An ERP study on initial second language vocabulary learning.

PubMed

Yum, Yen Na; Midgley, Katherine J; Holcomb, Phillip J; Grainger, Jonathan

2014-04-01

This study examined the very initial phases of orthographic and semantic acquisition in monolingual native English speakers learning Chinese words under controlled laboratory conditions. Participants engaged in 10 sessions of vocabulary learning, four of which were used to obtain ERPs. Performance in behavioral tests improved over sessions, and these data were used to define fast and slow learners. Most important is that ERPs in the two groups of learners revealed qualitatively distinct learning patterns. Only fast learners showed a left-lateralized increase in N170 amplitude with training. Furthermore, only fast learners showed an increased N400 amplitude with training, with a distinct anterior distribution. Slow learners, on the other hand, showed a posterior positive effect, with increasingly positive-going waveforms in occipital sites as training progressed. Possible mechanisms underlying these qualitative differences are discussed. Copyright © 2014 Society for Psychophysiological Research.

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

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

Thwaites, D; Holloway, L; Bailey, M

2015-06-15

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction andmore » mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.« less

Independent tasks scheduling in cloud computing via improved estimation of distribution algorithm

NASA Astrophysics Data System (ADS)

Sun, Haisheng; Xu, Rui; Chen, Huaping

2018-04-01

To minimize makespan for scheduling independent tasks in cloud computing, an improved estimation of distribution algorithm (IEDA) is proposed to tackle the investigated problem in this paper. Considering that the problem is concerned with multi-dimensional discrete problems, an improved population-based incremental learning (PBIL) algorithm is applied, which the parameter for each component is independent with other components in PBIL. In order to improve the performance of PBIL, on the one hand, the integer encoding scheme is used and the method of probability calculation of PBIL is improved by using the task average processing time; on the other hand, an effective adaptive learning rate function that related to the number of iterations is constructed to trade off the exploration and exploitation of IEDA. In addition, both enhanced Max-Min and Min-Min algorithms are properly introduced to form two initial individuals. In the proposed IEDA, an improved genetic algorithm (IGA) is applied to generate partial initial population by evolving two initial individuals and the rest of initial individuals are generated at random. Finally, the sampling process is divided into two parts including sampling by probabilistic model and IGA respectively. The experiment results show that the proposed IEDA not only gets better solution, but also has faster convergence speed.

Collaborative Supervised Learning for Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

2011-01-01

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

NASA Astrophysics Data System (ADS)

Nelson, Kevin; Corbin, George; Blowers, Misty

2014-05-01

Machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

Spontaneous brain activity predicts learning ability of foreign sounds.

PubMed

Ventura-Campos, Noelia; Sanjuán, Ana; González, Julio; Palomar-García, María-Ángeles; Rodríguez-Pujadas, Aina; Sebastián-Gallés, Núria; Deco, Gustavo; Ávila, César

2013-05-29

Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.

Inverse analysis of turbidites by machine learning

NASA Astrophysics Data System (ADS)

Naruse, H.; Nakao, K.

2017-12-01

This study aims to propose a method to estimate paleo-hydraulic conditions of turbidity currents from ancient turbidites by using machine-learning technique. In this method, numerical simulation was repeated under various initial conditions, which produces a data set of characteristic features of turbidites. Then, this data set of turbidites is used for supervised training of a deep-learning neural network (NN). Quantities of characteristic features of turbidites in the training data set are given to input nodes of NN, and output nodes are expected to provide the estimates of initial condition of the turbidity current. The optimization of weight coefficients of NN is then conducted to reduce root-mean-square of the difference between the true conditions and the output values of NN. The empirical relationship with numerical results and the initial conditions is explored in this method, and the discovered relationship is used for inversion of turbidity currents. This machine learning can potentially produce NN that estimates paleo-hydraulic conditions from data of ancient turbidites. We produced a preliminary implementation of this methodology. A forward model based on 1D shallow-water equations with a correction of density-stratification effect was employed. This model calculates a behavior of a surge-like turbidity current transporting mixed-size sediment, and outputs spatial distribution of volume per unit area of each grain-size class on the uniform slope. Grain-size distribution was discretized 3 classes. Numerical simulation was repeated 1000 times, and thus 1000 beds of turbidites were used as the training data for NN that has 21000 input nodes and 5 output nodes with two hidden-layers. After the machine learning finished, independent simulations were conducted 200 times in order to evaluate the performance of NN. As a result of this test, the initial conditions of validation data were successfully reconstructed by NN. The estimated values show very small deviation from the true parameters. Comparing to previous inverse modeling of turbidity currents, our methodology is superior especially in the efficiency of computation. Also, our methodology has advantage in extensibility and applicability to various sediment transport processes such as pyroclastic flows or debris flows.

Search and Evaluation of Learning Materials for Ophthalmology, 1973-1975.

ERIC Educational Resources Information Center

Prince, Arlene Leinbach; And Others

A study of instructional materials, especially media, available for ophthalmology teaching was initiated at the University of Washington School of Medicine in March 1973. Some 115 catalog and distribution sources were contracted, from which more than 200 potential titles were identified. Fifty three materials were reviewed and evaluated. Six…

Stochastic game theory: for playing games, not just for doing theory.

PubMed

Goeree, J K; Holt, C A

1999-09-14

Recent theoretical advances have dramatically increased the relevance of game theory for predicting human behavior in interactive situations. By relaxing the classical assumptions of perfect rationality and perfect foresight, we obtain much improved explanations of initial decisions, dynamic patterns of learning and adjustment, and equilibrium steady-state distributions.

Steps toward a Formative Evaluation of NSDL. Technical Report

ERIC Educational Resources Information Center

Bikson, Tora K.; Kalra, Nidhi; Galway, Lionel A.; Agnew, Grace

2011-01-01

The National Science Foundation's (NSF) National Science Digital Library/Distributed Learning (NSDL) program turned 10 years old in 2010. This report presents results of a preliminary program evaluation carried out by RAND and is organized around three principal goals: (1) Provide an initial evaluation of NSDL based on existing information…

Tanoak: History, ecology and values

Treesearch

Susan Frankel

2013-01-01

To combat sudden oak death (SOD), scientists needed to understand its primary host – tanoak, Notholithocarpus densiflorus (Hook. & Arn.) Manos, Cannon & S. H. Oh (Fagaceae), so research was initiated on its distribution, utilization and natural history. This Madrono Special Issue presents much of what we have learned, over the past 10 years...

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

PubMed Central

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-01-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment. PMID:27922592

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

NASA Astrophysics Data System (ADS)

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-12-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.

PubMed

Bradbury, Kyle; Saboo, Raghav; L Johnson, Timothy; Malof, Jordan M; Devarajan, Arjun; Zhang, Wuming; M Collins, Leslie; G Newell, Richard

2016-12-06

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Health Worker Focused Distributed Simulation for Improving Capability of Health Systems in Liberia.

PubMed

Gale, Thomas C E; Chatterjee, Arunangsu; Mellor, Nicholas E; Allan, Richard J

2016-04-01

The main goal of this study was to produce an adaptable learning platform using virtual learning and distributed simulation, which can be used to train health care workers, across a wide geographical area, key safety messages regarding infection prevention control (IPC). A situationally responsive agile methodology, Scrum, was used to develop a distributed simulation module using short 1-week iterations and continuous synchronous plus asynchronous communication including end users and IPC experts. The module contained content related to standard IPC precautions (including handwashing techniques) and was structured into 3 distinct sections related to donning, doffing, and hazard perception training. Using Scrum methodology, we were able to link concepts applied to best practices in simulation-based medical education (deliberate practice, continuous feedback, self-assessment, and exposure to uncommon events), pedagogic principles related to adult learning (clear goals, contextual awareness, motivational features), and key learning outcomes regarding IPC, as a rapid response initiative to the Ebola outbreak in West Africa. Gamification approach has been used to map learning mechanics to enhance user engagement. The developed IPC module demonstrates how high-frequency, low-fidelity simulations can be rapidly designed using scrum-based agile methodology. Analytics incorporated into the tool can help demonstrate improved confidence and competence of health care workers who are treating patients within an Ebola virus disease outbreak region. These concepts could be used in a range of evolving disasters where rapid development and communication of key learning messages are required.

Distributed Learning: Revitalizing Anesthesiology Training in Resource-Limited Ethiopia.

PubMed

Patel, Krupa B; Dooley, Morgan; Abate, Ananya; Moll, Vanessa

2017-01-01

Ethiopia has a significant paucity of available health-care workers. Despite the increasing number of medical schools, there are not enough physician instructors. Furthermore, availability and standardization of postgraduate training are lacking. Modalities of e-learning have been shown to be successful when used to impart medical education in other resource-limited countries. The Emory University and Addis Ababa University (AAU) Departments of Anesthesiology have formed a collaboration with the intent of improving the AAU Anesthesiology residency program, one of two postgraduate training programs for anesthesiology in Ethiopia. An initial educational needs assessment identified areas in the existing training program that required improvement. In this pilot study, we describe how the current classroom-based curriculum is augmented by the introduction of interactive educational sessions and distributed learning in the form of video lectures. Video lectures covered topics based on areas identified by Ethiopian residents and faculty. Interactive sessions included hands-on ultrasound workshops and epidural placement practicums, a journal club, problem-based learning sessions, and a mock code simulation. Assessment of the additions of the newly introduced blended learning technique was conducted via pre- and posttests on the topics presented. Pre- to posttest score averages increased from 54.5% to 83.6%. An expansion of educational resources and modes of didactics are needed to fill the gaps that exist in Ethiopian anesthesiology training. Incorporating distributed learning into the existing didactic structure may lead to more efficacious instruction resulting in a higher retention rate of information.

The U.S. Army's Impact on the History of Distance Education

ERIC Educational Resources Information Center

Duncan, Steve

2005-01-01

One of the most significant events that heralded the Department of Defense's commitment to distance education was the Advanced Distributed Learning (ADL) Initiative, which held its kickoff meeting in Washington, DC in 1997. This meeting provided the army and other military services the endorsement that had been lacking relative to implementing…

Demonstrating the Impact of a Distributed Leadership Approach in Higher Education

ERIC Educational Resources Information Center

Jones, Sandra; Harvey, Marina; Hamilton, Jillian; Bevacqua, John; Egea, Kathy; McKenzie, Jo

2017-01-01

Higher education is under pressure to advance from a singular focus on assessment of outputs (measurements) to encompass the impact (influence) of initiatives across all aspects of academic endeavour (research, learning and teaching, and leadership). This paper focuses on the implications of this shift for leadership in higher education.…

Programmatic, Systematic, Automatic: An Online Course Accessibility Support Model

ERIC Educational Resources Information Center

Bastedo, Kathleen; Sugar, Amy; Swenson, Nancy; Vargas, Jessica

2013-01-01

Over the past few years, there has been a noticeable increase in the number of requests for online course material accommodations at the University of Central Florida (UCF). In response to these requests, UCF's Center for Distributed Learning (CDL) formed new teams, reevaluated its processes, and initiated a partnership with UCF's Student…

Open Distribution of Virtual Containers as a Key Framework for Open Educational Resources and STEAM Subjects

ERIC Educational Resources Information Center

Corbi, Alberto; Burgos, Daniel

2017-01-01

This paper presents how virtual containers enhance the implementation of STEAM (science, technology, engineering, arts, and math) subjects as Open Educational Resources (OER). The publication initially summarizes the limitations of delivering open rich learning contents and corresponding assignments to students in college level STEAM areas. The…

A Hardware Testbed for Distributed Learning, Estimation, and Approximation Theory with Sensor Vehicle Networks

DTIC Science & Technology

2012-04-25

Virginia Tech VAL. Because of the excellent performance of the Trimble-based systems that were tested in the past, the Trimble subsidy Applanix was...initially contacted for available systems. The lowest cost, turnkey Trimble/ Applanix the POS LV 210 far exceeded the performance requirements of the

Foreward. Tanoak: History, ecology and values.

Treesearch

S.J. Frankel

2013-01-01

To combat sudden oak death (SOD), scientists needed to understand its primary host – tanoak, Notholithocarpus densiflorus (Hook. & Arn.) Manos, Cannon & S. H. Oh (Fagaceae), so research was initiated on its distribution, utilization and natural history. This Madrono Special Issue presents much of what we have learned, over the past 10 years...

Constraints on the Transfer of Perceptual Learning in Accented Speech

PubMed Central

Eisner, Frank; Melinger, Alissa; Weber, Andrea

2013-01-01

The perception of speech sounds can be re-tuned through a mechanism of lexically driven perceptual learning after exposure to instances of atypical speech production. This study asked whether this re-tuning is sensitive to the position of the atypical sound within the word. We investigated perceptual learning using English voiced stop consonants, which are commonly devoiced in word-final position by Dutch learners of English. After exposure to a Dutch learner’s productions of devoiced stops in word-final position (but not in any other positions), British English (BE) listeners showed evidence of perceptual learning in a subsequent cross-modal priming task, where auditory primes with devoiced final stops (e.g., “seed”, pronounced [si:th]), facilitated recognition of visual targets with voiced final stops (e.g., SEED). In Experiment 1, this learning effect generalized to test pairs where the critical contrast was in word-initial position, e.g., auditory primes such as “town” facilitated recognition of visual targets like DOWN. Control listeners, who had not heard any stops by the speaker during exposure, showed no learning effects. The generalization to word-initial position did not occur when participants had also heard correctly voiced, word-initial stops during exposure (Experiment 2), and when the speaker was a native BE speaker who mimicked the word-final devoicing (Experiment 3). The readiness of the perceptual system to generalize a previously learned adjustment to other positions within the word thus appears to be modulated by distributional properties of the speech input, as well as by the perceived sociophonetic characteristics of the speaker. The results suggest that the transfer of pre-lexical perceptual adjustments that occur through lexically driven learning can be affected by a combination of acoustic, phonological, and sociophonetic factors. PMID:23554598

A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

NASA Astrophysics Data System (ADS)

Castelletti, Davide; Demir, Begüm; Bruzzone, Lorenzo

2014-10-01

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ɛ-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

MEMORE: An Environment for Data Collection and Analysis on the Use of Computers in Education

ERIC Educational Resources Information Center

Goldschmidt, Ronaldo; Fernandes de Souza, Isabel; Norris, Monica; Passos, Claudio; Ferlin, Claudia; Cavalcanti, Maria Claudia; Soares, Jorge

2016-01-01

The use of computers as teaching and learning tools plays a particularly important role in modern society. Within this scenario, Brazil launched its own version of the "One Laptop per Child" (OLPC) program, and this initiative, termed PROUCA, has already distributed hundreds of low-cost laptops for educational purposes in many Brazilian…

Category representations in the brain are both discretely localized and widely distributed.

PubMed

Shehzad, Zarrar; McCarthy, Gregory

2018-06-01

Whether category information is discretely localized or represented widely in the brain remains a contentious issue. Initial functional MRI studies supported the localizationist perspective that category information is represented in discrete brain regions. More recent fMRI studies using machine learning pattern classification techniques provide evidence for widespread distributed representations. However, these latter studies have not typically accounted for shared information. Here, we find strong support for distributed representations when brain regions are considered separately. However, localized representations are revealed by using analytical methods that separate unique from shared information among brain regions. The distributed nature of shared information and the localized nature of unique information suggest that brain connectivity may encourage spreading of information but category-specific computations are carried out in distinct domain-specific regions. NEW & NOTEWORTHY Whether visual category information is localized in unique domain-specific brain regions or distributed in many domain-general brain regions is hotly contested. We resolve this debate by using multivariate analyses to parse functional MRI signals from different brain regions into unique and shared variance. Our findings support elements of both models and show information is initially localized and then shared among other regions leading to distributed representations being observed.

Implementing a Healthy Food Distribution Program: A Supply Chain Strategy to Increase Fruit and Vegetable Access in Underserved Areas.

PubMed

DeFosset, Amelia R; Kwan, Allison; Rizik-Baer, Daniel; Gutierrez, Luis; Gase, Lauren N; Kuo, Tony

2018-05-24

Increasing access to fresh produce in small retail venues could improve the diet of people in underserved communities. However, small retailers face barriers to stocking fresh produce. In 2014, an innovative distribution program, Community Markets Purchasing Real and Affordable Foods (COMPRA), was launched in Los Angeles with the aim of making it more convenient and profitable for small retailers to stock fresh produce. Our case study describes the key processes and lessons learned in the first 2 years of implementing COMPRA. Considerable investments in staff capacity and infrastructure were needed to launch COMPRA. Early successes included significant week-to-week increases in the volume of produce distributed. Leveraging partnerships, maintaining a flexible operational and funding structure, and broadly addressing store owners' needs contributed to initial gains. We describe key challenges and next steps to scaling the program. Lessons learned from implementing COMPRA could inform other jurisdictions considering supply-side approaches to increase access to healthy food.

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.

Lessons Learned through the Development and Publication of AstroImageJ

NASA Astrophysics Data System (ADS)

Collins, Karen

2018-01-01

As lead author of the scientific image processing software package AstroImageJ (AIJ), I will discuss the reasoning behind why we decided to release AIJ to the public, and the lessons we learned related to the development, publication, distribution, and support of AIJ. I will also summarize the AIJ code language selection, code documentation and testing approaches, code distribution, update, and support facilities used, and the code citation and licensing decisions. Since AIJ was initially developed as part of my graduate research and was my first scientific open source software publication, many of my experiences and difficulties encountered may parallel those of others new to scientific software publication. Finally, I will discuss the benefits and disadvantages of releasing scientific software that I now recognize after having AIJ in the public domain for more than five years.

Organisational learning and self-adaptation in dynamic disaster environments.

PubMed

Corbacioglu, Sitki; Kapucu, Naim

2006-06-01

This paper examines the problems associated with inter-organisational learning and adaptation in the dynamic environments that characterise disasters. The research uses both qualitative and quantitative methods to investigate whether organisational learning took place during and in the time in between five disaster response operations in Turkey. The availability of information and its exchange and distribution within and among organisational actors determine whether self-adaptation happens in the course of a disaster response operation. Organisational flexibility supported by an appropriate information infrastructure creates conditions conducive to essential interaction and permits the flow of information. The study found that no significant organisational learning occurred within Turkish disaster management following the earthquakes in Erzincan (1992), Dinar (1995) and Ceyhan (1998). By contrast, the 'symmetry-breaking' Marmara earthquake of 1999 initiated a 'double loop' learning process that led to change in the organisational, technical and cultural aspects of Turkish disaster management, as revealed by the Duzce earthquake response operations.

Experiential Learning through Civic Engagement as a Response to Intimate Partner Violence in the Community

ERIC Educational Resources Information Center

Wardle, Elizabeth Ann; Furgerson, Karen; Davis, Rebecca; Schultz, Tara

2015-01-01

Initially, survey research was conducted to examine the economic impact on domestic violence within a two-county area in South Texas. Surveys were distributed to the police departments in these areas to obtain this information. Once the data were collected, there was evidence that the downturn in the economy was having an effect on family…

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

PubMed

Pernkopf, Franz

2008-12-01

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

Event-Triggered Distributed Control of Nonlinear Interconnected Systems Using Online Reinforcement Learning With Exploration.

PubMed

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-09-07

In this paper, a distributed control scheme for an interconnected system composed of uncertain input affine nonlinear subsystems with event triggered state feedback is presented by using a novel hybrid learning scheme-based approximate dynamic programming with online exploration. First, an approximate solution to the Hamilton-Jacobi-Bellman equation is generated with event sampled neural network (NN) approximation and subsequently, a near optimal control policy for each subsystem is derived. Artificial NNs are utilized as function approximators to develop a suite of identifiers and learn the dynamics of each subsystem. The NN weight tuning rules for the identifier and event-triggering condition are derived using Lyapunov stability theory. Taking into account, the effects of NN approximation of system dynamics and boot-strapping, a novel NN weight update is presented to approximate the optimal value function. Finally, a novel strategy to incorporate exploration in online control framework, using identifiers, is introduced to reduce the overall cost at the expense of additional computations during the initial online learning phase. System states and the NN weight estimation errors are regulated and local uniformly ultimately bounded results are achieved. The analytical results are substantiated using simulation studies.

The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas.

PubMed

Ecker, Joseph R; Geschwind, Daniel H; Kriegstein, Arnold R; Ngai, John; Osten, Pavel; Polioudakis, Damon; Regev, Aviv; Sestan, Nenad; Wickersham, Ian R; Zeng, Hongkui

2017-11-01

A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans. Copyright © 2017 Elsevier Inc. All rights reserved.

Memory Impairment in Multiple Sclerosis is Due to a Core Deficit in Initial Learning

PubMed Central

DeLuca, John; Leavitt, Victoria M.; Chiaravalloti, Nancy; Wylie, Glenn

2013-01-01

Persons with multiple sclerosis (MS) suffer memory impairment, but research on the nature of MS-related memory problems is mixed. Some have argued for a core deficit in retrieval, while others have identified deficient initial learning as the core deficit. We used a selective reminding paradigm to determine whether deficient initial learning or delayed retrieval represents the primary memory deficit in 44 persons with MS. Brain atrophy was measured from high-resolution MRIs. Regression analyses examined the impact of brain atrophy on (a) initial learning and delayed retrieval separately, and then (b) delayed retrieval controlling for initial learning. Brain atrophy was negatively associated with both initial learning and delayed retrieval (ps < .01), but brain atrophy was unrelated to retrieval when controlling for initial learning (p > .05). In addition, brain atrophy was associated with inefficient learning across initial acquisition trials, and brain atrophy was unrelated to delayed recall among MS subjects who successfully acquired the word list (although such learning frequently required many exposures). Taken together, memory deficits in MS are a result of deficits in initial learning; moreover, initial learning mediates the relationship between brain atrophy and subsequent retrieval, thereby supporting the core learning-deficit hypothesis of memory impairment in MS. PMID:23832311

Distributed data networks: a blueprint for Big Data sharing and healthcare analytics.

PubMed

Popovic, Jennifer R

2017-01-01

This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources. © 2016 New York Academy of Sciences.

The origins of word learning: Brain responses of 3-month-olds indicate their rapid association of objects and words.

PubMed

Friedrich, Manuela; Friederici, Angela D

2017-03-01

The present study explored the origins of word learning in early infancy. Using event-related potentials (ERP) we monitored the brain activity of 3-month-old infants when they were repeatedly exposed to several initially novel words paired consistently with each the same initially novel objects or inconsistently with different objects. Our results provide strong evidence that these young infants extract statistic regularities in the distribution of the co-occurrences of objects and words extremely quickly. The data suggest that this ability is based on the rapid formation of associations between the neural representations of objects and words, but that the new associations are not retained in long-term memory until the next day. The type of brain response moreover indicates that, unlike in older infants, in 3-month-olds a semantic processing stage is not involved. Their ability to combine words with meaningful information is caused by a primary learning mechanism that enables the formation of proto-words and acts as a precursor for the acquisition of genuine words. © 2015 John Wiley & Sons Ltd.

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 effectiveness of flipped classroom learning model in secondary physics classroom setting

NASA Astrophysics Data System (ADS)

Prasetyo, B. D.; Suprapto, N.; Pudyastomo, R. N.

2018-03-01

The research aimed to describe the effectiveness of flipped classroom learning model on secondary physics classroom setting during Fall semester of 2017. The research object was Secondary 3 Physics group of Singapore School Kelapa Gading. This research was initiated by giving a pre-test, followed by treatment setting of the flipped classroom learning model. By the end of the learning process, the pupils were given a post-test and questionnaire to figure out pupils' response to the flipped classroom learning model. Based on the data analysis, 89% of pupils had passed the minimum criteria of standardization. The increment level in the students' mark was analysed by normalized n-gain formula, obtaining a normalized n-gain score of 0.4 which fulfil medium category range. Obtains from the questionnaire distributed to the students that 93% of students become more motivated to study physics and 89% of students were very happy to carry on hands-on activity based on the flipped classroom learning model. Those three aspects were used to generate a conclusion that applying flipped classroom learning model in Secondary Physics Classroom setting is effectively applicable.

The next level of distributed learning: the introduction of the personal digital assistant.

PubMed

McKenney, Robert R

2004-01-01

Handheld technology has grown in both popularity and capabilities. Studies continue to be done on their impact in numerous fields. At The Ohio State University Medical Center, a handheld program was started in 2001, initially involving third- and fourth-year medical students and residents. The presence of these digital devices presented the opportunity to examine their use in taking traditional materials and delivering them in a personal digital assistant-friendly format. The objective was to offer these materials within an "anytime anywhere" set-up, thereby positively affecting the learning experience while also laying the foundation for other such uses.

The Ocean Observatories Initiative Data Management and QA/QC: Lessons Learned and the Path Ahead

NASA Astrophysics Data System (ADS)

Vardaro, M.; Belabbassi, L.; Garzio, L. M.; Knuth, F.; Smith, M. J.; Kerfoot, J.; Crowley, M. F.

2016-02-01

The Ocean Observatories Initiative (OOI) is a multi-decadal, NSF-funded program that will provide long-term, near real-time cabled and telemetered measurements of climate variability, ocean circulation, ecosystem dynamics, air-sea exchange, seafloor processes, and plate-scale geodynamics. The OOI platforms consist of seafloor sensors, fixed moorings, and mobile assets containing over 700 operational instruments in the Atlantic and Pacific oceans. Rutgers University operates the Cyberinfrastructure (CI) component of the OOI, which acquires, processes and distributes data to scientists, researchers, educators and the public. It will also provide observatory mission command and control, data assessment and distribution, and long-term data management. The Rutgers Data Management Team consists of a data manager and four data evaluators, who are tasked with ensuring data completeness and quality, as well as interaction with OOI users to facilitate data delivery and utility. Here we will discuss the procedures developed to guide the data team workflow, the automated QC algorithms and human-in-the-loop (HITL) annotations that are used to flag suspect data (whether due to instrument failures, biofouling, or unanticipated events), system alerts and alarms, long-term data storage and CF (Climate and Forecast) standard compliance, and the lessons learned during construction and the first several months of OOI operations.

Importance of astrocytes for potassium ion (K+) homeostasis in brain and glial effects of K+ and its transporters on learning.

PubMed

Hertz, Leif; Chen, Ye

2016-12-01

Initial clearance of extracellular K + ([K + ] o ) following neuronal excitation occurs by astrocytic uptake, because elevated [K + ] o activates astrocytic but not neuronal Na + ,K + -ATPases. Subsequently, astrocytic K + is re-released via Kir4.1 channels after distribution in the astrocytic functional syncytium via gap junctions. The dispersal ensures widespread release, preventing renewed [K + ] o increase and allowing neuronal Na + ,K + -ATPase-mediated re-uptake. Na + ,K + -ATPase operation creates extracellular hypertonicity and cell shrinkage which is reversed by the astrocytic cotransporter NKCC1. Inhibition of Kir channels by activation of specific PKC isotypes may decrease syncytial distribution and enable physiologically occurring [K + ] o increases to open L-channels for Ca 2+ , activating [K + ] o -stimulated gliotransmitter release and regulating gap junctions. Learning is impaired when [K + ] o is decreased to levels mainly affecting astrocytic membrane potential or Na + ,K + -ATPase or by abnormalities in its α2 subunit. It is enhanced by NKCC1-mediated ion and water uptake during the undershoot, reversing neuronal inactivity, but impaired in migraine with aura in which [K + ] o is highly increased. Vasopressin augments NKCC1 effects and facilitates learning. Enhanced myelination, facilitated by astrocytic-oligodendrocytic gap junctions also promotes learning. Copyright © 2016 Elsevier Ltd. All rights reserved.

