a) designing a prototype, b) performing a formative evaluation and c) a prototype revision. Implementation stage is done through a limited trial. A limited trial was conducted on 24 and 26 August 2015 in Class VII 2 SMPN 12 Padang. The evaluation phase was conducted in the form of experiments at SMPN 1 Padang, SMPN 12 Padang and SMP National Padang. Based on the development research done, the syntax model problem solving for science learning at junior high school consists of the introduction, observation, initial problems, data collection, data organization, data analysis/generalization, and communicating.

The discovery of structural form

PubMed Central

Kemp, Charles; Tenenbaum, Joshua B.

2008-01-01

Algorithms for finding structure in data have become increasingly important both as tools for scientific data analysis and as models of human learning, yet they suffer from a critical limitation. Scientists discover qualitatively new forms of structure in observed data: For instance, Linnaeus recognized the hierarchical organization of biological species, and Mendeleev recognized the periodic structure of the chemical elements. Analogous insights play a pivotal role in cognitive development: Children discover that object category labels can be organized into hierarchies, friendship networks are organized into cliques, and comparative relations (e.g., “bigger than” or “better than”) respect a transitive order. Standard algorithms, however, can only learn structures of a single form that must be specified in advance: For instance, algorithms for hierarchical clustering create tree structures, whereas algorithms for dimensionality-reduction create low-dimensional spaces. Here, we present a computational model that learns structures of many different forms and that discovers which form is best for a given dataset. The model makes probabilistic inferences over a space of graph grammars representing trees, linear orders, multidimensional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the underlying structure of a variety of physical, biological, and social domains. Our approach brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development. PMID:18669663

Collective learning and optimal consensus decisions in social animal groups.

PubMed

Kao, Albert B; Miller, Noam; Torney, Colin; Hartnett, Andrew; Couzin, Iain D

2014-08-01

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

Collective Learning and Optimal Consensus Decisions in Social Animal Groups

PubMed Central

Kao, Albert B.; Miller, Noam; Torney, Colin; Hartnett, Andrew; Couzin, Iain D.

2014-01-01

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context. PMID:25101642

Using E-Learning to Train Youth Workers: The Bell Experience

ERIC Educational Resources Information Center

Marquart, Matthea; Rizzi, Zora Jones; Parikh, Amita Desai

2010-01-01

A national provider of afterschool and summer programming plans to expand quickly into new regions, bringing its successful model of out-of-school learning to more children in disadvantaged schools and neighborhoods. A large number of staff members must be trained in the provider's program model in a short window of time. The organization needs to…

Rectangular Array Model Supporting Students' Spatial Structuring in Learning Multiplication

ERIC Educational Resources Information Center

Shanty, Nenden Octavarulia; Wijaya, Surya

2012-01-01

We examine how rectangular array model can support students' spatial structuring in learning multiplication. To begin, we define what we mean by spatial structuring as the mental operation of constructing an organization or form for an object or set of objects. For that reason, the eggs problem was chosen as the starting point in which the…

Teaching to Emerge: Toward a Bottom-Up Pedagogy

ERIC Educational Resources Information Center

Brailas, Alexios; Koskinas, Konstantinos; Alexias, George

2017-01-01

This paper focuses on the conceptual model of an academic course inspired by complexity theory. In the proposed conceptual model, the aim of teaching is to form a learning organization: a knowledge community with emergent properties that cannot be reduced to any linear combination of the properties of its parts. In this approach, the learning of…

The Global Classroom Model Simultaneous Campus-and Home-Based Education Using Videoconferencing

ERIC Educational Resources Information Center

Weitze, Charlotte Laerke; Ørngreen, Rikke

2014-01-01

This paper presents and discusses findings about how students, teachers, and the organization experience a start-up-project applying videoconferences between campus and home. This is new territory for adult learning centers. The research is based on the "Global Classroom Model" as it is implemented and used at an adult learning center in…

The Development of Learning Services Assessment Instrument Model of Islamic Higher Education

ERIC Educational Resources Information Center

Zurqoni

2017-01-01

The Islamic higher educations have an important role in producing the qualified graduates. Therefore, the organization of the Islamic higher educations must be coupled with the quality of learning services. Some institutes and universities are optimizing their learning services, while the others tend to choose different service patterns since…

Model Centers Program for Learning Disabled Children: Historical Perspective.

ERIC Educational Resources Information Center

Senf, Gerald M.

This document describes the present federal effort on behalf of learning disabled children, beginning with its recent history. It traces the field of learning disabilities as a subspecialty within education from 1963, when a steering committee was appointed to organize a symposium on "The Child with Minimal Brain Dysfunction," through the Learning…

E-Business and Online Learning: Connections and Opportunities for Vocational Education and Training.

ERIC Educational Resources Information Center

Mitchell, John

Australian vocational education and training (VET) providers show increasing interest in using electronic technology to provide online learning, student services, and business functions, according to a study that included a literature review, Internet search, interviews with organizations that use e-business models for online learning, analysis of…

Architectures for Distributed and Complex M-Learning Systems: Applying Intelligent Technologies

ERIC Educational Resources Information Center

Caballe, Santi, Ed.; Xhafa, Fatos, Ed.; Daradoumis, Thanasis, Ed.; Juan, Angel A., Ed.

2009-01-01

Over the last decade, the needs of educational organizations have been changing in accordance with increasingly complex pedagogical models and with the technological evolution of e-learning environments with very dynamic teaching and learning requirements. This book explores state-of-the-art software architectures and platforms used to support…

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…

Messy Design: Organic Planning for Blended Learning

ERIC Educational Resources Information Center

Rankin, Andrea; Luzeckyj, Ann; Haggis, Jane; Gare, Callum

2016-01-01

In this paper we argue that a messy design process does not mitigate against sharing and transfer of artefacts across educational domains. In fact, such a process can aid in developing a model for learning and teaching that is reusable and authentic. We describe the planning and design of an integrated and interactive blended learning environment…

Writing-to-Learn, Writing-to-Communicate, & Scientific Literacy

ERIC Educational Resources Information Center

Balgopal, Meena; Wallace, Alison

2013-01-01

Writing-to-learn (WTL) is an effective instructional and learning strategy that centers on the process of organizing and articulating ideas, as opposed to writing-to-communicate, which centers on the finished written product. We describe a WTL model that we have developed and tested with various student groups over several years. With effective…

Some New Theoretical Issues in Systems Thinking Relevant for Modelling Corporate Learning

ERIC Educational Resources Information Center

Minati, Gianfranco

2007-01-01

Purpose: The purpose of this paper is to describe fundamental concepts and theoretical challenges with regard to systems, and to build on these in proposing new theoretical frameworks relevant to learning, for example in so-called learning organizations. Design/methodology/approach: The paper focuses on some crucial fundamental aspects introduced…

Knowledge Transfer among Projects Using a Learn-Forget Model

ERIC Educational Resources Information Center

Tukel, Oya I.; Rom, Walter O.; Kremic, Tibor

2008-01-01

Purpose: The purpose of this paper is to analyze the impact of learning in a project-driven organization and demonstrate analytically how the learning, which takes place during the execution of successive projects, and the forgetting that takes place during the dormant time between the project executions, can impact performance and productivity in…

Disease Modeling via Large-Scale Network Analysis

DTIC Science & Technology

2015-05-20

SECURITY CLASSIFICATION OF: A central goal of genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit...guarantees for the methods. In the past, we have developed predictive methods general enough to apply to potentially any genetic trait, varying from... genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit problem of predicting the association of genes with

Achieving "organic compositionality" through self-organization: reviews on brain-inspired robotics experiments.

PubMed

Tani, Jun; Nishimoto, Ryunosuke; Paine, Rainer W

2008-05-01

The current paper examines how compositional structures can self-organize in given neuro-dynamical systems when robot agents are forced to learn multiple goal-directed behaviors simultaneously. Firstly, we propose a basic model accounting for the roles of parietal-premotor interactions for representing skills for goal-directed behaviors. The basic model had been implemented in a set of robotics experiments employing different neural network architectures. The comparative reviews among those experimental results address the issues of local vs distributed representations in representing behavior and the effectiveness of level structures associated with different sensory-motor articulation mechanisms. It is concluded that the compositional structures can be acquired "organically" by achieving generalization in learning and by capturing the contextual nature of skilled behaviors under specific conditions. Furthermore, the paper discusses possible feedback for empirical neuroscience studies in the future.

The Study and Design of Adaptive Learning System Based on Fuzzy Set Theory

NASA Astrophysics Data System (ADS)

Jia, Bing; Zhong, Shaochun; Zheng, Tianyang; Liu, Zhiyong

Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

Why do organizations not learn from incidents? Bottlenecks, causes and conditions for a failure to effectively learn.

PubMed

Drupsteen, Linda; Hasle, Peter

2014-11-01

If organizations would be able to learn more effectively from incidents that occurred in the past, future incidents and consequential injury or damage can be prevented. To improve learning from incidents, this study aimed to identify limiting factors, i.e. the causes of the failure to effectively learn. In seven organizations focus groups were held to discuss factors that according to employees contributed to the failure to learn. By use of a model of the learning from incidents process, the steps, where difficulties for learning arose, became visible, and the causes for these difficulties could be studied. Difficulties were identified in multiple steps of the learning process, but most difficulties became visible when planning actions, which is the phase that bridges the gap from incident investigation to actions for improvement. The main causes for learning difficulties, which were identified by the participants in this study, were tightly related to the learning process, but some indirect causes - or conditions - such as lack of ownership and limitations in expertise were also mentioned. The results illustrate that there are two types of causes for the failure to effectively learn: direct causes and indirect causes, here called conditions. By actively and systematically studying learning, more conditions might be identified and indicators for a successful learning process may be determined. Studying the learning process does, however, require a shift from learning from incidents to learning to learn. Copyright © 2014 Elsevier Ltd. All rights reserved.

Situated learning theory: adding rate and complexity effects via Kauffman's NK model.

PubMed

Yuan, Yu; McKelvey, Bill

2004-01-01

For many firms, producing information, knowledge, and enhancing learning capability have become the primary basis of competitive advantage. A review of organizational learning theory identifies two approaches: (1) those that treat symbolic information processing as fundamental to learning, and (2) those that view the situated nature of cognition as fundamental. After noting that the former is inadequate because it focuses primarily on behavioral and cognitive aspects of individual learning, this paper argues the importance of studying learning as interactions among people in the context of their environment. It contributes to organizational learning in three ways. First, it argues that situated learning theory is to be preferred over traditional behavioral and cognitive learning theories, because it treats organizations as complex adaptive systems rather than mere information processors. Second, it adds rate and nonlinear learning effects. Third, following model-centered epistemology, it uses an agent-based computational model, in particular a "humanized" version of Kauffman's NK model, to study the situated nature of learning. Using simulation results, we test eight hypotheses extending situated learning theory in new directions. The paper ends with a discussion of possible extensions of the current study to better address key issues in situated learning.

Supervised Learning for Detection of Duplicates in Genomic Sequence Databases.

PubMed

Chen, Qingyu; Zobel, Justin; Zhang, Xiuzhen; Verspoor, Karin

2016-01-01

First identified as an issue in 1996, duplication in biological databases introduces redundancy and even leads to inconsistency when contradictory information appears. The amount of data makes purely manual de-duplication impractical, and existing automatic systems cannot detect duplicates as precisely as can experts. Supervised learning has the potential to address such problems by building automatic systems that learn from expert curation to detect duplicates precisely and efficiently. While machine learning is a mature approach in other duplicate detection contexts, it has seen only preliminary application in genomic sequence databases. We developed and evaluated a supervised duplicate detection method based on an expert curated dataset of duplicates, containing over one million pairs across five organisms derived from genomic sequence databases. We selected 22 features to represent distinct attributes of the database records, and developed a binary model and a multi-class model. Both models achieve promising performance; under cross-validation, the binary model had over 90% accuracy in each of the five organisms, while the multi-class model maintains high accuracy and is more robust in generalisation. We performed an ablation study to quantify the impact of different sequence record features, finding that features derived from meta-data, sequence identity, and alignment quality impact performance most strongly. The study demonstrates machine learning can be an effective additional tool for de-duplication of genomic sequence databases. All Data are available as described in the supplementary material.

A Theory of How Columns in the Neocortex Enable Learning the Structure of the World

PubMed Central

Hawkins, Jeff; Ahmad, Subutai; Cui, Yuwei

2017-01-01

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed. PMID:29118696

A Model of Sustainability for Professional Organizations: Using a Learning Management System to Offer Continuing Education

ERIC Educational Resources Information Center

Sparrow, Gregory S.

2017-01-01

Professional membership organizations have long maintained their exposure and revenue stream through a variety of traditional avenues, most notably memberships, sponsored conferences, and professional journals. The synergy of this three-tiered model has depended on a certain enhanced status derived from membership benefits and proprietary…

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

ERIC Educational Resources Information Center

Brostoff, Anita

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

Molecular mechanisms underlying formation of long-term reward memories and extinction memories in the honeybee (Apis mellifera)

PubMed Central

2014-01-01

The honeybee (Apis mellifera) has long served as an invertebrate model organism for reward learning and memory research. Its capacity for learning and memory formation is rooted in the ecological need to efficiently collect nectar and pollen during summer to ensure survival of the hive during winter. Foraging bees learn to associate a flower's characteristic features with a reward in a way that resembles olfactory appetitive classical conditioning, a learning paradigm that is used to study mechanisms underlying learning and memory formation in the honeybee. Due to a plethora of studies on appetitive classical conditioning and phenomena related to it, the honeybee is one of the best characterized invertebrate model organisms from a learning psychological point of view. Moreover, classical conditioning and associated behavioral phenomena are surprisingly similar in honeybees and vertebrates, suggesting a convergence of underlying neuronal processes, including the molecular mechanisms that contribute to them. Here I review current thinking on the molecular mechanisms underlying long-term memory (LTM) formation in honeybees following classical conditioning and extinction, demonstrating that an in-depth analysis of the molecular mechanisms of classical conditioning in honeybees might add to our understanding of associative learning in honeybees and vertebrates. PMID:25225299

Communications and control for electric power systems: Power flow classification for static security assessment

NASA Technical Reports Server (NTRS)

Niebur, D.; Germond, A.

1993-01-01

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.

PubMed

Hamker, Fred H; Wiltschut, Jan

2007-09-01

Most computational models of coding are based on a generative model according to which the feedback signal aims to reconstruct the visual scene as close as possible. We here explore an alternative model of feedback. It is derived from studies of attention and thus, probably more flexible with respect to attentive processing in higher brain areas. According to this model, feedback implements a gain increase of the feedforward signal. We use a dynamic model with presynaptic inhibition and Hebbian learning to simultaneously learn feedforward and feedback weights. The weights converge to localized, oriented, and bandpass filters similar as the ones found in V1. Due to presynaptic inhibition the model predicts the organization of receptive fields within the feedforward pathway, whereas feedback primarily serves to tune early visual processing according to the needs of the task.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

An application of adaptive learning to malfunction recovery

NASA Technical Reports Server (NTRS)

Cruz, R. E.

1986-01-01

A self-organizing controller is developed for a simplified two-dimensional aircraft model. The Controller learns how to pilot the aircraft through a navigational mission without exceeding pre-established position and velocity limits. The controller pilots the aircraft by activating one of eight directional actuators at all times. By continually monitoring the aircraft's position and velocity with respect to the mission, the controller progressively modifies its decision rules to improve the aircraft's performance. When the controller has learned how to pilot the aircraft, two actuators fail permanently. Despite this malfunction, the controller regains proficiency at its original task. The experimental results reported show the controller's capabilities for self-organizing control, learning, and malfunction recovery.

Quantitative Predictive Models for Systemic Toxicity (SOT)

EPA Science Inventory

Models to identify systemic and specific target organ toxicity were developed to help transition the field of toxicology towards computational models. By leveraging multiple data sources to incorporate read-across and machine learning approaches, a quantitative model of systemic ...

Deep Learning through Reusable Learning Objects in an MBA Program

ERIC Educational Resources Information Center

Rufer, Rosalyn; Adams, Ruifang Hope

2013-01-01

It has well been established that it is important to be able to leverage any organization's processes and core competencies to sustain its competitive advantage. Thus, one learning objective of an online MBA is to teach students how to apply the VRIO (value, rarity, inimitable, operationalized) model, developed by Barney and Hesterly (2006), in…

Toward a Model for the Conceptual Understanding of Personal Learning Environments: A Case Study

ERIC Educational Resources Information Center

Ivanova, Malinka; Chatti, Mohamed Amine

2011-01-01

The development of Personal Learning Environments (PLEs) is in the scope of research groups and educators aiming to propose suitable mechanisms for the organization of self-controlled and self-directed learning, providing students with tools and services for access to content and human intelligence inside and outside the educational institutions.…

A Dynamic Programming Approach to Identifying the Shortest Path in Virtual Learning Environments

ERIC Educational Resources Information Center

Fazlollahtabar, Hamed

2008-01-01

E-learning has been widely adopted as a promising solution by many organizations to offer learning-on-demand opportunities to individual employees (learners) in order to reduce training time and cost. While successful information systems models have received much attention among researchers, little research has been conducted to assess the success…

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

PubMed

Liu, Yuzhe; Gopalakrishnan, Vanathi

2017-03-01

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

Using Graphic Organizers in Intercultural Education

ERIC Educational Resources Information Center

Ciascai, Liliana

2009-01-01

Graphic organizers are instruments of representation, illustration and modeling of information. In the educational practice they are used for building, and systematization of knowledge. Graphic organizers are instruments that addressed mostly visual learning style, but their use is beneficial to all learners. In this paper we illustrate the use of…

Organizational Learning: A Review of the Literature with Implications for HRD Professionals.

ERIC Educational Resources Information Center

Dixon, Nancy M.

1992-01-01

A model of organizational learning includes information acquisition, information distribution/interpretation, meaning making, organizational memory, and information retrieval. Human resource development professionals have techniques for increasing competence in individuals, but they must also do so for organizations. (SK)

Creating Clay Models of a Human Torso as an Alternative to Dissection

ERIC Educational Resources Information Center

Shipley, Gwendolyn

2010-01-01

Instead of dissecting animals, students create small clay models of human internal organs to demonstrate their understanding of the positioning and interlocking shapes of the organs. Not only is this approach more environmentally friendly, it also forces them to learn human anatomy--which is more relevant to them than the anatomy of other…

Creating a Learning Organization in Law Enforcement: Maturity Levels for Police Oversight Agencies

ERIC Educational Resources Information Center

Filstad, Cathrine; Gottschalk, Petter

2010-01-01

Purpose: The purpose of this paper is to conceptualize a stage model for maturity levels for police oversight agencies. Design/methodology/approach: The paper is based on a literature review covering police oversight organizations and stages of growth models. Findings: As a conceptual paper, the main findings are related to the appropriateness of…

The Development of a Virtual 3D Model of the Renal Corpuscle from Serial Histological Sections for E-Learning Environments

ERIC Educational Resources Information Center

Roth, Jeremy A.; Wilson, Timothy D.; Sandig, Martin

2015-01-01

Histology is a core subject in the anatomical sciences where learners are challenged to interpret two-dimensional (2D) information (gained from histological sections) to extrapolate and understand the three-dimensional (3D) morphology of cells, tissues, and organs. In gross anatomical education 3D models and learning tools have been associated…

Applying Organ Clearance Concepts in a Clinical Setting

PubMed Central

2008-01-01

Objective To teach doctor of pharmacy (PharmD) students how to apply organ clearance concepts in a clinical setting in order to optimize dose management, select the right drug product, and promote better patient-centered care practices. Design A student-focused 5-hour topic entitled "Organ Clearance Concepts: Modeling and Clinical Applications" was developed and delivered to second-year PharmD students. Active-learning techniques, such as reading assignments and thought-provoking questions, and collaborative learning techniques, such as small groups, were used. Student learning was assessed using application cards and a minute paper. Assessment Overall student responses to topic presentation were overwhelmingly positive. The teaching strategies here discussed allowed students to play an active role in their own learning process and provided the necessary connection to keep them motivated, as mentioned in the application cards and minute paper assessments. Students scored an average of 88% on the examination given at the end of the course. Conclusion By incorporating active-learning and collaborative-learning techniques in presenting material on organ clearance concept, students gained a more thorough knowledge of dose management and drug-drug interactions than if the concepts had been presented using a traditional lecture format. This knowledge will help students in solving critical patient situations in a real-world context. PMID:19214275

Molecular Mechanisms Underlying Formation of Long-Term Reward Memories and Extinction Memories in the Honeybee ("Apis Mellifera")

ERIC Educational Resources Information Center

Eisenhardt, Dorothea

2014-01-01

The honeybee ("Apis mellifera") has long served as an invertebrate model organism for reward learning and memory research. Its capacity for learning and memory formation is rooted in the ecological need to efficiently collect nectar and pollen during summer to ensure survival of the hive during winter. Foraging bees learn to associate a…

Electronic Learning Systems in Hong Kong Business Organizations: A Study of Early and Late Adopters

ERIC Educational Resources Information Center

Chan, Simon C. H.; Ngai, Eric W. T.

2012-01-01

Based on the diffusion of innovation theory (E. M. Rogers, 1983, 1995), the authors examined the antecedents of the adoption of electronic learning (e-learning) systems by using a time-based assessment model (R. C. Beatty, J. P. Shim, & M. C. Jones, 2001), which classified adopters into categories upon point in time when adopting e-learning…

Getting the Gist: Relating Text Processing Research to Reading and Writing Instruction for Learning Disabled Secondary Students. Long Island University Transition Project Learning How to Learn: A High School/College Linkage Model to Expand Higher Educational Opportunities for Learning Disabled Students. Position Paper Series: Document No. 5.

ERIC Educational Resources Information Center

Seidenberg, Pearl L.