Distance Learning: A Way of Life-Long Learning

DTIC Science & Technology

2005-09-01

promise of future benefits. 15. SUBJECT TERMS training, educational technology , distributed learning , distance learning , collaboration, online instruction...knowledge." - Aristotle Introduction Modern learning technology assumes various names: distance learning , distributed training, computer-based...training, web-based learning , or advanced distributed learning . No matter the name, the basic concept is using computer technology for instruction with no

Summative Evaluation of the Learning Initiatives Program (LIP). Final Report

ERIC Educational Resources Information Center

Human Resources and Skills Development Canada, 2005

2005-01-01

The Learning Initiatives Program (LIP), formerly the Learning Initiatives Fund (LIF), is a contribution program which was established in 1994 to encourage and support initiatives that contribute to the development of a results-oriented, accessible, relevant and accountable learning system in Canada. Through this program, Human Resources and Skills…

Effectiveness of an e-Learning Platform for Image Interpretation Education of Medical Staff and Students.

PubMed

Ogura, Akio; Hayashi, Norio; Negishi, Tohru; Watanabe, Haruyuki

2018-05-09

Medical staff must be able to perform accurate initial interpretations of radiography to prevent diagnostic errors. Education in medical image interpretation is an ongoing need that is addressed by text-based and e-learning platforms. The effectiveness of these methods has been previously reported. Here, we describe the effectiveness of an e-learning platform used for medical image interpretation education. Ten third-year medical students without previous experience in chest radiography interpretation were provided with e-learning instructions. Accuracy of diagnosis using chest radiography was provided before and after e-learning education. We measured detection accuracy for two image groups: nodular shadow and ground-glass shadow. We also distributed the e-learning system to the two groups and analyzed the effectiveness of education for both types of image shadow. The mean correct answer rate after the 2-week e-learning period increased from 34.5 to 72.7%. Diagnosis of the ground glass shadow improved significantly more than that of the mass shadow. Education using the e-leaning platform is effective for interpretation of chest radiography results. E-learning is particularly effective for the interpretation of chest radiography images containing ground glass shadow.

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

NASA Astrophysics Data System (ADS)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Improvements in State and Local Planning for Mass Dispensing of Medical Countermeasures: The Technical Assistance Review Program, United States, 2007-2014.

PubMed

Renard, Paul G; Vagi, Sara J; Reinold, Chris M; Silverman, Brenda L; Avchen, Rachel N

2017-09-01

To evaluate and describe outcomes of state and local medical countermeasure preparedness planning, which is critical to ensure rapid distribution and dispensing of a broad spectrum of life-saving medical assets during a public health emergency. We used 2007 to 2014 state and local data collected from the Centers for Disease Control and Prevention's Technical Assistance Review. We calculated descriptive statistics from 50 states and 72 local Cities Readiness Initiative jurisdictions that participated in the Technical Assistance Review annually. From 2007 to 2014, the average overall Technical Assistance Review score increased by 13% for states and 41% for Cities Readiness Initiative jurisdictions. In 2014, nearly half of states achieved the maximum possible overall score (100), and 94% of local Cities Readiness Initiative jurisdictions achieved a score of 90 or more. Despite challenges, effective and timely medical countermeasure distribution and dispensing is possible with appropriate planning, staff, and resources. However, vigilance in training, exercising, and improving plans from lessons learned in a sustained, coordinated way is critical to ensure continued public health preparedness success.

Estimating the Attack Ratio of Dengue Epidemics under Time-varying Force of Infection using Aggregated Notification Data

NASA Astrophysics Data System (ADS)

Coelho, Flavio Codeço; Carvalho, Luiz Max De

2015-12-01

Quantifying the attack ratio of disease is key to epidemiological inference and public health planning. For multi-serotype pathogens, however, different levels of serotype-specific immunity make it difficult to assess the population at risk. In this paper we propose a Bayesian method for estimation of the attack ratio of an epidemic and the initial fraction of susceptibles using aggregated incidence data. We derive the probability distribution of the effective reproductive number, Rt, and use MCMC to obtain posterior distributions of the parameters of a single-strain SIR transmission model with time-varying force of infection. Our method is showcased in a data set consisting of 18 years of dengue incidence in the city of Rio de Janeiro, Brazil. We demonstrate that it is possible to learn about the initial fraction of susceptibles and the attack ratio even in the absence of serotype specific data. On the other hand, the information provided by this approach is limited, stressing the need for detailed serological surveys to characterise the distribution of serotype-specific immunity in the population.

The Pioneering Role of the Vaccine Safety Datalink Project (VSD) to Advance Collaborative Research and Distributed Data Networks

PubMed Central

Fahey, Kevin R.

2015-01-01

Introduction: Large-scale distributed data networks consisting of diverse stakeholders including providers, patients, and payers are changing health research in terms of methods, speed and efficiency. The Vaccine Safety Datalink (VSD) set the stage for expanded involvement of health plans in collaborative research. Expanding Surveillance Capacity and Progress Toward a Learning Health System: From an initial collaboration of four integrated health systems with fewer than 10 million covered lives to 16 diverse health plans with nearly 100 million lives now in the FDA Sentinel, the expanded engagement of health plan researchers has been essential to increase the value and impact of these efforts. The collaborative structure of the VSD established a pathway toward research efforts that successfully engage all stakeholders in a cohesive rather than competitive manner. The scientific expertise and methodology developed through the VSD such as rapid cycle analysis (RCA) to conduct near real-time safety surveillance allowed for the development of the expanded surveillance systems that now exist. Building on Success and Lessons Learned: These networks have learned from and built on the knowledge base and infrastructure created by the VSD investigators. This shared technical knowledge and experience expedited the development of systems like the FDA’s Mini-Sentinel and the Patient Centered Outcomes Research Institute (PCORI)’s PCORnet Conclusion: This narrative reviews the evolution of the VSD, its contribution to other collaborative research networks, longer-term sustainability of this type of distributed research, and how knowledge gained from the earlier efforts can contribute to a continually learning health system. PMID:26793736

Automated Planning and Scheduling for Space Mission Operations

NASA Technical Reports Server (NTRS)

Chien, Steve; Jonsson, Ari; Knight, Russell

2005-01-01

Research Trends: a) Finite-capacity scheduling under more complex constraints and increased problem dimensionality (subcontracting, overtime, lot splitting, inventory, etc.) b) Integrated planning and scheduling. c) Mixed-initiative frameworks. d) Management of uncertainty (proactive and reactive). e) Autonomous agent architectures and distributed production management. e) Integration of machine learning capabilities. f) Wider scope of applications: 1) analysis of supplier/buyer protocols & tradeoffs; 2) integration of strategic & tactical decision-making; and 3) enterprise integration.

Technology and Its Use in Education: Present Roles and Future Prospects

ERIC Educational Resources Information Center

Courville, Keith

2011-01-01

(Purpose) This article describes two current trends in Educational Technology: distributed learning and electronic databases. (Findings) Topics addressed in this paper include: (1) distributed learning as a means of professional development; (2) distributed learning for content visualization; (3) usage of distributed learning for educational…

Designing Distributed Learning Environments with Intelligent Software Agents

ERIC Educational Resources Information Center

Lin, Fuhua, Ed.

2005-01-01

"Designing Distributed Learning Environments with Intelligent Software Agents" reports on the most recent advances in agent technologies for distributed learning. Chapters are devoted to the various aspects of intelligent software agents in distributed learning, including the methodological and technical issues on where and how intelligent agents…

Distributed Learning Metadata Standards

ERIC Educational Resources Information Center

McClelland, Marilyn

2004-01-01

Significant economies can be achieved in distributed learning systems architected with a focus on interoperability and reuse. The key building blocks of an efficient distributed learning architecture are the use of standards and XML technologies. The goal of plug and play capability among various components of a distributed learning system…

Teachers' Personal Learning Networks (PLNs): Exploring the Nature of Self-Initiated Professional Learning Online

ERIC Educational Resources Information Center

Tour, Ekaterina

2017-01-01

In the field of Literacy Studies, online spaces have been recognised as providing many opportunities for spontaneous and self-initiated learning. While some progress has been made in understanding these important learning experiences, little attention has been paid to teachers' self-initiated professional learning. Contributing to the debates…

Assessment of the University of Michigan's dental hygiene partnership with the Huron Valley Boys & Girls Club: a study of students' and staffs' perceptions and service learning outcomes.

PubMed

Christensen Brydges, Sarah; Gwozdek, Anne E

2011-01-01

The Boys & Girls Club of America (BGCA) requires a health curriculum be taught. With the assistance of the University of Michigan (UM) Dental Hygiene program, these requirements have been addressed at the Huron Valley Boys & Girls Club (HVBGC) through dental hygiene students presenting oral health education to club members throughout the year. This study assessed the outcomes and benefits of the service learning initiative between the UM Dental Hygiene Program and the HVBGC from both the students' and staffs' perceptions. Three surveys were distributed: one to the HVBGC staff, one to UM's Dental Hygiene class of 2012 (with no service learning experience at the HVBGC) and one to UM Dental Hygiene classes of 2010 and 2011 (most of whom had experience at the HVBGC). Qualitative and quantitative data were collected and evaluated. The respondents from the class of 2012 were less knowledgeable about the BGCA and access to care issues. The members of the classes of 2010 and 2011, 79% of whom had HVBGC experience, identified they had benefitted from this service learning experience. The HVBGC staff survey indicated a high level of satisfaction with the student presentations and felt their curricular requirements were being met. Future topics of safety, orthodontics and gardening/nutrition were identified. This study indicates the service learning initiative has been beneficial for both the UM Dental Hygiene students and the HVBGC. Future studies should use a longitudinal design to obtain baseline and post-service learning data.

Set size manipulations reveal the boundary conditions of perceptual ensemble learning.

PubMed

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-11-01

Recent evidence suggests that observers can grasp patterns of feature variations in the environment with surprising efficiency. During visual search tasks where all distractors are randomly drawn from a certain distribution rather than all being homogeneous, observers are capable of learning highly complex statistical properties of distractor sets. After only a few trials (learning phase), the statistical properties of distributions - mean, variance and crucially, shape - can be learned, and these representations affect search during a subsequent test phase (Chetverikov, Campana, & Kristjánsson, 2016). To assess the limits of such distribution learning, we varied the information available to observers about the underlying distractor distributions by manipulating set size during the learning phase in two experiments. We found that robust distribution learning only occurred for large set sizes. We also used set size to assess whether the learning of distribution properties makes search more efficient. The results reveal how a certain minimum of information is required for learning to occur, thereby delineating the boundary conditions of learning of statistical variation in the environment. However, the benefits of distribution learning for search efficiency remain unclear. Copyright © 2017 Elsevier Ltd. All rights reserved.

Students' and residents' perceptions regarding technology in medical training.

PubMed

Briscoe, Gregory W; Fore Arcand, Lisa G; Lin, Terence; Johnson, Joel; Rai, Aanmol; Kollins, Kevin

2006-01-01

This pilot study provides firsthand feedback from medical students and residents in training regarding their perceptions of technology in medicine. The authors distributed an e-mail invitation to an anonymous Web-based survey to medical students and residents in two different U.S. training institutions. Respondents unanimously expressed that technology skills were important in medical training and felt it most important to learn about electronic medical records and accessing scientific information on the Internet. At the point of patient care, trainees' preferred reference sources were the Internet and PDA, in that order. Most clinical trainees felt PDAs were critical in patient care and met their clinical needs, and they were most likely to use them for medication reference. The majority of trainees preferred printed media over digital media for initial learning, but the converse for referencing. Instructor-led small groups were viewed as the best environment in which to receive instruction. Trainees in medical education are technologically savvy and provide invaluable feedback regarding initiation, development and refinement of technological systems in medical training.

Enhancing ASTRO101 Student Engagement Using Student-Created ScienceSKETCHES

NASA Astrophysics Data System (ADS)

Slater, Timothy F.; Slater, Stephanie

2016-01-01

As astronomy teaching faculty are changing their teaching strategies from those less desirable approaches that allow students to passively listen to professor-centered, information-lectures to more desirable, active-student engagement classrooms characterized by active learning, ASTRO 101 professors are looking for more ways to help students learn to participate in authentic scientific practices. This is consistent with notion advocated by the NRC that students should practice scientific thinking, scientific discourse, and scientific practices while learning science. Noticing that much informal scientific discussion is mediated by sketches—such as those occasionally lively discussions held after hours during scientific conferences—scholars at the CAPER Center for Astronomy & Physics Education Research have been piloting a series of active learning tasks where students are challenged to create scientific drawings to illustrate their understanding of astronomical phenomena or structures. Known informally as ScienceSKETCHES, examples of these tasks challenge students to illustrate: the spectral curve differences between high and low mass stars; the differences among galaxy shapes; the distribution of stars for the Andromeda Galaxy in terms of luminosity versus temperature; old and young planetary surfaces; or the relationships between distances and speeds of orbiting objects. Although our initial testing has focused on predominately on paper and pencil tasks, with the occasional cell phone picture of a ScienceSKETCH being texted to the professor, the electronic-based teaching world is nearly ready to support these sorts of drawing tasks. Already, the ability to complete and submit scientific sketches is becoming commonplace across electronic learning platforms, including shared white-boarding in many desktop videoconferencing systems, and handheld device learning systems for interactive classrooms, like those from Learning Catalytics, among many others. Our initial results suggest that this strategy is worthwhile line of research and development for a wide range of astronomy education researchers and curriculum developers.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

PubMed

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

2018-01-01

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

Key Barriers to the Implementation of Solar Energy in Nigeria: A Critical Analysis

NASA Astrophysics Data System (ADS)

Abdullahi, D.; Suresh, S.; Renukappa, S.; Oloke, D.

2017-08-01

Nigeria, potentially, has abundant sunshine throughout the year, making it full thirst for solar energy generation. Even though, the country’s solar energy projects have not realised a fair result over the years, due to many barriers associated with initiatives implementation. Therefore, the entire power sector remains incapacitated to generate, transmit and distribute a clean, affordable and sustainable energy to assist economic growth. The research integrated five African counterpart’s solar energy initiatives, barriers, policies and strategies adopted as a lesson learned to Nigeria. Inadequate solar initiative’s research, lack of technological know-how, short-term policies, lack of awareness and political instability are the major barriers that made the implementation of solar initiatives almost impossible in Nigeria. The shock of the barriers therefore, constitutes a major negative contribution to the crippling of the power sector in the state. Future research will concentrate on initiatives for mitigating solar and other renewable energy barriers.

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

A Framework for Mobile Learning for Enhancing Learning in Higher Education

ERIC Educational Resources Information Center

Barreh, Kadar Abdillahi; Abas, Zoraini Wati

2015-01-01

As mobile learning becomes increasingly pervasive, many higher education institutions have initiated a number of mobile learning initiatives to support their traditional learning modes. This study proposes a framework for mobile learning for enhancing learning in higher education. This framework for mobile learning is based on research conducted…

Learning transitive verbs from single-word verbs in the input by young children acquiring English.

PubMed

Ninio, Anat

2016-09-01

The environmental context of verbs addressed by adults to young children is claimed to be uninformative regarding the verbs' meaning, yielding the Syntactic Bootstrapping Hypothesis that, for verb learning, full sentences are needed to demonstrate the semantic arguments of verbs. However, reanalysis of Gleitman's (1990) original data regarding input to a blind child revealed the context of single-word parental verbs to be more transparent than that of sentences. We tested the hypothesis that English-speaking children learn their early verbs from parents' single-word utterances. Distribution of single-word transitive verbs produced by a large sample of young children was strongly predicted by the relative token frequency of verbs in parental single-word utterances, but multiword sentences had no predictive value. Analysis of the interactive context showed that objects of verbs are retrievable by pragmatic inference, as is the meaning of the verbs. Single-word input appears optimal for learning an initial vocabulary of verbs.

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

PubMed Central

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

PubMed

Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

2016-01-01

Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

Accelerating the development of formal thinking in middle and high school students II: Postproject effects on science achievement

NASA Astrophysics Data System (ADS)

Shayer, Michael; Adey, Philip S.

A one-year lag was found between the effect of an intervention intended to promote formal operational thinking in students initially 11 or 12 years of age and the appearance of substantial science achievement in the experimental groups. A one-year lag was also reported on cognitive development: Whereas at the end of the two-year intervention the experimental groups were up to 0.9 ahead of the control groups, one year later the differential on Piagetian measures had disappeared, but the experimentals now showed better science achievement of even greater magnitude. Although the control groups showed normal distribution both on science achievement and cognitive development, the experimental groups showed bi- or trimodal distribution. Between one-half and one-quarter of the students involved in the experiment in different groups showed effects of the order of 2 both on cognitive development and science achievement; some students appeared unaffected (compared with the controls), and others demonstrated modest effects on science achievement. An age/gender interaction is reported: the most substantial effects were found in boys initially aged 12+ and girls initially 11+. The only group to show no effects was boys initially aged 11+. It is suggested that the intervention methods may have favored the abstract analytical learning style as described by Cohen 1986.

End-to-end learning for digital hologram reconstruction

NASA Astrophysics Data System (ADS)

Xu, Zhimin; Zuo, Si; Lam, Edmund Y.

2018-02-01

Digital holography is a well-known method to perform three-dimensional imaging by recording the light wavefront information originating from the object. Not only the intensity, but also the phase distribution of the wavefront can then be computed from the recorded hologram in the numerical reconstruction process. However, the reconstructions via the traditional methods suffer from various artifacts caused by twin-image, zero-order term, and noise from image sensors. Here we demonstrate that an end-to-end deep neural network (DNN) can learn to perform both intensity and phase recovery directly from an intensity-only hologram. We experimentally show that the artifacts can be effectively suppressed. Meanwhile, our network doesn't need any preprocessing for initialization, and is comparably fast to train and test, in comparison with the recently published learning-based method. In addition, we validate that the performance improvement can be achieved by introducing a prior on sparsity.

Distributional Language Learning: Mechanisms and Models of ategory Formation.

PubMed

Aslin, Richard N; Newport, Elissa L

2014-09-01

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

Barriers to Change: Findings from Three Literacy Professional Learning Initiatives

ERIC Educational Resources Information Center

Parsons, Allison Ward; Parsons, Seth A.; Morewood, Aimee; Ankrum, Julie W.

2016-01-01

In this article, we describe lessons learned from three separate literacy professional learning initiatives that took place in elementary schools in three different locations: high-poverty urban, medium-poverty rural, and low-poverty suburban. The professional learning initiatives were also diverse in scope: one was a three-year, school-wide…

A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems

DTIC Science & Technology

1990-11-01

Intelligence Systems," in Distributed Artifcial Intelligence , vol. II, L. Gasser and M. Huhns (eds), Pitman, London, 1989, pp. 413-430. Shaw, M. Harrow, B...IDTIC FILE COPY A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems N Michael I. Shaw...SUBTITLE 5. FUNDING NUMBERS A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems 6

Manifold Regularized Multitask Feature Learning for Multimodality Disease Classification

PubMed Central

Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

2015-01-01

Multimodality based methods have shown great advantages in classification of Alzheimer’s disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. PMID:25277605

Multisource Data Classification Using A Hybrid Semi-supervised Learning Scheme

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

Vatsavai, Raju; Bhaduri, Budhendra L; Shekhar, Shashi

2009-01-01

In many practical situations thematic classes can not be discriminated by spectral measurements alone. Often one needs additional features such as population density, road density, wetlands, elevation, soil types, etc. which are discrete attributes. On the other hand remote sensing image features are continuous attributes. Finding a suitable statistical model and estimation of parameters is a challenging task in multisource (e.g., discrete and continuous attributes) data classification. In this paper we present a semi-supervised learning method by assuming that the samples were generated by a mixture model, where each component could be either a continuous or discrete distribution. Overall classificationmore » accuracy of the proposed method is improved by 12% in our initial experiments.« less

New Media Vision for IYA

NASA Astrophysics Data System (ADS)

Gay, P. L.; Koppelman, M.

2008-11-01

The International Year of Astronomy New Media Committee seeks to provide and promote online astronomy experiences in the places that people work, play and learn; create content that will expose people to astronomy, provide them regular content, and create special opportunities for learning; distribute content for active (pull) and passive (push) channels and through guerilla marketing technique; use a diverse suite of technologies to reach people on multiple platforms and in diverse online settings. To make these goals a reality we have brought together a diverse group of astronomy new media practitioners to both mentor grass roots efforts and spearhead national initiatives. You are invited to partner you programs with the New Media Task Group. In this paper we lay out are goals and define our vision.

Neural initialization of audiovisual integration in prereaders at varying risk for developmental dyslexia.

PubMed

I Karipidis, Iliana; Pleisch, Georgette; Röthlisberger, Martina; Hofstetter, Christoph; Dornbierer, Dario; Stämpfli, Philipp; Brem, Silvia

2017-02-01

Learning letter-speech sound correspondences is a major step in reading acquisition and is severely impaired in children with dyslexia. Up to now, it remains largely unknown how quickly neural networks adopt specific functions during audiovisual integration of linguistic information when prereading children learn letter-speech sound correspondences. Here, we simulated the process of learning letter-speech sound correspondences in 20 prereading children (6.13-7.17 years) at varying risk for dyslexia by training artificial letter-speech sound correspondences within a single experimental session. Subsequently, we acquired simultaneously event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) scans during implicit audiovisual presentation of trained and untrained pairs. Audiovisual integration of trained pairs correlated with individual learning rates in right superior temporal, left inferior temporal, and bilateral parietal areas and with phonological awareness in left temporal areas. In correspondence, a differential left-lateralized parietooccipitotemporal ERP at 400 ms for trained pairs correlated with learning achievement and familial risk. Finally, a late (650 ms) posterior negativity indicating audiovisual congruency of trained pairs was associated with increased fMRI activation in the left occipital cortex. Taken together, a short (<30 min) letter-speech sound training initializes audiovisual integration in neural systems that are responsible for processing linguistic information in proficient readers. To conclude, the ability to learn grapheme-phoneme correspondences, the familial history of reading disability, and phonological awareness of prereading children account for the degree of audiovisual integration in a distributed brain network. Such findings on emerging linguistic audiovisual integration could allow for distinguishing between children with typical and atypical reading development. Hum Brain Mapp 38:1038-1055, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Lessons learned from the U.S. Geological Survey abandoned mine lands initiative: 1997-2002

USGS Publications Warehouse

Kimball, Briant A.; Church, Stan E.; Besser, John M.

2006-01-01

Growth of the United States has been facilitated, in part, by hard-rock mining in the Rocky Mountains. Abandoned and inactive mines cause many significant environmental concerns in hundreds of watersheds. Those who have responsibility to address these environmental concerns must have a basic level of scientific information about mining and mine wastes in a watershed prior to initiating remediation activities. To demonstrate what information is needed and how to obtain that information, the U.S. Geological Survey implemented the Abandoned Mine Lands (AML) Initiative from 1997 to 2002 with demonstration studies in the Boulder River watershed in Montana and the Animas River watershed in Colorado. The AML Initiative included collection and analysis of geologic, hydrologic, geochemical, geophysical, and biological data. The synergy of this interdisciplinary analysis produced a perspective of the environmental concerns that could not have come from a single discipline. Two examples of these perspectives include (1) the combination of hydrological tracer techniques, structural geology, and geophysics help to understand the spatial distribution of loading to the streams in a way that cannot be evaluated by monitoring at a catchment outlet, and (2) the combination of toxicology and hydrology combine to illustrate that seasonal variability of toxicity conditions occurs. Lessons have been learned by listening to and collaborating with land-management agencies to understand their needs and by applying interdisciplinary methods to answer their questions.

Improvements in State and Local Planning for Mass Dispensing of Medical Countermeasures: The Technical Assistance Review Program, United States, 2007–2014

PubMed Central

Vagi, Sara J.; Reinold, Chris M.; Silverman, Brenda L.; Avchen, Rachel N.

2017-01-01

Objectives. To evaluate and describe outcomes of state and local medical countermeasure preparedness planning, which is critical to ensure rapid distribution and dispensing of a broad spectrum of life-saving medical assets during a public health emergency. Methods. We used 2007 to 2014 state and local data collected from the Centers for Disease Control and Prevention’s Technical Assistance Review. We calculated descriptive statistics from 50 states and 72 local Cities Readiness Initiative jurisdictions that participated in the Technical Assistance Review annually. Results. From 2007 to 2014, the average overall Technical Assistance Review score increased by 13% for states and 41% for Cities Readiness Initiative jurisdictions. In 2014, nearly half of states achieved the maximum possible overall score (100), and 94% of local Cities Readiness Initiative jurisdictions achieved a score of 90 or more. Conclusions. Despite challenges, effective and timely medical countermeasure distribution and dispensing is possible with appropriate planning, staff, and resources. However, vigilance in training, exercising, and improving plans from lessons learned in a sustained, coordinated way is critical to ensure continued public health preparedness success. PMID:28892441

Spatial and reversal learning in the Morris water maze are largely resistant to six hours of REM sleep deprivation following training

PubMed Central

Walsh, Christine M.; Booth, Victoria; Poe, Gina R.

2011-01-01

This first test of the role of REM (rapid eye movement) sleep in reversal spatial learning is also the first attempt to replicate a much cited pair of papers reporting that REM sleep deprivation impairs the consolidation of initial spatial learning in the Morris water maze. We hypothesized that REM sleep deprivation following training would impair both hippocampus-dependent spatial learning and learning a new target location within a familiar environment: reversal learning. A 6-d protocol was divided into the initial spatial learning phase (3.5 d) immediately followed by the reversal phase (2.5 d). During the 6 h following four or 12 training trials/day of initial or reversal learning phases, REM sleep was eliminated and non-REM sleep left intact using the multiple inverted flowerpot method. Contrary to our hypotheses, REM sleep deprivation during four or 12 trials/day of initial spatial or reversal learning did not affect training performance. However, some probe trial measures indicated REM sleep-deprivation–associated impairment in initial spatial learning with four trials/day and enhancement of subsequent reversal learning. In naive animals, REM sleep deprivation during normal initial spatial learning was followed by a lack of preference for the subsequent reversal platform location during the probe. Our findings contradict reports that REM sleep is essential for spatial learning in the Morris water maze and newly reveal that short periods of REM sleep deprivation do not impair concurrent reversal learning. Effects on subsequent reversal learning are consistent with the idea that REM sleep serves the consolidation of incompletely learned items. PMID:21677190

Learning, Labour and Union Learning Representatives: Promoting Workplace Learning

ERIC Educational Resources Information Center

Ball, Malcolm

2011-01-01

The initiative by the Trades Union Congress (TUC) and affiliated trade unions in the UK to appoint trade union learning representatives (ULRs), to promote learning among their members, is a significant development in adult learning. Understandably, the initiative has attracted the attention of academic researchers, but primarily from the…

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

PubMed

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

Two generalizations of Kohonen clustering

NASA Technical Reports Server (NTRS)

Bezdek, James C.; Pal, Nikhil R.; Tsao, Eric C. K.

1993-01-01

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ.

Developmental Changes in Cross-Situational Word Learning: The Inverse Effect of Initial Accuracy

ERIC Educational Resources Information Center

Fitneva, Stanka A.; Christiansen, Morten H.