Many learning disabled secondary school students have difficulties with text organization in both subject area reading and expository writing. Problems may include difficulty in following the main ideas in text, recognizing the main text topics and their interrelationships, or recognizing the subordinate and superordinate ideas and examples.…

Integrated Experiential Education: Definitions and a Conceptual Model

ERIC Educational Resources Information Center

Fenton, Lara; Gallant, Karen

2016-01-01

Universities are currently embracing community engagement strategies to increase opportunities for student learning in community settings such as community organizations. Experiential learning is often touted as the pedagogy underlying such experiences. We undertook a research project exploring the challenges and benefits for students and faculty…

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Animal models of female pelvic organ prolapse: lessons learned

PubMed Central

Couri, Bruna M; Lenis, Andrew T; Borazjani, Ali; Paraiso, Marie Fidela R; Damaser, Margot S

2012-01-01

Pelvic organ prolapse is a vaginal protrusion of female pelvic organs. It has high prevalence worldwide and represents a great burden to the economy. The pathophysiology of pelvic organ prolapse is multifactorial and includes genetic predisposition, aberrant connective tissue, obesity, advancing age, vaginal delivery and other risk factors. Owing to the long course prior to patients becoming symptomatic and ethical questions surrounding human studies, animal models are necessary and useful. These models can mimic different human characteristics – histological, anatomical or hormonal, but none present all of the characteristics at the same time. Major animal models include knockout mice, rats, sheep, rabbits and nonhuman primates. In this article we discuss different animal models and their utility for investigating the natural progression of pelvic organ prolapse pathophysiology and novel treatment approaches. PMID:22707980

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

PubMed

Aoki, Kenichi; Wakano, Joe Yuichiro; Lehmann, Laurent

2012-06-01

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium. Copyright © 2012 Elsevier Inc. All rights reserved.

Developing PFC representations using reinforcement learning

PubMed Central

Reynolds, Jeremy R.; O'Reilly, Randall C.

2009-01-01

From both functional and biological considerations, it is widely believed that action production, planning, and goal-oriented behaviors supported by the frontal cortex are organized hierarchically (Fuster, 1990, Koechlin, Ody, & Kouneiher, 2003, & Miller, Galanter, & Pribram, 1960) However, the nature of the different levels of the hierarchy remains unclear, and little attention has been paid to the origins of such a hierarchy. We address these issues through biologically-inspired computational models that develop representations through reinforcement learning. We explore several different factors in these models that might plausibly give rise to a hierarchical organization of representations within the PFC, including an initial connectivity hierarchy within PFC, a hierarchical set of connections between PFC and subcortical structures controlling it, and differential synaptic plasticity schedules. Simulation results indicate that architectural constraints contribute to the segregation of different types of representations, and that this segregation facilitates learning. These findings are consistent with the idea that there is a functional hierarchy in PFC, as captured in our earlier computational models of PFC function and a growing body of empirical data. PMID:19591977

CAN-Care: an innovative model of practice-based learning.

PubMed

Raines, Deborah A

2006-01-01

The "Collaborative Approach to Nursing Care" (CAN-Care) Model of practice-based education is designed to meet the unique learning needs of the accelerated nursing program student. The model is based on a synergistic partnership between the academic and service settings, the vision of which is to create an innovative practice-based learning model, resulting in a positive experience for both the student and unit-based nurse. Thus, the objectives of quality outcomes for both the college and Health Care Organization are fulfilled. Specifically, the goal is the education of nurses ready to meet the challenges of caring for persons in the complex health care environment of the 21st century.

The Emergence of Organizing Structure in Conceptual Representation.

PubMed

Lake, Brenden M; Lawrence, Neil D; Tenenbaum, Joshua B

2018-06-01

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form-where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist. Copyright © 2018 Cognitive Science Society, Inc.

Deep SOMs for automated feature extraction and classification from big data streaming

NASA Astrophysics Data System (ADS)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Learning Organization Models and Their Application to the U.S. Army

DTIC Science & Technology

2016-06-01

activities from informal to formal. At the informal end resides unanticipated experiences which result in learning either consciously or unconsciously, new...A systems Approach to Quantum Improvement and Global Success. New York: McGraw-Hill. Marsick, V. J., & Watkins, K. E. (1999). Facilitating

Enhancing Undergraduate Agro-Ecological Laboratory Employment through Experiential Learning

ERIC Educational Resources Information Center

Grossman, J. M.; Patel, M.; Drinkwater, L. E.

2010-01-01

We piloted an educational model, the Sustainable Agriculture Scholars Program, linking research in organic agriculture to experiential learning activities for summer undergraduate employees in 2007 and 2008. Our objectives were to: (1) further student understanding of sustainable agriculture research, (2) increase student interest in sustainable…

Physics textbooks from the viewpoint of network structures

NASA Astrophysics Data System (ADS)

Králiková, Petra; Teleki, Aba

2017-01-01

We can observe self-organized networks all around us. These networks are, in general, scale invariant networks described by the Bianconi-Barabasi model. The self-organized networks (networks formed naturally when feedback acts on the system) show certain universality. These networks, in simplified models, have scale invariant distribution (Pareto distribution type I) and parameter α has value between 2 and 5. The textbooks are extremely important in the learning process and from this reason we studied physics textbook at the level of sentences and physics terms (bipartite network). The nodes represent physics terms, sentences, and pictures, tables, connected by links (by physics terms and transitional words and transitional phrases). We suppose that learning process are more robust and goes faster and easier if the physics textbook has a structure similar to structures of self-organized networks.

Spiking Neurons in a Hierarchical Self-Organizing Map Model Can Learn to Develop Spatial and Temporal Properties of Entorhinal Grid Cells and Hippocampal Place Cells

PubMed Central

Pilly, Praveen K.; Grossberg, Stephen

2013-01-01

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation. PMID:23577130

From Symbols, Stories and Social Artifacts to Social Architecture and Agency: The Discourse of Learning and the Decline of "Organizational Culture" in the "New Work Order."

ERIC Educational Resources Information Center

Siegel, Irwin H.

The concept of organizational culture has been central to the development of concepts, such as the learning organization and organizational learning, which are important within the field of adult education. However, the functionalist models of organizational culture, which have often relied on ethnographic and/or anecdotal studies of organizations…

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

PubMed

Russek, Evan M; Momennejad, Ida; Botvinick, Matthew M; Gershman, Samuel J; Daw, Nathaniel D

2017-09-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

PubMed Central

Botvinick, Matthew M.

2017-01-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation. PMID:28945743

Engagement studios: students and communities working to address the determinants of health.

PubMed

Bainbridge, Lesley; Grossman, Susan; Dharamsi, Shafik; Porter, Jill; Wood, Victoria

2014-01-01

This article presents an innovative model for interprofessional community-oriented learning. The Engagement Studios model involves a partnership between community organizations and students as equal partners in conversations and activities aimed at addressing issues of common concern as they relate to the social determinants of health. Interprofessional teams of students from health and non-health disciplines work with community partners to identify priority community issues and explore potential solutions. The student teams work with a particular community organization, combining their unique disciplinary perspectives to develop a project proposal, which addresses the community issues that have been jointly identified. Approved proposals receive a small budget to implement the project. In this paper we present the Engagement Studios model and share lessons learned from a pilot of this educational initiative.

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

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

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

Constructivist Learning of Anatomy: Gaining Knowledge by Creating Anatomical Casts

ERIC Educational Resources Information Center

Hermiz, David J.; O'Sullivan, Daniel J.; Lujan, Heidi L.; DiCarlo, Stephen E.

2011-01-01

Educators are encouraged to provide inquiry-based, collaborative, and problem solving activities that enhance learning and promote curiosity, skepticism, objectivity, and the use of scientific reasoning. Making anatomical casts or models by injecting solidifying substances into organs is an example of a constructivist activity for achieving these…

Assessment in the Learning Organization: Shifting the Paradigm.

ERIC Educational Resources Information Center

Costa, Arthur L., Ed.; Kallick, Bena, Ed.

This collection provides a new perspective for understanding what assessment can do to promote continuous improvement in education. The concepts of systems thinking, continued learning, mental models, shared vision, and team building are highlighted in the selections, which include: (1) "A Systems Approach to Assessing School Culture"…

Key Elements of a Successful Drive toward Marketing Strategy Making

ERIC Educational Resources Information Center

Cann, Cynthia W.; George, Marie A.

2003-01-01

A conceptual model is presented that depicts the relationship between an internal marketing function and an organization's readiness to learn. Learning and marketing orientations are identified as components to marketing strategy making. Key organizational functions, including communication and decision-making, are utilized in a framework for…

Developing Preservice Elementary Teachers' Knowledge and Practices through Modeling-Centered Scientific Inquiry

ERIC Educational Resources Information Center

Schwarz, Christina

2009-01-01

Preservice elementary teachers face many challenges in learning how to teach science effectively, such as engaging students in science, organizing instruction, and developing a productive learning community. This paper reports on several iterative cycles of design-based research aimed at fostering preservice teachers' principled reasoning around…

Reflections on Leadership Characteristics Necessary To Develop and Sustain Learning School Communities.

ERIC Educational Resources Information Center

Diggins, Patrick B.

1997-01-01

Reflects on what schools must do to become genuine learning organizations. Traditional organizational culture was typically inward looking, centralized, and insular. Bureaucratic systems make schools structurally ineffective. Mintzberg's varied government and normative-control models are less suitable for education than Alfred C. Crane's…

Neural network applications in telecommunications

NASA Technical Reports Server (NTRS)

Alspector, Joshua

1994-01-01

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Personal Factors Impacting College Student Success: Constructing College Learning Effectiveness Inventory (CLEI)

ERIC Educational Resources Information Center

Kim, Eunhee; Newton, Fred B.; Downey, Ronald G.; Benton, Stephen L.

2010-01-01

The College Learning Effectiveness Inventory, a new assessment tool identifying personal variables important to college student success, was constructed using empirical approaches grounded in a conceptual model. The exploratory and confirmatory studies revealed the six-underlying factors: Academic Self-Efficacy, Organization and Attention to…

Learning Portfolio Analysis and Mining for SCORM Compliant Environment

ERIC Educational Resources Information Center

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

2006-01-01

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

Cooperative Charter Schools: New Enterprises in Instructional Delivery.

ERIC Educational Resources Information Center

Hanson, Katherine L.; Hentschke, Guilbert C.

A wide variety of charter schools has emerged since the first charter was granted in 1991. Six distinct models include schools managed by grassroots organizations, schools focused on special student populations, schools centered around distance learning or home learning, business-managed schools, schools structured as teacher cooperatives, and…

Student Learning in an International Context: Examining Motivations for Education Transfer

ERIC Educational Resources Information Center

Roberts, Darbi

2016-01-01

This chapter examines the underlying motivations behind why institutions and organizations decide to apply particular policies and practices. By applying a lens of five diffusion models--learning, imitation, competition, normative, and coercion--to understand these motivations, decision makers and implementers will make better choices for…

A Digital Ecosystems Model of Assessment Feedback on Student Learning

ERIC Educational Resources Information Center

Gomez, Stephen; Andersson, Holger; Park, Julian; Maw, Stephen; Crook, Anne; Orsmond, Paul

2013-01-01

The term ecosystem has been used to describe complex interactions between living organisms and the physical world. The principles underlying ecosystems can also be applied to complex human interactions in the digital world. As internet technologies make an increasing contribution to teaching and learning practice in higher education, the…

Cross-organism learning method to discover new gene functionalities.

PubMed

Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

2016-04-01

Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones, without influence of the evolutionary distance between the considered organisms. The generated ranked lists of reliably predicted annotations, which describe novel gene functionalities and have an associated likelihood value, are very valuable both to complement available annotations, for better coverage in biomedical knowledge discovery analyses, and to quicken the annotation curation process, by focusing it on the prioritized novel annotations predicted. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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).

Improving classroom quality with the RULER Approach to Social and Emotional Learning: proximal and distal outcomes.

PubMed

Hagelskamp, Carolin; Brackett, Marc A; Rivers, Susan E; Salovey, Peter

2013-06-01

The RULER Approach to Social and Emotional Learning ("RULER") is designed to improve the quality of classroom interactions through professional development and classroom curricula that infuse emotional literacy instruction into teaching-learning interactions. Its theory of change specifies that RULER first shifts the emotional qualities of classrooms, which are then followed, over time, by improvements in classroom organization and instructional support. A 2-year, cluster randomized controlled trial was conducted to test hypotheses derived from this theory. Sixty-two urban schools either integrated RULER into fifth- and sixth-grade English language arts (ELA) classrooms or served as comparison schools, using their standard ELA curriculum only. Results from multilevel modeling with baseline adjustments and structural equation modeling support RULER's theory of change. Compared to classrooms in comparison schools, classrooms in RULER schools exhibited greater emotional support, better classroom organization, and more instructional support at the end of the second year of program delivery. Improvements in classroom organization and instructional support at the end of Year 2 were partially explained by RULER's impacts on classroom emotional support at the end of Year 1. These findings highlight the important contribution of emotional literacy training and development in creating engaging, empowering, and productive learning environments.

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling.

PubMed

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic-semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory-semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Hierarchical pictorial structures for simultaneously localizing multiple organs in volumetric pre-scan CT

NASA Astrophysics Data System (ADS)

Montillo, Albert; Song, Qi; Das, Bipul; Yin, Zhye

2015-03-01

Parsing volumetric computed tomography (CT) into 10 or more salient organs simultaneously is a challenging task with many applications such as personalized scan planning and dose reporting. In the clinic, pre-scan data can come in the form of very low dose volumes acquired just prior to the primary scan or from an existing primary scan. To localize organs in such diverse data, we propose a new learning based framework that we call hierarchical pictorial structures (HPS) which builds multiple levels of models in a tree-like hierarchy that mirrors the natural decomposition of human anatomy from gross structures to finer structures. Each node of our hierarchical model learns (1) the local appearance and shape of structures, and (2) a generative global model that learns probabilistic, structural arrangement. Our main contribution is twofold. First we embed the pictorial structures approach in a hierarchical framework which reduces test time image interpretation and allows for the incorporation of additional geometric constraints that robustly guide model fitting in the presence of noise. Second we guide our HPS framework with the probabilistic cost maps extracted using random decision forests using volumetric 3D HOG features which makes our model fast to train and fast to apply to novel test data and posses a high degree of invariance to shape distortion and imaging artifacts. All steps require approximate 3 mins to compute and all organs are located with suitably high accuracy for our clinical applications such as personalized scan planning for radiation dose reduction. We assess our method using a database of volumetric CT scans from 81 subjects with widely varying age and pathology and with simulated ultra-low dose cadaver pre-scan data.

What's Inside Bodies? Learning about Skeletons and Other Organ Systems of Vertebrate Animals.

ERIC Educational Resources Information Center

Tunnicliffe, Sue Dale; Reiss, Michael

This paper describes a study of young children's understanding of what is on the inside of animals--skeletons and other organ systems. The study uses 2-D drawings based on the idea that a drawing is the representational model and is the outward expression of the mental model. The 617 drawings made by participants in the study were awarded one of…

Status of Standards Implementation in Anchorage Secondary Schools: A Concerns Based Acceptance Model (CBAM) Review, 2001-2002.

ERIC Educational Resources Information Center

Fenton, Ray

The Concerns Based Acceptance Model (CBAM) has been a key element in developing and assessing the implementation of science and mathematics programs over the past 20 years. CBAM provides an organized approach to assessing where people stand as they learn about, and accept, changes in organizations. This study examined the status of the adoption of…

Building "Bob": A Project Exploring the Human Body at Western Illinois University Preschool Center

ERIC Educational Resources Information Center

Brouette, Scott

2008-01-01

When the children at Western Illinois University Preschool Center embarked on a study of human bodies, they decided to build a life-size model of a body, organ by organ from the inside out, to represent some of the things they were learning. This article describes the building of "Bob," the human body model, highlighting the children's…

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

PubMed

Chung, Michael Jae-Yoon; Friesen, Abram L; Fox, Dieter; Meltzoff, Andrew N; Rao, Rajesh P N

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

PubMed Central

Chung, Michael Jae-Yoon; Friesen, Abram L.; Fox, Dieter; Meltzoff, Andrew N.; Rao, Rajesh P. N.

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration. PMID:26536366

Efficient self-organizing multilayer neural network for nonlinear system modeling.

PubMed

Han, Hong-Gui; Wang, Li-Dan; Qiao, Jun-Fei

2013-07-01

It has been shown extensively that the dynamic behaviors of a neural system are strongly influenced by the network architecture and learning process. To establish an artificial neural network (ANN) with self-organizing architecture and suitable learning algorithm for nonlinear system modeling, an automatic axon-neural network (AANN) is investigated in the following respects. First, the network architecture is constructed automatically to change both the number of hidden neurons and topologies of the neural network during the training process. The approach introduced in adaptive connecting-and-pruning algorithm (ACP) is a type of mixed mode operation, which is equivalent to pruning or adding the connecting of the neurons, as well as inserting some required neurons directly. Secondly, the weights are adjusted, using a feedforward computation (FC) to obtain the information for the gradient during learning computation. Unlike most of the previous studies, AANN is able to self-organize the architecture and weights, and to improve the network performances. Also, the proposed AANN has been tested on a number of benchmark problems, ranging from nonlinear function approximating to nonlinear systems modeling. The experimental results show that AANN can have better performances than that of some existing neural networks. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Operationalizing the Learning Health Care System in an Integrated Delivery System

PubMed Central

Psek, Wayne A.; Stametz, Rebecca A.; Bailey-Davis, Lisa D.; Davis, Daniel; Darer, Jonathan; Faucett, William A.; Henninger, Debra L.; Sellers, Dorothy C.; Gerrity, Gloria

2015-01-01

Introduction: The Learning Health Care System (LHCS) model seeks to utilize sophisticated technologies and competencies to integrate clinical operations, research and patient participation in order to continuously generate knowledge, improve care, and deliver value. Transitioning from concept to practical application of an LHCS presents many challenges but can yield opportunities for continuous improvement. There is limited literature and practical experience available in operationalizing the LHCS in the context of an integrated health system. At Geisinger Health System (GHS) a multi-stakeholder group is undertaking to enhance organizational learning and develop a plan for operationalizing the LHCS system-wide. We present a framework for operationalizing continuous learning across an integrated delivery system and lessons learned through the ongoing planning process. Framework: The framework focuses attention on nine key LHCS operational components: Data and Analytics; People and Partnerships; Patient and Family Engagement; Ethics and Oversight; Evaluation and Methodology; Funding; Organization; Prioritization; and Deliverables. Definitions, key elements and examples for each are presented. The framework is purposefully broad for application across different organizational contexts. Conclusion: A realistic assessment of the culture, resources and capabilities of the organization related to learning is critical to defining the scope of operationalization. Engaging patients in clinical care and discovery, including quality improvement and comparative effectiveness research, requires a defensible ethical framework that undergirds a system of strong but flexible oversight. Leadership support is imperative for advancement of the LHCS model. Findings from our ongoing work within the proposed framework may inform other organizations considering a transition to an LHCS. PMID:25992388

Blended Learning Implementation in “Guru Pembelajar” Program

NASA Astrophysics Data System (ADS)

Mahdan, D.; Kamaludin, M.; Wendi, H. F.; Simanjuntak, M. V.

2018-02-01

The rapid development of information and communication technology (ICT), especially the internet, computers and communication devices requires the innovation in learning; one of which is Blended Learning. The concept of Blended Learning is the mixing of face-to-face learning models by learning online. Blended learning used in the learner teacher program organized by the Indonesian department of education and culture that a program to improve the competence of teachers, called “Guru Pembelajar” (GP). Blended learning model is perfect for learning for teachers, due to limited distance and time because online learning can be done anywhere and anytime. but the problems that arise from the implementation of this activity are many teachers who do not follow the activities because teachers, especially the elderly do not want to follow the activities because they cannot use computers and the internet, applications that are difficult to understand by participants, unstable internet connection in the area where the teacher lives and facilities and infrastructure are not adequate.

Computational Cognitive Neuroscience Modeling of Sequential Skill Learning

DTIC Science & Technology

2016-09-21

101 EAST 27TH STREET STE 4308 AUSTIN , TX 78712 09/21/2016 Final Report DISTRIBUTION A: Distribution approved for public release. Air Force Research ...5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) The University of Texas at Austin 108 E Dean Keeton Stop A8000 Austin , TX ...AFRL-AFOSR-VA-TR-2016-0320 Computational Cognitive Neuroscience Modeling of Sequential Skill Learning David Schnyer UNIVERSITY OF TEXAS AT AUSTIN

Learning organizations, internal marketing, and organizational commitment in hospitals

PubMed Central

2014-01-01

Background Knowledge capital is becoming more important to healthcare establishments, especially for hospitals that are facing changing societal and industrial patterns. Hospital staff must engage in a process of continual learning to improve their healthcare skills and provide a superior service to their patients. Internal marketing helps hospital administrators to improve the quality of service provided by nursing staff to their patients and allows hospitals to build a learning culture and enhance the organizational commitment of its nursing staff. Our empirical study provides nursing managers with a tool to allow them to initiate a change in the attitudes of nurses towards work, by constructing a new ‘learning organization’ and using effective internal marketing. Methods A cross-sectional design was employed. Two hundred questionnaires were distributed to nurses working in either a medical centre or a regional hospital in Taichung City, Taiwan, and 114 valid questionnaires were returned (response rate: 57%). The entire process of distribution and returns was completed between 1 October and 31 October 2009. Hypothesis testing was conducted using structural equation modelling. Results A significant positive correlation was found between the existence of a ‘learning organization’, internal marketing, and organizational commitment. Internal marketing was a mediator between creating a learning organization and organizational commitment. Conclusion Nursing managers may be able to apply the creation of a learning organization to strategies that can strengthen employee organizational commitment. Further, when promoting the creation of a learning organization, managers can coordinate their internal marketing practices to enhance the organizational commitment of nurses. PMID:24708601

The Experience of a Distance Learning Organization in a Private Higher Educational Institution in the Republic of Tatarastan (Russia): From Idea to Realization

ERIC Educational Resources Information Center

Akhmetova, Daniya; Vorontsova, Liliya; Morozova, Ilona Gennadyevna

2013-01-01

The article is devoted to the unique experience of distance learning development in the conditions of Russian reality. The model of distance learning in the Institute of Economics, Management and Law (Kazan city, Russia) is created on the basis of educational sphere diagnosis taking into account foreign and Russian experience. The specificity of…

A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties.