2017-01-01

Intuitively, the accuracy of initial word-referent mappings should be positively correlated with the outcome of learning. Yet recent evidence suggests an inverse effect of initial accuracy in adults, whereby greater accuracy of initial mappings is associated with poorer outcomes in a cross-situational learning task. Here, we examine the impact of…

The effects of initial participation motivations on learning engagement in transition training for future general practitioners in rural China: perceived deterrents as mediator

PubMed Central

Cui, Guan-yu; Yao, Mei-lin; Zhang, Xia; Guo, Yan-kui; Li, Hui-min; Yao, Xiu-ping

2016-01-01

Background For the shortage of high-quality general practitioners (GPs) in China's rural areas, Chinese government has taken steps to encourage rural specialists to participate in transition training for future GPs. Specialists’ initial participation motivations and their perceived deterrents during training may play important roles for their learning engagement in the transition training. This study aimed at revealing the relationships among the variables of initial participation motivations, perceived deterrents in training, and learning engagement. Methods A questionnaire survey was used in this study. A total of 156 rural specialists who participated in transition training for future GPs filled out the questionnaire, which consisted of the measurements of initial participation motivations, perceived deterrents, and learning engagement in training. The data about specialists’ demographic variables were collected at the same time. Results The variance of initial escape/stimulations motivation significantly predicted the variance of learning engagement through the full mediating role of perceived deterrents in training. In addition, initial educational preparation motivations predicted the variance of learning engagement directly. Conclusions Specialists’ initial participation motivations and perceived deterrents in training played important roles for learning engagement in the transition training. PMID:27340086

Neural correlates of skill acquisition: decreased cortical activity during a serial interception sequence learning task.

PubMed

Gobel, Eric W; Parrish, Todd B; Reber, Paul J

2011-10-15

Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of the frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. Copyright © 2011 Elsevier Inc. All rights reserved.

Neural Correlates of Skill Acquisition: Decreased Cortical Activity During a Serial Interception Sequence Learning Task

PubMed Central

Gobel, Eric W.; Parrish, Todd B.; Reber, Paul J.

2011-01-01

Learning of complex motor skills requires learning of component movements as well as the sequential structure of their order and timing. Using a Serial Interception Sequence Learning (SISL) task, participants learned a sequence of precisely timed interception responses through training with a repeating sequence. Following initial implicit learning of the repeating sequence, functional MRI data were collected during performance of that known sequence and compared with activity evoked during novel sequences of actions, novel timing patterns, or both. Reduced activity was observed during the practiced sequence in a distributed bilateral network including extrastriate occipital, parietal, and premotor cortical regions. These reductions in evoked activity likely reflect improved efficiency in visuospatial processing, spatio-motor integration, motor planning, and motor execution for the trained sequence, which is likely supported by nondeclarative skill learning. In addition, the practiced sequence evoked increased activity in the left ventral striatum and medial prefrontal cortex, while the posterior cingulate was more active during periods of better performance. Many prior studies of perceptual-motor skill learning have found increased activity in motor areas of frontal cortex (e.g., motor and premotor cortex, SMA) and striatal areas (e.g., the putamen). The change in activity observed here (i.e., decreased activity across a cortical network) may reflect skill learning that is predominantly expressed through more accurate performance rather than decreased reaction time. PMID:21771663

The surprisingly high human efficiency at learning to recognize faces

PubMed Central

Peterson, Matthew F.; Abbey, Craig K.; Eckstein, Miguel P.

2009-01-01

We investigated the ability of humans to optimize face recognition performance through rapid learning of individual relevant features. We created artificial faces with discriminating visual information heavily concentrated in single features (nose, eyes, chin or mouth). In each of 2500 learning blocks a feature was randomly selected and retained over the course of four trials, during which observers identified randomly sampled, noisy face images. Observers learned the discriminating feature through indirect feedback, leading to large performance gains. Performance was compared to a learning Bayesian ideal observer, resulting in unexpectedly high learning compared to previous studies with simpler stimuli. We explore various explanations and conclude that the higher learning measured with faces cannot be driven by adaptive eye movement strategies but can be mostly accounted for by suboptimalities in human face discrimination when observers are uncertain about the discriminating feature. We show that an initial bias of humans to use specific features to perform the task even though they are informed that each of four features is equally likely to be the discriminatory feature would lead to seemingly supra-optimal learning. We also examine the possibility of inefficient human integration of visual information across the spatially distributed facial features. Together, the results suggest that humans can show large performance improvement effects in discriminating faces as they learn to identify the feature containing the discriminatory information. PMID:19000918

45 CFR 2516.600 - How are funds for school-based service-learning programs distributed?

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 4 2010-10-01 2010-10-01 false How are funds for school-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2516.600 How are funds for school-based service-learning programs distributed? (a) Of...

45 CFR 2516.600 - How are funds for school-based service-learning programs distributed?

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 4 2011-10-01 2011-10-01 false How are funds for school-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE SCHOOL-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2516.600 How are funds for school-based service-learning programs distributed? (a) Of...

45 CFR 2517.600 - How are funds for community-based service-learning programs distributed?

Code of Federal Regulations, 2010 CFR

2010-10-01

... 45 Public Welfare 4 2010-10-01 2010-10-01 false How are funds for community-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE COMMUNITY-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2517.600 How are funds for community-based service-learning programs distributed? All...

45 CFR 2517.600 - How are funds for community-based service-learning programs distributed?

Code of Federal Regulations, 2011 CFR

2011-10-01

... 45 Public Welfare 4 2011-10-01 2011-10-01 false How are funds for community-based service-learning... (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE COMMUNITY-BASED SERVICE-LEARNING PROGRAMS Distribution of Funds § 2517.600 How are funds for community-based service-learning programs distributed? All...

Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study

PubMed Central

Teinonen, Tuomas; Tervaniemi, Mari; Huotilainen, Minna

2016-01-01

Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0–100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200–400 ms and 450–650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns. PMID:27617967

Optimizing one-shot learning with binary synapses.

PubMed

Romani, Sandro; Amit, Daniel J; Amit, Yali

2008-08-01

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.

An Efficacious Measurement of Learning Initiatives: E-Learning Systems, Learning-Organization Culture, Knowledge Creation, and Innovativeness

ERIC Educational Resources Information Center

Grundhoefer, Raymie

2013-01-01

The purpose of this research is twofold: (a) develop a validated measure for learning initiatives based on knowledge-creation theory and (b) conduct a quantitative study to investigate the relationships between electronic learning systems, learning-organization culture, efficacious knowledge creation (EKC), and innovativeness. Although Cheng-Chang…

Study of distributed learning as a solution to category proliferation in Fuzzy ARTMAP based neural systems.

PubMed

Parrado-Hernández, Emilio; Gómez-Sánchez, Eduardo; Dimitriadis, Yannis A

2003-09-01

An evaluation of distributed learning as a means to attenuate the category proliferation problem in Fuzzy ARTMAP based neural systems is carried out, from both qualitative and quantitative points of view. The study involves two original winner-take-all (WTA) architectures, Fuzzy ARTMAP and FasArt, and their distributed versions, dARTMAP and dFasArt. A qualitative analysis of the distributed learning properties of dARTMAP is made, focusing on the new elements introduced to endow Fuzzy ARTMAP with distributed learning. In addition, a quantitative study on a selected set of classification problems points out that problems have to present certain features in their output classes in order to noticeably reduce the number of recruited categories and achieve an acceptable classification accuracy. As part of this analysis, distributed learning was successfully adapted to a member of the Fuzzy ARTMAP family, FasArt, and similar procedures can be used to extend distributed learning capabilities to other Fuzzy ARTMAP based systems.

Creating and Nurturing Distributed Asynchronous Learning Environments.

ERIC Educational Resources Information Center

Kochtanek, Thomas R.; Hein, Karen K.

2000-01-01

Describes the evolution of a university course from a face-to-face experience to a Web-based asynchronous learning environment. Topics include cognition and learning; distance learning and distributed learning; student learning communities and the traditional classroom; the future as it relates to education and technology; collaborative student…

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

PubMed

Deist, Timo M; Jochems, A; van Soest, Johan; Nalbantov, Georgi; Oberije, Cary; Walsh, Seán; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Dekker, Andre; Lambin, Philippe

2017-06-01

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future 'big data' infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade [Formula: see text]. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The performance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine.

Dynamic changes in network activations characterize early learning of a natural language.

PubMed

Plante, Elena; Patterson, Dianne; Dailey, Natalie S; Kyle, R Almyrde; Fridriksson, Julius

2014-09-01

Those who are initially exposed to an unfamiliar language have difficulty separating running speech into individual words, but over time will recognize both words and the grammatical structure of the language. Behavioral studies have used artificial languages to demonstrate that humans are sensitive to distributional information in language input, and can use this information to discover the structure of that language. This is done without direct instruction and learning occurs over the course of minutes rather than days or months. Moreover, learners may attend to different aspects of the language input as their own learning progresses. Here, we examine processing associated with the early stages of exposure to a natural language, using fMRI. Listeners were exposed to an unfamiliar language (Icelandic) while undergoing four consecutive fMRI scans. The Icelandic stimuli were constrained in ways known to produce rapid learning of aspects of language structure. After approximately 4 min of exposure to the Icelandic stimuli, participants began to differentiate between correct and incorrect sentences at above chance levels, with significant improvement between the first and last scan. An independent component analysis of the imaging data revealed four task-related components, two of which were associated with behavioral performance early in the experiment, and two with performance later in the experiment. This outcome suggests dynamic changes occur in the recruitment of neural resources even within the initial period of exposure to an unfamiliar natural language. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of multi-level social learning for sustainable landscapes: perspective of a development initiative in Bergslagen, Sweden.

PubMed

Axelsson, Robert; Angelstam, Per; Myhrman, Lennart; Sädbom, Stefan; Ivarsson, Milis; Elbakidze, Marine; Andersson, Kenneth; Cupa, Petr; Diry, Christian; Doyon, Frederic; Drotz, Marcus K; Hjorth, Arne; Hermansson, Jan Olof; Kullberg, Thomas; Lickers, F Henry; McTaggart, Johanna; Olsson, Anders; Pautov, Yurij; Svensson, Lennart; Törnblom, Johan

2013-03-01

To implement policies about sustainable landscapes and rural development necessitates social learning about states and trends of sustainability indicators, norms that define sustainability, and adaptive multi-level governance. We evaluate the extent to which social learning at multiple governance levels for sustainable landscapes occur in 18 local development initiatives in the network of Sustainable Bergslagen in Sweden. We mapped activities over time, and interviewed key actors in the network about social learning. While activities resulted in exchange of experiences and some local solutions, a major challenge was to secure systematic social learning and make new knowledge explicit at multiple levels. None of the development initiatives used a systematic approach to secure social learning, and sustainability assessments were not made systematically. We discuss how social learning can be improved, and how a learning network of development initiatives could be realized.

Statistical learning of speech sounds is most robust during the period of perceptual attunement.

PubMed

Liu, Liquan; Kager, René

2017-12-01

Although statistical learning has been shown to be a domain-general mechanism, its constraints, such as its interactions with perceptual development, are less well understood and discussed. This study is among the first to investigate the distributional learning of lexical pitch in non-tone-language-learning infants, exploring its interaction with language-specific perceptual attunement during the first 2years after birth. A total of 88 normally developing Dutch infants of 5, 11, and 14months were tested via a distributional learning paradigm and were familiarized on a unimodal or bimodal distribution of high-level versus high-falling tones in Mandarin Chinese. After familiarization, they were tested on a tonal contrast that shared equal distributional information in either modality. At 5months, infants in both conditions discriminated the contrast, whereas 11-month-olds showed discrimination only in the bimodal condition. By 14months, infants failed to discriminate the contrast in either condition. Results indicate interplay between infants' long-term linguistic experience throughout development and short-term distributional learning during the experiment, and they suggest that the influence of tonal distributional learning varies along the perceptual attunement trajectory, such that opportunities for distributional learning effects appear to be constrained in the beginning and at the end of perceptual attunement. The current study contributes to previous research by demonstrating an effect of age on learning from distributional cues. Copyright © 2017 Elsevier Inc. All rights reserved.

Commentary on "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning"

ERIC Educational Resources Information Center

Hewitt, Jim

2015-01-01

The article, "Distributed Revisiting: An Analytic for Retention of Coherent Science Learning" is an interesting study that operates at the intersection of learning theory and learning analytics. The authors observe that the relationship between learning theory and research in the learning analytics field is constrained by several…

Sustainability Factors for E-Learning Initiatives

ERIC Educational Resources Information Center

Gunn, Cathy

2010-01-01

This paper examines the challenges that "grass roots" e-learning initiatives face in trying to become sustainable. A cross-institutional study focused on local, rather than centrally driven, initiatives. A number of successful e-learning innovations were identified that had been driven by capable teachers seeking solutions to real…

Applications and Lessons Learned using Data from the Atmospheric Infrared Sounder

NASA Astrophysics Data System (ADS)

Ray, S. E.; Fetzer, E. J.; Olsen, E. T.; Lambrigtsen, B.; Pagano, T. S.; Teixeira, J.; Licata, S. J.; Hall, J. R.

2016-12-01

Applications and Lessons Learned using Data from the Atmospheric Infrared SounderSharon Ray, Jet Propulsion Laboratory, California Institute of Technology The Atmospheric Infrared Sounder (AIRS) on NASA's Aqua spacecraft has been returning daily global observations of Earth's atmospheric constituents and properties since 2002. With a 12-year data record and daily, global observations in near real-time, AIRS can play a role in applications that fall under many of the NASA Applied Sciences focus areas. AIRS' involvement in applications is two years in, so what have we learned and what are the pitfalls? AIRS has made gains in drought applications with products under consideration for inclusion in the U.S. Drought Monitor national map, as also with volcano rapid response with an internal alert system and automated products to help characterize plume extent. Efforts are underway with cold air aloft for aviation, influenza outbreak prediction, and vector borne disease. But challenges have occurred both in validation and in crossing the "valley of death" between products and decision makers. AIRS now has improved maps of standard products to be distributed in near real-time via NASA LANCE and by the Goddard DAAC as part of the Obama's administration Big Earth Data Initiative. In addition internal tools have been developed to support development and distribution of our application products. This talk will communicate the status of the AIRS applications effort along with lessons learned, and provide examples of new product imagery designed to best communicate AIRS data.

Assessing Students' Development in Learning Approaches According to Initial Learning Profiles: A Person-Oriented Perspective

ERIC Educational Resources Information Center

Vanthournout, Gert; Coertjens, Liesje; Gijbels, David; Donche, Vincent; Van Petegem, Peter

2013-01-01

Research regarding the development of students' learning approaches have at times reported unexpected or lack of expected changes. The current study explores the idea of differential developments in learning approaches according to students' initial learning profiles as a possible explanation for these outcomes. A learning profile is conceived as…

Teachers' Self-Initiated Professional Learning through Personal Learning Networks

ERIC Educational Resources Information Center

Tour, Ekaterina

2017-01-01

It is widely acknowledged that to be able to teach language and literacy with digital technologies, teachers need to engage in relevant professional learning. Existing formal models of professional learning are often criticised for being ineffective. In contrast, informal and self-initiated forms of learning have been recently recognised as…

Combining lived experience with the facilitation of enquiry-based learning: a 'trigger' for transformative learning.

PubMed

Stacey, G; Oxley, R; Aubeeluck, A

2015-09-01

What is known on the subject The values underpinning recovery-orientated practice are recited in the literature and influential in the content of mental health nurse education internationally. However, scepticism exists regarding the degree to which students' assimilate the principles of recovery into their practice due to the troublesome and challenging nature of learning at a transformational level, also known as threshold concept learning. Evaluation suggests that this combination of educational approaches positively influences students' prior understandings, beliefs and values in relation to the prospect for people with significant mental health problems to recover. The components of threshold concepts are useful as a deductive framework for the evaluation of educational initiatives which attempt to initiate transformative learning. While this forum clearly holds significant potential for student development, support and preparation is needed for both the student and the facilitator in order to enable the possibility of learning which influences attitudes, beliefs and practice. The aim of this paper is to discuss the potential for combining lived experience of mental distress with the facilitation of enquiry-based learning (EBL) to act as a trigger for transformative learning in the context of promoting the understanding of mental health 'recovery' in nurse education.The values underpinning recovery-orientated practice are recited in the literature and influential in mental health nurse education internationally. However, scepticism exists regarding the degree to which students assimilate into their practice. An open-ended was distributed to a cohort of pre-registration nursing students receiving the co-facilitated EBL (n = 112). Data demonstrated how the specific attributes of this educational approach were identified by students as impacting positively on ill-informed preconceptions, understanding of complex theory and their future practice. Results were considered in light of the identification of 'recovery' as a 'threshold concept' and offered evidence to support the value of this specific educational forum in the promotion of learning which is transformative, integrative, bounded and at times troublesome. Support and significant preparation is needed for both the student and the facilitator in order to enable the possibility of transformatory learning. © 2015 John Wiley & Sons Ltd.

Classification and recognition of dynamical models: the role of phase, independent components, kernels and optimal transport.

PubMed

Bissacco, Alessandro; Chiuso, Alessandro; Soatto, Stefano

2007-11-01

We address the problem of performing decision tasks, and in particular classification and recognition, in the space of dynamical models in order to compare time series of data. Motivated by the application of recognition of human motion in image sequences, we consider a class of models that include linear dynamics, both stable and marginally stable (periodic), both minimum and non-minimum phase, driven by non-Gaussian processes. This requires extending existing learning and system identification algorithms to handle periodic modes and nonminimum phase behavior, while taking into account higher-order statistics of the data. Once a model is identified, we define a kernel-based cord distance between models that includes their dynamics, their initial conditions as well as input distribution. This is made possible by a novel kernel defined between two arbitrary (non-Gaussian) distributions, which is computed by efficiently solving an optimal transport problem. We validate our choice of models, inference algorithm, and distance on the tasks of human motion synthesis (sample paths of the learned models), and recognition (nearest-neighbor classification in the computed distance). However, our work can be applied more broadly where one needs to compare historical data while taking into account periodic trends, non-minimum phase behavior, and non-Gaussian input distributions.

Exploring the Role of Distributed Learning in Distance Education at Allama Iqbal Open University: Academic Challenges at Postgraduate Level

ERIC Educational Resources Information Center

Bukhsh, Qadir; Chaudhary, Muhammad Ajmal

2015-01-01

Distributed learning is derived from the concept of distributed resources. Different institutions around the globe connected through network and the learners are diverse, located in the different cultures and communities. Distributed learning provides global standards of quality to all learners through synchronous and asynchronous communications…

Distributed Learning and Constructivist Philosophy (Uzaktan Ögretim Ve Yapilandirmaci Felsefe)

ERIC Educational Resources Information Center

Tekinarslan, Erkan

2003-01-01

Distance education and its new form of distributed learning have been used in many countries to provide education to people who need training. Recent developments in instructional technology enable the institutions to distribute their education to more people in distant places than ever before. The field of distributed learning has a lot of…

Thinking about Distributed Learning? Issues and Questions To Ponder.

ERIC Educational Resources Information Center

Sorg, Steven

2001-01-01

Introduces other articles in this issue devoted to distributed learning at metropolitan universities. Discusses issues that institutions should address if considering distributed learning: institutional goals and strategic plans, faculty development needs and capabilities, student support services, technical and personnel infrastructure, policies,…

e-Learning initiatives to support prescribing

PubMed Central

Maxwell, Simon; Mucklow, John

2012-01-01

Preparing medical students to prescribe is a major challenge of undergraduate education. They must develop an understanding of clinical pharmacology and acquire knowledge about drugs and therapeutics, as well as the skills to prescribe for individual patients in the face of multiple variables. The task of delivering the learning required to achieve these attributes relies upon limited numbers of teachers, who have increasingly busy clinical commitments. There is evidence that training is currently insufficient to meet the demands of the workplace. e-Learning provides an opportunity to improve the learning experience. The advantages for teachers are improved distribution of learning content, ease of update, standardization and tracking of learner activities. The advantages for learners are ease of access, greater interactivity and individual choice concerning the pace and mix of learning. Important disadvantages are the considerable resource required to develop e-Learning projects and difficulties in simulating some aspects of the real world prescribing experience. Pre-requisites for developing an e-Learning programme to support prescribing include academic expertise, institutional support, learning technology services and an effective virtual learning environment. e-Learning content might range from complex interactive learning sessions through to static web pages with links. It is now possible to simulate and provide feedback on prescribing decisions and this will improve with advances in virtual reality. Other content might include a student formulary, self-assessment exercises (e.g. calculations), a glossary and an on-line library. There is some evidence for the effectiveness of e-Learning but better research is required into its potential impact on prescribing. PMID:22509885

e-Learning initiatives to support prescribing.

PubMed

Maxwell, Simon; Mucklow, John

2012-10-01

Preparing medical students to prescribe is a major challenge of undergraduate education. They must develop an understanding of clinical pharmacology and acquire knowledge about drugs and therapeutics, as well as the skills to prescribe for individual patients in the face of multiple variables. The task of delivering the learning required to achieve these attributes relies upon limited numbers of teachers, who have increasingly busy clinical commitments. There is evidence that training is currently insufficient to meet the demands of the workplace. e-Learning provides an opportunity to improve the learning experience. The advantages for teachers are improved distribution of learning content, ease of update, standardization and tracking of learner activities. The advantages for learners are ease of access, greater interactivity and individual choice concerning the pace and mix of learning. Important disadvantages are the considerable resource required to develop e-Learning projects and difficulties in simulating some aspects of the real world prescribing experience. Pre-requisites for developing an e-Learning programme to support prescribing include academic expertise, institutional support, learning technology services and an effective virtual learning environment. e-Learning content might range from complex interactive learning sessions through to static web pages with links. It is now possible to simulate and provide feedback on prescribing decisions and this will improve with advances in virtual reality. Other content might include a student formulary, self-assessment exercises (e.g. calculations), a glossary and an on-line library. There is some evidence for the effectiveness of e-Learning but better research is required into its potential impact on prescribing. © 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.

Deep greedy learning under thermal variability in full diurnal cycles

NASA Astrophysics Data System (ADS)

Rauss, Patrick; Rosario, Dalton

2017-08-01

We study the generalization and scalability behavior of a deep belief network (DBN) applied to a challenging long-wave infrared hyperspectral dataset, consisting of radiance from several manmade and natural materials within a fixed site located 500 m from an observation tower. The collections cover multiple full diurnal cycles and include different atmospheric conditions. Using complementary priors, a DBN uses a greedy algorithm that can learn deep, directed belief networks one layer at a time and has two layers form to provide undirected associative memory. The greedy algorithm initializes a slower learning procedure, which fine-tunes the weights, using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of spectral data and their labels, despite significant data variability between and within classes due to environmental and temperature variation occurring within and between full diurnal cycles. We argue, however, that more questions than answers are raised regarding the generalization capacity of these deep nets through experiments aimed at investigating their training and augmented learning behavior.

Seos - EARSEL'S Project on Science Education Through Earth Observation for High Schools

NASA Astrophysics Data System (ADS)

Reuter, R.

2011-09-01

SEOS is an initiative for using remote sensing in science education curricula in high schools funded under the 6th Framework Programme of the European Commission (EC). Eleven partners from several European countries, in cooperation with the European Space Agency (ESA) and teachers from European high schools, created e-learning tutorials for science students in high schools. The tutorials cover many disciplines such as physics, biology, geography, mathematics and engineering, emphasising the interdisciplinary character of remote sensing. They are the core element of the SEOS Learning Management System, allowing teachers to create their own courses, to distribute already available or new worksheets to the students for homework and to collect the results. Forums are available for teachers, students and other users to exchange information and discuss topics relevant for their study.

Selecting a Laboratory Information Management System for Biorepositories in Low- and Middle-Income Countries: The H3Africa Experience and Lessons Learned

PubMed Central

Musinguzi, Henry; Lwanga, Newton; Kezimbira, Dafala; Kigozi, Edgar; Katabazi, Fred Ashaba; Wayengera, Misaki; Joloba, Moses Lutaakome; Abayomi, Emmanuel Akin; Swanepoel, Carmen; Croxton, Talishiea; Ozumba, Petronilla; Thankgod, Anazodo; van Zyl, Lizelle; Mayne, Elizabeth Sarah; Kader, Mukthar; Swartz, Garth

2017-01-01

Biorepositories in Africa need significant infrastructural support to meet International Society for Biological and Environmental Repositories (ISBER) Best Practices to support population-based genomics research. ISBER recommends a biorepository information management system which can manage workflows from biospecimen receipt to distribution. The H3Africa Initiative set out to develop regional African biorepositories where Uganda, Nigeria, and South Africa were successfully awarded grants to develop the state-of-the-art biorepositories. The biorepositories carried out an elaborate process to evaluate and choose a laboratory information management system (LIMS) with the aim of integrating the three geographically distinct sites. In this article, we review the processes, African experience, lessons learned, and make recommendations for choosing a biorepository LIMS in the African context.

Learning Initiatives in the Residential Setting. The First-Year Experience Monograph Series No. 48

ERIC Educational Resources Information Center

Luna, Gene, Ed.; Gahagan, Jimmie, Ed.

2008-01-01

In 2004, "Learning Reconsidered" urged educators to think more holistically about student learning and development. "Learning Initiatives in the Residential Setting" provides a framework for putting this call into action at large universities and small colleges alike. Chapters trace the history of learning in residence halls, discuss academic and…

Learning overcomplete representations from distributed data: a brief review

NASA Astrophysics Data System (ADS)

Raja, Haroon; Bajwa, Waheed U.

2016-05-01

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates the design of dictionary learning algorithms that consider distributed nature of data as one of the problem variables. Just like centralized settings, distributed dictionary learning problem can be posed in more than one way depending on the problem setup. Most notable distinguishing features are the online versus batch nature of data and the representative versus discriminative nature of the dictionaries. In this paper, several distributed dictionary learning algorithms that are designed to tackle different problem setups are reviewed. One of these algorithms is cloud K-SVD, which solves the dictionary learning problem for batch data in distributed settings. One distinguishing feature of cloud K-SVD is that it has been shown to converge to its centralized counterpart, namely, the K-SVD solution. On the other hand, no such guarantees are provided for other distributed dictionary learning algorithms. Convergence of cloud K-SVD to the centralized K-SVD solution means problems that are solvable by K-SVD in centralized settings can now be solved in distributed settings with similar performance. Finally, cloud K-SVD is used as an example to show the advantages that are attainable by deploying distributed dictionary algorithms for real world distributed datasets.

Three Years of the New Mexico Laptop Learning Initiative (NMLLI): Stumbling toward Innovation

ERIC Educational Resources Information Center

Rutledge, David; Duran, James; Carroll-Miranda, Joseph

2007-01-01

This article presents qualitative results of the first three years of the New Mexico Laptop Learning Initiative (NMLLI). Results suggest that teachers, students, and their communities support this initiative to improve student learning. Descriptive statistics were used during year two to further understand how the laptops were being used by…

Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics.

PubMed

Yuan, Chengzhi; Licht, Stephen; He, Haibo

2017-09-26

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

Academic learning for specialist nurses: a grounded theory study.

PubMed

Millberg, Lena German; Berg, Linda; Brämberg, Elisabeth Björk; Nordström, Gun; Ohlén, Joakim

2014-11-01

The aim was to explore the major concerns of specialist nurses pertaining to academic learning during their education and initial professional career. Specialist nursing education changed in tandem with the European educational reform in 2007. At the same time, greater demands were made on the healthcare services to provide evidence-based and safe patient-care. These changes have influenced specialist nursing programmes and consequently the profession. Grounded Theory guided the study. Data were collected by means of a questionnaire with open-ended questions distributed at the end of specialist nursing programmes in 2009 and 2010. Five universities were included. Further, individual, pair and group interviews were used to collect data from 12 specialist nurses, 5-14 months after graduation. A major concern for specialist nurses was that academic learning should be "meaningful" for their professional future. The specialist nurses' "meaningful academic learning process" was characterised by an ambivalence of partly believing in and partly being hesitant about the significance of academic learning and partly receiving but also lacking support. Specialist nurses were influenced by factors in two areas: curriculum and healthcare context. They felt that the outcome of contribution to professional confidence was critical in making academic learning meaningful. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning.