PubMed

Chartier, Sylvain; Giguère, Gyslain; Langlois, Dominic

2009-01-01

In this paper, we present a new recurrent bidirectional model that encompasses correlational, competitive and topological model properties. The simultaneous use of many classes of network behaviors allows for the unsupervised learning/categorization of perceptual patterns (through input compression) and the concurrent encoding of proximities in a multidimensional space. All of these operations are achieved within a common learning operation, and using a single set of defining properties. It is shown that the model can learn categories by developing prototype representations strictly from exposition to specific exemplars. Moreover, because the model is recurrent, it can reconstruct perfect outputs from incomplete and noisy patterns. Empirical exploration of the model's properties and performance shows that its ability for adequate clustering stems from: (1) properly distributing connection weights, and (2) producing a weight space with a low dispersion level (or higher density). In addition, since the model uses a sparse representation (k-winners), the size of topological neighborhood can be fixed, and no longer requires a decrease through time as was the case with classic self-organizing feature maps. Since the model's learning and transmission parameters are independent from learning trials, the model can develop stable fixed points in a constrained topological architecture, while being flexible enough to learn novel patterns.

Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

PubMed Central

Del Papa, Bruno; Priesemann, Viola

2017-01-01

Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences. PMID:28552964

Children's Participation in Ceremonial Life in Bali: Extending LOPI to Other Parts of the World.

PubMed

Corona, Yolanda; Putri, Dewa Ayu Eka; Quinteros, Graciela

2015-01-01

This chapter extends a model of how children in Indigenous communities of the Americas Learn by Observing and Pitching In (LOPI; Rogoff, 2014) to another region of the world, by examining which aspects of the model can be applied to the ways in which Balinese children learn with their peers and adults. We describe clear parallels in the role of observation and communication, the social organization of endeavors, and children's motivation to participate as they learn the music of gamelan (the traditional orchestra) that is used in religious ceremonies. © 2015 Elsevier Inc. All rights reserved.

Role of conceptual models in a physical therapy curriculum: application of an integrated model of theory, research, and clinical practice.

PubMed

Darrah, Johanna; Loomis, Joan; Manns, Patricia; Norton, Barbara; May, Laura

2006-11-01

The Department of Physical Therapy, University of Alberta, Edmonton, Alberta, Canada, recently implemented a Master of Physical Therapy (MPT) entry-level degree program. As part of the curriculum design, two models were developed, a Model of Best Practice and the Clinical Decision-Making Model. Both models incorporate four key concepts of the new curriculum: 1) the concept that theory, research, and clinical practice are interdependent and inform each other; 2) the importance of client-centered practice; 3) the terminology and philosophical framework of the World Health Organization's International Classification of Functioning, Disability, and Health; and 4) the importance of evidence-based practice. In this article the general purposes of models for learning are described; the two models developed for the MPT program are described; and examples of their use with curriculum design and teaching are provided. Our experiences with both the development and use of models of practice have been positive. The models have provided both faculty and students with a simple, systematic structured framework to organize teaching and learning in the MPT program.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

PubMed

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M; Loza, Maria Isabel; Munteanu, Cristian R; Pazos, Alejandro; García-Mera, Xerardo; González-Díaz, Humbert

2018-06-25

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC 50 , EC 50 , K i , etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

Understanding the complexity of redesigning care around the clinical microsystem.

PubMed

Barach, P; Johnson, J K

2006-12-01

The microsystem is an organizing design construct in which social systems cut across traditional discipline boundaries. Because of its interdisciplinary focus, the clinical microsystem provides a conceptual and practical framework for simplifying complex organizations that deliver care. It also provides an important opportunity for organizational learning. Process mapping and microworld simulation may be especially useful for redesigning care around the microsystem concept. Process mapping, in which the core processes of the microsystem are delineated and assessed from the perspective of how the individual interacts with the system, is an important element of the continuous learning cycle of the microsystem and the healthcare organization. Microworld simulations are interactive computer based models that can be used as an experimental platform to test basic questions about decision making misperceptions, cause-effect inferences, and learning within the clinical microsystem. Together these tools offer the user and organization the ability to understand the complexity of healthcare systems and to facilitate the redesign of optimal outcomes.

A new pattern associative memory model for image recognition based on Hebb rules and dot product

NASA Astrophysics Data System (ADS)

Gao, Mingyue; Deng, Limiao; Wang, Yanjiang

2018-04-01

A great number of associative memory models have been proposed to realize information storage and retrieval inspired by human brain in the last few years. However, there is still much room for improvement for those models. In this paper, we extend a binary pattern associative memory model to accomplish real-world image recognition. The learning process is based on the fundamental Hebb rules and the retrieval is implemented by a normalized dot product operation. Our proposed model can not only fulfill rapid memory storage and retrieval for visual information but also have the ability on incremental learning without destroying the previous learned information. Experimental results demonstrate that our model outperforms the existing Self-Organizing Incremental Neural Network (SOINN) and Back Propagation Neuron Network (BPNN) on recognition accuracy and time efficiency.

Teaching, Learning, and Leading with Schools and Communities: Preparing Sophisticated and Resilient Elementary STEM Educators

ERIC Educational Resources Information Center

Smetana, Lara K.; Coleman, Elizabeth R.; Ryan, Ann Marie; Tocci, Charles

2013-01-01

Loyola University Chicago's Teaching, Learning, and Leading With Schools and Communities (TLLSC) program is an ambitious break from traditional university-based teacher preparation models. This clinically based initial teacher preparation program, fully embedded in local schools and community organizations, takes an ecological perspective on the…

A Proposed Blueprint Model towards the Evaluation of Educational System in Iran

ERIC Educational Resources Information Center

Mehrafsha, S. Jahangir

2011-01-01

The pursuit of quality gave rise to the concept of Iran Universities as learning organizations. Iran Universities must have the capacity to learn if they are to survive the demands and requirements of the emerging times. This includes liberating traditional methodologies that are anchored on positivism and seemingly dependent on technical…

Developing a Structural Model on the Relationship among Motivational Beliefs, Self-Regulated Learning Strategies, and Achievement in Mathematics

ERIC Educational Resources Information Center

Fadlelmula, Fatma Kayan; Cakiroglu, Erdinc; Sungur, Semra

2015-01-01

This study examines the interrelationships among students' motivational beliefs (i.e. achievement goal orientations, perception of classroom goal structure, and self-efficacy), use of self-regulated learning strategies (i.e. elaboration, organization, and metacognitive self-regulation strategies), and achievement in mathematics, by proposing and…

The Student Support Team as a Professional Learning Community.

ERIC Educational Resources Information Center

DuFour, Richard; Guidice, Aida; Magee, Deborah; Martin, Patricia; Zivkovic, Barbara

This chapter discusses three emerging national trends that could serve as catalysts for fundamental change in student services programs. First is the concept of the learning organization, which offers a superior model for enhancing the effectiveness of institutions and the people within them. Second is the movement away from standardization and…

Managing the Transformation to an E-Learning Organisation.

ERIC Educational Resources Information Center

Simpson, Janet

The Douglas Mawson Institute (DMI) of TAFE (technical and further education), which operates more than four campuses in Australia with a diverse student body numbering approximately 17,000 (78 percent studying part-time), has initiated strategies to support the transformation to an e-learning organization. Major elements in the change model are…

Sequencing Language and Activities in Teaching High School Chemistry. A Report to the National Science Foundation.

ERIC Educational Resources Information Center

Abraham, Michael R.; Renner, John W.

A learning cycle consists of three phases: exploration; conceptual invention; and expansion of an idea. These phases parallel Piaget's functioning model of assimilation, disequilibrium and accomodation, and organization respectively. The learning cycle perceives students as actors rather than reactors to the environment. Inherent in that…

Human Resource Development to Facilitate Experiential Learning: The Case of Yahoo Japan

ERIC Educational Resources Information Center

Matsuo, Makoto

2015-01-01

Although work experiences are recognized as important mechanisms for developing leaders in organizations, existing research has focused primarily on work assignments rather than on human resource development (HRD) systems that promote experiential learning of managers. The primary goal of this study was to develop an HRD model for facilitating…

A Phenomenological Exploration of Self-Directed Learning among Successful Minority Entrepreneurs

ERIC Educational Resources Information Center

Alexander, Nancy Hope

2013-01-01

This transcendental, phenomenological study explored the Self-directed learning (SDL) of 10 successful minority entrepreneurs. Two SDL theories serve as lenses for the study, Spear and Mocker's (1984) Organizing Circumstance and Brockett and Heimstra's (1991) Personal Responsibility Orientation model. Five themes emerged from the data:…

A Collaborative Model for Influencing the Technology Integration Behaviors and Dispositions of Preservice Social Studies Teachers

ERIC Educational Resources Information Center

Jones, Raymond; Cunningham, Ann; Stewart, Loraine Moses

2005-01-01

Collaboration among faculty can enhance the learning experience for preservice teachers and reinforce the integral role of technology in teaching, learning, and professional development in social studies education. Organized around the Performance Profiles outlined by the National Educational Technology Standards for Teachers (NETS[middle dot]T),…

Multiple organ definition in CT using a Bayesian approach for 3D model fitting

NASA Astrophysics Data System (ADS)

Boes, Jennifer L.; Weymouth, Terry E.; Meyer, Charles R.

1995-08-01

Organ definition in computed tomography (CT) is of interest for treatment planning and response monitoring. We present a method for organ definition using a priori information about shape encoded in a set of biometric organ models--specifically for the liver and kidney-- that accurately represents patient population shape information. Each model is generated by averaging surfaces from a learning set of organ shapes previously registered into a standard space defined by a small set of landmarks. The model is placed in a specific patient's data set by identifying these landmarks and using them as the basis for model deformation; this preliminary representation is then iteratively fit to the patient's data based on a Bayesian formulation of the model's priors and CT edge information, yielding a complete organ surface. We demonstrate this technique using a set of fifteen abdominal CT data sets for liver surface definition both before and after the addition of a kidney model to the fitting; we demonstrate the effectiveness of this tool for organ surface definition in this low-contrast domain.

Entrepreneurial organizations: the driving force for improving quality in the healthcare industry.

PubMed

Borkowski, Nancy; Gordon, Jean

2006-01-01

Using DiMaggio and Powell's concept of institutional isomorphism, the authors explain why healthcare lags behind other industries in innovating new production functions that address quality. Healthcare finns can "learn" to be entrepreneurial organizations within Stevenson's 'entrepreneurial versus administrative behavior' framework and Covin and Slevin's model of an entrepreneurial organization's required culture and behavioral support structure.

Charting the energy landscape of metal/organic interfaces via machine learning

NASA Astrophysics Data System (ADS)

Scherbela, Michael; Hörmann, Lukas; Jeindl, Andreas; Obersteiner, Veronika; Hofmann, Oliver T.

2018-04-01

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

Charting the energy landscape of metal/organic interfaces via machine learning

DOE PAGES

Scherbela, Michael; Hormann, Lukas; Jeindl, Andreas; ...

2018-04-17

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. Here in this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. Finally, we demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

Charting the energy landscape of metal/organic interfaces via machine learning

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

Scherbela, Michael; Hormann, Lukas; Jeindl, Andreas

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. Here in this work, we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected density functional theory (DFT) calculations. Finally, we demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.

PubMed

Kaplan, Bernhard A; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

PubMed Central

Kaplan, Bernhard A.; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID:24570657

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model

PubMed Central

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P.; Terrace, Herbert S.

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort’s success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models. PMID:26407227

Implicit Value Updating Explains Transitive Inference Performance: The Betasort Model.

PubMed

Jensen, Greg; Muñoz, Fabian; Alkan, Yelda; Ferrera, Vincent P; Terrace, Herbert S

2015-01-01

Transitive inference (the ability to infer that B > D given that B > C and C > D) is a widespread characteristic of serial learning, observed in dozens of species. Despite these robust behavioral effects, reinforcement learning models reliant on reward prediction error or associative strength routinely fail to perform these inferences. We propose an algorithm called betasort, inspired by cognitive processes, which performs transitive inference at low computational cost. This is accomplished by (1) representing stimulus positions along a unit span using beta distributions, (2) treating positive and negative feedback asymmetrically, and (3) updating the position of every stimulus during every trial, whether that stimulus was visible or not. Performance was compared for rhesus macaques, humans, and the betasort algorithm, as well as Q-learning, an established reward-prediction error (RPE) model. Of these, only Q-learning failed to respond above chance during critical test trials. Betasort's success (when compared to RPE models) and its computational efficiency (when compared to full Markov decision process implementations) suggests that the study of reinforcement learning in organisms will be best served by a feature-driven approach to comparing formal models.

Web Image Retrieval Using Self-Organizing Feature Map.

ERIC Educational Resources Information Center

Wu, Qishi; Iyengar, S. Sitharama; Zhu, Mengxia

2001-01-01

Provides an overview of current image retrieval systems. Describes the architecture of the SOFM (Self Organizing Feature Maps) based image retrieval system, discussing the system architecture and features. Introduces the Kohonen model, and describes the implementation details of SOFM computation and its learning algorithm. Presents a test example…

An experimental ward. Improving care and learning.

PubMed

Ronan, L; Stoeckle, J D

1992-01-01

The rapidly changing health care system is still largely organized according to old, and increasingly outdated models. The contemporary demands of patient care and residency training call for an experimental ward, which can develop and test new techniques in hospital organization and the delivery of care in a comprehensive way.

Learning Organization Practices.

ERIC Educational Resources Information Center

1997

This document contains three papers from a symposium on learning organization practices. "Learning Lenses of Leading Organizations: Best Practices Survey" (Laurel S. Jeris) shows that successful learning organizations view learning initiatives through multiple lenses with a clear, sustained focus on strategic outcomes. "Dimensions…

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

PubMed Central

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J.

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic–semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory–semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model. PMID:24688478

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.

Lessons Learned from 2 Decades of Modelling Forest Dead Organic Matter and Soil Carbon at the National Scale

NASA Astrophysics Data System (ADS)

Shaw, C.; Kurz, W. A.; Metsaranta, J.; Bona, K. A.; Hararuk, O.; Smyth, C.

2017-12-01

The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is a forest carbon budget model that operates on individual stands. It is applied from regional to national-scales in Canada for national and international reporting of GHG emissions and removals and in support of analyses of forest sector mitigation options and other scientific and policy questions. This presentation will review the history and continuous improvement process of representations of dead organic matter (DOM) and soil carbon modelling. Early model versions in which dead organic matter (DOM) pools only included litter, downed deadwood and soil, to the current version where these pools are estimated separately to better compare model estimates against field measurements, or new pools have been added. Uncertainty analyses consistently point at soil C pools as large sources of uncertainty. With the new ground plot measurements from the National Forest Inventory, and with a newly compiled forest soil carbon database, we have recently completed a model data assimilation exercise that helped reduce parameter uncertainties. Lessons learned from the continuous improvement process will be summarised and we will discuss how model modification have led to improved representation of DOM and soil carbon dynamics. We conclude by suggesting future research priorities that can advance DOM and soil carbon modelling in Canadian forest ecosystems.

Room-temperature and temperature-dependent QSRR modelling for predicting the nitrate radical reaction rate constants of organic chemicals using ensemble learning methods.

PubMed

Gupta, S; Basant, N; Mohan, D; Singh, K P

2016-07-01

Experimental determinations of the rate constants of the reaction of NO3 with a large number of organic chemicals are tedious, and time and resource intensive; and the development of computational methods has widely been advocated. In this study, we have developed room-temperature (298 K) and temperature-dependent quantitative structure-reactivity relationship (QSRR) models based on the ensemble learning approaches (decision tree forest (DTF) and decision treeboost (DTB)) for predicting the rate constant of the reaction of NO3 radicals with diverse organic chemicals, under OECD guidelines. Predictive powers of the developed models were established in terms of statistical coefficients. In the test phase, the QSRR models yielded a correlation (r(2)) of >0.94 between experimental and predicted rate constants. The applicability domains of the constructed models were determined. An attempt has been made to provide the mechanistic interpretation of the selected features for QSRR development. The proposed QSRR models outperformed the previous reports, and the temperature-dependent models offered a much wider applicability domain. This is the first report presenting a temperature-dependent QSRR model for predicting the nitrate radical reaction rate constant at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards NO3 radicals in the atmosphere, hence, their persistence and exposure risk assessment.

Sustainability of healthcare improvement: what can we learn from learning theory?

PubMed Central

2012-01-01

Background Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Methods Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Results Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. Conclusions The improved understanding of the clinical system represented a change in mental models of employees that influenced how the organization changed its performance. By applying the framework of organizational learning, we learned that changes originating from a new mental model represent double-loop learning. In double-loop learning, deeper system properties are changed, and consequently changes are more likely to be sustained. PMID:22863199

Sustainability of healthcare improvement: what can we learn from learning theory?

PubMed

Hovlid, Einar; Bukve, Oddbjørn; Haug, Kjell; Aslaksen, Aslak Bjarne; von Plessen, Christian

2012-08-03

Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. The improved understanding of the clinical system represented a change in mental models of employees that influenced how the organization changed its performance. By applying the framework of organizational learning, we learned that changes originating from a new mental model represent double-loop learning. In double-loop learning, deeper system properties are changed, and consequently changes are more likely to be sustained.

Correcting the SIMPLE Model of Free Recall

ERIC Educational Resources Information Center

Lee, Michael D.; Pooley, James P.

2013-01-01

The scale-invariant memory, perception, and learning (SIMPLE) model developed by Brown, Neath, and Chater (2007) formalizes the theoretical idea that scale invariance is an important organizing principle across numerous cognitive domains and has made an influential contribution to the literature dealing with modeling human memory. In the context…

The Self-Help Group Model: A Review

ERIC Educational Resources Information Center

Jaques, Marceline E.; Patterson, Kathleen M.

1974-01-01

Self-help mutual aid groups are organized by peers who share a common problem. Through group identification, mutual support, and modeling, behavior is directed toward learning a new coping life style. The self-help group model is considered here as a viable and necessary part of a total rehabilitation service system. (Author)

Building a Global Learning Organization: Lessons from the World's Top Corporations.

ERIC Educational Resources Information Center

Marquardt, Michael J.

1995-01-01

Research on 50 organizations elicited 19 attributes of learning organizations: individual learning, group learning, streamlined structure, corporate learning culture, empowerment, environmental scanning, knowledge creation/transfer, learning technology, quality, learning strategy, supportive atmosphere, teamwork/networking, vision, acculturation,…

An Organizational Learning Framework for Patient Safety.

PubMed

Edwards, Marc T

Despite concerted effort to improve quality and safety, high reliability remains a distant goal. Although this likely reflects the challenge of organizational change, persistent controversy over basic issues suggests that weaknesses in conceptual models may contribute. The essence of operational improvement is organizational learning. This article presents a framework for identifying leverage points for improvement based on organizational learning theory and applies it to an analysis of current practice and controversy. Organizations learn from others, from defects, from measurement, and from mindfulness. These learning modes correspond with contemporary themes of collaboration, no blame for human error, accountability for performance, and managing the unexpected. The collaborative model has dominated improvement efforts. Greater attention to the underdeveloped modes of organizational learning may foster more rapid progress in patient safety by increasing organizational capabilities, strengthening a culture of safety, and fixing more of the process problems that contribute to patient harm.

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

Student nurses' experiences of the clinical learning environment in relation to the organization of supervision: a questionnaire survey.

PubMed

Sundler, Annelie J; Björk, Maria; Bisholt, Birgitta; Ohlsson, Ulla; Engström, Agneta Kullén; Gustafsson, Margareta

2014-04-01

The aim was to investigate student nurses' experiences of the clinical learning environment in relation to how the supervision was organized. The clinical environment plays an essential part in student nurses' learning. Even though different models for supervision have been previously set forth, it has been stressed that there is a need both of further empirical studies on the role of preceptorship in undergraduate nursing education and of studies comparing different models. A cross-sectional study with comparative design was carried out with a mixed method approach. Data were collected from student nurses in the final term of the nursing programme at three universities in Sweden by means of a questionnaire. In general the students had positive experiences of the clinical learning environment with respect to pedagogical atmosphere, leadership style of the ward manager, premises of nursing, supervisory relationship, and role of the nurse preceptor and nurse teacher. However, there were significant differences in their ratings of the supervisory relationship (p<0.001) and the pedagogical atmosphere (p 0.025) depending on how the supervision was organized. Students who had the same preceptor all the time were more satisfied with the supervisory relationship than were those who had different preceptors each day. Students' comments on the supervision confirmed the significance of the preceptor and the supervisory relationship. The organization of the supervision was of significance with regard to the pedagogical atmosphere and the students' relation to preceptors. Students with the same preceptor throughout were more positive concerning the supervisory relationship and the pedagogical atmosphere. © 2013.

Is the Recall of Verbal-Spatial Information from Working Memory Affected by Symptoms of ADHD?

ERIC Educational Resources Information Center

Caterino, Linda C.; Verdi, Michael P.