PubMed

D'Angelo, Egidio; Mapelli, Lisa; Casellato, Claudia; Garrido, Jesus A; Luque, Niceto; Monaco, Jessica; Prestori, Francesca; Pedrocchi, Alessandra; Ros, Eduardo

2016-04-01

The cerebellum is involved in learning and memory of sensory motor skills. However, the way this process takes place in local microcircuits is still unclear. The initial proposal, casted into the Motor Learning Theory, suggested that learning had to occur at the parallel fiber-Purkinje cell synapse under supervision of climbing fibers. However, the uniqueness of this mechanism has been questioned, and multiple forms of long-term plasticity have been revealed at various locations in the cerebellar circuit, including synapses and neurons in the granular layer, molecular layer and deep-cerebellar nuclei. At present, more than 15 forms of plasticity have been reported. There has been a long debate on which plasticity is more relevant to specific aspects of learning, but this question turned out to be hard to answer using physiological analysis alone. Recent experiments and models making use of closed-loop robotic simulations are revealing a radically new view: one single form of plasticity is insufficient, while altogether, the different forms of plasticity can explain the multiplicity of properties characterizing cerebellar learning. These include multi-rate acquisition and extinction, reversibility, self-scalability, and generalization. Moreover, when the circuit embeds multiple forms of plasticity, it can easily cope with multiple behaviors endowing therefore the cerebellum with the properties needed to operate as an effective generalized forward controller.

Distributed leadership, team working and service improvement in healthcare.

PubMed

Boak, George; Dickens, Victoria; Newson, Annalisa; Brown, Louise

2015-01-01

The purpose of this paper is to analyse the introduction of distributed leadership and team working in a therapy department in a healthcare organisation and to explore the factors that enabled the introduction to be successful. This paper used a case study methodology. Qualitative and quantitative information was gathered from one physiotherapy department over a period of 24 months. Distributed leadership and team working were central to a number of system changes that were initiated by the department, which led to improvements in patient waiting times for therapy. The paper identifies six factors that appear to have influenced the successful introduction of distributed learning and team working in this case. This is a single case study. It would be interesting to explore whether these factors are found in other cases where distributed leadership is introduced in healthcare organisations. The paper provides an example of successful introduction of distributed leadership, which has had a positive impact on services to patients. Other therapy teams may consider how the approach may be adopted or adapted to their own circumstances. Although distributed leadership is thought to be important in healthcare, particularly when organisational change is needed, there are very few studies of the practicalities of how it can be introduced.

Query Health: standards-based, cross-platform population health surveillance

PubMed Central

Klann, Jeffrey G; Buck, Michael D; Brown, Jeffrey; Hadley, Marc; Elmore, Richard; Weber, Griffin M; Murphy, Shawn N

2014-01-01

Objective Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Materials and methods Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. Results We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. Discussions This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Conclusions Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites. PMID:24699371

Query Health: standards-based, cross-platform population health surveillance.

PubMed

Klann, Jeffrey G; Buck, Michael D; Brown, Jeffrey; Hadley, Marc; Elmore, Richard; Weber, Griffin M; Murphy, Shawn N

2014-01-01

Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

Applying Distributed Learning Theory in Online Business Communication Courses.

ERIC Educational Resources Information Center

Walker, Kristin

2003-01-01

Focuses on the critical use of technology in online formats that entail relatively new teaching media. Argues that distributed learning theory is valuable for teachers of online business communication courses for several reasons. Discusses the application of distributed learning theory to the teaching of business communication online. (SG)

Initiating undergraduate medical students into communities of research practise: what do supervisors recommend?

PubMed Central

2010-01-01

Background Much has been written in the educational literature on the value of communities of practise in enhancing student learning. Here, we take the experience of senior undergraduate medical students involved in short-term research as a member of a team as a paradigm for learning in a community of practise. Based on feedback from experienced supervisors, we offer recommendations for initiating students into the research culture of their team. In so doing, we endeavour to create a bridge between theory and practise through disseminating advice on good supervisory practise, where the supervisor is perceived as an educator responsible for designing the research process to optimize student learning. Methods Using the questionnaire design tool SurveyMonkey and comprehensive lists of contact details of staff who had supervised research projects at the University of Edinburgh during 1995 - 2008, current and previous supervisors were invited to recommend procedures which they had found successful in initiating students into the research culture of a team. Text responses were then coded in the form of derivative recommendations and categorized under general themes and sub-themes. Results Using the chi-square tests of linear trend and association, evidence was found for a positive trend towards more experienced supervisors offering responses (χ2 = 16.833, p < 0.0005, n = 215) while there was a lack of evidence of bias in the gender distribution of respondents (χ2 = 0.482, p = 0.487, n = 203), respectively. A total of 126 codes were extracted from the text responses of 65 respondents. These codes were simplified to form a complete list of 52 recommendations, which were in turn categorized under seven derivative overarching themes, the most highly represented themes being Connecting the student with others and Cultivating self-efficacy in research competence. Conclusions Through the design of a coding frame for supervisor responses, a wealth of ideas has been captured to make communities of research practise effective mediums for undergraduate student learning. The majority of these recommendations are underpinned by educational theory and have the potential to take the learner beyond the stage of initiation to that of integration within their community of research practise. PMID:21092088

Simple Automatic File Exchange (SAFE) to Support Low-Cost Spacecraft Operation via the Internet

NASA Technical Reports Server (NTRS)

Baker, Paul; Repaci, Max; Sames, David

1998-01-01

Various issues associated with Simple Automatic File Exchange (SAFE) are presented in viewgraph form. Specific topics include: 1) Packet telemetry, Internet IP networks and cost reduction; 2) Basic functions and technical features of SAFE; 3) Project goals, including low-cost satellite transmission to data centers to be distributed via an Internet; 4) Operations with a replicated file protocol; 5) File exchange operation; 6) Ground stations as gateways; 7) Lessons learned from demonstrations and tests with SAFE; and 8) Feedback and future initiatives.

Institutionalizing Blended Learning into Joint Training: A Case Study and Ten Recommendations

DTIC Science & Technology

2014-12-01

mail.mil pbockelman@mesh.dsci.com ABSTRACT In 2011, the Joint Staff J7 (Joint Training) directorate initiated the Continuum of eLearning project in...Orlando, FL. 14. ABSTRACT In 2011, the Joint Staff J7 (Joint Training) directorate initiated the Continuum of eLearning project in order to integrate...dispersed organizations still poses significant challenges. The Joint Staff J7, Deputy Director for Joint Training initiated the Continuum of eLearning

The TENOR Architecture for Advanced Distributed Learning and Intelligent Training

DTIC Science & Technology

2002-01-01

called TENOR, for Training Education Network on Request. There have been a number of recent learning systems developed that leverage off Internet...AG2-14256 AIAA 2002-1054 The TENOR Architecture for Advanced Distributed Learning and Intelligent Training C. Tibaudo, J. Kristl and J. Schroeder...COVERED 4. TITLE AND SUBTITLE The TENOR Architecture for Advanced Distributed Learning and Intelligent Training 5a. CONTRACT NUMBER F33615-00-M

Luck and Learning: Feedback Contingencies and Initial Success in Verbal Discrimination Learning.

ERIC Educational Resources Information Center

Schneider, H. G.; Ferrante, A. P.

1983-01-01

A total of 90 undergraduate volunteers learned a 12-pair, low-frequency verbal discrimination list. Independent variables were feedback (positive only, negative only, or both) and initial success (17, 50, or 83 percent correct on the first trial). While the main effect of feedback was not significant, that of initial success was. (Author/RH)

FODEM: Developing Digital Learning Environments in Widely Dispersed Learning Communities

ERIC Educational Resources Information Center

Suhonen, Jarkko; Sutinen, Erkki

2006-01-01

FODEM (FOrmative DEvelopment Method) is a design method for developing digital learning environments for widely dispersed learning communities. These are communities in which the geographical distribution and density of learners is low when compared to the kind of learning communities in which there is a high distribution and density of learners…

E-Learning System Overview Based on Semantic Web

ERIC Educational Resources Information Center

Alsultanny, Yas A.

2006-01-01

The challenge of the semantic web is the provision of distributed information with well-defined meaning, understandable for different parties. e-Learning is efficient task relevant and just-in-time learning grown from the learning requirements of the new dynamically changing, distributed business world. In this paper we design an e-Learning system…

Learning from Simulation Design to Develop Better Experiential Learning Initiatives: An Integrative Approach

ERIC Educational Resources Information Center

Canhoto, Ana Isabel; Murphy, Jamie

2016-01-01

Simulations offer engaging learning experiences, via the provision of feedback or the opportunities for experimentation. However, they lack important attributes valued by marketing educators and employers. This article proposes a "back to basics" look at what constitutes an effective experiential learning initiative. Drawing on the…

CSCL in Teacher Training: What Learning Tasks Lead to Collaboration?

ERIC Educational Resources Information Center

Lockhorst, Ditte; Admiraal, Wilfried; Pilot, Albert

2010-01-01

Professional teacher communities appear to be positively related to student learning, teacher learning, teacher practice and school culture. Teacher collaboration is a significant element of these communities. In initial teacher training as well as in-service training and other initiatives for teacher learning, collaborative skills should be…

"More Confident Going into College": Lessons Learned from Multiple Stakeholders in a New Blended Learning Initiative

ERIC Educational Resources Information Center

Whiteside, Aimee L.; Garrett Dikkers, Amy; Lewis, Somer

2016-01-01

This article examined a blended learning initiative in a large suburban high school in the Midwestern region of the United States. It employed a single-case exploratory design approach to learn about the experience of administrators, teachers, students, and parents. Using Zimmerman's Self-Regulated Learning (SRL) Theory as a guiding framework,…

Alignment in Teacher Education and Distribution of Leadership: An Example Concerning Learning Study

ERIC Educational Resources Information Center

Nilsson, Ingrid

2008-01-01

The critical aspects distribution of professional leadership, alignment in learning and research close to practices, were lifted forward in order to exemplify a research project with learning study as an approach for alignment between teacher education and practice, and as consequence an instrument for distribution of power. The results showed…

A distributed algorithm for machine learning

NASA Astrophysics Data System (ADS)

Chen, Shihong

2018-04-01

This paper considers a distributed learning problem in which a group of machines in a connected network, each learning its own local dataset, aim to reach a consensus at an optimal model, by exchanging information only with their neighbors but without transmitting data. A distributed algorithm is proposed to solve this problem under appropriate assumptions.

Learning about and from a Distribution of Program Impacts Using Multisite Trials

ERIC Educational Resources Information Center

Raudenbush, Stephen W.; Bloom, Howard S.

2015-01-01

The present article provides a synthesis of the conceptual and statistical issues involved in using multisite randomized trials to learn about and from a distribution of heterogeneous program impacts across individuals and/or program sites. Learning "about" such a distribution involves estimating its mean value, detecting and quantifying…

Quality in-training initiative--a solution to the need for education in quality improvement: results from a survey of program directors.

PubMed

Kelz, Rachel R; Sellers, Morgan M; Reinke, Caroline E; Medbery, Rachel L; Morris, Jon; Ko, Clifford

2013-12-01

The Next Accreditation System and the Clinical Learning Environment Review Program will emphasize practice-based learning and improvement and systems-based practice. We present the results of a survey of general surgery program directors to characterize the current state of quality improvement in graduate surgical education and introduce the Quality In-Training Initiative (QITI). In 2012, a 20-item survey was distributed to 118 surgical residency program directors from ACS NSQIP-affiliated hospitals. The survey content was developed in collaboration with the QITI to identify program director opinions regarding education in practice-based learning and improvement and systems-based practice, to investigate the status of quality improvement education in their respective programs, and to quantify the extent of resident participation in quality improvement. There was a 57% response rate. Eighty-five percent of program directors (n = 57) reported that education in quality improvement is essential to future professional work in the field of surgery. Only 28% (n = 18) of programs reported that at least 50% of their residents track and analyze their patient outcomes, compare them with norms/benchmarks/published standards, and identify opportunities to make practice improvements. Program directors recognize the importance of quality improvement efforts in surgical practice. Subpar participation in basic practice-based learning and improvement activities at the resident level reflects the need for support of these educational goals. The QITI will facilitate programmatic compliance with goals for quality improvement education. Copyright © 2013 American College of Surgeons. All rights reserved.

Preparing Children To Read and Learn: An Education Initiative of Laura Bush.

ERIC Educational Resources Information Center

Department of Education, Washington, DC.

Noting that teaching reading is one of the Bush Administration's top domestic priorities, this pamphlet introduces the Ready to Read, Ready to Learn education initiative of First Lady Laura Bush. The goals of the initiative are to ensure that all young children are ready to read and learn when they enter their first classroom, and to ensure that…

Learning neuroendoscopy with an exoscope system (video telescopic operating monitor): Early clinical results.

PubMed

Parihar, Vijay; Yadav, Y R; Kher, Yatin; Ratre, Shailendra; Sethi, Ashish; Sharma, Dhananjaya

2016-01-01

Steep learning curve is found initially in pure endoscopic procedures. Video telescopic operating monitor (VITOM) is an advance in rigid-lens telescope systems provides an alternative method for learning basics of neuroendoscopy with the help of the familiar principle of microneurosurgery. The aim was to evaluate the clinical utility of VITOM as a learning tool for neuroendoscopy. Video telescopic operating monitor was used 39 cranial and spinal procedures and its utility as a tool for minimally invasive neurosurgery and neuroendoscopy for initial learning curve was studied. Video telescopic operating monitor was used in 25 cranial and 14 spinal procedures. Image quality is comparable to endoscope and microscope. Surgeons comfort improved with VITOM. Frequent repositioning of scope holder and lack of stereopsis is initial limiting factor was compensated for with repeated procedures. Video telescopic operating monitor is found useful to reduce initial learning curve of neuroendoscopy.

Successfully recruiting a multicultural population: the DASH-Sodium experience.

PubMed

Kennedy, Betty M; Conlin, Paul R; Ernst, Denise; Reams, Patrice; Charleston, Jeanne B; Appel, Lawrence J

2005-01-01

Recruiting practices employed by the four clinical centers participating in the Dietary Approaches to Stop Hypertension (DASH)-Sodium trial were examined to assess the most successful method of obtaining participants and to describe pertinent learning experiences gained as a result of the trial. The primary recruitment strategies employed by each center were mass mailing brochures (direct, coupon packs, or other) and mass media (advertisements in newspapers, radio, and television spots). Of 412 randomized participants, 265 (64%) were from mass distribution of brochures, 62 (15%) mass media, and 85 (21%) were prior study participants, referred by word-of-mouth, or reported coming from screening events and presentations. Although the most successful method of recruitment was mass mailing brochures, three times as many brochures were distributed to obtain similar success as in the initial DASH trial.

Active Learning Using Arbitrary Binary Valued Queries

DTIC Science & Technology

1990-10-01

active learning in the sense that the learner has complete choice in the information received. Specifically, we allow the learner to ask arbitrary yes...no questions. We consider both active learning under a fixed distribution and distribution-free active learning . In the case of active learning , the...a concept class is actively learnable iff it is finite, so that active learning is in fact less powerful than the usual passive learning model. We

Strategies for Impact: Enabling E-Learning Project Initiatives

ERIC Educational Resources Information Center

Csete, Josephine; Evans, Jennifer

2013-01-01

Purpose: The paper aims to focus on institutional initiatives to embed e-learning in a university in Hong Kong, from 2006-12, through large-scale funding of 43 e-learning projects. It outlines the guiding principles behind the university's e-learning development and discusses the significance of various procedures and practices in project…

Technology Enhanced Learning: A Case Study of NPTEL

ERIC Educational Resources Information Center

Nitonde, Rohidas

2018-01-01

Technology Enhanced Learning (TEL) is a major 21st century trend in Higher Education. There are several government initiatives in India towards e-learning. National Programme on Technology Enhanced Learning (NPTEL) is one of the major initiatives. The present paper is an assessment of various aspects of this programme. It aims at evaluating the…

Virtual Virtuosos: A Case Study in Learning Music in Virtual Learning Environments in Spain

ERIC Educational Resources Information Center

Alberich-Artal, Enric; Sangra, Albert

2012-01-01

In recent years, the development of Information and Communication Technologies (ICT) has contributed to the generation of a number of interesting initiatives in the field of music education and training in virtual learning environments. However, music education initiatives employing virtual learning environments have replicated and perpetuated the…

Action Learning: Developing Leaders and Supporting Change in a Healthcare Context

ERIC Educational Resources Information Center

Doyle, Louise

2014-01-01

This account of practice outlines how action learning was used as the key component of a leadership development initiative for managers in an acute hospital setting. It explains how the initiative was conceived, why action learning was chosen and how action learning principles were incorporated. Insights into the outcomes and considerations for…

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks.

PubMed

Carpenter, Gail A.

1997-11-01

A class of adaptive resonance theory (ART) models for learning, recognition, and prediction with arbitrarily distributed code representations is introduced. Distributed ART neural networks combine the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons. With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. With a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, which permits fast as well as slow learning without catastrophic forgetting. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference between coding node activation and an adaptive threshold. Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LTD) for higher frequency test inputs. This paradoxical behavior is traced to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also helps stabilize memory. Without the match-reset-search system, dART becomes a type of distributed competitive learning network.

Framing ICT-Enabled Innovation for Learning: The Case of One-to-One Learning Initiatives in Europe

ERIC Educational Resources Information Center

Bocconi, Stefania; Kampylis, Panagiotis; Punie, Yves

2013-01-01

This article discusses 1:1 learning initiatives in Europe in the context of a mapping framework of ICT-enabled innovation for learning. The aim of the framework, visualised as a spider's web, is two-fold: (i) to provide a further understanding of the nature of ICT-enabled innovation for learning; and (ii) to depict the impact of existing and…

Conceptual Commitments of the LIDA Model of Cognition

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

Information Acquisition, Analysis and Integration

DTIC Science & Technology

2016-08-03

of sensing and processing, theory, applications, signal processing, image and video processing, machine learning , technology transfer. 16. SECURITY... learning . 5. Solved elegantly old problems like image and video debluring, intro- ducing new revolutionary approaches. 1 DISTRIBUTION A: Distribution...Polatkan, G. Sapiro, D. Blei, D. B. Dunson, and L. Carin, “ Deep learning with hierarchical convolution factor analysis,” IEEE 6 DISTRIBUTION A

Evaluation and lessons learned from an undergraduate service learning course providing youth-focused relationship education.

PubMed

McElwain, Alyssa; Finnegan, Vanessa; Whittaker, Angela; Kerpelman, Jennifer; Adler-Baeder, Francesca; Duke, Adrienne

2016-10-01

Adolescent romantic relationships are known to have a significant impact on individual well-being and development. However, few teens experience formal education about the knowledge and skills necessary for building healthy romantic relationships. In response, a statewide relationship education initiative was developed at a large university in a Southeastern state. Undergraduates who enrolled in a service learning course in Human Development and Family Studies partnered with this initiative and implemented a relationship education program targeting high school students. A service learning model is used in this initiative because it offers opportunities for students' professional development and experiential learning. The present article provides a formative and illustrative summative evaluation of the service learning program. Specifically, the primary aims of this paper are to 1) provide an overview of the service learning course components; 2) describe preparation of the service learning students and their implementation of the relationship education program; 3) discuss challenges and lessons learned; and 4) offer initial evidence of effectiveness by showing change in targeted outcomes for the high school student recipients of the relationship education program. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Computer for Every Student and Teacher: Lessons Learned about Planning and Implementing a Successful 1:1 Learning Initiative in Schools

ERIC Educational Resources Information Center

Corn, Jenifer O.; Oliver, Kevin M.; Hess, Clara E.; Halstead, Elizabeth O.; Argueta, Rodolfo; Patel, Ruchi K.; Tingen, Jennifer; Huff, Jessica D.

2010-01-01

Twelve high schools in North Carolina piloted a 1:1 learning initiative, where every student and teacher received a laptop computer with wireless Internet access provided throughout the school. The overall goals of the initiative were to improve teaching practices; increase student achievement; and better prepare students for work, citizenship,…

Fifteen years of external quality assessment in leukemia/lymphoma immunophenotyping in The Netherlands and Belgium: A way forward.

PubMed

Preijers, Frank W M B; van der Velden, Vincent H J; Preijers, Tim; Brooimans, Rik A; Marijt, Erik; Homburg, Christa; van Montfort, Kees; Gratama, Jan W

2016-05-01

In 1985, external quality assurance was initiated in the Netherlands to reduce the between-laboratory variability of leukemia/lymphoma immunophenotyping and to improve diagnostic conclusions. This program consisted of regular distributions of test samples followed by biannual plenary participant meetings in which results were presented and discussed. A scoring system was developed in which the quality of results was rated by systematically reviewing the pre-analytical, analytical, and post-analytical assay stages using three scores, i.e., correct (A), minor fault (B), and major fault (C). Here, we report on 90 consecutive samples distributed to 40-61 participating laboratories between 1998 and 2012. Most samples contained >20% aberrant cells, mainly selected from mature lymphoid malignancies (B or T cell) and acute leukemias (myeloid or lymphoblastic). In 2002, minimally required monoclonal antibody (mAb) panels were introduced, whilst methodological guidelines for all three assay stages were implemented. Retrospectively, we divided the study into subsequent periods of 4 ("initial"), 4 ("learning"), and 7 years ("consolidation") to detect "learning effects." Uni- and multivariate models showed that analytical performance declined since 2002, but that post-analytical performance improved during the entire period. These results emphasized the need to improve technical aspects of the assay, and reflected improved interpretational skills of the participants. A strong effect of participant affiliation in all three assay stages was observed: laboratories in academic and large peripheral hospitals performed significantly better than those in small hospitals. © 2015 International Clinical Cytometry Society. © 2015 International Clinical Cytometry Society.

A Framework for a Computer System to Support Distributed Cooperative Learning

ERIC Educational Resources Information Center

Chiu, Chiung-Hui

2004-01-01

To develop a computer system to support cooperative learning among distributed students; developers should consider the foundations of cooperative learning. This article examines the basic elements that make cooperation work and proposes a framework for such computer supported cooperative learning (CSCL) systems. This framework is constituted of…

Learning fast accurate movements requires intact frontostriatal circuits

PubMed Central

Shabbott, Britne; Ravindran, Roshni; Schumacher, Joseph W.; Wasserman, Paula B.; Marder, Karen S.; Mazzoni, Pietro

2013-01-01

The basal ganglia are known to play a crucial role in movement execution, but their importance for motor skill learning remains unclear. Obstacles to our understanding include the lack of a universally accepted definition of motor skill learning (definition confound), and difficulties in distinguishing learning deficits from execution impairments (performance confound). We studied how healthy subjects and subjects with a basal ganglia disorder learn fast accurate reaching movements. We addressed the definition and performance confounds by: (1) focusing on an operationally defined core element of motor skill learning (speed-accuracy learning), and (2) using normal variation in initial performance to separate movement execution impairment from motor learning abnormalities. We measured motor skill learning as performance improvement in a reaching task with a speed-accuracy trade-off. We compared the performance of subjects with Huntington's disease (HD), a neurodegenerative basal ganglia disorder, to that of premanifest carriers of the HD mutation and of control subjects. The initial movements of HD subjects were less skilled (slower and/or less accurate) than those of control subjects. To factor out these differences in initial execution, we modeled the relationship between learning and baseline performance in control subjects. Subjects with HD exhibited a clear learning impairment that was not explained by differences in initial performance. These results support a role for the basal ganglia in both movement execution and motor skill learning. PMID:24312037

Distributional Learning of Lexical Tones: A Comparison of Attended vs. Unattended Listening.

PubMed

Ong, Jia Hoong; Burnham, Denis; Escudero, Paola

2015-01-01

This study examines whether non-tone language listeners can acquire lexical tone categories distributionally and whether attention in the training phase modulates the effect of distributional learning. Native Australian English listeners were trained on a Thai lexical tone minimal pair and their performance was assessed using a discrimination task before and after training. During Training, participants either heard a Unimodal distribution that would induce a single central category, which should hinder their discrimination of that minimal pair, or a Bimodal distribution that would induce two separate categories that should facilitate their discrimination. The participants either heard the distribution passively (Experiments 1A and 1B) or performed a cover task during training designed to encourage auditory attention to the entire distribution (Experiment 2). In passive listening (Experiments 1A and 1B), results indicated no effect of distributional learning: the Bimodal group did not outperform the Unimodal group in discriminating the Thai tone minimal pairs. Moreover, both Unimodal and Bimodal groups improved above chance on most test aspects from Pretest to Posttest. However, when participants' auditory attention was encouraged using the cover task (Experiment 2), distributional learning was found: the Bimodal group outperformed the Unimodal group on a novel test syllable minimal pair at Posttest relative to at Pretest. Furthermore, the Bimodal group showed above-chance improvement from Pretest to Posttest on three test aspects, while the Unimodal group only showed above-chance improvement on one test aspect. These results suggest that non-tone language listeners are able to learn lexical tones distributionally but only when auditory attention is encouraged in the acquisition phase. This implies that distributional learning of lexical tones is more readily induced when participants attend carefully during training, presumably because they are better able to compute the relevant statistics of the distribution.

Distributional Learning of Lexical Tones: A Comparison of Attended vs. Unattended Listening

PubMed Central

Ong, Jia Hoong; Burnham, Denis; Escudero, Paola

2015-01-01

This study examines whether non-tone language listeners can acquire lexical tone categories distributionally and whether attention in the training phase modulates the effect of distributional learning. Native Australian English listeners were trained on a Thai lexical tone minimal pair and their performance was assessed using a discrimination task before and after training. During Training, participants either heard a Unimodal distribution that would induce a single central category, which should hinder their discrimination of that minimal pair, or a Bimodal distribution that would induce two separate categories that should facilitate their discrimination. The participants either heard the distribution passively (Experiments 1A and 1B) or performed a cover task during training designed to encourage auditory attention to the entire distribution (Experiment 2). In passive listening (Experiments 1A and 1B), results indicated no effect of distributional learning: the Bimodal group did not outperform the Unimodal group in discriminating the Thai tone minimal pairs. Moreover, both Unimodal and Bimodal groups improved above chance on most test aspects from Pretest to Posttest. However, when participants’ auditory attention was encouraged using the cover task (Experiment 2), distributional learning was found: the Bimodal group outperformed the Unimodal group on a novel test syllable minimal pair at Posttest relative to at Pretest. Furthermore, the Bimodal group showed above-chance improvement from Pretest to Posttest on three test aspects, while the Unimodal group only showed above-chance improvement on one test aspect. These results suggest that non-tone language listeners are able to learn lexical tones distributionally but only when auditory attention is encouraged in the acquisition phase. This implies that distributional learning of lexical tones is more readily induced when participants attend carefully during training, presumably because they are better able to compute the relevant statistics of the distribution. PMID:26214002

Using Activity Theory to Evaluate a Professional Learning and Development Initiative in the Use of Narrative Assessment

ERIC Educational Resources Information Center

Bourke, Roseanna; Mentis, Mandia; O'Neill, John

2013-01-01

Analysis of the impact of professional learning and development (PLD) programmes for educators is complex. This article presents an analysis of a PLD initiative in which classroom teachers learned to use narrative assessment for students with "high" and "very high" learning needs. Using Cultural Historical Activity Theory…

Professional Learning in Initial Teacher Education: Vision in the Constructivist Conception of Teaching and Learning

ERIC Educational Resources Information Center

Tang, Sylvia Y. F.; Wong, Angel K. Y.; Cheng, May M. H.

2012-01-01

With the constructivist view of learning as a conceptual lens, this paper examines student teachers' professional learning in initial teacher education (ITE). A mixed-method study was conducted with student teachers of a Bachelor of Education Programme in Hong Kong. The quantitative element of the study reveals that student teachers held a…

Correlates of Individual, and Age-Related, Differences in Short-Term Learning

ERIC Educational Resources Information Center

Zhang, Zhiyong; Davis, Hasker P.; Salthouse, Timothy A.; Tucker-Drob, Elliot M.

2007-01-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task…

Investments in Professional Learning Must Change: The Goals Are Ambitious, the Stakes Are High--And Resources Are the Key

ERIC Educational Resources Information Center

Killion, Joellen; Hirsh, Stephanie

2013-01-01

Adapted from the brief "Meet the Promise of Content Standards: Investing in Professional Learning," this article draws on the work of Learning Forward's initiative, Transforming Professional Learning to Prepare College- and Career-Ready Students: Implementing the Common Core. This multidimensional initiative is focused on developing…

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

PubMed

Breining, Bonnie; Nozari, Nazbanou; Rapp, Brenda

2016-04-01

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

Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation.

PubMed

Zhao, Wei; Wang, Han

2016-06-28

Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages.

Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation

PubMed Central

Zhao, Wei; Wang, Han

2016-01-01

Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages. PMID:27367691

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Parallel Distributed Processing at 25: further explorations in the microstructure of cognition.

PubMed

Rogers, Timothy T; McClelland, James L

2014-08-01

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called "deep networks"-systems that build on key ideas presented in the PDP volumes. Copyright © 2014 Cognitive Science Society, Inc.

The Secret Is in the Sound

PubMed Central

Christiansen, Morten H.; Onnis, Luca; Hockema, Stephen A.

2009-01-01

When learning language young children are faced with many seemingly formidable challenges, including discovering words embedded in a continuous stream of sounds and determining what role these words play in syntactic constructions. We suggest that knowledge of phoneme distributions may play a crucial part in helping children segment words and determine their lexical category, and propose an integrated model of how children might go from unsegmented speech to lexical categories. We corroborated this theoretical model using a two-stage computational analysis of a large corpus of English child-directed speech. First, we used transition probabilities between phonemes to find words in unsegmented speech. Second, we used distributional information about word edges—the beginning and ending phonemes of words—to predict whether the segmented words from the first stage were nouns, verbs, or something else. The results indicate that discovering lexical units and their associated syntactic category in child-directed speech is possible by attending to the statistics of single phoneme transitions and word-initial and final phonemes. Thus, we suggest that a core computational principle in language acquisition is that the same source of information is used to learn about different aspects of linguistic structure. PMID:19371361

Achieving effective learning effects in the blended course: a combined approach of online self-regulated learning and collaborative learning with initiation.

PubMed

Tsai, Chia-Wen

2011-09-01

In many countries, undergraduates are required to take at least one introductory computer course to enhance their computer literacy and computing skills. However, the application software education in Taiwan can hardly be deemed as effective in developing students' practical computing skills. The author applied online self-regulated learning (SRL) and collaborative learning (CL) with initiation in a blended computing course and examined the effects of different combinations on enhancing students' computing skills. Four classes, comprising 221 students, participated in this study. The online SRL and CL with initiation (G1, n = 53), online CL with initiation (G2, n = 68), and online CL without initiation (G3, n = 68) were experimental groups, and the last class, receiving traditional lecture (G4, n = 32), was the control group. The results of this study show that students who received the intervention of online SRL and CL with initiation attained significantly best grades for practical computing skills, whereas those that received the traditional lectures had statistically poorest grades among the four classes. The implications for schools and educators who plan to provide online or blended learning for their students, particularly in computing courses, are also provided in this study.

Optimal Reward Functions in Distributed Reinforcement Learning

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Tumer, Kagan

2000-01-01

We consider the design of multi-agent systems so as to optimize an overall world utility function when (1) those systems lack centralized communication and control, and (2) each agents runs a distinct Reinforcement Learning (RL) algorithm. A crucial issue in such design problems is to initialize/update each agent's private utility function, so as to induce best possible world utility. Traditional 'team game' solutions to this problem sidestep this issue and simply assign to each agent the world utility as its private utility function. In previous work we used the 'Collective Intelligence' framework to derive a better choice of private utility functions, one that results in world utility performance up to orders of magnitude superior to that ensuing from use of the team game utility. In this paper we extend these results. We derive the general class of private utility functions that both are easy for the individual agents to learn and that, if learned well, result in high world utility. We demonstrate experimentally that using these new utility functions can result in significantly improved performance over that of our previously proposed utility, over and above that previous utility's superiority to the conventional team game utility.

Create a good learning environment and motivate active learning enthusiasm

NASA Astrophysics Data System (ADS)

Bi, Weihong; Fu, Guangwei; Fu, Xinghu; Zhang, Baojun; Liu, Qiang; Jin, Wa

2017-08-01

In view of the current poor learning initiative of undergraduates, the idea of creating a good learning environment and motivating active learning enthusiasm is proposed. In practice, the professional tutor is allocated and professional introduction course is opened for college freshman. It can promote communication between the professional teachers and students as early as possible, and guide students to know and devote the professional knowledge by the preconceived form. Practice results show that these solutions can improve the students interest in learning initiative, so that the active learning and self-learning has become a habit in the classroom.

Students' perception of the learning environment in a distributed medical programme.

PubMed

Veerapen, Kiran; McAleer, Sean

2010-09-24

The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites.

Incremental Support Vector Machine Framework for Visual Sensor Networks

NASA Astrophysics Data System (ADS)

Awad, Mariette; Jiang, Xianhua; Motai, Yuichi

2006-12-01

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

Learning Novel Musical Pitch via Distributional Learning

ERIC Educational Resources Information Center

Ong, Jia Hoong; Burnham, Denis; Stevens, Catherine J.

2017-01-01

Because different musical scales use different sets of intervals and, hence, different musical pitches, how do music listeners learn those that are in their native musical system? One possibility is that musical pitches are acquired in the same way as phonemes, that is, via distributional learning, in which learners infer knowledge from the…

Multimedia Instruction Initiative: Building Faculty Competence.

ERIC Educational Resources Information Center

Haile, Penelope J.

Hofstra University began a university-wide initiative to enhance classroom instruction with multimedia technology and foster collaborative approaches to learning. The Multimedia Instruction Initiative emphasized teamwork among faculty, students, and computer center support staff to develop a technology-enriched learning environment supported by…

Innovations in Online Learning: Moving beyond No Significant Difference. The Pew Symposia in Learning and Technology (4th, Phoenix, Arizona, December 8-9, 2000).

ERIC Educational Resources Information Center

Twigg, Carol A.

Symposium participants gathered to discuss how to move online learning beyond being "as good as" traditional education. Participants were asked to analyze their assumptions about distributed learning, identify the strengths of each type of distributed learning discussed, and explore what needs to be done to improve online education. This paper…

Online learning in paediatrics: a student-led web-based learning modality.

PubMed

Gill, Peter; Kitney, Lauren; Kozan, Daniel; Lewis, Melanie

2010-03-01

undergraduate medical education is shifting away from traditional didactic methods towards a more self-directed learning environment. E-learning has emerged as a vital learning modality that allows students to apply key principles to practical scenarios in a truly personalised approach.  at the University of Alberta, paediatrics is taught longitudinally, with lectures distributed throughout the preclinical curriculum and concentrated in the 8-week paediatric clinical clerkship. As a result, students entering clerkship lack core foundational knowledge and clinical skills. PedsCases (http://www.pedscases.com) is a student-driven interactive website designed to achieve the learning outcomes identified by the competency-based paediatric curriculum. This open-access e-learning tool is a comprehensive peer-reviewed learning resource that incorporates various learning modalities. Material is student generated and peer reviewed by staff paediatricians to ensure validity, accuracy and usefulness. After 17 months, PedsCases contains 216 questions, 19 cases, 11 flashcard-type quizzes, 11 podcasts and two clinical videos, and has had 2148 unique visitors from 73 different countries. PedsCases is one of the top five references returned by Internet search engines for the phrase 'paediatrics for medical students'. PedsCases is a collaborative resource created for and by medical students that provides an opportunity for active self-directed learning while disseminating knowledge in an evidence-based, interactive and clinically relevant fashion. PedsCases encourages students to take an active role in their education and drive medical education initiatives in response to the evolving curriculum. As the focus of medical education shifts towards independent learning, student-led educational tools such as PedsCases have emerged as essential resources for students. © Blackwell Publishing Ltd 2010.

Organisational Learning and Employees' Intrinsic Motivation

ERIC Educational Resources Information Center

Remedios, Richard; Boreham, Nick

2004-01-01

This study examined the effects of organisational learning initiatives on employee motivation. Four initiatives consistent with theories of organisational learning were a priori ranked in terms of concepts that underpin intrinsic-motivation theory. Eighteen employees in a UK petrochemical company were interviewed to ascertain their experiences of…

A Cognitive Approach to e-Learning

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

Greitzer, Frank L.; Rice, Douglas M.; Eaton, Sharon L.

2003-12-01

Like traditional classroom instruction, distributed learning derives from passive training paradigms. Just as student-centered classroom teaching methods have been applied over several decades of classroom instruction, interactive approaches have been encouraged for distributed learning. While implementation of multimedia-based training features may appear to produce active learning, sophisticated use of multimedia features alone does not necessarily enhance learning. This paper describes the results of applying cognitive science principles to enhance learning in a student-centered, distributed learning environment, and lessons learned in developing and delivering this training. Our interactive, scenario-based approach exploits multimedia technology within a systematic, cognitive framework for learning. Themore » basis of the application of cognitive principles is the innovative use of multimedia technology to implement interaction elements. These simple multimedia interactions, which are used to support new concepts, are later combined with other interaction elements to create more complex, integrated practical exercises. This technology-based approach may be applied in a variety of training and education contexts, but is especially well suited for training of equipment operators and maintainers. For example, it has been used in a sustainment training application for the United States Army's Combat Support System Automated Information System Interface (CAISI). The CAISI provides a wireless communications capability that allows various logistics systems to communicate across the battlefield. Based on classroom training material developed by the CAISI Project Office, the Pacific Northwest National Laboratory designed and developed an interactive, student-centered distributed-learning application for CAISI operators and maintainers. This web-based CAISI training system is also distributed on CD media for use on individual computers, and material developed for the computer-based course can be used in the classroom. In addition to its primary role in sustainment training, this distributed learning course can complement or replace portions of the classroom instruction, thus supporting a blended learning solution.« less

Coordinated, Collaborative and Coherent: Developing and Implementing E-Learning Guidelines within a National Tertiary Education System

ERIC Educational Resources Information Center

Suddaby, Gordon; Milne, John

2008-01-01

Purpose: The paper aims to discusses two complementary initiatives focussed on developing and implementing e-learning guidelines to support good pedagogy in e-learning practice. Design/methodology/approach: The first initiative is the development of a coherent set of open access e-learning guidelines for the New Zealand tertiary sector. The second…

The Open Learning Initiative: Measuring the Effectiveness of the OLI Statistics Course in Accelerating Student Learning

ERIC Educational Resources Information Center

Lovett, Marsha; Meyer, Oded; Thille, Candace

2008-01-01

The Open Learning Initiative (OLI) is an open educational resources project at Carnegie Mellon University that began in 2002 with a grant from The William and Flora Hewlett Foundation. OLI creates web-based courses that are designed so that students can learn effectively without an instructor. In addition, the courses are often used by instructors…

A Framework to Support Global Corporate M-Learning: Learner Initiative and Technology Acceptance across Cultures

ERIC Educational Resources Information Center

Farrell, Wendy

2015-01-01

Corporations are growing more and more international and accordingly need to train and develop an increasingly diverse and dispersed employee based. M-learning seems like it may be the solution if it can cross cultures. Learner initiative has been shown to be a disadvantage of distant learning environments, which would include m-learning.…

How collaborative governance can facilitate quality learning for sustainability in cities: A comparative case study of Bristol, Kitakyushu and Tongyeong

NASA Astrophysics Data System (ADS)

Ofei-Manu, Paul; Didham, Robert J.; Byun, Won Jung; Phillips, Rebecca; Dickella Gamaralalage, Premakumara Jagath; Rees, Sian

2017-09-01

Quality learning for sustainability can have a transformative effect in terms of promoting empowerment, leadership and wise investments in individual and collective lives and regenerating the local economies of cities, making them more inclusive, safe, resilient and sustainable. It can also help cities move towards achieving the United Nations Sustainable Development Goals (SDGs). Effecting the transformation of cities into Learning Cities, however, requires changes in the structure of governance. Drawing on interviews with key informants as well as secondary data, this article examines how collaborative governance has facilitated quality learning for sustainability in Bristol (United Kingdom), Kitakyushu (Japan) and Tongyeong (Republic of Korea). Focusing on a conceptual framework and practical application of learning initiatives, this comparative study reveals how these cities' governance mechanisms and institutional structures supported initiatives premised on cooperative learning relationships. While recognising differences in the scope and depth of the learning initiatives and the need for further improvements, the authors found evidence of general support for the governance structures and mechanisms for learning in these cities. The authors conclude by recommending that (1) to implement the Learning Cities concept based on UNESCO's Key Features of Learning Cities, recognition should be given to existing sustainability-related learning initiatives in cities; (2) collaborative governance of the Learning Cities concept at both local and international levels should be streamlined; and (3) UNESCO's Global Network of Learning Cities could serve as a hub for sharing education/learning resources and experiences for other international city-related programmes as an important contribution to the implementation of the SDGs.

Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion.

PubMed

Fierimonte, Roberto; Scardapane, Simone; Uncini, Aurelio; Panella, Massimo

2016-08-26

Distributed learning refers to the problem of inferring a function when the training data are distributed among different nodes. While significant work has been done in the contexts of supervised and unsupervised learning, the intermediate case of Semi-supervised learning in the distributed setting has received less attention. In this paper, we propose an algorithm for this class of problems, by extending the framework of manifold regularization. The main component of the proposed algorithm consists of a fully distributed computation of the adjacency matrix of the training patterns. To this end, we propose a novel algorithm for low-rank distributed matrix completion, based on the framework of diffusion adaptation. Overall, the distributed Semi-supervised algorithm is efficient and scalable, and it can preserve privacy by the inclusion of flexible privacy-preserving mechanisms for similarity computation. The experimental results and comparison on a wide range of standard Semi-supervised benchmarks validate our proposal.

Perceptual learning in Williams syndrome: looking beyond averages.

PubMed

Gervan, Patricia; Gombos, Ferenc; Kovacs, Ilona

2012-01-01

Williams Syndrome is a genetically determined neurodevelopmental disorder characterized by an uneven cognitive profile and surprisingly large neurobehavioral differences among individuals. Previous studies have already shown different forms of memory deficiencies and learning difficulties in WS. Here we studied the capacity of WS subjects to improve their performance in a basic visual task. We employed a contour integration paradigm that addresses occipital visual function, and analyzed the initial (i.e. baseline) and after-learning performance of WS individuals. Instead of pooling the very inhomogeneous results of WS subjects together, we evaluated individual performance by expressing it in terms of the deviation from the average performance of the group of typically developing subjects of similar age. This approach helped us to reveal information about the possible origins of poor performance of WS subjects in contour integration. Although the majority of WS individuals showed both reduced baseline and reduced learning performance, individual analysis also revealed a dissociation between baseline and learning capacity in several WS subjects. In spite of impaired initial contour integration performance, some WS individuals presented learning capacity comparable to learning in the typically developing population, and vice versa, poor learning was also observed in subjects with high initial performance levels. These data indicate a dissociation between factors determining initial performance and perceptual learning.

Emergence of small-world structure in networks of spiking neurons through STDP plasticity.

PubMed

Basalyga, Gleb; Gleiser, Pablo M; Wennekers, Thomas

2011-01-01

In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.

Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).

PubMed

Buss, Aaron T; Wifall, Tim; Hazeltine, Eliot; Spencer, John P

2014-02-01

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

Social marketing of condoms in India.

PubMed

Thapa, S; Prasad, C V; Rao, P H; Severy, L J; Rao, S R

1994-01-01

Contraceptive social marketing is a way of supplying contraceptives to consumers who cannot afford to buy them at full market price, yet are not reached by the free public distribution program. The process involves supplying a subsidized product through existing commercial distribution networks, using the mass media and other retail marketing techniques to commercially advertise the products. India was the first country to introduce this concept to its family planning program. India's social marketing program is also the largest in the world. Over the past 25 years, total condom sales in India have expanded under the program from less than 10 million per year to more than one billion. The authors present an overview of India's social marketing initiative, describe the firms participating in the program, and summarize the lessons learned from the social marketing experience. Problems and prospects, and experiences and implications are discussed.

Developing a Hypothetical Learning Trajectory for the Sampling Distribution of the Sample Means

NASA Astrophysics Data System (ADS)

Syafriandi

2018-04-01

Special types of probability distribution are sampling distributions that are important in hypothesis testing. The concept of a sampling distribution may well be the key concept in understanding how inferential procedures work. In this paper, we will design a hypothetical learning trajectory (HLT) for the sampling distribution of the sample mean, and we will discuss how the sampling distribution is used in hypothesis testing.

Fast phonetic learning occurs already in 2-to-3-month old infants: an ERP study

PubMed Central

Wanrooij, Karin; Boersma, Paul; van Zuijen, Titia L.

2014-01-01

An important mechanism for learning speech sounds in the first year of life is “distributional learning,” i.e., learning by simply listening to the frequency distributions of the speech sounds in the environment. In the lab, fast distributional learning has been reported for infants in the second half of the first year; the present study examined whether it can also be demonstrated at a much younger age, long before the onset of language-specific speech perception (which roughly emerges between 6 and 12 months). To investigate this, Dutch infants aged 2 to 3 months were presented with either a unimodal or a bimodal vowel distribution based on the English /æ/~/ε/ contrast, for only 12 minutes. Subsequently, mismatch responses (MMRs) were measured in an oddball paradigm, where one half of the infants in each group heard a representative [æ] as the standard and a representative [ε] as the deviant, and the other half heard the same reversed. The results (from the combined MMRs during wakefulness and active sleep) disclosed a larger MMR, implying better discrimination of [æ] and [ε], for bimodally than unimodally trained infants, thus extending an effect of distributional training found in previous behavioral research to a much younger age when speech perception is still universal rather than language-specific, and to a new method (using event-related potentials). Moreover, the analysis revealed a robust interaction between the distribution (unimodal vs. bimodal) and the identity of the standard stimulus ([æ] vs. [ε]), which provides evidence for an interplay between a perceptual asymmetry and distributional learning. The outcomes show that distributional learning can affect vowel perception already in the first months of life. PMID:24701203

Team Learning in Technology-Mediated Distributed Teams

ERIC Educational Resources Information Center

Andres, Hayward P.; Shipps, Belinda P.

2010-01-01

This study examines technological, educational/learning, and social affordances associated with the facilitation of project-based learning and problem solving in technology-mediated distributed teams. An empirical interpretive research approach using direct observation is used to interpret, evaluate and rate observable manifested behaviors and…

Stochastic associative memory

NASA Astrophysics Data System (ADS)

Baumann, Erwin W.; Williams, David L.

1993-08-01

Artificial neural networks capable of learning and recalling stochastic associations between non-deterministic quantities have received relatively little attention to date. One potential application of such stochastic associative networks is the generation of sensory 'expectations' based on arbitrary subsets of sensor inputs to support anticipatory and investigate behavior in sensor-based robots. Another application of this type of associative memory is the prediction of how a scene will look in one spectral band, including noise, based upon its appearance in several other wavebands. This paper describes a semi-supervised neural network architecture composed of self-organizing maps associated through stochastic inter-layer connections. This 'Stochastic Associative Memory' (SAM) can learn and recall non-deterministic associations between multi-dimensional probability density functions. The stochastic nature of the network also enables it to represent noise distributions that are inherent in any true sensing process. The SAM architecture, training process, and initial application to sensor image prediction are described. Relationships to Fuzzy Associative Memory (FAM) are discussed.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Park, Won-Woong; Kim, Dong-Kyu; Im, Yong-Taek; Bureerat, Sujin; Kwon, Hyuck-Cheol; Chun, Myung-Sik

2015-04-01

This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

From Indoctrination to Initiation: A Non-Coercive Approach to Faith-Learning Integration

ERIC Educational Resources Information Center

Reichard, Joshua D.

2013-01-01

This article contributes to ongoing discussions related to the nature, scope, and methods of faith-learning integration. The "initiation" approach developed by Tim McDonough (2011) is adapted to faith-learning integration in an attempt to bridge polarizing discussions regarding indoctrination versus rational autonomy and critical…

Achieving Sustainability in Learning and Teaching Initiatives

ERIC Educational Resources Information Center

Brew, Angela; Cahir, Jayde

2014-01-01

Universities have a long history of change in learning and teaching to suit various government initiatives and institutional priorities. Academic developers now are frequently required to address strategic learning and teaching priorities. This paper asks how, in such a context, academic developers can ensure that work they do in relation to one…

Engaging FCS Partners in an International Service Learning Initiative

ERIC Educational Resources Information Center

Keino, Leah C.; Torrie, Margaret C.; Hausafus, Cheryl O.; Trost, Betty C.

2010-01-01

Several definitions of service learning exist. For this initiative, the authors used Torres and Sinton's (2000) definition that students are learning about social issues and applying new knowledge to real problems in their communities. This project entailed a partnership of committed citizens of different groups (middle, secondary, and university…

Monitoring Implementation of Active Learning Classrooms at Lethbridge College, 2014-2015

ERIC Educational Resources Information Center

Benoit, Andy

2017-01-01

Having experienced preliminary success in designing two active learning classrooms, Lethbridge College developed an additional eight active learning classrooms as part of a three-year initiative spanning 2014-2017. Year one of the initiative entailed purchasing new audio-visual equipment and classroom furniture followed by installation. This…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

A Connected History of Health and Education: Learning Together toward a Better City

ERIC Educational Resources Information Center

Howard, Joanne; Howard, Diane; Dotson, Ebbin

2015-01-01

The infrastructure, financial, and human resource histories of health and education are offered as key components of future strategic planning initiatives in learning cities, and 10 key components of strategic planning initiatives designed to enhance the health and wealth of citizens of learning cities are discussed.

A Database for Decision-Making in Training and Distributed Learning Technology

DTIC Science & Technology

1998-04-01

developer must answer these questions: ♦ Who will develop the courseware? Should we outsource ? ♦ What media should we use? How much will it cost? ♦ What...to develop , the database can be useful for answering staffing questions and planning transitions to technology- assisted courses. The database...of distributed learning curricula in com- parison to traditional methods. To develop a military-wide distributed learning plan, the existing course

Procedural instructions, principles, and examples: how to structure instructions for procedural tasks to enhance performance, learning, and transfer.

PubMed

Eiriksdottir, Elsa; Catrambone, Richard

2011-12-01

The goal of this article is to investigate how instructions can be constructed to enhance performance and learning of procedural tasks. Important determinants of the effectiveness of instructions are type of instructions (procedural information, principles, and examples) and pedagogical goal (initial performance, learning, and transfer). Procedural instructions describe how to complete tasks in a stepwise manner, principles describe rules governing the tasks, and examples demonstrate how instances of the task are carried out. The authors review the research literature associated with each type of instruction to identify factors determining effectiveness for different pedagogical goals. The results suggest a trade-off between usability and learnability. Specific instructions help initial performance, whereas more general instructions, requiring problem solving, help learning and transfer. Learning from instructions takes cognitive effort, and research suggests that learners typically opt for low effort. However, it is possible to meet both goals of good initial performance and learning with methods such as fading and by combining different types of instructions. How instructions are constructed influences their effectiveness for the goals of good initial performance, learning, and transfer, and it is therefore important for researchers and practitioners alike to define the pedagogical goal of instructions. If the goal is good initial performance, then instructions should highly resemble the task at hand (e.g., in the form of detailed procedural instructions and examples), but if the goal is good learning and transfer, then instructions should be more abstract, inducing learners to expend the necessary cognitive effort for learning.

Modeling Learning and Memory Using Verbal Learning Tests: Results From ACTIVE

PubMed Central

Gross, Alden L.

2013-01-01

Objective. To investigate the influence of memory training on initial recall and learning. Method. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Results. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen’s d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p < .001). Findings were replicated using the HVLT (decline in initial recall, d = 0.60, p = .01; pre- and posttraining acceleration in learning, d = 3.10, p < .001). Because of the immediate training boost, the memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. Discussion. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning. PMID:22929389

The relationship among learning, health beliefs, alcohol consumption, and tobacco use of primigravidas.

PubMed

Strychar, I M; Griffith, W S; Conry, R F

1990-01-01

The purposes of this study were to identify how pregnant women learned about alcohol consumption and tobacco use, and to identify the relationship between learning, health beliefs and behaviours. Determining how pregnant women learned was based upon Tough's and Knowles' view of learning and consisted of identifying knowledge levels, resources utilized, advice given, time in learning, and initiators of learning episodes. The ex post facto research design involved one-hour interviews with 128 primigravidas at 8 hospitals in British Columbia, 75% of the sample consumed alcoholic beverages before becoming pregnant and these women reduced their intake by an average of 82%; 39% smoked cigarettes before becoming pregnant and these women reduced their cigarette smoking by an average of 52%. Drinkers were advised not to consume alcoholic beverages during pregnancy, whereas smokers were told by friends and family members that it was okay to smoke during pregnancy. Engagement in other-initiated learning episodes was found to be correlated with reduced alcohol intake (p less than or equal to .05); whereas, health beliefs were not correlated with reduced alcohol intake. Neither self-initiated nor other-initiated learning was associated with reduced cigarette smoking; however, perceived risk was associated with reduced cigarette smoking. Knowledge about smoking was associated with health beliefs, suggesting that learning may be indirectly related to smoking behaviours. This study should be replicated with a larger sample to determine the directionality of the association between learning, beliefs and behaviours.

Modeling learning and memory using verbal learning tests: results from ACTIVE.

PubMed

Gross, Alden L; Rebok, George W; Brandt, Jason; Tommet, Doug; Marsiske, Michael; Jones, Richard N

2013-03-01

To investigate the influence of memory training on initial recall and learning. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen's d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p < .001). Findings were replicated using the HVLT (decline in initial recall, d = 0.60, p = .01; pre- and posttraining acceleration in learning, d = 3.10, p < .001). Because of the immediate training boost, the memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning.

Student assessment of the educational benefits of using a CD-ROM for instruction of basic surgical skills.

PubMed

Howe, Lisa M; Boothe, Harry W; Hartsfield, Sandee M

2005-01-01

At Texas A&M University, introductory-level surgical lecture and laboratory notes were converted to a CD-ROM format that included illustrative photographs as well as instructional videos demonstrating the basic surgical skills that all students were required to master. The CD-ROM was distributed to all students in place of traditional paper notes in the second-year surgical class in the professional veterinary curriculum. The study reported here was designed to evaluate the educational benefits of the use of the CD-ROM in place of traditional paper notes by examining the attitudes and practices of students before and after exposure to the CD-ROM format. An anonymous survey was distributed to students in the second-year introductory surgery course on the first day of class and again on the last day of class. Responses to questions were tabulated, response frequencies determined, and Chi-square analysis performed to determine differences between initial and final responses. On the final survey, 89 per cent of students responded that the instructional videos definitely helped them prepare for the laboratory, and 77 per cent responded that they were more likely to practice techniques learned from the CD-ROM videos than those learned from traditional study materials. The majority of students believed that the CD-ROM improved both the course (60 per cent) and their learning experience (62 per cent) as compared to traditional paper notes. Including instructional videos on the CD-ROM enhanced the educational experience of the students by promoting preparedness for laboratories and promoting practice of techniques learned from the videos outside of the laboratory.