2012-01-01

Objective: The Kulhavy model for text learning using organized spatial displays proposes that learning will be increased when participants view visual images prior to related text. In contrast to previous studies, this study also included students who exhibited symptoms of ADHD. Method: Participants were presented with either a map-text or…

Impact of Expert Teaching Quality on Novice Academic Performance in the Jigsaw Cooperative Learning Method

ERIC Educational Resources Information Center

Berger, Roland; Hänze, Martin

2015-01-01

We assessed the impact of expert students' instructional quality on the academic performance of novice students in 12th-grade physics classes organized in an expert model of cooperative learning ("jigsaw classroom"). The instructional quality of 129 expert students was measured by a newly developed rating system. As expected, when…

Using an "Open Approach" to Create a New, Innovative Higher Education Model

ERIC Educational Resources Information Center

Huggins, Susan; Smith, Peter

2015-01-01

Navigating learning, formal or informal, can be overwhelming, confusing, and impersonal. With more options than ever, the process of deciding what, where, and when can be overwhelming to a learner. The concept of Open College at Kaplan University (OC@KU) was to bring organization, purpose, and personalization of learning caused by vast resources…

Transfer of Learning from Management Development Programmes: Testing the Holton Model

ERIC Educational Resources Information Center

Kirwan, Cyril; Birchall, David

2006-01-01

Transfer of learning from management development programmes has been described as the effective and continuing application back at work of the knowledge and skills gained on those programmes. It is a very important issue for organizations today, given the large amounts of investment in these programmes and the small amounts of that investment that…

Automatic Sound Generation for Spherical Objects Hitting Straight Beams Based on Physical Models.

ERIC Educational Resources Information Center

Rauterberg, M.; And Others

Sounds are the result of one or several interactions between one or several objects at a certain place and in a certain environment; the attributes of every interaction influence the generated sound. The following factors influence users in human/computer interaction: the organization of the learning environment, the content of the learning tasks,…

Do written mandatory accreditation standards for residential care positively model learning organizations? Textual and critical discourse analysis.

PubMed

Bell, Erica; Robinson, Andrew; See, Catherine

2013-11-01

Unprecedented global population ageing accompanied by increasing complexity of aged care present major challenges of quality in aged care. In the business literature, Senge's theory of adaptive learning organisations offers a model of organisational quality. However, while accreditation of national standards is an increasing mechanism for achieving quality in aged care, there are anecdotal concerns it creates a 'minimum standards compliance mentality' and no evidence about whether it reinforces learning organisations. The research question was 'Do mandatory national accreditation standards for residential aged care, as they are written, positively model learning organisations?'. Automatic text analysis was combined with critical discourse analysis to analyse the presence of learning concepts from Senge's learning organisation theory in an exhaustive sample of national accreditation standards from 7 countries. The two stages of analysis were: (1) quantitative mapping of the presence of learning organisation concepts in standards using Bayesian-based textual analytics software and (2) qualitative critical discourse analysis to further examine how the language of standards so identified may be modelling learning organisation concepts. The learning concepts 'training', 'development', 'knowledge', and 'systems' are present with relative frequencies of 19%, 11%, 10%, and 10% respectively in the 1944 instances, in paragraph-sized text blocks, considered. Concepts such as 'team', 'integration', 'learning', 'change' and 'innovation' occur with 7%, 6%, 5%, 5%, and 1% relative frequencies respectively. Learning concepts tend to co-occur with negative rather than positive sentiment language in the 3176 instances in text blocks containing sentiment language. Critical discourse analysis suggested that standards generally use the language of organisational change and learning in limited ways that appear to model 'learning averse' communities of practice and organisational cultures. The aged care quality challenge and the role of standards need rethinking. All standards implicitly or explicitly model an organisation of some type. If standards can model a limited and negative learning organisation language, they could model a well-developed and positive learning organisation language. In the context of the global aged care crisis, the modelling of learning organisations is probably critical for minimal competence in residential aged care and certainly achievable in the language of standards. Copyright © 2013 Elsevier Ltd. All rights reserved.

A bio-inspired kinematic controller for obstacle avoidance during reaching tasks with real robots.

PubMed

Srinivasa, Narayan; Bhattacharyya, Rajan; Sundareswara, Rashmi; Lee, Craig; Grossberg, Stephen

2012-11-01

This paper describes a redundant robot arm that is capable of learning to reach for targets in space in a self-organized fashion while avoiding obstacles. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle-free space using the Direction-to-Rotation Transform (DIRECT). Unlike prior DIRECT models, the learning process in this work was realized using an online Fuzzy ARTMAP learning algorithm. The DIRECT-based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not having experienced them during learning. The DIRECT model was extended based on a novel reactive obstacle avoidance direction (DIRECT-ROAD) model to enable redundant robots to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevented the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, a self-organized process of mental rehearsals of movements was modeled, inspired by human and animal experiments on reaching, to generate plans for movement execution using DIRECT-ROAD in complex environments. These mental rehearsals or plans are self-generated by using the Fuzzy ARTMAP algorithm to retrieve multiple solutions for reaching each target while accounting for all the obstacles in its environment. The key aspects of the proposed novel controller were illustrated first using simple examples. Experiments were then performed on real robot platforms to demonstrate successful obstacle avoidance during reaching tasks in real-world environments. Copyright © 2012 Elsevier Ltd. All rights reserved.

Learning the organization: a model for health system analysis for new nurse administrators.

PubMed

Clark, Mary Jo

2004-01-01

Health systems are large and complex organizations in which multiple components and processes influence system outcomes. In order to effectively position themselves in such organizations, nurse administrators new to a system must gain a rapid understanding of overall system operation. Such understanding is facilitated by use of a model for system analysis. The model presented here examines the dynamic interrelationships between and among internal and external elements as they affect system performance. External elements to be analyzed include environmental factors and characteristics of system clientele. Internal elements flow from the mission and goals of the system and include system culture, services, resources, and outcomes.

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

NASA Astrophysics Data System (ADS)

Bahtiar; Rahayu, Y. S.; Wasis

2018-01-01

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

Evolution of learning in fluctuating environments: when selection favors both social and exploratory individual learning.

PubMed

Borenstein, Elhanan; Feldman, Marcus W; Aoki, Kenichi

2008-03-01

Cumulative cultural change requires organisms that are capable of both exploratory individual learning and faithful social learning. In our model, an organism's phenotype is initially determined innately (by its genotypic value) or by social learning (copying a phenotype from the parental generation), and then may or may not be modified by individual learning (exploration around the initial phenotype). The environment alternates periodically between two states, each defined as a certain range of phenotypes that can survive. These states may overlap, in which case the same phenotype can survive in both states, or they may not. We find that a joint social and exploratory individual learning strategy-the strategy that supports cumulative culture-is likely to spread when the environmental states do not overlap. In particular, when the environmental states are contiguous and mutation is allowed among the genotypic values, this strategy will spread in either moderately or highly stable environments, depending on the exact nature of the individual learning applied. On the other hand, natural selection often favors a social learning strategy without exploration when the environmental states overlap. We find only partial support for the "consensus" view, which holds that individual learning, social learning, and innate determination of behavior will evolve at short, intermediate, and long environmental periodicities, respectively.

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Structural drift: the population dynamics of sequential learning.

PubMed

Crutchfield, James P; Whalen, Sean

2012-01-01

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream "teacher" and then pass samples from the model to their downstream "student". It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.

Machine learning of molecular electronic properties in chemical compound space

NASA Astrophysics Data System (ADS)

Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand; Vazquez-Mayagoitia, Alvaro; Hansen, Katja; Tkatchenko, Alexandre; Müller, Klaus-Robert; Anatole von Lilienfeld, O.

2013-09-01

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ‘quantum machine’ is similar, and sometimes superior, to modern quantum-chemical methods—at negligible computational cost.

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.

PubMed

Fernandez, Michael; Boyd, Peter G; Daff, Thomas D; Aghaji, Mohammad Zein; Woo, Tom K

2014-09-04

In this work, we have developed quantitative structure-property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.

Phase Transitions in a Model for Social Learning via the Internet

NASA Astrophysics Data System (ADS)

Bordogna, Clelia M.; Albano, Ezequiel V.

Based on the concepts of educational psychology, sociology and statistical physics, a mathematical model for a new type of social learning process that takes place when individuals interact via the Internet is proposed and studied. The noise of the interaction (misunderstandings, lack of well organized participative activities, etc.) dramatically restricts the number of individuals that can be efficiently in mutual contact and drives phase transitions between ``ordered states'' such as the achievements of the individuals are satisfactory and ``disordered states'' with negligible achievements.

Vision for a treasured resource. Part 2. Nurse manager learning needs.

PubMed

Horvath, K J; Aroian, J F; Secatore, J A; Alpert, H; Costa, M J; Powers, E; Stengrevics, S S

1997-04-01

Part 1 in this two-part series focused on the meaning of significant incidents in nurse managers' practice related to role implementation. Explanation of the authors' application of the Manager as Developer Model (MADM) as a useful model for organizing and understanding some of the data was also discussed. Part 2 describes significant incidents in nurse managers' practice related to their ongoing learning needs. The authors address issues of performance counseling and intervention versus coaching and make recommendations for management development programs.

Provider-agency fit in substance abuse treatment organizations: implications for learning climate, morale, and evidence-based practice implementation.

PubMed

Ramsey, Alex T; van den Berk-Clark, Carissa

2015-05-12

Substance abuse agencies have been slow to adopt and implement evidence-based practices (EBPs), due in part to poor provider morale and organizational climates that are not conducive to successful learning and integration of these practices. Person-organization fit theory suggests that alignment, or fit, between provider- and agency-level characteristics regarding the implementation of EBPs may influence provider morale and organizational learning climate and, thus, implementation success. The current study hypothesized that discrepancies, or lack of fit, between provider- and agency-level contextual factors would negatively predict provider morale and organizational learning climate, outcomes shown to be associated with successful EBP implementation. Direct service providers (n = 120) from four substance abuse treatment agencies responded to a survey involving provider morale, organizational learning climate, agency expectations for EBP use, agency resources for EBP use, and provider attitudes towards EBP use. Difference scores between combinations of provider- and agency-level factors were computed to model provider-agency fit. Quadratic regression analyses were conducted to more adequately and comprehensively model the level of the dependent variables across the entire "fit continuum". Discrepancies, or misfit, between agency expectations and provider attitudes and between agency resources and provider attitudes were associated with poorer provider morale and weaker organizational learning climate. For all hypotheses, the curvilinear model of provider-agency discrepancies significantly predicted provider morale and organizational learning climate, indicating that both directions of misfit (provider factors more favorable than agency factors, and vice-versa) were detrimental to morale and climate. However, outcomes were most negative when providers viewed EBPs favorably, but perceived that agency expectations and resources were less supportive of EBP use. The current research benefits from a strong theoretical framework, consistent findings, and significant practical implications for substance abuse treatment agencies. Comprehensive attempts to strengthen outcomes related to EBP implementation must consider both provider- and agency-level characteristics regarding EBP use. Organizational efforts to more closely align provider attitudes and agency priorities will likely constitute a key strategy in fostering the implementation of EBPs in substance abuse treatment organizations.

Theories, models and frameworks used in capacity building interventions relevant to public health: a systematic review.

PubMed

Bergeron, Kim; Abdi, Samiya; DeCorby, Kara; Mensah, Gloria; Rempel, Benjamin; Manson, Heather

2017-11-28

There is limited research on capacity building interventions that include theoretical foundations. The purpose of this systematic review is to identify underlying theories, models and frameworks used to support capacity building interventions relevant to public health practice. The aim is to inform and improve capacity building practices and services offered by public health organizations. Four search strategies were used: 1) electronic database searching; 2) reference lists of included papers; 3) key informant consultation; and 4) grey literature searching. Inclusion and exclusion criteria are outlined with included papers focusing on capacity building, learning plans, professional development plans in combination with tools, resources, processes, procedures, steps, model, framework, guideline, described in a public health or healthcare setting, or non-government, government, or community organizations as they relate to healthcare, and explicitly or implicitly mention a theory, model and/or framework that grounds the type of capacity building approach developed. Quality assessment were performed on all included articles. Data analysis included a process for synthesizing, analyzing and presenting descriptive summaries, categorizing theoretical foundations according to which theory, model and/or framework was used and whether or not the theory, model or framework was implied or explicitly identified. Nineteen articles were included in this review. A total of 28 theories, models and frameworks were identified. Of this number, two theories (Diffusion of Innovations and Transformational Learning), two models (Ecological and Interactive Systems Framework for Dissemination and Implementation) and one framework (Bloom's Taxonomy of Learning) were identified as the most frequently cited. This review identifies specific theories, models and frameworks to support capacity building interventions relevant to public health organizations. It provides public health practitioners with a menu of potentially usable theories, models and frameworks to support capacity building efforts. The findings also support the need for the use of theories, models or frameworks to be intentional, explicitly identified, referenced and for it to be clearly outlined how they were applied to the capacity building intervention.

Information extraction from multi-institutional radiology reports.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinical and research use. Although efforts to structure some radiology report information through predefined templates are beginning to bear fruit, a large portion of radiology report information is entered in free text. The free text format is a major obstacle for rapid extraction and subsequent use of information by clinicians, researchers, and healthcare information systems. This difficulty is due to the ambiguity and subtlety of natural language, complexity of described images, and variations among different radiologists and healthcare organizations. As a result, radiology reports are used only once by the clinician who ordered the study and rarely are used again for research and data mining. In this work, machine learning techniques and a large multi-institutional radiology report repository are used to extract the semantics of the radiology report and overcome the barriers to the re-use of radiology report information in clinical research and other healthcare applications. We describe a machine learning system to annotate radiology reports and extract report contents according to an information model. This information model covers the majority of clinically significant contents in radiology reports and is applicable to a wide variety of radiology study types. Our automated approach uses discriminative sequence classifiers for named-entity recognition to extract and organize clinically significant terms and phrases consistent with the information model. We evaluated our information extraction system on 150 radiology reports from three major healthcare organizations and compared its results to a commonly used non-machine learning information extraction method. We also evaluated the generalizability of our approach across different organizations by training and testing our system on data from different organizations. Our results show the efficacy of our machine learning approach in extracting the information model's elements (10-fold cross-validation average performance: precision: 87%, recall: 84%, F1 score: 85%) and its superiority and generalizability compared to the common non-machine learning approach (p-value<0.05). Our machine learning information extraction approach provides an effective automatic method to annotate and extract clinically significant information from a large collection of free text radiology reports. This information extraction system can help clinicians better understand the radiology reports and prioritize their review process. In addition, the extracted information can be used by researchers to link radiology reports to information from other data sources such as electronic health records and the patient's genome. Extracted information also can facilitate disease surveillance, real-time clinical decision support for the radiologist, and content-based image retrieval. Copyright © 2015 Elsevier B.V. All rights reserved.

Dorsoventral and Proximodistal Hippocampal Processing Account for the Influences of Sleep and Context on Memory (Re)consolidation: A Connectionist Model

PubMed Central

Lines, Justin

2017-01-01

The context in which learning occurs is sufficient to reconsolidate stored memories and neuronal reactivation may be crucial to memory consolidation during sleep. The mechanisms of context-dependent and sleep-dependent memory (re)consolidation are unknown but involve the hippocampus. We simulated memory (re)consolidation using a connectionist model of the hippocampus that explicitly accounted for its dorsoventral organization and for CA1 proximodistal processing. Replicating human and rodent (re)consolidation studies yielded the following results. (1) Semantic overlap between memory items and extraneous learning was necessary to explain experimental data and depended crucially on the recurrent networks of dorsal but not ventral CA3. (2) Stimulus-free, sleep-induced internal reactivations of memory patterns produced heterogeneous recruitment of memory items and protected memories from subsequent interference. These simulations further suggested that the decrease in memory resilience when subjects were not allowed to sleep following learning was primarily due to extraneous learning. (3) Partial exposure to the learning context during simulated sleep (i.e., targeted memory reactivation) uniformly increased memory item reactivation and enhanced subsequent recall. Altogether, these results show that the dorsoventral and proximodistal organization of the hippocampus may be important components of the neural mechanisms for context-based and sleep-based memory (re)consolidations. PMID:28757864

Factors influencing the adoption of E-learning in Tabriz University of Medical Sciences.

PubMed

Abdekhoda, Mohammadhiwa; Dehnad, Afsaneh; Ghazi Mirsaeed, Sayd Javad; Zarea Gavgani, Vahideh

2016-01-01

Background: Electronic Learning (E-learning), is the use of electronic technology in education via computer and the internet. Despite its slow adoption by faculty members, e-learning provides several benefits to individuals and organizations. This study was conducted to determine the factors influencing the adoption of e-learning by faculty members in Tabriz University of Medical Sciences. Methods: This was a cross- sectional study, in which a sample of 190 faculty members of Tabriz University of Medical Sciences was randomly selected, using stratified sampling. A Conceptual Path Model of Unified Theory of Acceptance and Use of Technology (UTAUT) was applied to assess the faculty members' attitude towards e-learning. The collected data were analyzed by SPSS16, using descriptive statistics and regression analysis. The model was tested by structural equation modeling (SEM) and was finally represented by Analysis of Moment Structures. Results: The results evidenced that UTAUT model explains about 56% of the variance for adoption of elearning. The findings also revealed that performance expectancy, effort expectancy, social influences and behavior indentation had direct and significant effects on faculty members' behavior towards the use of e-learning. However, facilitated condition had no significant effects on the use of e-learning. Conclusion: The authorized model provides considerable insight for perception and anticipation of faculty members' behaviors in adopting e-learning. The survey clearly identified significant and non-significant factors that may affect the adoption of e-learning. The results of this study could help the policy makers when successful adoption of e-learning is in their agenda.

Factors influencing the adoption of E-learning in Tabriz University of Medical Sciences

PubMed Central

Abdekhoda, Mohammadhiwa; Dehnad, Afsaneh; Ghazi Mirsaeed, Sayd Javad; Zarea Gavgani, Vahideh

2016-01-01

Background: Electronic Learning (E-learning), is the use of electronic technology in education via computer and the internet. Despite its slow adoption by faculty members, e-learning provides several benefits to individuals and organizations. This study was conducted to determine the factors influencing the adoption of e-learning by faculty members in Tabriz University of Medical Sciences. Methods: This was a cross- sectional study, in which a sample of 190 faculty members of Tabriz University of Medical Sciences was randomly selected, using stratified sampling. A Conceptual Path Model of Unified Theory of Acceptance and Use of Technology (UTAUT) was applied to assess the faculty members’ attitude towards e-learning. The collected data were analyzed by SPSS16, using descriptive statistics and regression analysis. The model was tested by structural equation modeling (SEM) and was finally represented by Analysis of Moment Structures. Results: The results evidenced that UTAUT model explains about 56% of the variance for adoption of elearning. The findings also revealed that performance expectancy, effort expectancy, social influences and behavior indentation had direct and significant effects on faculty members’ behavior towards the use of e-learning. However, facilitated condition had no significant effects on the use of e-learning. Conclusion: The authorized model provides considerable insight for perception and anticipation of faculty members’ behaviors in adopting e-learning. The survey clearly identified significant and non-significant factors that may affect the adoption of e-learning. The results of this study could help the policy makers when successful adoption of e-learning is in their agenda. PMID:28491832

Organizational Change from Scientific Management to the Learning Organization--Implications for New Work Systems.

ERIC Educational Resources Information Center

Fusch, Gene E.

Western enterprises confront an era of global competition in which industry leaders can no longer overlook negative effects originating from past Taylorist and autocratic organizational structures. Corporate leaders are exploring innovative methods to change their organizations from the Taylorist model to workplace environments that foster worker…

Ontological Relations and the Capability Maturity Model Applied in Academia

ERIC Educational Resources Information Center

de Oliveira, Jerônimo Moreira; Campoy, Laura Gómez; Vilarino, Lilian

2015-01-01

This work presents a new approach to the discovery, identification and connection of ontological elements within the domain of characterization in learning organizations. In particular, the study can be applied to contexts where organizations require planning, logic, balance, and cognition in knowledge creation scenarios, which is the case for the…

A Conceptual Model for Supporting Para-Teacher Learning in an Indian Non-Governmental Organization (NGO)

ERIC Educational Resources Information Center

Raval, Harini; McKenney, Susan; Pieters, Jules

2010-01-01

Non-governmental organizations (NGOs) are being recognized globally for their influential role in realizing the UN Millennium Development Goal of education for all in developing countries. NGOs mostly employ untrained para-educators for grassroots activities. The professional development of these teachers is critical for NGO effectiveness, yet…

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Learning, climate and the evolution of cultural capacity.

PubMed

Whitehead, Hal

2007-03-21

Patterns of environmental variation influence the utility, and thus evolution, of different learning strategies. I use stochastic, individual-based evolutionary models to assess the relative advantages of 15 different learning strategies (genetic determination, individual learning, vertical social learning, horizontal/oblique social learning, and contingent combinations of these) when competing in variable environments described by 1/f noise. When environmental variation has little effect on fitness, then genetic determinism persists. When environmental variation is large and equal over all time-scales ("white noise") then individual learning is adaptive. Social learning is advantageous in "red noise" environments when variation over long time-scales is large. Climatic variability increases with time-scale, so that short-lived organisms should be able to rely largely on genetic determination. Thermal climates usually are insufficiently red for social learning to be advantageous for species whose fitness is very determined by temperature. In contrast, population trajectories of many species, especially large mammals and aquatic carnivores, are sufficiently red to promote social learning in their predators. The ocean environment is generally redder than that on land. Thus, while individual learning should be adaptive for many longer-lived organisms, social learning will often be found in those dependent on the populations of other species, especially if they are marine. This provides a potential explanation for the evolution of a prevalence of social learning, and culture, in humans and cetaceans.

Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies.

PubMed

McElreath, Richard; Bell, Adrian V; Efferson, Charles; Lubell, Mark; Richerson, Peter J; Waring, Timothy

2008-11-12

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.

Learning the Gestalt rule of collinearity from object motion.

PubMed

Prodöhl, Carsten; Würtz, Rolf P; von der Malsburg, Christoph

2003-08-01

The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses spatiotemporal retinal filtering, which is very sensitive to changes in the visual input. We show that it is crucial for successful learning to use the correlation of the transient responses instead of the sustained ones. As a consequence, learning works best with video sequences of moving objects. The model addresses a special case of the fundamental question of what represents the necessary a priori knowledge the brain is equipped with at birth so that the self-organized process of structuring by experience can be successful.