Envisioning the future of wildlife in a changing climate: Collaborative learning for adaptation planning

USGS Publications Warehouse

LeDee, Olivia E.; Karasov, W.H.; Martin, Karl J.; Meyer, Michael W.; Ribic, Christine; Van Deelen, Timothy R.

2011-01-01

Natural resource managers are tasked with assessing the impacts of climate change on conservation targets and developing adaptation strategies to meet agency goals. The complex, transboundary nature of climate change demands the collaboration of scientists, managers, and stakeholders in this effort. To share, integrate, and apply knowledge from these diverse perspectives, we must engage in social learning. In 2009, we initiated a process to engage university researchers and agency scientists and managers in collaborative learning to assess the impacts of climate change on terrestrial fauna in the state of Wisconsin, USA. We constructed conceptual Bayesian networks to depict the influence of climate change, key biotic and abiotic factors, and existing stressors on the distribution and abundance of 3 species: greater prairie-chicken (Tympanuchus cupido), wood frog (Lithobates sylvaticus), and Karner blue butterfly (Plebejus melissa samuelis). For each species, we completed a 2-stage expert review that elicited dialogue on information gaps, management opportunities, and research priorities. From our experience, collaborative network modeling proved to be a powerful tool to develop a common vision of the potential impacts of climate change on conservation targets.

Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept.

PubMed

Jochems, Arthur; Deist, Timo M; van Soest, Johan; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Lambin, Philippe; Dekker, Andre

2016-12-01

One of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital. Clinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)). A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.be/nQpqMIuHyOk). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer. We show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%CI, 0.51-0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets. Distributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.

Distributional learning aids linguistic category formation in school-age children.

PubMed

Hall, Jessica; Owen VAN Horne, Amanda; Farmer, Thomas

2018-05-01

The goal of this study was to determine if typically developing children could form grammatical categories from distributional information alone. Twenty-seven children aged six to nine listened to an artificial grammar which contained strategic gaps in its distribution. At test, we compared how children rated novel sentences that fit the grammar to sentences that were ungrammatical. Sentences could be distinguished only through the formation of categories of words with shared distributional properties. Children's ratings revealed that they could discriminate grammatical and ungrammatical sentences. These data lend support to the hypothesis that distributional learning is a potential mechanism for learning grammatical categories in a first language.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks

PubMed Central

Räsänen, Okko; Nagamine, Tasha; Mesgarani, Nima

2017-01-01

Infants’ speech perception adapts to the phonemic categories of their native language, a process assumed to be driven by the distributional properties of speech. This study investigates whether deep neural networks (DNNs), the current state-of-the-art in distributional feature learning, are capable of learning phoneme-like representations of speech in an unsupervised manner. We trained DNNs with unlabeled and labeled speech and analyzed the activations of each layer with respect to the phones in the input segments. The analyses reveal that the emergence of phonemic invariance in DNNs is dependent on the availability of phonemic labeling of the input during the training. No increased phonemic selectivity of the hidden layers was observed in the purely unsupervised networks despite successful learning of low-dimensional representations for speech. This suggests that additional learning constraints or more sophisticated models are needed to account for the emergence of phone-like categories in distributional learning operating on natural speech. PMID:29359204

Training strategy for convolutional neural networks in pedestrian gender classification

NASA Astrophysics Data System (ADS)

Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min

2017-06-01

In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.

Neural Mechanisms Behind Identification of Leptokurtic Noise and Adaptive Behavioral Response

PubMed Central

d'Acremont, Mathieu; Bossaerts, Peter

2016-01-01

Large-scale human interaction through, for example, financial markets causes ceaseless random changes in outcome variability, producing frequent and salient outliers that render the outcome distribution more peaked than the Gaussian distribution, and with longer tails. Here, we study how humans cope with this evolutionary novel leptokurtic noise, focusing on the neurobiological mechanisms that allow the brain, 1) to recognize the outliers as noise and 2) to regulate the control necessary for adaptive response. We used functional magnetic resonance imaging, while participants tracked a target whose movements were affected by leptokurtic noise. After initial overreaction and insufficient subsequent correction, participants improved performance significantly. Yet, persistently long reaction times pointed to continued need for vigilance and control. We ran a contrasting treatment where outliers reflected permanent moves of the target, as in traditional mean-shift paradigms. Importantly, outliers were equally frequent and salient. There, control was superior and reaction time was faster. We present a novel reinforcement learning model that fits observed choices better than the Bayes-optimal model. Only anterior insula discriminated between the 2 types of outliers. In both treatments, outliers initially activated an extensive bottom-up attention and belief network, followed by sustained engagement of the fronto-parietal control network. PMID:26850528

Privacy-preserving backpropagation neural network learning.

PubMed

Chen, Tingting; Zhong, Sheng

2009-10-01

With the development of distributed computing environment , many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the privacy preservation notion to original learning algorithms. In this paper, we focus on preserving the privacy in an important learning model, multilayer neural networks. We present a privacy-preserving two-party distributed algorithm of backpropagation which allows a neural network to be trained without requiring either party to reveal her data to the other. We provide complete correctness and security analysis of our algorithms. The effectiveness of our algorithms is verified by experiments on various real world data sets.

VISIONS2 Learning for Life Initiative. Final Report.

ERIC Educational Resources Information Center

Orangeburg-Calhoun Technical Coll., Orangeburg, SC.

During the Learning for Life Initiative, a technical college and an adult education center partnered with two area businesses to develop and deliver job-specific workplace literacy and basic skills training to employees. Major activities of the initiative included the following: comprehensive staff development program for all project instructors,…

Student Initiatives in Urban Elementary Science Classrooms

ERIC Educational Resources Information Center

Lewis, Scott; Lee, Okhee; Santau, Alexandra; Cone, Neporcha

2010-01-01

Student initiatives play an important role in inquiry-based science with all students, including English language learning (ELL) students. This study examined initiatives that elementary students made as they participated in an intervention to promote science learning and English language development over a three-year period. In addition, the…

Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief

ERIC Educational Resources Information Center

Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally

2012-01-01

This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…

Measures of Student Success with Textbook Transformations: The Affordable Learning Georgia Initiative

ERIC Educational Resources Information Center

Croteau, Emily

2017-01-01

In 2014, the state of Georgia's budget supported a University System of Georgia (USG) initiative: Affordable Learning Georgia (ALG). The initiative was implemented via Textbook Transformation Grants, which provided grants to USG faculty, libraries and librarians, and institutions to "transform their use of textbooks and other learning…

Improving Initial Assessment in Work-Based Learning.

ERIC Educational Resources Information Center

Green, Muriel

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

The Scholarship of Teaching: The CEET Initiative on Teaching and Learning. A Faculty Development Program on Teaching and Learning and Classroom Research. Volumes 1-4. October 2005-December 2006

ERIC Educational Resources Information Center

Scarborough, Jule Dee

2007-01-01

This Northern Illinois University College of Engineering and Engineering Technology (CEET) initiative represents the authors' first attempt to prepare engineering and technology professors for teaching to improve student learning and the Scholarship of Teaching. This college portfolio is nontraditional in that it combines a learning paper approach…

Exploring Initiative as a Signal of Knowledge Co-Construction During Collaborative Problem Solving.

PubMed

Howard, Cynthia; Di Eugenio, Barbara; Jordan, Pamela; Katz, Sandra

2017-08-01

Peer interaction has been found to be conducive to learning in many settings. Knowledge co-construction (KCC) has been proposed as one explanatory mechanism. However, KCC is a theoretical construct that is too abstract to guide the development of instructional software that can support peer interaction. In this study, we present an extensive analysis of a corpus of peer dialogs that we collected in the domain of introductory Computer Science. We show that the notion of task initiative shifts correlates with both KCC and learning. Speakers take task initiative when they contribute new content that advances problem solving and that is not invited by their partner; if initiative shifts between the partners, it indicates they both contribute to problem solving. We found that task initiative shifts occur more frequently within KCC episodes than outside. In addition, task initiative shifts within KCC episodes correlate with learning for low pre-testers, and total task initiative shifts correlate with learning for high pre-testers. As recognizing task initiative shifts does not require as much deep knowledge as recognizing KCC, task initiative shifts as an indicator of productive collaboration are potentially easier to model in instructional software that simulates a peer. Copyright © 2016 Cognitive Science Society, Inc.

Developing Army Leaders: Lessons for Teaching Critical Thinking in Distributed, Resident, and Mixed-Delivery Venues

DTIC Science & Technology

2014-01-01

Based and Affective Theories of Learning Outcomes to New Methods of Training Evaluation,” Journal of Applied Psychology Monograph, Vol. 2, No. 2, 1993...officers. Thus, the Command and Staff General School offers non-resident alternatives for the Common Core: an advanced distributed learning (ADL...course delivered online and a course combining in-person instruction and distributed learning taught in The Army School System (TASS). This report

Advancements in Distributed Learning (ADL) Environment in Support of Transformation

DTIC Science & Technology

2017-01-01

REPORT TR-HFM-212 Advancements in Distributed Learning (ADL) Environment in Support of Transformation (Progrès en apprentissage distribué (ADL) à...l’appui de la transformation ) This report documents the findings of Task Group 212. The primary objective of this Task Group was to explore an agile...STO TECHNICAL REPORT TR-HFM-212 Advancements in Distributed Learning (ADL) Environment in Support of Transformation (Progrès en apprentissage

Fostering Distributed Science Learning through Collaborative Technologies

ERIC Educational Resources Information Center

Vazquez-Abad, Jesus; Brousseau, Nancy; Guillermina, Waldegg C.; Vezina, Mylene; Martinez, Alicia D.; de Verjovsky, Janet Paul

2004-01-01

TACTICS (French and Spanish acronym standing for Collaborative Work and Learning in Science with Information and Communications Technologies) is an ongoing project aimed at investigating a distributed community of learning and practice in which information and communications technologies (ICT) take the role of collaborative tools to support social…

Learning from Multiple Collaborating Intelligent Tutors: An Agent-based Approach.

ERIC Educational Resources Information Center

Solomos, Konstantinos; Avouris, Nikolaos

1999-01-01

Describes an open distributed multi-agent tutoring system (MATS) and discusses issues related to learning in such open environments. Topics include modeling a one student-many teachers approach in a computer-based learning context; distributed artificial intelligence; implementation issues; collaboration; and user interaction. (Author/LRW)

Fostering Self-Regulation in Distributed Learning

ERIC Educational Resources Information Center

Terry, Krista P.; Doolittle, Peter

2006-01-01

Although much has been written about fostering self-regulated learning in traditional classroom settings, there has been little that addresses how to facilitate self-regulated learning skills in distributed and online environments. This article will examine some such strategies by specifically focusing on time management. Specific principles for…

Towards a Better Distributed Framework for Learning Big Data

DTIC Science & Technology

2017-06-14

UNLIMITED: PB Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT This work aimed at solving issues in distributed machine learning. The PI’s team proposed...communication load. Finally, the team proposed the parallel least-squares policy iteration (parallel LSPI) to parallelize a reinforcement policy learning. 15

Alberta Learning: Early Development Instrument Pilot Project Evaluation.

ERIC Educational Resources Information Center

Meaney, Wanda; Harris-Lorenze, Elayne

The Early Development Instrument (EDI) was designed by McMaster University to measure the outcomes of childrens early years as they influence their readiness to learn at school. The EDI was piloted in several Canadian cities in recent years through two national initiatives. Building on these initiatives, Alberta Learning piloted the EDI as a…

Integrating LMSs in the Educational Process: Greek Teachers' Initial Perceptions about LAMS

ERIC Educational Resources Information Center

Papadakis, Spyros; Dovros, Nikos; Paschalis, Giorgos; Rossiou, Eleni

2012-01-01

E-learning with the use of Learning Management Systems, has been increasingly adopted in Primary, Secondary and Higher Education with the expectation to increase students' motivation and infuse activity-centred learning strategies with various educational benefits. This study has investigated the initial perceptions of Greek teachers about the…

An Examination of the Transformative Learning Potential of Alternative Spring Breaks

ERIC Educational Resources Information Center

Mann, Jessica; DeAngelo, Linda

2016-01-01

As institutions seek to offer students an educational experience equipped with opportunities to develop as not only active learners but also engaged citizens, service-learning initiatives in the form of alternative spring breaks (ASB) have become prevalent. This study examines the potential of ASBs, as service-learning initiatives, to deliver a…

Teacher Beliefs Regarding Learning, Pedagogy, and the Use of Technology in Higher Education

ERIC Educational Resources Information Center

Jääskelä, Päivikki; Häkkinen, Päivi; Rasku-Puttonen, Helena

2017-01-01

This study examines university teachers' beliefs about the role of technology in achieving the pedagogical aims of learning within teaching development initiatives at a Finnish university. The initiatives targeted technology adoption in teaching and learning and were enhanced within teacher groups, with support from a university-level network…

Exploring the Living Learning Laboratory: An Approach to Strengthen Campus Sustainability Initiatives by Using Sustainability Science Approach

ERIC Educational Resources Information Center

Zen, Irina Safitri

2017-01-01

Purpose: The paper aims to explore and analyse the potential of campus living learning laboratory (LLL) as an integrated mechanism to provide the innovative and creative teaching and learning experiences, robust research output and strengthening the campus sustainability initiatives by using the sustainability science approach.…

Building Comprehensive High School Guidance Programs through the Smaller Learning Communities Model

ERIC Educational Resources Information Center

Harper, Geralyn

2013-01-01

Despite many reform initiatives, including the federally funded initiative titled the Smaller Learning Communities' (SLC) Model, many students are still underexposed to comprehensive guidance programs. The purpose of this mixed method project study was to examine which components in a comprehensive guidance program for the learning academies at a…

Developing Cooperative Learning in Initial Teacher Education: Indicators for Implementation

ERIC Educational Resources Information Center

Jolliffe, Wendy; Snaith, Jessica

2017-01-01

This paper examines the impact of supporting pre-service teachers to use cooperative learning in one initial teacher education institution in England. In a context where the government requires all teacher education to be "school-led" and where school partners do not commonly use cooperative learning (Baines, Rubie-Davies, and Blatchford…

Laptops Meet Schools, One-One Draw: M-Learning for Secondary Students with Literacy Difficulties

ERIC Educational Resources Information Center

Conway, Paul F.; Amberson, Jessica

2011-01-01

Mobile technology-enhanced literacy initiatives have become a focus of efforts to support learning for students with literacy difficulties. The "Laptops Initiative for Post-Primary Students with Dyslexia or other Reading/Writing Difficulties" offers insights into and addresses questions about ICT policy making regarding m-learning technologies for…

Action Learning--A Process Which Supports Organisational Change Initiatives

ERIC Educational Resources Information Center

Joyce, Pauline

2012-01-01

This paper reflects on how action learning sets (ALSs) were used to support organisational change initiatives. It sets the scene with contextualising the inclusion of change projects in a masters programme. Action learning is understood to be a dynamic process where a team meets regularly to help individual members address issues through a highly…

Appalachian Rural Systemic Initiative: An Account of a Service Learning Collaboration of Two Science Educators.

ERIC Educational Resources Information Center

Brown, Sherri L.; Lashley, Terry L.

To fulfill a service-learning course requirement at the University of Tennessee, Knoxville (UTK), two science-education doctoral students provided professional development to rural teachers and principals participating in the Appalachian Rural Systemic Initiative (ARSI). This paper begins with descriptions of service learning objectives, both in…

Verbal and visual memory in patients with early Parkinson's disease: effect of levodopa.

PubMed

Singh, Sumit; Behari, Madhuri

2006-03-01

The effect of initiation of levodopa therapy on the memory functions in patients with Parkinson's disease remains poorly understood. To evaluate the effect of initiation of levodopa therapy on memory, in patients with early Parkinson's disease. Prospective case control study. Seventeen patients with early Parkinson's disease were evaluated for verbal memory using Rey's auditory verbal learning test, and visual memory using the Benton's visual retention test and Form sequence learning test. UPDRS scores, Hoehn and Yahr's Staging and Schwab and England scores of Activities of daily living. Hamilton's depression rating scale and MMSE were also evaluated. Six controls were also evaluated according to similar study protocol. Levodopa was then prescribed to the cases. Same tests were repeated on all the subjects after 12 weeks. The mean age of the patients was 59.8 (+ 12.9 yrs); mean disease duration of 3.26 (+ 2.06 yrs). The mean UPDRS scores of patients were 36.52 (+ 15.84). Controls were of a similar age and sex distribution. A statistically significant improvement in the scores on the UPDRS, Hamilton's depression scale, Schwab and England scale, and a statistically significant deterioration in the scores of visual memory was observed in patients with PD after starting levodopa, as compared to their baseline scores. There was no correlation between degree of deterioration and the dose of levodopa. Initiation of levodopa therapy in patients with early and stable Parkinson's disease is associated with deterioration in visual memory functions, with relative preservation of the verbal memory.

Synchrony detection and amplification by silicon neurons with STDP synapses.

PubMed

Bofill-i-petit, Adria; Murray, Alan F

2004-09-01

Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.

Students' perception of the learning environment in a distributed medical programme

PubMed Central

Veerapen, Kiran; McAleer, Sean

2010-01-01

Background The learning environment of a medical school has a significant impact on students' achievements and learning outcomes. The importance of equitable learning environments across programme sites is implicit in distributed undergraduate medical programmes being developed and implemented. Purpose To study the learning environment and its equity across two classes and three geographically separate sites of a distributed medical programme at the University of British Columbia Medical School that commenced in 2004. Method The validated Dundee Ready Educational Environment Survey was sent to all students in their 2nd and 3rd year (classes graduating in 2009 and 2008) of the programme. The domains of the learning environment surveyed were: students' perceptions of learning, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere, and students' social self-perceptions. Mean scores, frequency distribution of responses, and inter- and intrasite differences were calculated. Results The perception of the global learning environment at all sites was more positive than negative. It was characterised by a strongly positive perception of teachers. The work load and emphasis on factual learning were perceived negatively. Intersite differences within domains of the learning environment were more evident in the pioneer class (2008) of the programme. Intersite differences consistent across classes were largely related to on-site support for students. Conclusions Shared strengths and weaknesses in the learning environment at UBC sites were evident in areas that were managed by the parent institution, such as the attributes of shared faculty and curriculum. A greater divergence in the perception of the learning environment was found in domains dependent on local arrangements and social factors that are less amenable to central regulation. This study underlines the need for ongoing comparative evaluation of the learning environment at the distributed sites and interaction between leaders of these sites. PMID:20922033

Correlates of individual, and age-related, differences in short-term learning.

PubMed

Zhang, Zhiyong; Davis, Hasker P; Salthouse, Timothy A; Tucker-Drob, Elliot M

2007-07-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task was fluid or crystallized intelligence associated with learning. Although there were moderate correlations among the level parameters across the verbal and spatial tasks, the learning parameters were not significantly correlated with one another across task modalities. These results are inconsistent with the existence of a general (e.g., material-independent) learning ability.

Probabilistic Reversal Learning in Schizophrenia: Stability of Deficits and Potential Causal Mechanisms.

PubMed

Reddy, Lena Felice; Waltz, James A; Green, Michael F; Wynn, Jonathan K; Horan, William P

2016-07-01

Although individuals with schizophrenia show impaired feedback-driven learning on probabilistic reversal learning (PRL) tasks, the specific factors that contribute to these deficits remain unknown. Recent work has suggested several potential causes including neurocognitive impairments, clinical symptoms, and specific types of feedback-related errors. To examine this issue, we administered a PRL task to 126 stable schizophrenia outpatients and 72 matched controls, and patients were retested 4 weeks later. The task involved an initial probabilistic discrimination learning phase and subsequent reversal phases in which subjects had to adjust their responses to sudden shifts in the reinforcement contingencies. Patients showed poorer performance than controls for both the initial discrimination and reversal learning phases of the task, and performance overall showed good test-retest reliability among patients. A subgroup analysis of patients (n = 64) and controls (n = 49) with good initial discrimination learning revealed no between-group differences in reversal learning, indicating that the patients who were able to achieve all of the initial probabilistic discriminations were not impaired in reversal learning. Regarding potential contributors to impaired discrimination learning, several factors were associated with poor PRL, including higher levels of neurocognitive impairment, poor learning from both positive and negative feedback, and higher levels of indiscriminate response shifting. The results suggest that poor PRL performance in schizophrenia can be the product of multiple mechanisms. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Modeling the Delivery Physiology of Distributed Learning Systems.

ERIC Educational Resources Information Center

Paquette, Gilbert; Rosca, Ioan

2003-01-01

Discusses instructional delivery models and their physiology in distributed learning systems. Highlights include building delivery models; types of delivery models, including distributed classroom, self-training on the Web, online training, communities of practice, and performance support systems; and actors (users) involved, including experts,…

Learning procedures from interactive natural language instructions

NASA Technical Reports Server (NTRS)

Huffman, Scott B.; Laird, John E.

1994-01-01

Despite its ubiquity in human learning, very little work has been done in artificial intelligence on agents that learn from interactive natural language instructions. In this paper, the problem of learning procedures from interactive, situated instruction is examined in which the student is attempting to perform tasks within the instructional domain, and asks for instruction when it is needed. Presented is Instructo-Soar, a system that behaves and learns in response to interactive natural language instructions. Instructo-Soar learns completely new procedures from sequences of instruction, and also learns how to extend its knowledge of previously known procedures to new situations. These learning tasks require both inductive and analytic learning. Instructo-Soar exhibits a multiple execution learning process in which initial learning has a rote, episodic flavor, and later executions allow the initially learned knowledge to be generalized properly.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Bose-Einstein condensates form in heuristics learned by ciliates deciding to signal 'social' commitments.

PubMed

Clark, Kevin B

2010-03-01

Fringe quantum biology theories often adopt the concept of Bose-Einstein condensation when explaining how consciousness, emotion, perception, learning, and reasoning emerge from operations of intact animal nervous systems and other computational media. However, controversial empirical evidence and mathematical formalism concerning decoherence rates of bioprocesses keep these frameworks from satisfactorily accounting for the physical nature of cognitive-like events. This study, inspired by the discovery that preferential attachment rules computed by complex technological networks obey Bose-Einstein statistics, is the first rigorous attempt to examine whether analogues of Bose-Einstein condensation precipitate learned decision making in live biological systems as bioenergetics optimization predicts. By exploiting the ciliate Spirostomum ambiguum's capacity to learn and store behavioral strategies advertising mating availability into heuristics of topologically invariant computational networks, three distinct phases of strategy use were found to map onto statistical distributions described by Bose-Einstein, Fermi-Dirac, and classical Maxwell-Boltzmann behavior. Ciliates that sensitized or habituated signaling patterns to emit brief periods of either deceptive 'harder-to-get' or altruistic 'easier-to-get' serial escape reactions began testing condensed on initially perceived fittest 'courting' solutions. When these ciliates switched from their first strategy choices, Bose-Einstein condensation of strategy use abruptly dissipated into a Maxwell-Boltzmann computational phase no longer dominated by a single fittest strategy. Recursive trial-and-error strategy searches annealed strategy use back into a condensed phase consistent with performance optimization. 'Social' decisions performed by ciliates showing no nonassociative learning were largely governed by Fermi-Dirac statistics, resulting in degenerate distributions of strategy choices. These findings corroborate previous work demonstrating ciliates with improving expertise search grouped 'courting' assurances at quantum efficiencies and verify efficient processing by primitive 'social' intelligences involves network forms of Bose-Einstein condensation coupled to preceding thermodynamic-sensitive computational phases. 2009 Elsevier Ireland Ltd. All rights reserved.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Distributed Emotions in the Design of Learning Technologies

ERIC Educational Resources Information Center

Kim, Beaumie; Kim, Mi Song

2010-01-01

Learning is a social activity, which requires interactions with the environment, tools, people, and also ourselves (e.g., our previous experiences). Each interaction provides different meanings to learners, and the associated emotion affects their learning and performance. With the premise that emotion and cognition are distributed, the authors…

Social Networks and Performance in Distributed Learning Communities

ERIC Educational Resources Information Center

Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

2012-01-01

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

ERIC Educational Resources Information Center

Deng, Lawrence Y.; Keh, Huan-Chao; Liu, Yi-Jen

2010-01-01

More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model.…

A Framework for Open, Flexible and Distributed Learning.

ERIC Educational Resources Information Center

Khan, Badrul H.

Designing open, flexible distance learning systems on the World Wide Web requires thoughtful analysis and investigation combined with an understanding of both the Web's attributes and resources and the ways instructional design principles can be applied to tap the Web's potential. A framework for open, flexible, and distributed learning has been…

A Critical Analysis of Job-Embedded Professional Learning within a Distributed Leadership Framework

ERIC Educational Resources Information Center

Campoli, Ashley Jimerson

2011-01-01

Leadership style and professional learning have been linked to student achievement. Studies have linked leadership styles such as distributed leadership to job-embedded professional learning. However, research is mixed when these two constructs are related to student achievement. This study evaluated the relationship between distributed…

Distributed Scaffolding in a Service-Learning Course

ERIC Educational Resources Information Center

Smagorinsky, Peter; Clayton, Christopher M.; Johnson, Lindy L.

2015-01-01

This article argues that the instructional scaffolding metaphor may be reconceived as distributed scaffolding when multiple means of influence are provided in a service-learning setting. In the service-learning course described here, the professor's role is largely as designer of activity settings for preservice teacher candidates, through…

ENERGY-NET (Energy, Environment and Society Learning Network): Best Practices to Enhance Informal Geoscience Learning

NASA Astrophysics Data System (ADS)

Rossi, R.; Elliott, E. M.; Bain, D.; Crowley, K. J.; Steiner, M. A.; Divers, M. T.; Hopkins, K. G.; Giarratani, L.; Gilmore, M. E.

2014-12-01

While energy links all living and non-living systems, the integration of energy, the environment, and society is often not clearly represented in 9 - 12 classrooms and informal learning venues. However, objective public learning that integrates these components is essential for improving public environmental literacy. ENERGY-NET (Energy, Environment and Society Learning Network) is a National Science Foundation funded initiative that uses an Earth Systems Science framework to guide experimental learning for high school students and to improve public learning opportunities regarding the energy-environment-society nexus in a Museum setting. One of the primary objectives of the ENERGY-NET project is to develop a rich set of experimental learning activities that are presented as exhibits at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania (USA). Here we detail the evolution of the ENERGY-NET exhibit building process and the subsequent evolution of exhibit content over the past three years. While preliminary plans included the development of five "exploration stations" (i.e., traveling activity carts) per calendar year, the opportunity arose to create a single, larger topical exhibit per semester, which was assumed to have a greater impact on museum visitors. Evaluative assessments conducted to date reveal important practices to be incorporated into ongoing exhibit development: 1) Undergraduate mentors and teen exhibit developers should receive additional content training to allow richer exhibit materials. 2) The development process should be distributed over as long a time period as possible and emphasize iteration. This project can serve as a model for other collaborations between geoscience departments and museums. In particular, these practices may streamline development of public presentations and increase the effectiveness of experimental learning activities.

Statistical learning in songbirds: from self-tutoring to song culture.

PubMed

Fehér, Olga; Ljubičić, Iva; Suzuki, Kenta; Okanoya, Kazuo; Tchernichovski, Ofer

2017-01-05

At the onset of vocal development, both songbirds and humans produce variable vocal babbling with broadly distributed acoustic features. Over development, these vocalizations differentiate into the well-defined, categorical signals that characterize adult vocal behaviour. A broadly distributed signal is ideal for vocal exploration, that is, for matching vocal production to the statistics of the sensory input. The developmental transition to categorical signals is a gradual process during which the vocal output becomes differentiated and stable. But does it require categorical input? We trained juvenile zebra finches with playbacks of their own developing song, produced just a few moments earlier, updated continuously over development. Although the vocalizations of these self-tutored (ST) birds were initially broadly distributed, birds quickly developed categorical signals, as fast as birds that were trained with a categorical, adult song template. By contrast, siblings of those birds that received no training (isolates) developed phonological categories much more slowly and never reached the same level of category differentiation as their ST brothers. Therefore, instead of simply mirroring the statistical properties of their sensory input, songbirds actively transform it into distinct categories. We suggest that the early self-generation of phonological categories facilitates the establishment of vocal culture by making the song easier to transmit at the micro level, while promoting stability of shared vocabulary at the group level over generations.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Authors.

Canada's new medical school: The Northern Ontario School of Medicine: social accountability through distributed community engaged learning.

PubMed

Strasser, Roger P; Lanphear, Joel H; McCready, William G; Topps, Maureen H; Hunt, D Dan; Matte, Marie C

2009-10-01

Like many rural regions around the world, Northern Ontario has a chronic shortage of doctors. Recognizing that medical graduates who have grown up in a rural area are more likely to practice in the rural setting, the Government of Ontario, Canada, decided in 2001 to establish a new medical school in the region with a social accountability mandate to contribute to improving the health of the people and communities of Northern Ontario. The Northern Ontario School of Medicine (NOSM) is a joint initiative of Laurentian University and Lakehead University, which are located 700 miles apart. This paper outlines the development and implementation of NOSM, Canada's first new medical school in more than 30 years. NOSM is a rural distributed community-based medical school which actively seeks to recruit students into its MD program who come from Northern Ontario or from similar northern, rural, remote, Aboriginal, Francophone backgrounds. The holistic, cohesive curriculum for the MD program relies heavily on electronic communications to support distributed community engaged learning. In the classroom and in clinical settings, students explore cases from the perspective of physicians in Northern Ontario. Clinical education takes place in a wide range of community and health service settings, so that the students experience the diversity of communities and cultures in Northern Ontario. NOSM graduates will be skilled physicians ready and able to undertake postgraduate training anywhere, but with a special affinity for and comfort with pursuing postgraduate training and clinical practice in Northern Ontario.

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

PubMed

Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

2018-05-01

Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

Developing a service improvement initiative for people with learning disabilities in hospice settings.

PubMed

Springall, Fiona

2018-03-21

People with learning disabilities are often marginalised in healthcare, including in hospice settings, and as a result may not receive effective end of life care. Research in hospice settings has identified that many staff lack confidence, skills and knowledge in caring for people with learning disabilities, which can have a negative effect on the care these individuals receive. To address these issues, the author has proposed a service improvement initiative, which she developed as part of her learning disability nursing degree programme. This proposed initiative aimed to enhance end of life care for people with learning disabilities through the implementation of a community learning disability link nurse in the hospice setting. ©2018 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result

NASA Astrophysics Data System (ADS)

Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.

2018-04-01

The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.

Distributed Leadership and Digital Collaborative Learning: A Synergistic Relationship?

ERIC Educational Resources Information Center

Harris, Alma; Jones, Michelle; Baba, Suria

2013-01-01

This paper explores the synergy between distributed leadership and digital collaborative learning. It argues that distributed leadership offers an important theoretical lens for understanding and explaining how digital collaboration is best supported and led. Drawing upon evidence from two online educational platforms, the paper explores the…

Project Evidence: Responding to the Changing Professional Learning Needs of Mentors in Initial Teacher Education

ERIC Educational Resources Information Center

Allen, Jeanne Maree; White, Simone; Sim, Cheryl

2017-01-01

This positioning paper seeks to contribute to the knowledge base of the changing professional learning needs of supervising or mentor teachers in initial teacher education. To do so, we draw from the work of "Project Evidence," an Australian Office of Learning and Teaching funded project, designed to support teacher education through the…

Understanding Evaluation of Learning Support in Mathematics and Statistics

ERIC Educational Resources Information Center

MacGillivray, Helen; Croft, Tony

2011-01-01

With rapid and continuing growth of learning support initiatives in mathematics and statistics found in many parts of the world, and with the likelihood that this trend will continue, there is a need to ensure that robust and coherent measures are in place to evaluate the effectiveness of these initiatives. The nature of learning support brings…

Internet-Based Public Health E-Learning Student Perceptions: An Evaluation from the People's Open Access Education Initiative (Peoples-uni)

ERIC Educational Resources Information Center

Awofeso, Niyi; Philip, Keir; Heller, Richard F.

2012-01-01

Current public health training infrastructure and facilitators in most developing nations are insufficient relative to public health service delivery needs. We examined five areas of student perceptions of a web-based public health learning initiative, the Peoples-uni, which focused on: reasons for enrolling, learning expectations; technical…

Barriers and Opportunities of e-Learning Implementation in Iraq: A Case of Public Universities

ERIC Educational Resources Information Center

Al-Azawei, Ahmed; Parslow, Patrick; Lundqvist, Karsten

2016-01-01

Although the implementation of e-learning initiatives has reached advanced stages in developed countries, it is still in its infancy in many developing nations and the Middle East in particular. Recently, few public universities in Iraq have initiated limited attempts to use e-learning alongside traditional classrooms. However, different obstacles…

Practices and Strategies of Self-Initiated Language Learning in an Online Social Network Discussion Forum: A Descriptive Case Study

ERIC Educational Resources Information Center

Hsieh, Hsiu-Wei

2012-01-01

The proliferation of information and communication technologies and the prevalence of online social networks have facilitated the opportunities for informal learning of foreign languages. However, little educational research has been conducted on how individuals utilize those social networks to take part in self-initiated language learning without…

English at Your Fingertips: Learning Initiatives for Rural Areas

ERIC Educational Resources Information Center

Bekaryan, Lilit; Soghomonyan, Zaruhi; Harutyunyan, Arusyak

2017-01-01

The present paper addresses the practice of a new English classroom on the model of a free e-learning programme in the context of adult education in Armenia, a country where English is taught as a second foreign language. The research reviews the results and impact of an online English language learning programme initiated for those vulnerable…

The Workplace Learner: How to Align Training Initiatives with Individual Learning Competencies.

ERIC Educational Resources Information Center

Rothwell, William J.

This book explains how work organizations can create a workplace climate that encourages real-time, on-the-job learning and development of competent workplace learners, who are wiling and able to seize the initiative for identifying their own learning experiences and evaluating the results. The following are among the topics discussed: (1) the…

Looking at OER with a Critical Eye: Strengthening OER Initiatives by Focusing on Student Learning

ERIC Educational Resources Information Center

Pierce, Matthew

2016-01-01

This paper discusses aspects of adopting, adapting, and building Open Educational Resources (OER) that have the potential to influence student learning but are sometimes overlooked by OER advocates. The author makes recommendations for ensuring that OER initiatives have a positive impact on student learning and argues that librarians can be…

Leading Change in Tissue Viability Best Practice: An Action Learning Programme for Link Nurse Practitioners

ERIC Educational Resources Information Center

Kellie, Jean; Henderson, Eileen; Milsom, Brian; Crawley, Hayley

2010-01-01

This account of practice reports on an action learning initiative designed and implemented in partnership between a regional NHS Acute Trust and a UK Business School. The central initiative was the implementation of an action learning programme entitled "Leading change in tissue viability best practice: a development programme for Link Nurse…

Evaluation of Massachusetts Expanded Learning Time (ELT) Initiative: Implementation and Outcomes after Four Years

ERIC Educational Resources Information Center

Boulay, Beth; Gamse, Beth; Checkoway, Amy; Maree, Kenyon; Fox, Lindsay

2011-01-01

The Massachusetts Department of Elementary and Secondary Education (ESE) has supported a multi-year study of the Expanded Learning Time (ELT) initiative to learn about the process and impact of ELT. Abt Associates Inc. is conducting this research. The study has two components: 1) a planning and implementation component that explores the…

"Ready To Learn" Transmedia Demonstration Station Study: A Report to the CPB-PBS "Ready to Learn Initiative"

ERIC Educational Resources Information Center

Pasnik, Shelley; Llorente, Carlin

2012-01-01

The 2012 Transmedia Demonstration Stations program study is part of the multiyear CPB-PBS "Ready To Learn" summative evaluation initiative by Education Development Center, Inc., (EDC) and SRI International (SRI). Through a series of related studies, the authors are documenting, and, whenever possible, measuring the impact of PBS KIDS…

Implementing and Sustaining Higher Education Service-Learning Initiatives: Revisiting Young et al.'s Organizational Tactics

ERIC Educational Resources Information Center

Bennett, Dawn; Sunderland, Naomi; Bartleet, Brydie-Leigh; Power, Anne

2016-01-01

Although the value of service-learning opportunities has long been aligned to student engagement, global citizenship, and employability, the rhetoric can be far removed from the reality of coordinating such activities within higher education. This article stems from arts-based service-learning initiatives with Indigenous communities in Australia.…

Learning at Air Navigation Services after Initial Training

ERIC Educational Resources Information Center

Teperi, Anna-Maria; Leppanen, Anneli

2010-01-01

Purpose: This study aims to find out the means used for individual, group and organizational learning at work at one air navigation service provider after the initial training period. The study also aims to find out what practices need to be improved to enhance learning at work. Design/methodology/approach: The data for the study were collected…

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

PubMed

Power, Daniel A; Watson, Richard A; Szathmáry, Eörs; Mills, Rob; Powers, Simon T; Doncaster, C Patrick; Czapp, Błażej

2015-12-08

The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions.

Distributed deep learning networks among institutions for medical imaging.

PubMed

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Epistemology and expectations survey about experimental physics: Development and initial results

NASA Astrophysics Data System (ADS)

Zwickl, Benjamin M.; Hirokawa, Takako; Finkelstein, Noah; Lewandowski, H. J.

2014-06-01

In response to national calls to better align physics laboratory courses with the way physicists engage in research, we have developed an epistemology and expectations survey to assess how students perceive the nature of physics experiments in the contexts of laboratory courses and the professional research laboratory. The Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS) evaluates students' epistemology at the beginning and end of a semester. Students respond to paired questions about how they personally perceive doing experiments in laboratory courses and how they perceive an experimental physicist might respond regarding their research. Also, at the end of the semester, the E-CLASS assesses a third dimension of laboratory instruction, students' reflections on their course's expectations for earning a good grade. By basing survey statements on widely embraced learning goals and common critiques of teaching labs, the E-CLASS serves as an assessment tool for lab courses across the undergraduate curriculum and as a tool for physics education research. We present the development, evidence of validation, and initial formative assessment results from a sample that includes 45 classes at 20 institutions. We also discuss feedback from instructors and reflect on the challenges of large-scale online administration and distribution of results.

Integrating Experiential and Distributional Data to Learn Semantic Representations

ERIC Educational Resources Information Center

Andrews, Mark; Vigliocco, Gabriella; Vinson, David

2009-01-01

The authors identify 2 major types of statistical data from which semantic representations can be learned. These are denoted as "experiential data" and "distributional data". Experiential data are derived by way of experience with the physical world and comprise the sensory-motor data obtained through sense receptors. Distributional data, by…

Assessing Distributed Leadership for Learning and Teaching Quality: A Multi-Institutional Study

ERIC Educational Resources Information Center

Carbone, Angela; Evans, Julia; Ross, Bella; Drew, Steve; Phelan, Liam; Lindsay, Katherine; Cottman, Caroline; Stoney, Susan; Ye, Jing

2017-01-01

Distributed leadership has been explored internationally as a leadership model that will promote and advance excellence in learning and teaching in higher education. This paper presents an assessment of how effectively distributed leadership was enabled at five Australian institutions implementing a collaborative teaching quality development…

Instructional Design Issues in a Distributed Collaborative Engineering Design (CED) Instructional Environment

ERIC Educational Resources Information Center

Koszalka, Tiffany A.; Wu, Yiyan

2010-01-01

Changes in engineering practices have spawned changes in engineering education and prompted the use of distributed learning environments. A distributed collaborative engineering design (CED) course was designed to engage engineering students in learning about and solving engineering design problems. The CED incorporated an advanced interactive…

An Evaluation of Short-Term Distributed Online Learning Events

ERIC Educational Resources Information Center

Barker, Bradley; Brooks, David

2005-01-01

The purpose of this study was to evaluate the effectiveness of short-term distributed online training events using an adapted version of the compressed evaluation form developed by Wisher and Curnow (1998). Evaluating online distributed training events provides insight into course effectiveness, the contribution of prior knowledge to learning, and…

The widest practicable dissemination: The NASA technical report server

NASA Technical Reports Server (NTRS)

Nelson, Michael L.; Gottlich, Gretchen L.; Bianco, David J.; Binkley, Robert L.; Kellogg, Yvonne D.; Paulson, Sharon S.; Beaumont, Chris J.; Schmunk, Robert B.; Kurtz, Michael; Accomazzi, Alberto

1995-01-01

The search for innovative methods to distribute NASA's information lead a gross-roots team to create the NASA Technical Report Server (NTRS), which uses the World Wide Web and other popular Internet-based information systems as search engines. The NTRS is an inter-center effort which provides uniform access to various distributed publication servers residing on the Internet. Users have immediate desktop access to technical publications from NASA centers and institutes. This paper presents the NTRS architecture, usage metrics, and the lessons learned while implementing and maintaining the services over the initial 6-month period. The NTRS is largely constructed with freely available software running on existing hardware. NTRS builds upon existing hardware and software, and the resulting additional exposure for the body of literature contained will allow NASA to ensure that its institutional knowledge base will continue to receive the widest practicable and appropriate dissemination.

Instructional Designers' Media Selection Practices for Distributed Problem-Based Learning Environments

ERIC Educational Resources Information Center

Fells, Stephanie

2012-01-01

The design of online or distributed problem-based learning (dPBL) is a nascent, complex design problem. Instructional designers are challenged to effectively unite the constructivist principles of problem-based learning (PBL) with appropriate media in order to create quality dPBL environments. While computer-mediated communication (CMC) tools and…

The Pathway Program: How a Collaborative, Distributed Learning Program Showed Us the Future of Social Work Education

ERIC Educational Resources Information Center

Morris, Teresa; Mathias, Christine; Swartz, Ronnie; Jones, Celeste A; Klungtvet-Morano, Meka

2013-01-01

This paper describes a three-campus collaborative, distributed learning program that delivers social work education to remote rural and desert communities in California via distance learning modalities. This "Pathway Program" provides accredited social work education for a career ladder beginning with advising and developing an academic…

Learning through Telepresence with iPads: Placing Schools in Local/Global Communities

ERIC Educational Resources Information Center

Meyer, Bente

2015-01-01

Distributed learning is a growing issue in education following the mainstreaming of technologies such as videoconferencing. However, though distance and distributed learning have been common in adult education and business since the 1990s little is still known about the use of videoconferencing in elementary education. This paper reports from…

Distributed Learning Enhances Relational Memory Consolidation

ERIC Educational Resources Information Center

Litman, Leib; Davachi, Lila

2008-01-01

It has long been known that distributed learning (DL) provides a mnemonic advantage over massed learning (ML). However, the underlying mechanisms that drive this robust mnemonic effect remain largely unknown. In two experiments, we show that DL across a 24 hr interval does not enhance immediate memory performance but instead slows the rate of…

Model of Distributed Learning Objects Repository for a Heterogenic Internet Environment

ERIC Educational Resources Information Center

Kaczmarek, Jerzy; Landowska, Agnieszka

2006-01-01

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

Perceptually Guided Photo Retargeting.

PubMed

Xia, Yingjie; Zhang, Luming; Hong, Richang; Nie, Liqiang; Yan, Yan; Shao, Ling

2016-04-22

We propose perceptually guided photo retargeting, which shrinks a photo by simulating a human's process of sequentially perceiving visually/semantically important regions in a photo. In particular, we first project the local features (graphlets in this paper) onto a semantic space, wherein visual cues such as global spatial layout and rough geometric context are exploited. Thereafter, a sparsity-constrained learning algorithm is derived to select semantically representative graphlets of a photo, and the selecting process can be interpreted by a path which simulates how a human actively perceives semantics in a photo. Furthermore, we learn the prior distribution of such active graphlet paths (AGPs) from training photos that are marked as esthetically pleasing by multiple users. The learned priors enforce the corresponding AGP of a retargeted photo to be maximally similar to those from the training photos. On top of the retargeting model, we further design an online learning scheme to incrementally update the model with new photos that are esthetically pleasing. The online update module makes the algorithm less dependent on the number and contents of the initial training data. Experimental results show that: 1) the proposed AGP is over 90% consistent with human gaze shifting path, as verified by the eye-tracking data, and 2) the retargeting algorithm outperforms its competitors significantly, as AGP is more indicative of photo esthetics than conventional saliency maps.

Once upon a time.... Storytelling to enhance teaching and learning.

PubMed

Lordly, Daphne

2007-01-01

The impact of storytelling in the classroom was examined, as was what motivates individuals to engage in storytelling. A storytelling methodology was introduced in an undergraduate nutrition course as an opportunity to enhance the teaching and learning environment. A 28-item, multi-part, self-administered survey was then distributed to the class (n=17). Survey responses (n=15, 88% response) indicate that educators' and students' storytelling can positively influence the learning environment. This occurs through the creation of a greater focus on personalized information, glimpses of real-life experience, a connection with a topic as participants recognize similarities in their own personal experience and knowledge, and connections between different topics and through the emphasis on key concepts. Stories initiate useful conversations about unexplored struggles within practice, such as the emotional dimension(s) of an issue or what it means to be professional. Students are motivated to participate in storytelling through an external focus on others (i.e., helping others to learn) and an internal focus on self (i.e., seeking a connection with others to promote social dialogue). Several challenges related to the use of storytelling in the classroom emerged. Storytelling develops ways of knowing and dialoguing about issues, which has the potential to influence how students will approach their professional practice.

Scheduling lessons learned from the Autonomous Power System

NASA Technical Reports Server (NTRS)

Ringer, Mark J.

1992-01-01

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler.

Contextual cueing in 3D visual search depends on representations in planar-, not depth-defined space.

PubMed

Zang, Xuelian; Shi, Zhuanghua; Müller, Hermann J; Conci, Markus

2017-05-01

Learning of spatial inter-item associations can speed up visual search in everyday life, an effect referred to as contextual cueing (Chun & Jiang, 1998). Whereas previous studies investigated contextual cueing primarily using 2D layouts, the current study examined how 3D depth influences contextual learning in visual search. In two experiments, the search items were presented evenly distributed across front and back planes in an initial training session. In the subsequent test session, the search items were either swapped between the front and back planes (Experiment 1) or between the left and right halves (Experiment 2) of the displays. The results showed that repeated spatial contexts were learned efficiently under 3D viewing conditions, facilitating search in the training sessions, in both experiments. Importantly, contextual cueing remained robust and virtually unaffected following the swap of depth planes in Experiment 1, but it was substantially reduced (to nonsignificant levels) following the left-right side swap in Experiment 2. This result pattern indicates that spatial, but not depth, inter-item variations limit effective contextual guidance. Restated, contextual cueing (even under 3D viewing conditions) is primarily based on 2D inter-item associations, while depth-defined spatial regularities are probably not encoded during contextual learning. Hence, changing the depth relations does not impact the cueing effect.

Perceptions of a continuing professional development portfolio model to enhance the scholarship of teaching and learning.

PubMed

Tofade, Toyin; Abate, Marie; Fu, Yunting

2014-04-01

To obtain feedback about the potential usefulness of a continuing professional development (CPD) portfolio for enhancing a faculty or practitioner's scholarship of teaching and learning (SoTL). A CPD portfolio approach to the SoTL was distributed in advance to registrants of the 2011 Annual AACP Teacher's Seminar. In an interactive workshop, faculty facilitators described a model for a CPD process applied to the development of an individual's SoTL. During the workshop, participants were asked to complete the initial sections of the portfolio to develop a personal plan for success in the SoTL. Post workshop, an evaluation form was distributed to the participants to obtain feedback about the CPD approach. Completed evaluation forms were collected, collated, and summarized. A total of 53 (14.1%) workshop participants completed the evaluation form of the 375 attendees. In all, 25 assistant professors, 14 associate professors, 4 full professors, 10 residents/students, 22 clinical, and 2 research faculty submitted evaluations. The proposed uses for the portfolio model selected most often by the responders were for personal development, faculty evaluation, increasing the SoTL, new faculty development, preceptor development, and residency training. A structured CPD portfolio model might be useful for the professional development of the SoTL.

Mayan Children's Creation of Learning Ecologies by Initiative and Cooperative Action.

PubMed

de León, Lourdes

2015-01-01

This chapter examines Mayan children's initiatives in creating their own learning environments in collaboration with others as they engage in culturally relevant endeavors of family and community life. To this end, I carry out a fine-grained ethnographic and linguistic analysis of the interactional emergence of learning ecologies. Erickson defines learning ecology as a socioecological system where participants mutually influence one another through verbal and nonverbal actions, as well as through other forms of semiotic communication (2010, 254). In analyzing learning ecologies, I adopt a "theory of action" approach, taking into account multimodal communication (e.g., talk, gesture, gaze, body positioning), participants' sociospatial organization, embodied action, objects, tools, and other culturally relevant materials brought together to build action (Goodwin, 2000, 2013; Hutchins, 1995). I use microethnographic analysis (Erickson, 1992) to bring to the surface central aspects of children's agentive roles in learning through "cooperative actions" (Goodwin, 2013) and "hands-on" experience (Ingold, 2007) the skills of competent members of their community. I examine three distinct Learning Ecologies created by children's initiatives among the Mayan children that I observed: (i) children requesting guidance to collaborate in a task, (ii) older children working on their own initiative with subsequent monitoring and correction from competent members, and (iii) children with near competence in a task with occasional monitoring and no guidance. I argue that these findings enrich and add power to models of family- and community-based learning such as Learning by Observing and Pitching In (Rogoff, 2014). © 2015 Elsevier Inc. All rights reserved.

Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems.

PubMed

Nishimoto, Ryu; Tani, Jun

2004-09-01

This study shows how sensory-action sequences of imitating finite state machines (FSMs) can be learned by utilizing the deterministic dynamics of recurrent neural networks (RNNs). Our experiments indicated that each possible combinatorial sequence can be recalled by specifying its respective initial state value and also that fractal structures appear in this initial state mapping after the learning converges. We also observed that the sequences of mimicking FSMs are encoded utilizing the transient regions rather than the invariant sets of the evolved dynamical systems of the RNNs.

Fast and Accurate Learning When Making Discrete Numerical Estimates.

PubMed

Sanborn, Adam N; Beierholm, Ulrik R

2016-04-01

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates.

Fast and Accurate Learning When Making Discrete Numerical Estimates

PubMed Central

Sanborn, Adam N.; Beierholm, Ulrik R.

2016-01-01

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates. PMID:27070155

Strengthening the evidence and action on multi-sectoral partnerships in public health: an action research initiative

PubMed Central

Willis, C. D.; Greene, J. K.; Abramowicz, A.; Riley, B. L.

2016-01-01

Abstract Introduction: The Public Health Agency of Canada’s Multi-sectoral Partnerships Initiative, administered by the Centre for Chronic Disease Prevention (CCDP), brings together diverse partners to design, implement and advance innovative approaches for improving population health. This article describes the development and initial priorities of an action research project (a learning and improvement strategy) that aims to facilitate continuous improvement of the CCDP’s partnership initiative and contribute to the evidence on multi-sectoral partnerships. Methods: The learning and improvement strategy for the CCDP’s multi-sectoral partnership initiative was informed by (1) consultations with CCDP staff and senior management, and (2) a review of conceptual frameworks to do with multi-sectoral partnerships. Consultations explored the development of the multi-sectoral initiative, barriers and facilitators to success, and markers of effectiveness. Published and grey literature was reviewed using a systematic search strategy with findings synthesized using a narrative approach. Results: Consultations and the review highlighted the importance of understanding partnership impacts, developing a shared vision, implementing a shared measurement system and creating opportunities for knowledge exchange. With that in mind, we propose a six-component learning and improvement strategy that involves (1) prioritizing learning needs, (2) mapping needs to evidence, (3) using relevant data-collection methods, (4) analyzing and synthesizing data, (5) feeding data back to CCDP staff and teams and (6) taking action. Initial learning needs include investigating partnership reach and the unanticipated effects of multi-sectoral partnerships for individuals, groups, organizations or communities. Conclusion: While the CCDP is the primary audience for the learning and improvement strategy, it may prove useful for a range of audiences, including other government departments and external organizations interested in capturing and sharing new knowledge generated from multi-sectoral partnerships. PMID:27284702

Designing Innovative Learning Environments to Foster Communities of Learners for Students in Initial Vocational Education

ERIC Educational Resources Information Center

Boersma, Annoesjka; ten Dam, Geert; Wardekker, Willem; Volman, Monique

2016-01-01

In this study, the concept of "community of learners" was used to improve initial vocational education. The framework of a 'community of learners for vocational orientation' that we present offers both a theoretical understanding of teaching-learning processes in initial vocational education and heuristics for the design of innovative…

Managing environmental knowledge through learning processes in Spanish hospitality companies.

PubMed

Cegarra-Navarro, Juan Gabriel; Martinez Martinez, Aurora

2010-11-01

The major focus of this research is to investigate whether environmental knowledge has any impact on organizational outcomes through an empirical investigation of 127 Spanish hospitality companies, using structural equation models. Our results show that environmental knowledge is an important determiner for developing organizational outcomes. However, this relationship is completed with just two related constructs: Firstly, the company's acquisition process plays a key role in managing the tension between the knowledge necessary to develop the appropriated environmental initiatives and current knowledge. Secondly, the company's distribution process also sheds light on tangible means for managers to enhance their company's outcomes through environmental knowledge.

Individual Learning Account Pilot Initiative: A Learning Tool for the 21st Century. Report to the OPM Director.

ERIC Educational Resources Information Center

President's Task Force on Federal Training Technology, Washington, DC.

The U.S. Office of Personnel Management (OPM) evaluated the feasibility of individual learning accounts (ILAs) as an approach to workforce development. Thirteen federal agencies volunteered to participate in the initiative. Together, they conducted a total of 17 pilot tests. Some pilot tests included all employees in the agency. Others targeted…

Workplace Mentoring Guide For Education, Business and Industry Partners of Connecticut's School-to-Career Initiative: Connecticut LEARNS.

ERIC Educational Resources Information Center

Connecticut State Dept. of Education, Hartford. Bureau of Career and Adult Education.

This document is a guide to workplace mentoring that is intended to assist individuals who are interested in or involved in placing students in work-based learning experiences as part of Connecticut's school-to-work initiative, Connecticut Learns. The following are among the topics discussed: (1) the purposes and principles of workplace mentoring;…

Interdisciplinary Interactions within a Small-Scale Research Initiative Investigating Animation Creation as a Means of Teaching and Learning

ERIC Educational Resources Information Center

Wishart, J. M.; Wakley, G.