What Campuses Need to Know about Organizational Learning and the Learning Organization

ERIC Educational Resources Information Center

Kezar, Adrianna

2005-01-01

This chapter provides an overview of the literature on organizational learning and the learning organization, sets out key concepts in each area, and reviews the way that organizational learning and the learning organization have been applied within higher education.

Training of Students’ Critical Thinking Skills through the implementation of a Modified Free Inquiry Model

NASA Astrophysics Data System (ADS)

Hadi, S. A.; Susantini, E.; Agustini, R.

2018-01-01

This research aimed at training students’ critical thinking skills through the implementation of a modified free inquiry learning model. The subjects of this research were 21 students of Mathematics Semester II. Using One-Group Pretest-Posttest Design, the data were analyzed descriptively using N-gain indicator. The results indicate that the modified free inquiry learning model was effective to train students’ critical thinking skills. The increase in the students’ critical thinking skills viewed from the value of N-Gain has a range of values with the categories of medium and high with a score between 0,25-0,95. Overall, the change in N-Gain score of each student and each indicator of critical thinking skills is as increasing with a moderate category. The increase of N-Gain value is resulted from the fact that the students were directly involved in organizing their learning process. These criteria indicate that the modified free inquiry learning model can be used to train students’ critical thinking skills on photosynthesis and cellular respiration materials. The results of this research are expected to be nationally implemented to familiarize students with andragogy learning style which places the students as the subjects of learning.

A Systemic Cause Analysis Model for Human Performance Technicians

ERIC Educational Resources Information Center

Sostrin, Jesse

2011-01-01

This article presents a systemic, research-based cause analysis model for use in the field of human performance technology (HPT). The model organizes the most prominent barriers to workplace learning and performance into a conceptual framework that explains and illuminates the architecture of these barriers that exist within the fabric of everyday…

Use of Molecular Models for Active Learning in Biochemistry Lecture Courses

ERIC Educational Resources Information Center

Hageman, James H.

2010-01-01

The pedagogical value of having biochemistry and organic chemistry students build and manipulate physical models of chemical species is well established in the literature. Nevertheless, for the most part, the use of molecular models is generally limited to several laboratory exercises or to demonstrations in the classroom setting. A simple…

Ten Steps to a Learning Organization.

ERIC Educational Resources Information Center

Kline, Peter; Saunders, Bernard

This guide provides a 10-step process for building a learning organization. It shows any organization how to develop and sustain an environment favorable to learning at every level, to reawaken and stimulate the power of learning in all members of the organization, and to harness the new learning that was generated to produce the maximum benefit…

A Survey of Computer Science Capstone Course Literature

ERIC Educational Resources Information Center

Dugan, Robert F., Jr.

2011-01-01

In this article, we surveyed literature related to undergraduate computer science capstone courses. The survey was organized around course and project issues. Course issues included: course models, learning theories, course goals, course topics, student evaluation, and course evaluation. Project issues included: software process models, software…

The brain as a system of nested but partially overlapping networks. Heuristic relevance of the model for brain physiology and pathology.

PubMed

Agnati, L F; Guidolin, D; Fuxe, K

2007-01-01

A new model of the brain organization is proposed. The model is based on the assumption that a global molecular network enmeshes the entire central nervous system. Thus, brain extra-cellular and intra-cellular molecular networks are proposed to communicate at the level of special plasma membrane regions (e.g., the lipid rafts) where horizontal molecular networks can represent input/output regions allowing the cell to have informational exchanges with the extracellular environment. Furthermore, some "pervasive signals" such as field potentials, pressure waves and thermal gradients that affect large parts of the brain cellular and molecular networks are discussed. Finally, at least two learning paradigms are analyzed taking into account the possible role of Volume Transmission: the so-called model of "temporal difference learning" and the "Turing B-unorganised machine". The relevance of this new view of brain organization for a deeper understanding of some neurophysiological and neuropathological aspects of its function is briefly discussed.

Distribution of Intelligence in Airborne Air-Defense Mission Systems

DTIC Science & Technology

2001-03-01

their ,,creator" has given them a structure - not only a program - allowing them to organize themselves, to learn and to adapt themselves to changing...self- organization capability. They are modelled on the structures of the unconscious mind. "• By contrast, fuzzy logic/fuzzy control has developed an...of these techniques as indicated in Fig. 3 is of particular importance for achieving unprecedented levels of self- organization capability and

Implicit Learning of Recursive Context-Free Grammars

PubMed Central

Rohrmeier, Martin; Fu, Qiufang; Dienes, Zoltan

2012-01-01

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning. PMID:23094021

Developing Conceptual Understanding and Procedural Fluency for Junior High School Students through Model-Facilitated Learning (MFL)

ERIC Educational Resources Information Center

Laswadi; Kusumah, Yaya S.; Darwis, Sutawanir; Afgani, Jarnawi D.

2016-01-01

Conceptual understanding (CU) and procedural fluency (PF) are two important mathematical competencies required by students. CU helps students organizing their knowledge into a coherent whole, and PF helps them to find the right solution of a problem. In order to enhance CU and PF, students need learning experiences in constructing knowledge and…

A Neurocomputational Account of Taxonomic Responding and Fast Mapping in Early Word Learning

ERIC Educational Resources Information Center

Mayor, Julien; Plunkett, Kim

2010-01-01

We present a neurocomputational model with self-organizing maps that accounts for the emergence of taxonomic responding and fast mapping in early word learning, as well as a rapid increase in the rate of acquisition of words observed in late infancy. The quality and efficiency of generalization of word-object associations is directly related to…

The remains of the dam: what have we learned from 15 years of US dam removals?

Treesearch

Gordon E. Grant; Sarah L. Lewis

2015-01-01

Important goals for studying dam removal are to learn how rivers respond to large and rapid introductions of sediment, and to develop predictive models to guide future dam removals. Achieving these goals requires organizing case histories systematically so that underlying physical mechanisms determining rates and styles of sediment erosion, transport, and deposition...

Part I: Reverse-docking studies of a squaramide-catalyzed conjugate addition of a diketone to a nitro-olefin Part II: The development of ChemSort: an education game for organic chemistry

NASA Astrophysics Data System (ADS)

Granger, Jenna Christine

Part 1: Reverse-docking studies of a squaramide-catalyzed conjugate addition of a diketone to a nitro-olefin. Asymmetric organocatalysis, the catalysis of asymmetric reactions by small organic molecules, is a rapidly growing field within organic synthesis. The ability to rationally design organocatalysts is therefore of increasing interest to organic chemists. Computational chemistry is quickly proving to be an extremely successful method for understanding and predicting the roles of organocatalysts, and therefore is certain to be of use in the rational design of such catalysts. A methodology for reverse-docking flexible organocatalysts to rigid transition state models of asymmetric reactions has been previously developed by the Deslongchamps group. The investigation of Rawal's squaramide-based organocatalyst for the addition of a diketone to a nitro-olefin is described, and the results of the reverse docking of Rawal's catalyst to the geometry optimized transition state models of the uncatalyzed reaction for both the R and S-product enantiomers are presented. The results of this study indicate a preference for binding of the organocatalyst to the R-enantiomer transition state model with a predicted enantiomeric excess of 99%, which is consistent with the experimental results. A plausible geometric model of the transition state for the catalyzed reaction is also presented. The success of this study demonstrates the credibility of using reverse docking methods for the rational design of asymmetric organocatalysts. Part 2: The development of ChemSort: an educational game for organic chemistry. With the advent of the millennial learner, we need to rethink traditional classroom approaches to science learning in terms of goals, approaches, and assessments. Digital simulations and games hold much promise in support of this educational shift. Although the idea of using games for education is not a new one, well-designed computer-based "serious games" are only beginning to emerge as exceptional tools for helping learners understand concepts and processes. The use of computer games for learning college-level organic chemistry is still relatively unexplored and underrepresented within the realm of "serious gaming". In this section, ideas for games as a way for teaching and learning organic chemistry will be introduced and the development process of ChemSort, a web-based Flash game for learning college-level organic chemistry topics, will be outlined. ChemSort is a path-based game, in which the player, or in this case the learner, must match the chemical structures with their appropriate functional groups. At the end of this section a 4-level useable prototype of ChemSort will be unveiled.

Integrating research, clinical care, and education in academic health science centers.

PubMed

King, Gillian; Thomson, Nicole; Rothstein, Mitchell; Kingsnorth, Shauna; Parker, Kathryn

2016-10-10

Purpose One of the major issues faced by academic health science centers (AHSCs) is the need for mechanisms to foster the integration of research, clinical, and educational activities to achieve the vision of evidence-informed decision making (EIDM) and optimal client care. The paper aims to discuss this issue. Design/methodology/approach This paper synthesizes literature on organizational learning and collaboration, evidence-informed organizational decision making, and learning-based organizations to derive insights concerning the nature of effective workplace learning in AHSCs. Findings An evidence-informed model of collaborative workplace learning is proposed to aid the alignment of research, clinical, and educational functions in AHSCs. The model articulates relationships among AHSC academic functions and sub-functions, cross-functional activities, and collaborative learning processes, emphasizing the importance of cross-functional activities in enhancing collaborative learning processes and optimizing EIDM and client care. Cross-functional activities involving clinicians, researchers, and educators are hypothesized to be a primary vehicle for integration, supported by a learning-oriented workplace culture. These activities are distinct from interprofessional teams, which are clinical in nature. Four collaborative learning processes are specified that are enhanced in cross-functional activities or teamwork: co-constructing meaning, co-learning, co-producing knowledge, and co-using knowledge. Practical implications The model provides an aspirational vision and insight into the importance of cross-functional activities in enhancing workplace learning. The paper discusses the conceptual and empirical basis to the model, its contributions and limitations, and implications for AHSCs. Originality/value The model's potential utility for health care is discussed, with implications for organizational culture and the promotion of cross-functional activities.

Gradient descent learning algorithm overview: a general dynamical systems perspective.

PubMed

Baldi, P

1995-01-01

Gives a unified treatment of gradient descent learning algorithms for neural networks using a general framework of dynamical systems. This general approach organizes and simplifies all the known algorithms and results which have been originally derived for different problems (fixed point/trajectory learning), for different models (discrete/continuous), for different architectures (forward/recurrent), and using different techniques (backpropagation, variational calculus, adjoint methods, etc.). The general approach can also be applied to derive new algorithms. The author then briefly examines some of the complexity issues and limitations intrinsic to gradient descent learning. Throughout the paper, the author focuses on the problem of trajectory learning.

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.

E-service learning: A pedagogic innovation for healthcare management education.

PubMed

Malvey, Donna M; Hamby, Eileen F; Fottler, Myron D

2006-01-01

This paper proposes an innovation in service learning that we identify as e-service learning. By adding the "e" to service learning, we create a service learning model that is dynamic, mediated by technology, and delivered online. This paper begins by examining service learning, which is a distinct learning concept. Service learning furnishes students with opportunities for applied learning through participation in projects and activities in community organizations. The authors then define and conceptualize e-service learning, including the anticipated outcomes of implementation such as enhanced access, quality, and cost effectiveness of healthcare management education. Because e-service learning is mediated by technology, we identify state of the art technologies that support e-service learning activities. In addition, possible e-service learning projects and activities that may be included in healthcare management courses such as finance, human resources, quality, service management/marketing and strategy are identified. Finally, opportunities for future research are suggested.

Chaos, Complexity, Learning, and the Learning Organization: Towards a Chaordic Enterprise

ERIC Educational Resources Information Center

van Eijnatten, Frans M.; Putnik, Goran D.

2004-01-01

In order to set the stage for this special issue, the prime concepts are defined: i.e. "chaos," "complexity," "learning" (individual and organizational), "learning organization," and "chaordic enterprise". Also, several chaos-and-complexity-related definitions of learning and learning organizations are provided. Next, the guest editors' main…

Learning Organisations--Reengineering Schools for Life Long Learning.

ERIC Educational Resources Information Center

O'Sullivan, Fergus

1997-01-01

Examines some key ideas behind the learning organization and explains why the concept is so powerful in contemporary contexts. Identifies various types of learning organizations, and suggests an analytical technique for relating styles of organizational learning to the environmental context. The key to becoming a learning organization is…

Teaching organization theory for healthcare management: three applied learning methods.

PubMed

Olden, Peter C

2006-01-01

Organization theory (OT) provides a way of seeing, describing, analyzing, understanding, and improving organizations based on patterns of organizational design and behavior (Daft 2004). It gives managers models, principles, and methods with which to diagnose and fix organization structure, design, and process problems. Health care organizations (HCOs) face serious problems such as fatal medical errors, harmful treatment delays, misuse of scarce nurses, costly inefficiency, and service failures. Some of health care managers' most critical work involves designing and structuring their organizations so their missions, visions, and goals can be achieved-and in some cases so their organizations can survive. Thus, it is imperative that graduate healthcare management programs develop effective approaches for teaching OT to students who will manage HCOs. Guided by principles of education, three applied teaching/learning activities/assignments were created to teach OT in a graduate healthcare management program. These educationalmethods develop students' competency with OT applied to HCOs. The teaching techniques in this article may be useful to faculty teaching graduate courses in organization theory and related subjects such as leadership, quality, and operation management.

Developing PFC representations using reinforcement learning.

PubMed

Reynolds, Jeremy R; O'Reilly, Randall C

2009-12-01

From both functional and biological considerations, it is widely believed that action production, planning, and goal-oriented behaviors supported by the frontal cortex are organized hierarchically [Fuster (1991); Koechlin, E., Ody, C., & Kouneiher, F. (2003). Neuroscience: The architecture of cognitive control in the human prefrontal cortex. Science, 424, 1181-1184; Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York: Holt]. However, the nature of the different levels of the hierarchy remains unclear, and little attention has been paid to the origins of such a hierarchy. We address these issues through biologically-inspired computational models that develop representations through reinforcement learning. We explore several different factors in these models that might plausibly give rise to a hierarchical organization of representations within the PFC, including an initial connectivity hierarchy within PFC, a hierarchical set of connections between PFC and subcortical structures controlling it, and differential synaptic plasticity schedules. Simulation results indicate that architectural constraints contribute to the segregation of different types of representations, and that this segregation facilitates learning. These findings are consistent with the idea that there is a functional hierarchy in PFC, as captured in our earlier computational models of PFC function and a growing body of empirical data.

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

PubMed Central

Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

2011-01-01

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

Diversity's Impact on the Executive Coaching Process

ERIC Educational Resources Information Center

Maltbia, Terrence E.; Power, Anne

2005-01-01

This paper presents a conceptual model intended to expand existing executive coaching processes used in organizations by building the strategic learning capabilities needed to integrate a diversity perspective into this emerging field of HRD practice. This model represents the early development of results from a Diversity Practitioner Study…

ITI: The Model. Integrated Thematic Instruction. Third Edition.

ERIC Educational Resources Information Center

Kovalik, Susan; Olsen, Karen

This book presents Integrated Thematic Instruction (ITI), a model for implementing a "brain-compatible" learning environment for students and teachers using a year-long theme to organize curriculum content and skills. The book's introduction identifies six "mismemes" (or mistaken ideas) that have hindered educational reform,…

Predictive Modeling in Adult Education

ERIC Educational Resources Information Center

Lindner, Charles L.

2011-01-01

The current economic crisis, a growing workforce, the increasing lifespan of workers, and demanding, complex jobs have made organizations highly selective in employee recruitment and retention. It is therefore important, to the adult educator, to develop models of learning that better prepare adult learners for the workplace. The purpose of…

Teaching RFID Information Systems Security

ERIC Educational Resources Information Center

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

2014-01-01

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

The Semantic Learning Organization

ERIC Educational Resources Information Center

Sicilia, Miguel-Angel; Lytras, Miltiadis D.

2005-01-01

Purpose: The aim of this paper is introducing the concept of a "semantic learning organization" (SLO) as an extension of the concept of "learning organization" in the technological domain. Design/methodology/approach: The paper takes existing definitions and conceptualizations of both learning organizations and Semantic Web technology to develop…

Developing seventh grade students' systems thinking skills in the context of the human circulatory system.

PubMed

Raved, Lena; Yarden, Anat

2014-01-01

Developing systems thinking skills in school can provide useful tools to deal with a vast amount of medical and health information that may help learners in decision making in their future lives as citizen. Thus, there is a need to develop effective tools that will allow learners to analyze biological systems and organize their knowledge. Here, we examine junior high school students' systems thinking skills in the context of the human circulatory system. A model was formulated for developing teaching and learning materials and for characterizing students' systems thinking skills. Specifically, we asked whether seventh grade students, who studied about the human circulatory system, acquired systems thinking skills, and what are the characteristics of those skills? Concept maps were used to characterize students' systems thinking components and examine possible changes in the students' knowledge structure. These maps were composed by the students before and following the learning process. The study findings indicate a significant improvement in the students' ability to recognize the system components and the processes that occur within the system, as well as the relationships between different levels of organization of the system, following the learning process. Thus, following learning students were able to organize the systems' components and its processes within a framework of relationships, namely the students' systems thinking skills were improved in the course of learning using the teaching and learning materials.

Developing Seventh Grade Students’ Systems Thinking Skills in the Context of the Human Circulatory System

PubMed Central

Raved, Lena; Yarden, Anat

2014-01-01

Developing systems thinking skills in school can provide useful tools to deal with a vast amount of medical and health information that may help learners in decision making in their future lives as citizen. Thus, there is a need to develop effective tools that will allow learners to analyze biological systems and organize their knowledge. Here, we examine junior high school students’ systems thinking skills in the context of the human circulatory system. A model was formulated for developing teaching and learning materials and for characterizing students’ systems thinking skills. Specifically, we asked whether seventh grade students, who studied about the human circulatory system, acquired systems thinking skills, and what are the characteristics of those skills? Concept maps were used to characterize students’ systems thinking components and examine possible changes in the students’ knowledge structure. These maps were composed by the students before and following the learning process. The study findings indicate a significant improvement in the students’ ability to recognize the system components and the processes that occur within the system, as well as the relationships between different levels of organization of the system, following the learning process. Thus, following learning students were able to organize the systems’ components and its processes within a framework of relationships, namely the students’ systems thinking skills were improved in the course of learning using the teaching and learning materials. PMID:25520948

[Learning how to learn for specialist further education].

PubMed

Breuer, G; Lütcke, B; St Pierre, M; Hüttl, S

2017-02-01

The world of medicine is becoming from year to year more complex. This necessitates efficient learning processes, which incorporate the principles of adult education but with unchanged periods of further education. The subject matter must be processed, organized, visualized, networked and comprehended. The learning process should be voluntary and self-driven with the aim of learning the profession and becoming an expert in a specialist field. Learning is an individual process. Despite this, the constantly cited learning styles are nowadays more controversial. An important factor is a healthy mixture of blended learning methods, which also use new technical possibilities. These include a multitude of e‑learning options and simulations, which partly enable situative learning in a "shielded" environment. An exemplary role model of the teacher and feedback for the person in training also remain core and sustainable aspects in medical further education.

Learning molecular energies using localized graph kernels.

PubMed

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-21

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Learning molecular energies using localized graph kernels

NASA Astrophysics Data System (ADS)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

Prespeech motor learning in a neural network using reinforcement.

PubMed

Warlaumont, Anne S; Westermann, Gert; Buder, Eugene H; Oller, D Kimbrough

2013-02-01

Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one's language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the different conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post-learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's post-learning productions were more likely to resemble the English vowels and vice versa. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Behavior-Based Circuit Model of How Outcome Expectations Organize Learned Behavior in Larval "Drosophila"

ERIC Educational Resources Information Center

Schleyer, Michael; Saumweber, Timo; Nahrendorf, Wiebke; Fischer, Benjamin; von Alpen, Desiree; Pauls, Dennis; Thum, Andreas; Gerber, Bertram

2011-01-01

Drosophila larvae combine a numerically simple brain, a correspondingly moderate behavioral complexity, and the availability of a rich toolbox for transgenic manipulation. This makes them attractive as a study case when trying to achieve a circuit-level understanding of behavior organization. From a series of behavioral experiments, we suggest a…

Designing an Online Course Content Structure Using a Design Patterns Approach

ERIC Educational Resources Information Center

Hathaway, Dawn; Norton, Priscilla

2013-01-01

Despite the central role that well organized and structured course content plays in engaging online learners with content, the authors point to the absence of guidelines for organizing content in ways that meet course learning goals. Recognizing the need for a design solution and, perhaps, the need for a new design model, "design…

A New Higher Education Curriculum in Organic Chemistry: What Questions Should Be Asked?

ERIC Educational Resources Information Center

Lafarge, David L.; Morge, Ludovic M.; Méheut, Martine M.

2014-01-01

Organic chemistry is often considered to be a difficult subject to teach and to learn, particularly as students prefer to resort to memorization alone rather than reasoning using models from chemical reactivity. Existing studies have led us to suggest principles for redefining the curriculum, ranging from its overall structure to the tasks given…

Students' Understandings of Acid Strength: How Meaningful Is Reliability When Measuring Alternative Conceptions

ERIC Educational Resources Information Center

Bretz, Stacey Lowery; McClary, LaKeisha

2015-01-01

Most organic chemistry reactions occur by a mechanism that includes acid-base chemistry, so it is important that students develop and learn to use correct conceptions of acids and acid strength. Recent studies have described undergraduate organic chemistry students' cognitive resources related to the Brønsted-Lowry acid model and the Lewis acid…

A Dozen Heads Are Better than One: Collaborative Writing in Genre-Based Pedagogy

ERIC Educational Resources Information Center

Caplan, Nigel A.; Farling, Monica

2017-01-01

Organizing writing instruction around genres rather than rhetorical modes can be a highly effective and engaging preparation for students' academic and professional writing needs. The teaching/learning cycle (TLC) is a highly scaffolded curriculum model for teaching target written genres. In the TLC, the organization of and linguistic choices in a…

ClearTK 2.0: Design Patterns for Machine Learning in UIMA

PubMed Central

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework. PMID:29104966

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

PubMed

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

Characteristics of health care organizations associated with learning and development: lessons from a pilot study.

PubMed

Nyström, Monica

2009-01-01

Characteristics of health care organizations associated with an ability to learn from experiences and to develop and manage change were explored in this study. Understanding of these characteristics is necessary to identify factors influencing success in learning from the past and achieving future health care quality objectives. A literature review of the quality improvement, strategic organizational development and change management, organizational learning, and microsystems fields identified 20 organizational characteristics, grouped under (a) organizational systems, (b) key actors, and (c) change management processes. Qualitative methods, using interviews, focus group reports, and archival records, were applied to find associations between identified characteristics and 6 Swedish health care units externally evaluated as delivering high-quality care. Strong support for a characteristic was defined as units having more than 4 sources describing the characteristic as an important success factor. Eighteen characteristics had strong support from at least 2 units. The strongest evidence was found for the following: (i) key actors have long-term commitment, provide support, and make sense of ambiguous situations; (ii) organizational systems encourage employee commitment, participation, and involvement; and (iii) change management processes are employed systematically. Based on the results, a new model of "characteristics associated with learning and development in health care organizations" is proposed.

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.

Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants.

PubMed

Walters, D M; Stringer, S M

2010-07-01

A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.

Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies.

PubMed

Oudeyer, P-Y; Gottlieb, J; Lopes, M

2016-01-01

This chapter studies the bidirectional causal interactions between curiosity and learning and discusses how understanding these interactions can be leveraged in educational technology applications. First, we review recent results showing how state curiosity, and more generally the experience of novelty and surprise, can enhance learning and memory retention. Then, we discuss how psychology and neuroscience have conceptualized curiosity and intrinsic motivation, studying how the brain can be intrinsically rewarded by novelty, complexity, or other measures of information. We explain how the framework of computational reinforcement learning can be used to model such mechanisms of curiosity. Then, we discuss the learning progress (LP) hypothesis, which posits a positive feedback loop between curiosity and learning. We outline experiments with robots that show how LP-driven attention and exploration can self-organize a developmental learning curriculum scaffolding efficient acquisition of multiple skills/tasks. Finally, we discuss recent work exploiting these conceptual and computational models in educational technologies, showing in particular how intelligent tutoring systems can be designed to foster curiosity and learning. © 2016 Elsevier B.V. All rights reserved.

Making of a Corporate University Model: Transition from Traditional Training to Learning Management System

ERIC Educational Resources Information Center

Ilyas, Mohammed

2017-01-01

Today organizations have adopted a corporate university model to meet their training requirements, a model that adds value to the business in terms of revenue and profit, improvement in customer retention, improved employee productivity, cost reduction and retention of talented employees. This paper highlights the radical change and an evolution…

Model Manipulation and Learning: Fostering Representational Competence with Virtual and Concrete Models

ERIC Educational Resources Information Center

Stull, Andrew T.; Hegarty, Mary

2016-01-01

This study investigated the development of representational competence among organic chemistry students by using 3D (concrete and virtual) models as aids for teaching students to translate between multiple 2D diagrams. In 2 experiments, students translated between different diagrams of molecules and received verbal feedback in 1 of the following 3…

The Learning Circle: A New Model of BSW Education for Alberta's Rural, Remote, and Aboriginal Communities.

ERIC Educational Resources Information Center

Zapf, M. K.; Bastien, B.; Bodor, R.; Carriere, J.; Pelech, W.

In 1998, a consortium including the University of Calgary (Alberta) and representatives from social service agencies and Native organizations developed a Bachelor of Social Work (BSW) model for delivery in rural, remote, and Aboriginal communities. The model called for innovative course content that was culturally and geographically relevant to…

Anatomical Knowledge Gain through a Clay-Modeling Exercise Compared to Live and Video Observations

ERIC Educational Resources Information Center

Kooloos, Jan G. M.; Schepens-Franke, Annelieke N.; Bergman, Esther M.; Donders, Rogier A. R. T.; Vorstenbosch, Marc A. T. M.

2014-01-01

Clay modeling is increasingly used as a teaching method other than dissection. The haptic experience during clay modeling is supposed to correspond to the learning effect of manipulations during exercises in the dissection room involving tissues and organs. We questioned this assumption in two pretest-post-test experiments. In these experiments,…

Answering Big Questions through Self-Organized Learning

ERIC Educational Resources Information Center

Rix, Sally

2017-01-01

Self-organized learning was developed by Professor Sugata Mitra of Newcastle University on the foundation of his firm belief that learning will emerge spontaneously when children are encouraged to be curious and are allowed to self-organize. There are seven dedicated Self-Organized Learning Environments (SOLEs) that Mitra established in…

Existential and Phenomenological Foundations of Currere: Self-Report in Curriculum Inquiry.

ERIC Educational Resources Information Center

Grumet, Madeleine R.

Traditional models for curriculum theory describe human development as a sum of its parts, organized in a hierarchy leading to operational competencies. The reconceptualist Currere model, originated by William Penar, develops horizontally with energy and learning moving outward and inward rather than upward in a linear trajectory. Educational…

Modeling-Mainstreaming: A Teacher Training Proposal.

ERIC Educational Resources Information Center

Bireley, Marlene; Mahan, Virginia

This document presents a learning model for training teachers to effectively deal with physically handicapped and mildly retarded children in their regular classroom. The modules are organized in the following fashion: Phase One; Development of an awareness of the concept of mainstreaming, of labels and their consequences, and of the psychological…

Positive Management Education: Creating Creative Minds, Passionate Hearts, and Kindred Spirits

ERIC Educational Resources Information Center

Karakas, Fahri

2011-01-01

The goal of this article is to explore positive management education, a practice-based teaching and learning model centered on positive organizational scholarship. Six signs of transformation in organizations are presented: complexity, community, creativity, spirituality, flexibility, and positivity. A model for positive management education is…

Nematodes: Model Organisms in High School Biology

ERIC Educational Resources Information Center

Bliss, TJ; Anderson, Margery; Dillman, Adler; Yourick, Debra; Jett, Marti; Adams, Byron J.; Russell, RevaBeth

2007-01-01

In a collaborative effort between university researchers and high school science teachers, an inquiry-based laboratory module was designed using two species of insecticidal nematodes to help students apply scientific inquiry and elements of thoughtful experimental design. The learning experience and model are described in this article. (Contains 4…

Strategic by Design: Iterative Approaches to Educational Planning

ERIC Educational Resources Information Center

Chance, Shannon

2010-01-01

Linear planning and decision-making models assume a level of predictability that is uncommon today. Such models inadequately address the complex variables found in higher education. When academic organizations adopt paired-down business strategies, they restrict their own vision. They fail to harness emerging opportunities or learn from their own…

Ground Water in a Fish Tank.

ERIC Educational Resources Information Center

Mayshark, Robin K.

1992-01-01

Describes creating a Model Aquatic/Terrestrial Ecosystem for use in helping students understand how water moves beneath the ground's surface. The model is constructed from a fish tank using rocks, soil, gravel, clay, and organic materials. Author describes possible cooperative-learning and problem-solving activities that can be done with this…

Learning by Doing versus Learning by Viewing: Three Experimental Comparisons of Learner-Generated versus Author-Provided Graphic Organizers

ERIC Educational Resources Information Center

Stull, Andrew T.; Mayer, Richard E.

2007-01-01

Do students learn more deeply from a passage when they attempt to construct their own graphic organizers (i.e., learning by doing) than when graphic organizers are provided (i.e., learning by viewing)? In 3 experiments, learners were tested on retention and transfer after reading a passage with author-provided graphic organizers or when asked to…

Communication and Control in Organizations: Applying the Work of James Thompson and Gregory Bateson to Interpretive Research.

DTIC Science & Technology

1985-07-01

learning ’, ’adaptations’, ’process’, and ’ abstraction ...hierarchy. Rather, he argues through a combina- tion of cybernetic epistemology, learning theory, and cognitive models, that patterned collective...systemic ability to perceive and respond to pattern and variance in the ’environment’. Each level of abstraction can be considered a ’meta’

Optimizing Learning. Proceedings of the Annual Society for the Advancement of Gifted Education Conference (6th, Edmonton, Alberta, Canada, September 29-30, 1995).

ERIC Educational Resources Information Center

Society for the Advancement of Gifted Education, Calgary (Alberta).

This conference proceedings focuses on structuring classrooms to optimize learning among Alberta (Canada) gifted students. The first paper, "Optimizing Parent Potential" (Trudy A. Harrold), describes a model and a process for helping parents acquire knowledge, organize their thinking, and act from a realistic base when dealing with their gifted…

The Quality Adaptation Model: Adaptation and Adoption of the Quality Standard ISO/IEC 19796-1 for Learning, Education, and Training

ERIC Educational Resources Information Center

Pawlowski, Jan M.

2007-01-01

In 2005, the new quality standard for learning, education, and training, ISO/IEC 19796-1, was published. Its purpose is to help educational organizations to develop quality systems and to improve the quality of their processes, products, and services. In this article, the standard is presented and compared to existing approaches, showing the…

Developing a Research Method for Testing New Curriculum Ideas: Report No. 1 on Using Marketing Research in a Lifelong Learning Organization.

ERIC Educational Resources Information Center

Haskins, Jack B.

A survey method was developed and used to determine interest in new course topics at the Learning Institute for Elders (LIFE) at the University of Central Florida. In the absence of a known validated method for course concept testing, this approach was modeled after the "message pretesting," or formative, research used and validated in…

Self-regulatory Behaviors and Approaches to Learning of Arts Students: A Comparison Between Professional Training and English Learning.

PubMed

Tseng, Min-Chen; Chen, Chia-Cheng

2017-06-01

This study investigated the self-regulatory behaviors of arts students, namely memory strategy, goal-setting, self-evaluation, seeking assistance, environmental structuring, learning responsibility, and planning and organizing. We also explored approaches to learning, including deep approach (DA) and surface approach (SA), in a comparison between students' professional training and English learning. The participants consisted of 344 arts majors. The Academic Self-Regulation Questionnaire and the Revised Learning Process Questionnaire were adopted to examine students' self-regulatory behaviors and their approaches to learning. The results show that a positive and significant correlation was found in students' self-regulatory behaviors between professional training and English learning. The results indicated that increases in using self-regulatory behaviors in professional training were associated with increases in applying self-regulatory behaviors in learning English. Seeking assistance, self-evaluation, and planning and organizing were significant predictors for learning English. In addition, arts students used the deep approach more often than the surface approach in both their professional training and English learning. A positive correlation was found in DA, whereas a negative correlation was shown in SA between students' self-regulatory behaviors and their approaches to learning. Students with high self-regulation adopted a deep approach, and they applied the surface approach less in professional training and English learning. In addition, a SEM model confirmed that DA had a positive influence; however, SA had a negative influence on self-regulatory behaviors.

Twelve tips for implementing whole-task curricula: how to make it work.

PubMed

Dolmans, Diana H J M; Wolfhagen, Ineke H A P; Van Merriënboer, Jeroen J G

2013-10-01

Whole-task models of learning and instructional design, such as problem-based learning, are nowadays very popular. Schools regularly encounter large problems when they implement whole-task curricula. The main aim of this article is to provide 12 tips that may help to make the implementation of a whole-task curriculum successful. Implementing whole-task curricula fails when the implementation is not well prepared. Requirements that must be met to make the implementation of whole task models into a success are described as twelve tips. The tips are organized in four clusters and refer to (1) the infrastructure, (2) the teachers, (3) the students, and (4) the management of the educational organization. Finally, the presented framework will be critically discussed and the importance of shared values and a change of culture is emphasized.

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training

PubMed Central

Kutafina, Ekaterina; Laukamp, David; Bettermann, Ralf; Schroeder, Ulrik; Jonas, Stephan M.

2016-01-01

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user’s hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98.30% (±1.26%) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills. PMID:27527167

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training.

PubMed

Kutafina, Ekaterina; Laukamp, David; Bettermann, Ralf; Schroeder, Ulrik; Jonas, Stephan M

2016-08-03

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user's hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills.

Creating a Learning Organization: A Case Study of Outcomes and Lessons Learned.

ERIC Educational Resources Information Center

Bierema, Laura L.; Berdish, David M.

1999-01-01

Discusses how organizations are gaining a competitive edge in a global business environment through learning and highlights a learning organization implementation case study of a division of Ford Motor Company. Examines the strategic initiative; performance improvement results; individual learning, including interpersonal development and…

From Doing to Learning: Changed focus during a pre-school learning study project on organic decomposition

NASA Astrophysics Data System (ADS)

Ljung-Djärf, Agneta; Magnusson, Andreas; Peterson, Sam

2014-03-01

We explored the use of the learning study (LS) model in developing Swedish pre-school science learning. This was done by analysing a 3-cycle LS project implemented to help a group of pre-school teachers (n = 5) understand their science educational practice, by collaboratively and systematically challenging it. Data consisted of video recordings of 1 screening (n = 7), 1 initial planning meeting, 3 analysis meetings, 3 interventions, and 78 individual test interviews with the children (n = 26). The study demonstrated that the teachers were initially uncomfortable with using scientific concepts and with maintaining the children's focus on the object of learning without framing it with play. During the project, we noted a shift in focus towards the object of learning and how to get the children to discern it. As teachers' awareness changed, enhanced learning was noted among the children. The study suggests that the LS model can promote pre-school science learning as follows: by building on, re-evaluating, and expanding children's experiences; and by helping the teachers focus on and contrast critical aspects of an object of learning, and to reflect on the use of play, imagination, and concepts and on directing the children's focus when doing so. Our research showed that the LS model holds promise to advance pre-school science learning by offering a theoretical tool useable to shift the focus from doing to learning while teaching science using learning activities.

A university and health care organization partnership to prepare nurses for evidence-based practice.

PubMed

Missal, Bernita; Schafer, Beth Kaiser; Halm, Margo A; Schaffer, Marjorie A

2010-08-01

This article describes a partnership model between a university and health care organizations for teaching graduate nursing research from a framework of evidence-based practice. Nurses from health care organizations identified topics for graduate students to search the literature and synthesize evidence for guiding nursing practice. Nurse educators mentored graduate students in conducting critical appraisals of the literature. Students learned how to search for the evidence, summarize the existing research findings, and translate the findings into practice recommendations. Through presenting and discussing their findings with key stakeholders, students learned how nurses planned to integrate the evidence into practice. Nurses used the evidence-based results to improve their practice in the two partner hospitals. The partnership stimulated action for further inquiry into best practices.

Software development: A paradigm for the future

NASA Technical Reports Server (NTRS)

Basili, Victor R.

1989-01-01

A new paradigm for software development that treats software development as an experimental activity is presented. It provides built-in mechanisms for learning how to develop software better and reusing previous experience in the forms of knowledge, processes, and products. It uses models and measures to aid in the tasks of characterization, evaluation and motivation. An organization scheme is proposed for separating the project-specific focus from the organization's learning and reuse focuses of software development. The implications of this approach for corporations, research and education are discussed and some research activities currently underway at the University of Maryland that support this approach are presented.

The Application of Learning Organization to Enhance Learning in Singapore Schools

ERIC Educational Resources Information Center

Retna, Kala S.; Ng, Pak Tee

2016-01-01

The rise of interest in the learning organization (LO) concept attests to the value of learning by individuals and organizations for continuous improvement and adaptability to the ever-changing environment. Although the LO concept originated from business contexts, it was subsequently extended to educational organizations, particularly to schools.…

Assessing the Learning Organization. Symposium 10. [Concurrent Symposium Session at AHRD Annual Conference, 2000.

ERIC Educational Resources Information Center

2000

This packet contains three papers from a symposium on assessing the learning organization. The first paper, "Relationship between Learning Organization Strategies and Performance Driver Outcomes" (Elwood F. Holton III, Sandra M. Kaiser), reports on a study of a new learning organization assessment instrument that was administered to 440…

Learning Gaps in a Learning Organization: Professionals' Values versus Management Values

ERIC Educational Resources Information Center

Parding, Karolina; Abrahamsson, Lena

2010-01-01

Purpose: The aim of this article is to challenge the concept of "the learning organization" as unproblematic and inherently good. Design/methodology/approach: The research looked at how teachers--as an example of public sector professionals in a work organization that claims to be a learning organization--view their conditions for…

Thermal Model Development for Ares I-X

NASA Technical Reports Server (NTRS)

Amundsen, Ruth M.; DelCorso, Joe

2008-01-01

Thermal analysis for the Ares I-X vehicle has involved extensive thermal model integration, since thermal models of vehicle elements came from several different NASA and industry organizations. Many valuable lessons were learned in terms of model integration and validation. Modeling practices such as submodel, analysis group and symbol naming were standardized to facilitate the later model integration. Upfront coordination of coordinate systems, timelines, units, symbols and case scenarios was very helpful in minimizing integration rework. A process for model integration was developed that included pre-integration runs and basic checks of both models, and a step-by-step process to efficiently integrate one model into another. Extensive use of model logic was used to create scenarios and timelines for avionics and air flow activation. Efficient methods of model restart between case scenarios were developed. Standardization of software version and even compiler version between organizations was found to be essential. An automated method for applying aeroheating to the full integrated vehicle model, including submodels developed by other organizations, was developed.

The impact of managers' perceptions of learning organizations on innovation in healthcare: sample of Turkey.

PubMed

Ugurluoglu, Ozgur; Ugurluoglu Aldogan, Ece; Dilmac, Elife

2013-01-01

Organizational learning is the process of increasing effective organizational activities through knowledge and understanding. Innovation is the creation of any product, service or process, which is new to a business unit. Significant amount of research on organizational learning place a central meaning on the fact that there is a positive relationship between organizational learning and innovation. Both organizational learning and innovation are essential for organizations to prepare for change. The aim of this study is to determine to what extent the identified learning organization dimensions are associated with innovation. The study used a quantitative non-experimental design employing statistical analysis via multiple regression and correlation methods to identify the relationships between the variables examined. Because the research was conducted in a non-experimental way, learning organization dimensions are referred to as predictor variables, and innovation is referred to as the criterion variable. Watkins and Marsick's Dimensions of the Learning Organization Questionnaire was used in the study. Questionnaires were distributed to 498 hospital managers and, 243 valid responses were used in this study. Therefore, 243 hospital managers working at 250 Ministry of Health (public) hospitals across Turkey participated in the study. Results demonstrate that there are significant and positive correlations between learning organization dimensions and innovation. Intercorrelations between learning organization dimensions and correlations between learning organization dimensions and innovation were average and high, respectively. Results further indicate that the dimensions of the learning organizations explained 66.5% of the variance for the innovation. Copyright © 2012 John Wiley & Sons, Ltd.

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

NASA Astrophysics Data System (ADS)

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

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

Learning-based stochastic object models for use in optimizing imaging systems

NASA Astrophysics Data System (ADS)

Dolly, Steven R.; Anastasio, Mark A.; Yu, Lifeng; Li, Hua

2017-03-01

It is widely known that the optimization of imaging systems based on objective, or task-based, measures of image quality via computer-simulation requires use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in anatomy within a specified ensemble of patients remains a challenging task. Because they are established by use of image data corresponding a single patient, previously reported numerical anatomical models lack of the ability to accurately model inter- patient variations in anatomy. In certain applications, however, databases of high-quality volumetric images are available that can facilitate this task. In this work, a novel and tractable methodology for learning a SOM from a set of volumetric training images is developed. The proposed method is based upon geometric attribute distribution (GAD) models, which characterize the inter-structural centroid variations and the intra-structural shape variations of each individual anatomical structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations learned from training data. By use of the GAD models, random organ shapes and positions can be generated and integrated to form an anatomical phantom. The randomness in organ shape and position will reflect the variability of anatomy present in the training data. To demonstrate the methodology, a SOM corresponding to the pelvis of an adult male was computed and a corresponding ensemble of phantoms was created. Additionally, computer-simulated X-ray projection images corresponding to the phantoms were computed, from which tomographic images were reconstructed.

Interdisciplinary Learning for Chemical Engineering Students from Organic Chemistry Synthesis Lab to Reactor Design to Separation

ERIC Educational Resources Information Center

Armstrong, Matt; Comitz, Richard L.; Biaglow, Andrew; Lachance, Russ; Sloop, Joseph

2008-01-01

A novel approach to the Chemical Engineering curriculum sequence of courses at West Point enabled our students to experience a much more realistic design process, which more closely replicated a real world scenario. Students conduct the synthesis in the organic chemistry lab, then conduct computer modeling of the reaction with ChemCad and…

Protecting and Promoting Indigenous Knowledge: Environmental Adult Education and Organic Agriculture

ERIC Educational Resources Information Center

Sumner, Jennifer

2008-01-01

Given today's pressing environmental issues, environmental adult educators can help us learn to live more sustainably. One of the models for more sustainable ways of life is organic agriculture, based in a knowledge system that works with nature, not against it. In order to understand this knowledge, we need to frame it in a way that captures all…

Neural Models of Spatial Orientation in Novel Environments

DTIC Science & Technology

1994-01-01

tool use, the problem of self-organizing body -centered spatial representations for movement planning and spatial orientation, and the problem of...meeting of the American Association for the Advancement of Science, Boston, February, 1993. 23. Grossberg, S., annual Linnaeus Lecture, Uppsala...Congress on Neural Networks entitled --A self-organizing neural network for learning a body -centered invariant representa- tion of 3-D target

Developing a Livebinder as Teaching Resource in Family & Consumer Sciences

ERIC Educational Resources Information Center

Miller, Cynthia L.

2015-01-01

The primary purpose of this paper is to explain how a digital tool, "LiveBinder," can be used for organizing online content and learning. The article explains why this digital tool should be utilized as a teaching resource and describes common uses. It also addresses how LiveBinders can be organized using shelves. A model LiveBinder of…

The polygonal model: A simple representation of biomolecules as a tool for teaching metabolism.

PubMed

Bonafe, Carlos Francisco Sampaio; Bispo, Jose Ailton Conceição; de Jesus, Marcelo Bispo

2018-01-01

Metabolism involves numerous reactions and organic compounds that the student must master to understand adequately the processes involved. Part of biochemical learning should include some knowledge of the structure of biomolecules, although the acquisition of such knowledge can be time-consuming and may require significant effort from the student. In this report, we describe the "polygonal model" as a new means of graphically representing biomolecules. This model is based on the use of geometric figures such as open triangles, squares, and circles to represent hydroxyl, carbonyl, and carboxyl groups, respectively. The usefulness of the polygonal model was assessed by undergraduate students in a classroom activity that consisted of "transforming" molecules from Fischer models to polygonal models and vice and versa. The survey was applied to 135 undergraduate Biology and Nursing students. Students found the model easy to use and we noted that it allowed identification of students' misconceptions in basic concepts of organic chemistry, such as in stereochemistry and organic groups that could then be corrected. The students considered the polygonal model easier and faster for representing molecules than Fischer representations, without loss of information. These findings indicate that the polygonal model can facilitate the teaching of metabolism when the structures of biomolecules are discussed. Overall, the polygonal model promoted contact with chemical structures, e.g. through drawing activities, and encouraged student-student dialog, thereby facilitating biochemical learning. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):66-75, 2018. © 2017 The International Union of Biochemistry and Molecular Biology.

CALM: Complex Adaptive System (CAS)-Based Decision Support for Enabling Organizational Change

NASA Astrophysics Data System (ADS)

Adler, Richard M.; Koehn, David J.

Guiding organizations through transformational changes such as restructuring or adopting new technologies is a daunting task. Such changes generate workforce uncertainty, fear, and resistance, reducing morale, focus and performance. Conventional project management techniques fail to mitigate these disruptive effects, because social and individual changes are non-mechanistic, organic phenomena. CALM (for Change, Adaptation, Learning Model) is an innovative decision support system for enabling change based on CAS principles. CALM provides a low risk method for validating and refining change strategies that combines scenario planning techniques with "what-if" behavioral simulation. In essence, CALM "test drives" change strategies before rolling them out, allowing organizations to practice and learn from virtual rather than actual mistakes. This paper describes the CALM modeling methodology, including our metrics for measuring organizational readiness to respond to change and other major CALM scenario elements: prospective change strategies; alternate futures; and key situational dynamics. We then describe CALM's simulation engine for projecting scenario outcomes and its associated analytics. CALM's simulator unifies diverse behavioral simulation paradigms including: adaptive agents; system dynamics; Monte Carlo; event- and process-based techniques. CALM's embodiment of CAS dynamics helps organizations reduce risk and improve confidence and consistency in critical strategies for enabling transformations.

Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

PubMed

Solouki, Saeed; Pooyan, Mohammad

2016-06-01

Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular structure of the cerebellum has been in the core of attention in theoretical and computational modeling. However, most of these models reflect some special features of the cerebellum without regarding the whole motor command computational process. In this paper, we try to make a logical relation between the most significant models of the cerebellum and introduce a new learning strategy to arrange the movement patterns: cerebellar modular arrangement and applying of movement patterns based on semi-supervised learning (CMAPS). We assume here the cerebellum like a big archive of patterns that has an efficient organization to classify and recall them. The main idea is to achieve an optimal use of memory locations by more than just a supervised learning and classification algorithm. Surely, more experimental and physiological researches are needed to confirm our hypothesis.

Learning Organization Profile of Educational Hospitals in Iran: Practice of Organizational Interlocking Systems.

PubMed

Mohebbifar, Rafat; Hashemi, Hassan Jahani; Rajaee, Roya; Najafi, Marziye; Etedal, Mahbobeh G H

2015-02-24

Organizational learning has been identified as necessary for different organizations to improve their performance in the changing and competitive environment. The main purpose of this research was to specify the learning organization profile of educational and health centers of Tehran and Qazvin Universities of Medical Sciences in Iran. The present research was conducted using a cross-sectional method in the academic year of 2013-2014. A staff of 530 from educational hospitals subordinated to Tehran and Qazvin universities of medical sciences participated in the research. The participants were selected using stratified random sampling. That is to say, a random sample of a proportionate size was selected from each hospital. The instrument for data collection was a Likert-scale questionnaire involving 50 items. The statistical techniques of ANOVA, t-test, Chi-square, correlation coefficients (Pearson and Spearman), and regression were utilized to analyze the data. All of them were performed using the Statistical Package for Social Sciences (SPSS) 16.0 for windows. the results indicated that 449 of participants (84.7%) had a B.S. degree and 78 of them (14.7%) had an M.S. or a Ph.D. degree. Among the fivefold dimensions of "Learning Organization" model (Learning, Organization, People, Knowledge, and Technology) in comparison of the two universities, the "people" dimension was the highest-rated dimension with the mean rating of 25.71±8.36 and the "learning" dimension was the lowest-rated dimension with the mean of 25.35±8.04. Comparison between the two universities yielded the result that educational hospitals in Tehran University of medical sciences with the rating of 126.56 had a more complete profile than that of educational hospitals in Qazvin university of medical sciences with the rating of 122.23. The hospitals of the two above-mentioned universities were, to a great extent, far from the characteristics of Learning Organization. In light of the massive mission of these centers to maintain and improve the community health and to train the skilled labor force, the centers should embark on updating the data and institutionalizing learning. Furthermore, to modify staff's behavior and performance and to achieve their goals, they should accentuate the importance of acquiring, creating, and transferring knowledge.

Adaptive functional systems: learning with chaos.

PubMed

Komarov, M A; Osipov, G V; Burtsev, M S

2010-12-01

We propose a new model of adaptive behavior that combines a winnerless competition principle and chaos to learn new functional systems. The model consists of a complex network of nonlinear dynamical elements producing sequences of goal-directed actions. Each element describes dynamics and activity of the functional system which is supposed to be a distributed set of interacting physiological elements such as nerve or muscle that cooperates to obtain certain goal at the level of the whole organism. During "normal" behavior, the dynamics of the system follows heteroclinic channels, but in the novel situation chaotic search is activated and a new channel leading to the target state is gradually created simulating the process of learning. The model was tested in single and multigoal environments and had demonstrated a good potential for generation of new adaptations. © 2010 American Institute of Physics.

Reinforcement learning of periodical gaits in locomotion robots

NASA Astrophysics Data System (ADS)

Svinin, Mikhail; Yamada, Kazuyaki; Ushio, S.; Ueda, Kanji

1999-08-01

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance- based reinforcement learning scheme, is used for sensory- motor control of an eight-legged mobile robot. Important feature of the classifier system is its ability to work with the continuous sensor space. The robot does not have a prior knowledge of the environment, its own internal model, and the goal coordinates. It is only assumed that the robot can acquire stable gaits by learning how to reach a light source. During the learning process the control system, is self-organized by reinforcement signals. Reaching the light source defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. Feasibility of the proposed self-organized system is tested under simulation and experiment. The control actions are specified at the leg level. It is shown that, as learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns.

Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data

PubMed Central

Perryman, Alexander L.; Stratton, Thomas P.; Ekins, Sean; Freundlich, Joel S.

2015-01-01

Purpose Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Methods Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). Results “Pruning” out the moderately unstable/moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 hour. Conclusions Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources. PMID:26415647

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.

PubMed

Perryman, Alexander L; Stratton, Thomas P; Ekins, Sean; Freundlich, Joel S

2016-02-01

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Creating an Innovative Learning Organization

ERIC Educational Resources Information Center

Salisbury, Mark

2010-01-01

This article describes how to create an innovative learning (iLearning) organization. It begins by discussing the life cycle of knowledge in an organization, followed by a description of the theoretical foundation for iLearning. Next, the article presents an example of iLearning, followed by a description of the distributed nature of work, the…

The Learning Organization: Theory into Practice.

ERIC Educational Resources Information Center

Otala, Matti

1995-01-01

Key elements of learning organizations are as follows: understanding strengths, weaknesses, threats, and opportunities; open-book management; streamlined processes; team spirit; lifelong learning and skill recycling; and removing anxiety. A learning organization consists of empowered, motivated people committed to improving continuously. (SK)

Universal effect of dynamical reinforcement learning mechanism in spatial evolutionary games

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Wu, Zhi-Xi; Wang, Bing-Hong

2012-06-01

One of the prototypical mechanisms in understanding the ubiquitous cooperation in social dilemma situations is the win-stay, lose-shift rule. In this work, a generalized win-stay, lose-shift learning model—a reinforcement learning model with dynamic aspiration level—is proposed to describe how humans adapt their social behaviors based on their social experiences. In the model, the players incorporate the information of the outcomes in previous rounds with time-dependent aspiration payoffs to regulate the probability of choosing cooperation. By investigating such a reinforcement learning rule in the spatial prisoner's dilemma game and public goods game, a most noteworthy viewpoint is that moderate greediness (i.e. moderate aspiration level) favors best the development and organization of collective cooperation. The generality of this observation is tested against different regulation strengths and different types of network of interaction as well. We also make comparisons with two recently proposed models to highlight the importance of the mechanism of adaptive aspiration level in supporting cooperation in structured populations.

Learning by Peers: An Alternative Learning Model for Digital Inclusion of Elderly People

NASA Astrophysics Data System (ADS)

de Sales, Márcia Barros; Silveira, Ricardo Azambuja; de Sales, André Barros; de Cássia Guarezi, Rita

This paper presents a model of digital inclusion for the elderly people, using learning by peers methodology. The model’s goal was valuing and promoting the potential capabilities of the elderly people by promoting some of them to instruct other elderly people to deal with computers and to use several software tools and internet services. The project involved 66 volunteering elderly people. However, 19 of them acted effectively as multipliers and the others as students. The process was observed through the empirical technique of interaction workshops. This technique was chosen for demanding direct participation of the people involved in real interaction. We worked with peer learning to facilitate the communication between elderly-learners and elderly-multipliers, due to the similarity in language, rhythm and life history, and because they felt more secure to develop the activities with people in their age group. This multiplying model can be used in centers, organizations and other entities that work with elderly people for their digital inclusion.

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.

Coupled Collaborative In-Class Activities and Individual Follow-Up Homework Promote Interactive Engagement and Improve Student Learning Outcomes in a College-Level "Environmental Geology" Course

ERIC Educational Resources Information Center

Arthurs, Leilani; Templeton, Alexis

2009-01-01

Interactive engagement pedagogies that emerge from a constructivist model of teaching and learning are often a challenge to implement in larger classes for a number of reasons including the physical layout of the classroom (e.g. fixed chairs in an amphitheater-style room), the logistics of organizing a large number of students into small…

Toward a Model of Organizations as Interpretation Systems.

DTIC Science & Technology

1983-09-01

interpretation. People are trying to interpret what they have done, define what they have learned, solve the problem of what they should do next. Building...converge upon an approximate interpretation. Managers may not agree fully about their perceptions ( Starbuck , 1976), but the thread of coherence among...meetings, telephone con- tact about complaints and questions) to learn shareholder’s opinions -16-j and to adapt to those opinions. Other Organizational

A New Concept Map Model for E-Learning Environments

NASA Astrophysics Data System (ADS)

Dattolo, Antonina; Luccio, Flaminia L.

Web-based education enables learners and teachers to access a wide quantity of continuously updated educational sources. In order to support the learning process, a system has to provide some fundamental features, such as simple mechanisms for the identification of the collection of “interesting” documents, adequate structures for storing, organizing and visualizing these documents, and appropriate mechanisms for creating personalized adaptive paths and views for learners.

Information-theoretic approach to interactive learning

NASA Astrophysics Data System (ADS)

Still, S.

2009-01-01

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.

Anatomical knowledge gain through a clay-modeling exercise compared to live and video observations.

PubMed

Kooloos, Jan G M; Schepens-Franke, Annelieke N; Bergman, Esther M; Donders, Rogier A R T; Vorstenbosch, Marc A T M

2014-01-01

Clay modeling is increasingly used as a teaching method other than dissection. The haptic experience during clay modeling is supposed to correspond to the learning effect of manipulations during exercises in the dissection room involving tissues and organs. We questioned this assumption in two pretest-post-test experiments. In these experiments, the learning effects of clay modeling were compared to either live observations (Experiment I) or video observations (Experiment II) of the clay-modeling exercise. The effects of learning were measured with multiple choice questions, extended matching questions, and recognition of structures on illustrations of cross-sections. Analysis of covariance with pretest scores as the covariate was used to elaborate the results. Experiment I showed a significantly higher post-test score for the observers, whereas Experiment II showed a significantly higher post-test score for the clay modelers. This study shows that (1) students who perform clay-modeling exercises show less gain in anatomical knowledge than students who attentively observe the same exercise being carried out and (2) performing a clay-modeling exercise is better in anatomical knowledge gain compared to the study of a video of the recorded exercise. The most important learning effect seems to be the engagement in the exercise, focusing attention and stimulating time on task. © 2014 American Association of Anatomists.

Analyzing Change in Students' Gene-to-Evolution Models in College-Level Introductory Biology

ERIC Educational Resources Information Center

Dauer, Joseph T.; Momsen, Jennifer L.; Speth, Elena Bray; Makohon-Moore, Sasha C.; Long, Tammy M.

2013-01-01

Research in contemporary biology has become increasingly complex and organized around understanding biological processes in the context of systems. To better reflect the ways of thinking required for learning about systems, we developed and implemented a pedagogical approach using box-and-arrow models (similar to concept maps) as a foundational…

Cesar Chavez--Grade Three Model Curriculum and Resources.

ERIC Educational Resources Information Center

California State Dept. of Education, Sacramento.

In this California state curriculum model for grade 3, "Continuity and Change," students study Cesar Chavez. The students learn about his relationship with immigrants, about his work with Fred Ross, and about his work in his own community. Students explore his work as a civil rights leader and labor organizer and the connection between…

Exploring Novel Tools for Assessing High School Students' Meaningful Understanding of Organic Reactions

ERIC Educational Resources Information Center

Vachliotis, Theodoros; Salta, Katerina; Vasiliou, Petroula; Tzougraki, Chryssa

2011-01-01

Systemic assessment questions (SAQs) are novel assessment tools used in the context of the Systemic Approach to Teaching and Learning (SATL) model. The purpose of this model is to enhance students' meaningful understanding of scientific concepts by use of constructivist concept mapping procedures, which emphasize the development of systems…

Toward Group Problem Solving Guidelines for 21st Century Teams

ERIC Educational Resources Information Center

Ranieri, Kathryn L.

2004-01-01

Effective problem-solving skills are critical in dealing with ambiguous and often complex issues in the present-day leaner and globally diverse organizations. Yet respected, well-established problem-solving models may be misaligned within the current work environment, particularly within a team context. Models learned from a more bureaucratic,…

Modeling and Simulation: PowerBoosting Productivity with Simulation.

ERIC Educational Resources Information Center

Riley, Suzanne

Minnesota high school students and teachers are learning the technology of simulation and integrating it into business and industrial technology courses. Modeling and simulation is the science of using software to construct a system within an organization and then running simulations of proposed changes to assess results before funds are spent. In…

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

Semantic Maps Capturing Organization Knowledge in e-Learning

NASA Astrophysics Data System (ADS)

Mavridis, Androklis; Koumpis, Adamantios; Demetriadis, Stavros N.

e-learning, shows much promise in accessibility and opportunity to learn, due to its asynchronous nature and its ability to transmit knowledge fast and effectively. However without a universal standard for online learning and teaching, many systems are proclaimed as “e-learning-compliant”, offering nothing more than automated services for delivering courses online, providing no additional enhancement to reusability and learner personalization. Hence, the focus is not on providing reusable and learner-centered content, but on developing the technology aspects of e-learning. This current trend has made it crucial to find a more refined definition of what constitutes knowledge in the e-learning context. We propose an e-learning system architecture that makes use of a knowledge model to facilitate continuous dialogue and inquiry-based knowledge learning, by exploiting the full benefits of the semantic web as a medium capable for supplying the web with formalized knowledge.

Limits of the Learning Organization: A Critical Look.

ERIC Educational Resources Information Center

Fenwick, Tara J.

The development of the "learning organization" may be traced to three converging trends: the tradition of organizational development; economic shifts to globalization, deregulation, and information-based industry; and total quality management. Learning organizations are generally characterized as follows: organizations that create…

Facing Up to the Learning Organization Challenge: Selected European Writings. Volume II. CEDEFOP Reference Series.

ERIC Educational Resources Information Center

Nyhan, Barry, Ed.; Kelleher, Michael, Ed.; Cressey, Peter, Ed.; Poell, Rob, Ed.

This volume, the second of a two-volume publication, comprises 15 papers that present the work of individual European projects dealing with learning within organizations. These five chapters in Part 1, The Meaning of the Learning Organization, examine the conceptual frameworks and dilemmas at the heart of the notion of the learning organization:…

Promoting Self-Directed Learning in a Learning Organization: Tools and Practices

ERIC Educational Resources Information Center

Rana, Sowath; Ardichvili, Alexandre; Polesello, Daiane

2016-01-01

Purpose: The purpose of this paper is to examine a set of practices that can help promote self-directed learning (SDL) in congruence with the goals of developing and maintaining a learning organization. Design/methodology/approach Findings from this study were derived from an extensive review of the SDL and the learning organization literature, as…

Coaching and leadership for the diffusion of innovation in health care: a different type of multi-organization improvement collaborative.

PubMed

Green, Paul L; Plsek, Paul E

2002-02-01

Health care organizations have suffered a steady decrease in operating margins in recent years while facing increased competition and pressure to provide ever-higher levels of customer service, quality of care, and innovation in delivery methodologies. The ability to rapidly find and implement changes that will lead to strategic improvement is critical. To assist member organizations in dealing with these issues, VHA Upper Midwest launched the Coaching and Leadership Initiative (VHA-CLI) in January 1999. The initiative was intended to develop new methods of collaborating for organizational learning of best practices, with a focus on generalizable change and deliberate leadership supports for deployment, diffusion, and sustainability. The emphasis was on the spread of ideas for improvement into all relevant corners of the organization. The structure of the VHA-CLI collaborative involved four waves of demonstration teams during 2 years. Each meeting of the collaborative included an executive session, team learning sessions (concepts applied to their improvement projects), and planning for the 6-month action period following the meeting. An important feature of the collaborative is the way in which teams in the various waves overlapped. For example, the Wave 1 team for a given organization came to a learning session in January 1999. At the second collaborative meeting in June 1999, the Wave 1 teams reported on the progress in their pilot sites. This meeting was also the kick-off session for the Wave 2 teams, which could learn about organizational culture and the improvement model from the efforts of their colleagues on Wave 1. Wave 1 teams also learned about and planned for spreading their efforts to other sites beyond the pilot. The pattern of multiple teams stretching across two waves of activity was repeated at every meeting of the collaborative. Each organization in the collaborative has achieved improved outcomes around its selected clinical topics. In total, 26 teams have made significant improvement in 17 different topic areas. In addition, each organization has been able to successfully spread tested improvements to other individuals, teams, or locations, and the improvement work has become easier and more rapid with each successive cycle. The learning process initiated by this project will continue for at least another year in the VHA Upper Midwest region and will be expanded as participating organizations in other regions enroll in the VHA's national effort.

Learning Organization.

ERIC Educational Resources Information Center

1996

This document consists of three papers presented at a symposium on the learning organization moderated by Roger Miller at the 1996 conference of the Academy of Human Resource Development (AHRD). "Creating a Learning Organization: A Case Study of Outcomes and Lessons Learned" (Laura L. Bierema, David M. Berdish) reports a study…

Regional Traffic Incident Management Programs : implementation guide

DOT National Transportation Integrated Search

2000-11-01

The purpose of this document is to assist organizations and their leaders in implementing and sustaining regional traffic incident management programs, both by examining some successful models, and by considering some of the lessons learned by early ...

CILT2000: Views of CILT2000.

ERIC Educational Resources Information Center

Collins, Allan

2002-01-01

Features comments on the Center for Innovative Learning Technologies (CILT) conference held in 2000 and addresses the five thematic areas around which the conference was organized, namely assessment, professional development, visualization and modeling, ubiquitous computing, and equity. (Author/YDS)

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

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

von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias

We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models ofmore » molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.« less

How the Group Investigation Model and the Six-Mirror Model Changed Teachers' Roles and Teachers' and Students' Attitudes towards Diversity

ERIC Educational Resources Information Center

Damini, Marialuisa

2014-01-01

This paper is based on research that demonstrates the positive effects of the cooperative learning model Group Investigation (GI) and the Six-Mirror model on teacher effectiveness in organizing and scaffolding CL activities, and changing students' and teachers' views of diversity. We explain how the connection between the two models…

The Learning Organization. Myths and Realities.

ERIC Educational Resources Information Center

Kerka, Sandra

Any type of organization can be a learning organization (LO) if it possesses certain characteristics: provide continuous learning opportunities, use learning to reach its goals, link individual performance with organizational performance, foster inquiry and dialogue, embrace creative tension as a source of energy and renewal, and be continuously…

The Adoption of On-Demand Learning in Organizations in the United States

ERIC Educational Resources Information Center

Cui, Lianbin

2010-01-01

There is a lack of studies on the current status of the use of on-demand learning in organizations and factors that may accelerate or hold back the acceptance and implementation of on-demand learning in organizations. The purpose of this study is to contribute to a better understanding of the adoption of on-demand learning in organizations in the…

Organization of brain tissue - Is the brain a noisy processor.

NASA Technical Reports Server (NTRS)

Adey, W. R.

1972-01-01

This paper presents some thoughts on functional organization in cerebral tissue. 'Spontaneous' wave and unit firing are considered as essential phenomena in the handling of information. Various models are discussed which have been suggested to describe the pseudorandom behavior of brain cells, leading to a view of the brain as an information processor and its role in learning, memory, remembering and forgetting.

Data Mining in Cyber Operations

DTIC Science & Technology

2014-07-01

information processing units intended to mimic the network of neurons in the human brain for performing pattern recognition  Self- organizing maps (SOM...patterns are mined from in order to influence the learning model . An exploratory attack does not alter the training process , but rather uses other...New Jersey: Prentice Hall. 21) Kohonen, T. (1982). Self- organized formation of topologically correct feature maps. Biological Cybernetics , 43, 59–69

Colour learning when foraging for nectar and pollen: bees learn two colours at once.

PubMed

Muth, Felicity; Papaj, Daniel R; Leonard, Anne S

2015-09-01

Bees are model organisms for the study of learning and memory, yet nearly all such research to date has used a single reward, nectar. Many bees collect both nectar (carbohydrates) and pollen (protein) on a single foraging bout, sometimes from different plant species. We tested whether individual bumblebees could learn colour associations with nectar and pollen rewards simultaneously in a foraging scenario where one floral type offered only nectar and the other only pollen. We found that bees readily learned multiple reward-colour associations, and when presented with novel floral targets generalized to colours similar to those trained for each reward type. These results expand the ecological significance of work on bee learning and raise new questions regarding the cognitive ecology of pollination. © 2015 The Author(s).

Analysis of the Effect of Sequencing Lecture and Laboratory Instruction on Student Learning and Motivation Towards Learning Chemistry in an Organic Chemistry Lecture Course

ERIC Educational Resources Information Center

Pakhira, Deblina

2012-01-01

Exposure to organic chemistry concepts in the laboratory can positively affect student performance, learning new chemistry concepts and building motivation towards learning chemistry in the lecture. In this study, quantitative methods were employed to assess differences in student performance, learning, and motivation in an organic chemistry…

Expert knowledge maps for knowledge management: a case study in Traditional Chinese Medicine research.

PubMed

Cui, Meng; Yang, Shuo; Yu, Tong; Yang, Ce; Gao, Yonghong; Zhu, Haiyan

2013-10-01

To design a model to capture information on the state and trends of knowledge creation, at both an individual and an organizational level, in order to enhance knowledge management. We designed a graph-theoretic knowledge model, the expert knowledge map (EKM), based on literature-based annotation. A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model. The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs, expert graphs, and expert-knowledge biography. Our model could help to reveal the hot topics, trends, and products of the research done by an organization. It can potentially be used to facilitate knowledge learning, sharing and decision-making among researchers, academicians, students, and administrators of organizations.

Analysis of the “naming game” with learning errors in communications

NASA Astrophysics Data System (ADS)

Lou, Yang; Chen, Guanrong

2015-07-01

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Analysis of the "naming game" with learning errors in communications.

PubMed

Lou, Yang; Chen, Guanrong

2015-07-16

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Hugh O'Brian Youth Leadership: Using a Theoretical Model at the Intersection of Youth Leadership Education and Service-Learning.

PubMed

Ray, Vicki Ferrence

2016-06-01

This chapter presents the Hugh O'Brian Youth Leadership (HOBY) program as a case study, examining their gradual process of shifting all programs to integrate leadership development and service. As an organization with over 4,000 volunteers and a nationwide scope, the change process was a challenge but resulted in benefits that fit the organizations' values. The social change model for leadership development (Higher Education Research Institute, ) was used as a guiding framework. © 2016 Wiley Periodicals, Inc., A Wiley Company.

Prespeech motor learning in a neural network using reinforcement☆

PubMed Central

Warlaumont, Anne S.; Westermann, Gert; Buder, Eugene H.; Oller, D. Kimbrough

2012-01-01

Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one’s language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the differ-ent conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network’s post learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network’s post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model’s post-learning productions were more likely to resemble the English vowels and vice versa. PMID:23275137

Creating and sustaining an academic-practice Partnership Engagement Model.

PubMed

Schaffer, Marjorie A; Schoon, Patricia M; Brueshoff, Bonnie L

2017-11-01

Public health clinical educators and practicing public health nurses (PHNs) are experiencing challenges in creating meaningful clinical learning experiences for nursing students due to an increase in nursing programs and greater workload responsibilities for both nursing faculty and PHNs. The Henry Street Consortium (HSC), a collaborative group of PHNs and nursing faculty, conducted a project to identify best practices for public health nursing student clinical learning experiences. Project leaders surveyed HSC members about preferences for teaching-learning strategies, facilitated development of resources and tools to guide learning, organized faculty/PHN pilot teams to test resources and tools with students, and evaluated the pilot team experiences through two focus groups. The analysis of the outcomes of the partnership engagement project led to the development of the Partnership Engagement Model (PEM), which may be used by nursing faculty and their public health practice partners to guide building relationships and sustainable partnerships for educating nursing students. © 2017 Wiley Periodicals, Inc.

From Learning Organization to Learning Community: Sustainability through Lifelong Learning

ERIC Educational Resources Information Center

Kearney, Judith; Zuber-Skerritt, Ortrun

2012-01-01

Purpose: This paper aims to: extend the concept of "The learning organization" to "The learning community," especially disadvantaged communities; demonstrate how leaders in a migrant community can achieve positive change at the personal, professional, team and community learning levels through participatory action learning and…

Learning molecular energies using localized graph kernels

DOE PAGES

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

2017-03-21

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning molecular energies using localized graph kernels

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

Ferré, Grégoire; Haut, Terry Scot; Barros, Kipton Marcos

We report that recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturallymore » incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. Finally, we benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.« less

Learning Organization Practices. [Concurrent Symposium Session at AHRD Annual Conference, 1998.

ERIC Educational Resources Information Center

1998

This document contains three papers from a symposium on learning organization practices. "The Beliefs of Managers as Facilitators of Learning in Learning Organizations" (Andrea D. Ellinger) reports selected findings from a qualitative study that examined the perceptions of managers regarding their beliefs, behaviors, triggers, and…

27 CFR 22.104 - Educational organizations, colleges of learning, and scientific universities.

Code of Federal Regulations, 2012 CFR

2012-04-01

..., colleges of learning, and scientific universities. 22.104 Section 22.104 Alcohol, Tobacco Products and...-free alcohol withdrawn by educational organizations, scientific universities, and colleges of learning... OF TAX-FREE ALCOHOL Use of Tax-Free Alcohol § 22.104 Educational organizations, colleges of learning...

27 CFR 22.104 - Educational organizations, colleges of learning, and scientific universities.

Code of Federal Regulations, 2013 CFR

2013-04-01

..., colleges of learning, and scientific universities. 22.104 Section 22.104 Alcohol, Tobacco Products and...-free alcohol withdrawn by educational organizations, scientific universities, and colleges of learning... OF TAX-FREE ALCOHOL Use of Tax-Free Alcohol § 22.104 Educational organizations, colleges of learning...

27 CFR 22.104 - Educational organizations, colleges of learning, and scientific universities.

Code of Federal Regulations, 2014 CFR

2014-04-01

..., colleges of learning, and scientific universities. 22.104 Section 22.104 Alcohol, Tobacco Products and...-free alcohol withdrawn by educational organizations, scientific universities, and colleges of learning... OF TAX-FREE ALCOHOL Use of Tax-Free Alcohol § 22.104 Educational organizations, colleges of learning...

The Learning Organization: An Undelivered Promise.

ERIC Educational Resources Information Center

Elkjaer, Bente

2001-01-01

Presents a case study on the development of a learning organization that did not last very long. Suggests that the reason for its demise was the way in which learning in the organization was understood and enacted. The case is evaluated against John Dewey's learning theory. (Contains 24 references.) (DDR)

Creating a longitudinal integrated clerkship with mutual benefits for an academic medical center and a community health system.

PubMed

Poncelet, Ann Noelle; Mazotti, Lindsay A; Blumberg, Bruce; Wamsley, Maria A; Grennan, Tim; Shore, William B

2014-01-01

The longitudinal integrated clerkship is a model of clinical education driven by tenets of social cognitive theory, situated learning, and workplace learning theories, and built on a foundation of continuity between students, patients, clinicians, and a system of care. Principles and goals of this type of clerkship are aligned with primary care principles, including patient-centered care and systems-based practice. Academic medical centers can partner with community health systems around a longitudinal integrated clerkship to provide mutual benefits for both organizations, creating a sustainable model of clinical training that addresses medical education and community health needs. A successful one-year longitudinal integrated clerkship was created in partnership between an academic medical center and an integrated community health system. Compared with traditional clerkship students, students in this clerkship had better scores on Clinical Performance Examinations, internal medicine examinations, and high perceptions of direct observation of clinical skills.Advantages for the academic medical center include mitigating the resources required to run a longitudinal integrated clerkship while providing primary care training and addressing core competencies such as systems-based practice, practice-based learning, and interprofessional care. Advantages for the community health system include faculty development, academic appointments, professional satisfaction, and recruitment.Success factors include continued support and investment from both organizations' leadership, high-quality faculty development, incentives for community-based physician educators, and emphasis on the mutually beneficial relationship for both organizations. Development of a longitudinal integrated clerkship in a community health system can serve as a model for developing and expanding these clerkship options for academic medical centers.

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.

Learning on Jupiter, learning on the Moon: the dark side of the G-force. Effects of gravity changes on neurovascular unit and modulation of learning and memory

PubMed Central

Porte, Yves; Morel, Jean-Luc

2012-01-01

On earth, gravity vector conditions the development of all living beings by physically imposing an axis along which to build their organism. Thus, during their whole life, they have to fight against this force not only to maintain their architectural organization but also to coordinate the communication between organs and keep their physiology in a balanced steady-state. In space, astronauts show physiological, psychological, and cognitive deregulations, ranging from bone decalcification or decrease of musculature, to depressive-like disorders, and spatial disorientation. Nonetheless, they are confronted to a great amount of physical changes in their environment such as solar radiations, loss of light-dark cycle, lack of spatial landmarks, confinement, and obviously a dramatic decrease of gravity force. It is thus very hard to selectively discriminate the strict role of gravity level alterations on physiological, and particularly cerebral, dysfunction. To this purpose, it is important to design autonomous models and apparatuses for behavioral phenotyping utilizable under modified gravity environments. Our team actually aims at working on this area of research. PMID:23015785

Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities

PubMed Central

2017-01-01

The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons’ outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several–but not all–types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life. PMID:28640825

Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

PubMed

MaBouDi, HaDi; Shimazaki, Hideaki; Giurfa, Martin; Chittka, Lars

2017-06-01

The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons' outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several-but not all-types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.

Leading the Higher Education IT Organization: Six Building Blocks of Success

ERIC Educational Resources Information Center

Laster, Stephen J.

2011-01-01

Many of the worries for IT leaders are new--and much broader. The traditional teaching, learning, and research models of the past will not be and cannot be the models for the future. These models break down as costs (human and financial) continue to grow faster than they can be funded, as digital natives change forever the nature of being "in…

Organizational Learning Culture, Learning Transfer Climate and Perceived Innovation in Jordanian Organizations

ERIC Educational Resources Information Center

Bates, Reid; Khasawneh, Samer

2005-01-01

This paper examines the relationship between organizational learning culture, learning transfer climate, and organizational innovation. The objective was to test the ability of learning organization culture to account for variance in learning transfer climate and subsequent organizational innovation, and to examine the role of learning transfer…

The Organization of Informal Learning

ERIC Educational Resources Information Center

Rogoff, Barbara; Callanan, Maureen; Gutiérrez, Kris D.; Erickson, Frederick

2016-01-01

Informal learning is often treated as simply an alternative to formal, didactic instruction. This chapter discusses how the organization of informal learning differs across distinct settings but with important commonalities distinguishing informal learning from formal learning: Informal learning is nondidactic, is embedded in meaningful activity,…

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

Appreciative Pedagogy: Constructing Positive Models for Learning.

ERIC Educational Resources Information Center

Yballe, Leodones; O'Connor, Dennis

2000-01-01

Appreciative inquiry, an approach focused on generation of a vision for an organization, may be adapted for management classes. Students and teachers conduct collaborative inquiry into successful experiences, creating positive images that generate positive action in the classroom. (SK)

An Exploratory Study of the Relationship between Self-Directed Learning and Senge's Five Disciplines Necessary to Become a Learning Organization: In a High-Tech Company

ERIC Educational Resources Information Center

Masier, Darren Joseph

2013-01-01

The manner by which organizations value their workforce has been shown to impact learning within organizations (Confessore, 1997; Marsick & Watkins, 2010; Weldy & Gillis, 2003). Leadership is faced with a multitude of options to facilitate learning (Cho, 2002; Confessore & Kops, 1998), and employee learning has been linked to…

Natural variation in learning rate and memory dynamics in parasitoid wasps: opportunities for converging ecology and neuroscience

PubMed Central

Hoedjes, Katja M.; Kruidhof, H. Marjolein; Huigens, Martinus E.; Dicke, Marcel; Vet, Louise E. M.; Smid, Hans M.

2011-01-01

Although the neural and genetic pathways underlying learning and memory formation seem strikingly similar among species of distant animal phyla, several more subtle inter- and intraspecific differences become evident from studies on model organisms. The true significance of such variation can only be understood when integrating this with information on the ecological relevance. Here, we argue that parasitoid wasps provide an excellent opportunity for multi-disciplinary studies that integrate ultimate and proximate approaches. These insects display interspecific variation in learning rate and memory dynamics that reflects natural variation in a daunting foraging task that largely determines their fitness: finding the inconspicuous hosts to which they will assign their offspring to develop. We review bioassays used for oviposition learning, the ecological factors that are considered to underlie the observed differences in learning rate and memory dynamics, and the opportunities for convergence of ecology and neuroscience that are offered by using parasitoid wasps as model species. We advocate that variation in learning and memory traits has evolved to suit an insect's lifestyle within its ecological niche. PMID:21106587

Online Self-Organizing Social Systems: The Decentralized Future of Online Learning.

ERIC Educational Resources Information Center

Wiley, David A.; Edwards, Erin K.

2002-01-01

Describes an online self-organizing social system (OSOSS) which allows large numbers of individuals to self-organize in a highly decentralized manner to solve problems and accomplish other goals. Topics include scalability and bandwidth in online learning; self-organization; learning objects; instructional design underlying OSOSS, including…

Women and Consciousness in the "Learning Organization": Emancipation or Exploitation?

ERIC Educational Resources Information Center

Mojab, Shahrzad; Gorman, Rachel

2003-01-01

Marxist-feminist analysis of the learning organization concept demonstrates that , instead of being emancipatory, it can function as a way to extract surplus value from labor and to maintain social control. Pressures on feminist-oriented nonprofit organizations to adopt the learning organization concept could erode consciousness raising and…

Innovation Diffusion Model in Higher Education: Case Study of E-Learning Diffusion

ERIC Educational Resources Information Center

Buc, Sanjana; Divjak, Blaženka

2015-01-01

The diffusion of innovation (DOI) is critical for any organization and especially nowadays for higher education institutions (HEIs) in the light of vast pressure of emerging educational technologies as well as of the demand of economy and society. DOI takes into account the initial and the implementation phase. The conceptual model of DOI in…

A Simple Computer-Aided Three-Dimensional Molecular Modeling for the Octant Rule

ERIC Educational Resources Information Center

Kang, Yinan; Kang, Fu-An

2011-01-01

The Moffitt-Woodward-Moscowitz-Klyne-Djerassi octant rule is one of the most successful empirical rules in organic chemistry. However, the lack of a simple effective modeling method for the octant rule in the past 50 years has posed constant difficulties for researchers, teachers, and students, particularly the young generations, to learn and…

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

ERIC Educational Resources Information Center

Hardof-Jaffe, Sharon; Aladjem, Ruthi

2018-01-01

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

Action Research in Professional Work: Developing New Practices through Design, Dialogue or Learning?

ERIC Educational Resources Information Center

Lahn, Leif Chr.

This paper examines action research that has been carried out in organizations consisting of predominantly highly educated personnel. The paper revolves around discussion of the Scandinavian model of action research, asking to what degree this model, which has been developed within the framework of industrial democracy, might also serve as a…

Creating learning environments.

PubMed

Ollier, D

1995-01-01

The Healthcare Forum Journal has compiled this compendium to serve as a resource in building learning organizations. Our aim is to help healthcare organizations, policymakers, and others (payers, providers, patients, physicians, and citizens) rethink the system of healthcare delivery by opening up a dialogue--the ideas presented in Sandra Seagal's interview, ¿The Pillars of Learning¿, provide the groundwork for understanding how human dynamics impact learning, and the further resources section offers readers an annotated bibliography on the subject, as well as a listing of organizations that focus on systems thinking and how to create organizations that continually learn.

Academic Staff's Perceptions of Characteristics of Learning Organization in a Higher Learning Institution

ERIC Educational Resources Information Center

Ali, Ali Khamis

2012-01-01

Purpose: The main objective of this study was to examine academic staff's perceptions of the characteristics of a learning organization within higher education: in this instance, the International Islamic University Malaysia (IIUM). The study also examined the relationship between the characteristics of a learning organization and satisfaction…

Restaurants as Learning Organizations: A Multiple-Site Case Study of U.S. Non-Chain Restaurants

ERIC Educational Resources Information Center

Boccia, Mark

2016-01-01

This study investigated the construct of the learning organization in the restaurant industry. Descriptive accounts of learning were gleaned from face-to-face interviews, focus groups, observations, document analysis, and data from the Dimensions of the Learning Organization Questionnaire (DLOQ) from 52 participants employed in three US…

Facilitating Expansive Learning in a Public Sector Organization

ERIC Educational Resources Information Center

Gustavsson, Maria

2009-01-01

The aim of this article is to discuss how learning opportunities can be organized to promote expansive learning in work practice. The discussion draws on results from a case study examining local development work and conditions that facilitate processes of expansive learning in a work team within a public sector organization in a Swedish…

The Individual|Collective Dialectic in the Learning Organization

ERIC Educational Resources Information Center

Lee, Yew-Jin; Roth, Wolff-Michael

2007-01-01

Purpose: The purpose of this paper is to answer two interrelated questions: "Who learns and how in the learning organization?". By implication, many theories of the learning organization are addressed that are based on a static and erroneous separation of individual and collective. Design/methodology/approach: Four episodes from a larger case…

A connectionist model of category learning by individuals with high-functioning autism spectrum disorder.

PubMed

Dovgopoly, Alexander; Mercado, Eduardo

2013-06-01

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

Combining Computational Modeling and Neuroimaging to Examine Multiple Category Learning Systems in the Brain

PubMed Central

Nomura, Emi M.; Reber, Paul J.

2012-01-01

Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the “off system” (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery. PMID:24962771

A neuromathematical model of human information processing and its application to science content acquisition

NASA Astrophysics Data System (ADS)

Anderson, O. Roger

The rate of information processing during science learning and the efficiency of the learner in mobilizing relevant information in long-term memory as an aid in transmitting newly acquired information to stable storage in long-term memory are fundamental aspects of science content acquisition. These cognitive processes, moreover, may be substantially related in tempo and quality of organization to the efficiency of higher thought processes such as divergent thinking and problem-solving ability that characterize scientific thought. As a contribution to our quantitative understanding of these fundamental information processes, a mathematical model of information acquisition is presented and empirically evaluated in comparison to evidence obtained from experimental studies of science content acquisition. Computer-based models are used to simulate variations in learning parameters and to generate the theoretical predictions to be empirically tested. The initial tests of the predictive accuracy of the model show close agreement between predicted and actual mean recall scores in short-term learning tasks. Implications of the model for human information acquisition and possible future research are discussed in the context of the unique theoretical framework of the model.

Large-scale functional models of visual cortex for remote sensing

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

Brumby, Steven P; Kenyon, Garrett; Rasmussen, Craig E

Neuroscience has revealed many properties of neurons and of the functional organization of visual cortex that are believed to be essential to human vision, but are missing in standard artificial neural networks. Equally important may be the sheer scale of visual cortex requiring {approx}1 petaflop of computation. In a year, the retina delivers {approx}1 petapixel to the brain, leading to massively large opportunities for learning at many levels of the cortical system. We describe work at Los Alamos National Laboratory (LANL) to develop large-scale functional models of visual cortex on LANL's Roadrunner petaflop supercomputer. An initial run of a simplemore » region VI code achieved 1.144 petaflops during trials at the IBM facility in Poughkeepsie, NY (June 2008). Here, we present criteria for assessing when a set of learned local representations is 'complete' along with general criteria for assessing computer vision models based on their projected scaling behavior. Finally, we extend one class of biologically-inspired learning models to problems of remote sensing imagery.« less

Acoustic signatures of sound source-tract coupling.

PubMed

Arneodo, Ezequiel M; Perl, Yonatan Sanz; Mindlin, Gabriel B

2011-04-01

Birdsong is a complex behavior, which results from the interaction between a nervous system and a biomechanical peripheral device. While much has been learned about how complex sounds are generated in the vocal organ, little has been learned about the signature on the vocalizations of the nonlinear effects introduced by the acoustic interactions between a sound source and the vocal tract. The variety of morphologies among bird species makes birdsong a most suitable model to study phenomena associated to the production of complex vocalizations. Inspired by the sound production mechanisms of songbirds, in this work we study a mathematical model of a vocal organ, in which a simple sound source interacts with a tract, leading to a delay differential equation. We explore the system numerically, and by taking it to the weakly nonlinear limit, we are able to examine its periodic solutions analytically. By these means we are able to explore the dynamics of oscillatory solutions of a sound source-tract coupled system, which are qualitatively different from those of a sound source-filter model of a vocal organ. Nonlinear features of the solutions are proposed as the underlying mechanisms of observed phenomena in birdsong, such as unilaterally produced "frequency jumps," enhancement of resonances, and the shift of the fundamental frequency observed in heliox experiments. ©2011 American Physical Society