2017-01-01

This paper reports an interdisciplinary research (IDR) initiative conducted by two lecturers from different university faculties who found they shared an interest in using animations to support teaching and learning. The research comprised an exploratory pilot to test the feasibility, and to explore the impact on learning, of having undergraduates…

Change at Work and Professional Learning: How Readiness to Change, Self-Determination and Personal Initiative Affect Individual Learning through Reflection

ERIC Educational Resources Information Center

Hetzner, Stefanie; Heid, Helmut; Gruber, Hans

2012-01-01

Reflection offers an important means to learn effectively from changes induced by the workplace. The authors examined readiness to change and work-related self-determination as preconditions for reflection at work and expected personal initiative--defined as "self-starting" and "proactive behaviour"--to have a mediating effect. The study tested…

Learning and Change in the Redesign of a Primary Health Care Initiative

ERIC Educational Resources Information Center

Rule, John; Dunston, Roger; Solomon, Nicky

2016-01-01

Purpose: This paper aims to provide an account of learning and change in the redesign of a primary health-care initiative in a large metropolitan city in Australia. Design/Methodology/ Approach: The paper is based on research exploring the place and role of learning in the re-making of health professional practices in a major New South Wales…

Power System Simulation for Policymaking and Making Policymakers

NASA Astrophysics Data System (ADS)

Cohen, Michael Ari

Power system simulation is a vital tool for anticipating, planning for and ultimately addressing future conditions on the power grid, especially in light of contemporary shifts in power generation, transmission and use that are being driven by a desire to utilize more environmentally responsible energy sources. This dissertation leverages power system simulation and engineering-economic analysis to provide initial answers to one open question about future power systems: how will high penetrations of distributed (rooftop) solar power affect the physical and economic operation of distribution feeders? We find that the overall impacts of distributed solar power (both positive and negative) on the feeders we modeled are minor compared to the overall cost of energy, but that there is on average a small net benefit provided by distributed generation. We then describe an effort to make similar analyses more accessible to a non-engineering (high school) audience by developing an educational video game called "Griddle" that is based on the same power system simulation techniques used in the first study. We describe the design and evaluation of Griddle and find that it demonstrates potential to provide students with insights about key power system learning objectives.

Product Distribution Theory for Control of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Lee, Chia Fan; Wolpert, David H.

2004-01-01

Product Distribution (PD) theory is a new framework for controlling Multi-Agent Systems (MAS's). First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (probability distribution of) the joint stare of the agents. Accordingly we can consider a team game in which the shared utility is a performance measure of the behavior of the MAS. For such a scenario the game is at equilibrium - the Lagrangian is optimized - when the joint distribution of the agents optimizes the system's expected performance. One common way to find that equilibrium is to have each agent run a reinforcement learning algorithm. Here we investigate the alternative of exploiting PD theory to run gradient descent on the Lagrangian. We present computer experiments validating some of the predictions of PD theory for how best to do that gradient descent. We also demonstrate how PD theory can improve performance even when we are not allowed to rerun the MAS from different initial conditions, a requirement implicit in some previous work.

A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

PubMed

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.

Orientation During Initial Learning and Subsequent Discrimination of Faces

NASA Technical Reports Server (NTRS)

Cohen, Malcolm M.; Holton, Emily M. (Technical Monitor)

1997-01-01

Discrimination of facial features degrades with stimulus rotation (e.g., the "Margaret Thatcher" effect). Thirty-two observers learned to discriminate between two upright, or two inverted, faces. Images, erect and rotated by +/-45deg, +/-90deg, +/-135deg and 180deg about the line of sight, were presented on a computer screen. Initial discriminative reaction times increased with stimulus rotation only for observers who learned the upright faces. Orientation during learning is critical in identifying faces subsequently seen at different orientations.

Spontaneous Group Learning in Ambient Learning Environments

NASA Astrophysics Data System (ADS)

Bick, Markus; Jughardt, Achim; Pawlowski, Jan M.; Veith, Patrick

Spontaneous Group Learning is a concept to form and facilitate face-to-face, ad-hoc learning groups in collaborative settings. We show how to use Ambient Intelligence to identify, support, and initiate group processes. Learners' positions are determined by widely used technologies, e.g., Bluetooth and WLAN. As a second step, learners' positions, tasks, and interests are visualized. Finally, a group process is initiated supported by relevant documents and services. Our solution is a starting point to develop new didactical solutions for collaborative processes.

Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic.

PubMed

van der Ham, Sabine; de Boer, Bart

2015-10-01

When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults' generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants' reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories.

Cognitive Bias for Learning Speech Sounds From a Continuous Signal Space Seems Nonlinguistic

PubMed Central

de Boer, Bart

2015-01-01

When learning language, humans have a tendency to produce more extreme distributions of speech sounds than those observed most frequently: In rapid, casual speech, vowel sounds are centralized, yet cross-linguistically, peripheral vowels occur almost universally. We investigate whether adults’ generalization behavior reveals selective pressure for communication when they learn skewed distributions of speech-like sounds from a continuous signal space. The domain-specific hypothesis predicts that the emergence of sound categories is driven by a cognitive bias to make these categories maximally distinct, resulting in more skewed distributions in participants’ reproductions. However, our participants showed more centered distributions, which goes against this hypothesis, indicating that there are no strong innate linguistic biases that affect learning these speech-like sounds. The centralization behavior can be explained by a lack of communicative pressure to maintain categories. PMID:27648212

Problem-Based Learning and Problem-Solving Tools: Synthesis and Direction for Distributed Education Environments.

ERIC Educational Resources Information Center

Friedman, Robert S.; Deek, Fadi P.

2002-01-01

Discusses how the design and implementation of problem-solving tools used in programming instruction are complementary with both the theories of problem-based learning (PBL), including constructivism, and the practices of distributed education environments. Examines how combining PBL, Web-based distributed education, and a problem-solving…

Redistributed Leadership for Sustainable Professional Learning Communities

ERIC Educational Resources Information Center

Hargreaves, Andy; Fink, Dean

2006-01-01

Distributed leadership in schools is not exclusive to professional learning communities; it is distributed in all schools, for good purposes and for bad, by design and by emergence. In this article, we describe a normative view of distributed leadership that tends to be a leadership of advocacy, and we offer a descriptive perspective that argues…

Technology, Learning and Instruction: Distributed Cognition in the Secondary English Classroom

ERIC Educational Resources Information Center

Gomez, Mary Louise; Schieble, Melissa; Curwood, Jen Scott; Hassett, Dawnene

2010-01-01

In this paper, we analyse interactions between secondary students and pre-service teachers in an online environment in order to understand how their meaning-making processes embody distributed cognition. We begin by providing a theoretical review of the ways in which literacy learning is distributed across learners, objects, tools, symbols,…

Some Technical Implications of Distributed Cognition on the Design on Interactive Learning Environments.

ERIC Educational Resources Information Center

Dillenbourg, Pierre

1996-01-01

Maintains that diagnosis, explanation, and tutoring, the functions of an interactive learning environment, are collaborative processes. Examines how human-computer interaction can be improved using a distributed cognition framework. Discusses situational and distributed knowledge theories and provides a model on how they can be used to redesign…

The Effects of Frequency, Distribution, Mode of Presentation, and First Language on Learning an Artificial Language

ERIC Educational Resources Information Center

Miyata, Munehiko

2011-01-01

This dissertation presents results from a series of experiments investigating adult learning of an artificial language and the effects that input frequency (high vs. low token frequency), frequency distribution (skewed vs. balanced), presentation mode (structured vs. scrambled), and first language (English vs. Japanese) have on such learning.…

An Autonomous Mobile Agent-Based Distributed Learning Architecture: A Proposal and Analytical Analysis

ERIC Educational Resources Information Center

Ahmed, Iftikhar; Sadeq, Muhammad Jafar

2006-01-01

Current distance learning systems are increasingly packing highly data-intensive contents on servers, resulting in the congestion of network and server resources at peak service times. A distributed learning system based on faded information field (FIF) architecture that employs mobile agents (MAs) has been proposed and simulated in this work. The…

A Self-Organizing Incremental Neural Network based on local distribution learning.

PubMed

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

"I'll Take Care of the Flowers!" Researching Agency through Initiatives across Different Learning Environments

ERIC Educational Resources Information Center

Kangas, Marjaana; Kopisto, Kaisa; Löfman, Krista; Salo, Laura; Krokfors, Leena

2017-01-01

This case study examined how the agency of a fifth-grade pupil appeared across different learning environments in the primary school context. In this study, agency is defined as the initiatives taken by an individual in interactive situations. The research question is: how does a pupil's agency manifest and vary through taking initiatives across…

Knowing and Learning about Science in Primary School "Communities of Science Practice": The Views of Participating Scientists in the "MyScience" Initiative

ERIC Educational Resources Information Center

Forbes, Anne; Skamp, Keith

2013-01-01

"MyScience" is a primary science education initiative in which being in a community of practice is integral to the learning process. One component of this initiative involves professional scientists interacting with primary school communities which are navigating their way towards sustainable "communities of practice" around the "domain" of…

2012 Preschool Pilot Study of PBS KIDS Transmedia Mathematics Content: A Report to the CPB-PBS "Ready to Learn Initiative"

ERIC Educational Resources Information Center

Pasnik, Shelley; Llorente, Carlin

2012-01-01

The 2012 Preschool Pilot Study of PBS KIDS Transmedia Mathematics Content (Preschool Pilot) is an important part of the authors' multiyear "Ready To Learn" (RTL) summative evaluation initiative. Through this initiative funded by the Corporation for Public Broadcasting (CPB) and Public Broadcasting Service (PBS), it was the responsibility…

Writing for publication: faculty development initiative using social learning theory.

PubMed

Sanderson, Bonnie K; Carter, Matt; Schuessler, Jenny B

2012-01-01

Demonstrating scholarly competency is an expectation for nurse faculty. However, there is hesitancy among some faculty to fully engage in scholarly activities. To strengthen a school of nursing's culture of scholarship, a faculty development writing initiative based on Social Learning Theory was implemented. The authors discuss this initiative to facilitate writing for publication productivity among faculty and the successful outcomes.

Exploring the Effects of Student-Centered Project-Based Learning with Initiation on Students' Computing Skills: A Quasi-Experimental Study of Digital Storytelling

ERIC Educational Resources Information Center

Tsai, Chia-Wen; Shen, Pei-Di; Lin, Rong-An

2015-01-01

This study investigated, via quasi-experiments, the effects of student-centered project-based learning with initiation (SPBL with Initiation) on the development of students' computing skills. In this study, 96 elementary school students were selected from four class sections taking a course titled "Digital Storytelling" and were assigned…

Accreditation of Prior Learning as a Lever for Lifelong Learning: Lessons Learnt from the New Opportunities Initiative, Portugal

ERIC Educational Resources Information Center

Carneiro, Roberto, Ed.

2011-01-01

The New Opportunities Initiative (NOI) is a Portuguese flagship programme to recognise and accredit prior learning (RPL, APL) and to endow low-skilled adults with upper secondary qualifications, which is defined as the minimum entry threshold to the exercise of a full citizenship in a knowledge-rich society. NOI's major achievement has been its…

Evaluation of the Massachusetts Expanded Learning Time (ELT) Initiative. Year Five Final Report: 2010-2011. Executive Summary

ERIC Educational Resources Information Center

Checkoway, Amy; Gamse, Beth; Velez, Melissa; Caven, Meghan; de la Cruz, Rodolfo; Donoghue, Nathaniel; Kliorys, Kristina; Linkow, Tamara; Luck, Rachel; Sahni, Sarah; Woodford, Michelle

2012-01-01

The Massachusetts Expanded Learning Time (ELT) initiative was established in 2005 with planning grants that allowed a limited number of schools to explore a redesign of their respective schedules and add time to their day or year. Participating schools are required to expand learning time by at least 300 hours per academic year to improve student…

Evaluation of the Massachusetts Expanded Learning Time (ELT) Initiative. Year Five Final Report: 2010-2011. Volume I

ERIC Educational Resources Information Center

Checkoway, Amy; Gamse, Beth; Velez, Melissa; Caven, Meghan; de la Cruz, Rodolfo; Donoghue, Nathaniel; Kliorys, Kristina; Linkow, Tamara; Luck, Rachel; Sahni, Sarah; Woodford, Michelle

2012-01-01

The Massachusetts Expanded Learning Time (ELT) initiative was established in 2005 with planning grants that allowed a limited number of schools to explore a redesign of their respective schedules and add time to their day or year. Participating schools are required to expand learning time by at least 300 hours per academic year to improve student…

Study of Preschool Parents and Caregivers Use of Technology and PBS KIDS Transmedia Resources: A Report to the CPB-PBS "Ready to Learn Initiative"

ERIC Educational Resources Information Center

Pasnik, Shelley; Llorente, Carlin

2012-01-01

Leaders of the CPB-PBS "Ready To Learn" Initiative understand the important role parents and caregivers play in ensuring young children's healthy development and academic learning. In order for young children, especially those living in traditionally underserved communities, to succeed at school and thrive outside of the classroom, educational…

Managing Learning for Performance.

ERIC Educational Resources Information Center

Kuchinke, K. Peter

1995-01-01

Presents findings of organizational learning literature that could substantiate claims of learning organization proponents. Examines four learning processes and their contribution to performance-based learning management: knowledge acquisition, information distribution, information interpretation, and organizational memory. (SK)

Optimal Battery Sizing in Photovoltaic Based Distributed Generation Using Enhanced Opposition-Based Firefly Algorithm for Voltage Rise Mitigation

PubMed Central

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem. PMID:25054184

Optimal battery sizing in photovoltaic based distributed generation using enhanced opposition-based firefly algorithm for voltage rise mitigation.

PubMed

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.

Distributed System Design Checklist

NASA Technical Reports Server (NTRS)

Hall, Brendan; Driscoll, Kevin

2014-01-01

This report describes a design checklist targeted to fault-tolerant distributed electronic systems. Many of the questions and discussions in this checklist may be generally applicable to the development of any safety-critical system. However, the primary focus of this report covers the issues relating to distributed electronic system design. The questions that comprise this design checklist were created with the intent to stimulate system designers' thought processes in a way that hopefully helps them to establish a broader perspective from which they can assess the system's dependability and fault-tolerance mechanisms. While best effort was expended to make this checklist as comprehensive as possible, it is not (and cannot be) complete. Instead, we expect that this list of questions and the associated rationale for the questions will continue to evolve as lessons are learned and further knowledge is established. In this regard, it is our intent to post the questions of this checklist on a suitable public web-forum, such as the NASA DASHLink AFCS repository. From there, we hope that it can be updated, extended, and maintained after our initial research has been completed.

Constraints on Circumstellar Dust Grain Sizes from High Spatial Resolution Observations in the Thermal Infrared

NASA Technical Reports Server (NTRS)

Bloemhof, E. E.; Danen, R. M.; Gwinn, C. R.

1996-01-01

We describe how high spatial resolution imaging of circumstellar dust at a wavelength of about 10 micron, combined with knowledge of the source spectral energy distribution, can yield useful information about the sizes of the individual dust grains responsible for the infrared emission. Much can be learned even when only upper limits to source size are available. In parallel with high-resolution single-telescope imaging that may resolve the more extended mid-infrared sources, we plan to apply these less direct techniques to interpretation of future observations from two-element optical interferometers, where quite general arguments may be made despite only crude imaging capability. Results to date indicate a tendency for circumstellar grain sizes to be rather large compared to the Mathis-Rumpl-Nordsieck size distribution traditionally thought to characterize dust in the general interstellar medium. This may mean that processing of grains after their initial formation and ejection from circumstellar atmospheres adjusts their size distribution to the ISM curve; further mid-infrared observations of grains in various environments would help to confirm this conjecture.

Novel-word learning deficits in Mandarin-speaking preschool children with specific language impairments.

PubMed

Chen, Yuchun; Liu, Huei-Mei

2014-01-01

Children with SLI exhibit overall deficits in novel word learning compared to their age-matched peers. However, the manifestation of the word learning difficulty in SLI was not consistent across tasks and the factors affecting the learning performance were not yet determined. Our aim is to examine the extent of word learning difficulties in Mandarin-speaking preschool children with SLI, and to explore the potent influence of existing lexical knowledge on to the word learning process. Preschool children with SLI (n=37) and typical language development (n=33) were exposed to novel words for unfamiliar objects embedded in stories. Word learning tasks including the initial mapping and short-term repetitive learning were designed. Results revealed that Mandarin-speaking preschool children with SLI performed as well as their age-peers in the initial form-meaning mapping task. Their word learning difficulty was only evidently shown in the short-term repetitive learning task under a production demand, and their learning speed was slower than the control group. Children with SLI learned the novel words with a semantic head better in both the initial mapping and repetitive learning tasks. Moderate correlations between stand word learning performances and scores on standardized vocabulary were found after controlling for children's age and nonverbal IQ. The results suggested that the word learning difficulty in children with SLI occurred in the process of establishing a robust phonological representation at the beginning stage of word learning. Also, implicit compound knowledge is applied to aid word learning process for children with and without SLI. We also provide the empirical data to validate the relationship between preschool children's word learning performance and their existing receptive vocabulary ability. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gains following perceptual learning are closely linked to the initial visual acuity.

PubMed

Yehezkel, Oren; Sterkin, Anna; Lev, Maria; Levi, Dennis M; Polat, Uri

2016-04-28

The goal of the present study was to evaluate the dependence of perceptual learning gains on initial visual acuity (VA), in a large sample of subjects with a wide range of VAs. A large sample of normally sighted and presbyopic subjects (N = 119; aged 40 to 63) with a wide range of uncorrected near visual acuities (VA, -0.12 to 0.8 LogMAR), underwent perceptual learning. Training consisted of detecting briefly presented Gabor stimuli under spatial and temporal masking conditions. Consistent with previous findings, perceptual learning induced a significant improvement in near VA and reading speed under conditions of limited exposure duration. Our results show that the improvements in VA and reading speed observed following perceptual learning are closely linked to the initial VA, with only a minor fraction of the observed improvement that may be attributed to the additional sessions performed by those with the worse VA.

Learning Companion Systems, Social Learning Systems, and the Global Social Learning Club.

ERIC Educational Resources Information Center

Chan, Tak-Wai

1996-01-01

Describes the development of learning companion systems and their contributions to the class of social learning systems that integrate artificial intelligence agents and use machine learning to tutor and interact with students. Outlines initial social learning projects, their programming languages, and weakness. Future improvements will include…

Distribution of Practice and Metacognition in Learning and Long-Term Retention of a Discrete Motor Task

ERIC Educational Resources Information Center

Dail, Teresa K.; Christina, Robert W.

2004-01-01

This study examined judgments of learning and the long-term retention of a discrete motor task (golf putting) as a function of practice distribution. The results indicated that participants in the distributed practice group performed more proficiently than those in the massed practice group during both acquisition and retention phases. No…

Learning Grammatical Categories from Distributional Cues: Flexible Frames for Language Acquisition

ERIC Educational Resources Information Center

St. Clair, Michelle C.; Monaghan, Padraic; Christiansen, Morten H.

2010-01-01

Numerous distributional cues in the child's environment may potentially assist in language learning, but what cues are useful to the child and when are these cues utilised? We propose that the most useful source of distributional cue is a flexible frame surrounding the word, where the language learner integrates information from the preceding and…

Quality Assurance in Distance Education: The Challenges to Be Addressed

ERIC Educational Resources Information Center

Stella, Antony; Gnanam, A.

2004-01-01

Integration of technology in all forms of education has narrowed down the gap between the on- and off-campus students and has resulted in the use of the more broad-based term "distributed learning". Consequently, distance learning is seen as a subset of distributed learning, focusing on students who may be separated in time and space from their…

A Framework System for Intelligent Support in Open Distributed Learning Environments--A Look Back from 16 Years Later

ERIC Educational Resources Information Center

Hoppe, H. Ulrich

2016-01-01

The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure…

Second Language Vocabulary Learning through Extensive Reading with Audio Support: How Do Frequency and Distribution of Occurrence Affect Learning?

ERIC Educational Resources Information Center

Webb, Stuart; Chang, Anna C-S.

2015-01-01

This study investigated (1) the extent of vocabulary learning through reading and listening to 10 graded readers, and (2) the relationship between vocabulary gain and the frequency and distribution of occurrence of 100 target words in the graded readers. The experimental design expanded on earlier studies that have typically examined incidental…

Bayesian network models for error detection in radiotherapy plans

NASA Astrophysics Data System (ADS)

Kalet, Alan M.; Gennari, John H.; Ford, Eric C.; Phillips, Mark H.

2015-04-01

The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.

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…

Distributional Vowel Training Is Less Effective for Adults than for Infants. A Study Using the Mismatch Response

PubMed Central

Wanrooij, Karin; Boersma, Paul; van Zuijen, Titia L.

2014-01-01

Distributional learning of speech sounds (i.e., learning from simple exposure to frequency distributions of speech sounds in the environment) has been observed in the lab repeatedly in both infants and adults. The current study is the first attempt to examine whether the capacity for using the mechanism is different in adults than in infants. To this end, a previous event-related potential study that had shown distributional learning of the English vowel contrast /æ/∼/ε/ in 2-to-3-month old Dutch infants was repeated with Dutch adults. Specifically, the adults were exposed to either a bimodal distribution that suggested the existence of the two vowels (as appropriate in English), or to a unimodal distribution that did not (as appropriate in Dutch). After exposure the participants were tested on their discrimination of a representative [æ] and a representative [ε], in an oddball paradigm for measuring mismatch responses (MMRs). Bimodally trained adults did not have a significantly larger MMR amplitude, and hence did not show significantly better neural discrimination of the test vowels, than unimodally trained adults. A direct comparison between the normalized MMR amplitudes of the adults with those of the previously tested infants showed that within a reasonable range of normalization parameters, the bimodal advantage is reliably smaller in adults than in infants, indicating that distributional learning is a weaker mechanism for learning speech sounds in adults (if it exists in that group at all) than in infants. PMID:25289935

Biologically based neural circuit modelling for the study of fear learning and extinction

NASA Astrophysics Data System (ADS)

Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

2016-11-01

The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

Interactions between statistical and semantic information in infant language development

PubMed Central

Lany, Jill; Saffran, Jenny R.

2013-01-01

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants’ weighting of these cues in subsequent word-learning tasks changes over the course of early language development. PMID:21884336

Sample Complexity Bounds for Differentially Private Learning

PubMed Central

Chaudhuri, Kamalika; Hsu, Daniel

2013-01-01

This work studies the problem of privacy-preserving classification – namely, learning a classifier from sensitive data while preserving the privacy of individuals in the training set. In particular, the learning algorithm is required in this problem to guarantee differential privacy, a very strong notion of privacy that has gained significant attention in recent years. A natural question to ask is: what is the sample requirement of a learning algorithm that guarantees a certain level of privacy and accuracy? We address this question in the context of learning with infinite hypothesis classes when the data is drawn from a continuous distribution. We first show that even for very simple hypothesis classes, any algorithm that uses a finite number of examples and guarantees differential privacy must fail to return an accurate classifier for at least some unlabeled data distributions. This result is unlike the case with either finite hypothesis classes or discrete data domains, in which distribution-free private learning is possible, as previously shown by Kasiviswanathan et al. (2008). We then consider two approaches to differentially private learning that get around this lower bound. The first approach is to use prior knowledge about the unlabeled data distribution in the form of a reference distribution chosen independently of the sensitive data. Given such a reference , we provide an upper bound on the sample requirement that depends (among other things) on a measure of closeness between and the unlabeled data distribution. Our upper bound applies to the non-realizable as well as the realizable case. The second approach is to relax the privacy requirement, by requiring only label-privacy – namely, that the only labels (and not the unlabeled parts of the examples) be considered sensitive information. An upper bound on the sample requirement of learning with label privacy was shown by Chaudhuri et al. (2006); in this work, we show a lower bound. PMID:25285183

Social Learning as a Way to Overcome Choice-Induced Preferences? Insights from Humans and Rhesus Macaques

PubMed Central

Monfardini, Elisabetta; Gaveau, Valérie; Boussaoud, Driss; Hadj-Bouziane, Fadila; Meunier, Martine

2012-01-01

Much theoretical attention is currently devoted to social learning. Yet, empirical studies formally comparing its effectiveness relative to individual learning are rare. Here, we focus on free choice, which is at the heart of individual reward-based learning, but absent in social learning. Choosing among two equally valued options is known to create a preference for the selected option in both humans and monkeys. We thus surmised that social learning should be more helpful when choice-induced preferences retard individual learning than when they optimize it. To test this prediction, the same task requiring to find which among two items concealed a reward was applied to rhesus macaques and humans. The initial trial was individual or social, rewarded or unrewarded. Learning was assessed on the second trial. Choice-induced preference strongly affected individual learning. Monkeys and humans performed much more poorly after an initial negative choice than after an initial positive choice. Comparison with social learning verified our prediction. For negative outcome, social learning surpassed or at least equaled individual learning in all subjects. For positive outcome, the predicted superiority of individual learning did occur in a majority of subjects (5/6 monkeys and 6/12 humans). A minority kept learning better socially though, perhaps due to a more dominant/aggressive attitude toward peers. Poor learning from errors due to over-valuation of personal choices is among the decision-making biases shared by humans and animals. The present study suggests that choice-immune social learning may help curbing this potentially harmful tendency. Learning from successes is an easier path. The present data suggest that whether one tends to walk it alone or with a peer’s help might depend on the social dynamics within the actor/observer dyad. PMID:22969703

Distance Learning Can Be as Effective as Traditional Learning for Medical Students in the Initial Assessment of Trauma Patients.

PubMed

Farahmand, Shervin; Jalili, Ebrahim; Arbab, Mona; Sedaghat, Mojtaba; Shirazi, Mandana; Keshmiri, Fatemeh; Azizpour, Arsalan; Valadkhani, Somayeh; Bagheri-Hariri, Shahram

2016-09-01

Distance learning is expanding and replacing the traditional academic medical settings. Managing trauma patients seems to be a prerequisite skill for medical students. This study has been done to evaluate the efficiency of distance learning on performing the initial assessment and management in trauma patients, compared with the traditional learning among senior medical students. One hundred and twenty senior medical students enrolled in this single-blind quasi-experimental study and were equally divided into the experimental (distance learning) and control group (traditional learning). All participants did a written MCQ before the study. The control group attended a workshop with a 50-minute lecture on initial management of trauma patients and a case simulation scenario followed by a hands-on session. On the other hand, the experimental group was given a DVD with a similar 50-minute lecture and a case simulation scenario, and they also attended a hands-on session to practice the skills. Both groups were evaluated by a trauma station in an objective structured clinical examination (OSCE) after a month. The performance in the experimental group was statistically better (P=0.001) in OSCE. Distance learning seems to be an appropriate adjunct to traditional learning.

Another Initiative? Where Does it Fit? A Unifying Framework and an Integrated Infrastructure for Schools to Address Barriers to Learning and Promote Healthy Development

ERIC Educational Resources Information Center

Center for Mental Health in Schools at UCLA, 2005

2005-01-01

This report was developed to highlight the current state of affairs and illustrate the value of a unifying framework and integrated infrastructure for the many initiatives, projects, programs, and services schools pursue in addressing barriers to learning and promoting healthy development. Specifically, it highlights how initiatives can be…

Effects of Changing Body Weight Distribution on Mediolateral Stability Control during Gait Initiation

PubMed Central

Caderby, Teddy; Yiou, Eric; Peyrot, Nicolas; de Viviés, Xavier; Bonazzi, Bruno; Dalleau, Georges

2017-01-01

During gait initiation, anticipatory postural adjustments (APA) precede the execution of the first step. It is generally acknowledged that these APA contribute to forward progression but also serve to stabilize the whole body in the mediolateral direction during step execution. Although previous studies have shown that changes in the distribution of body weight between both legs influence motor performance during gait initiation, it is not known whether and how such changes affect a person’s postural stability during this task. The aim of this study was to investigate the effects of changing initial body weight distribution between legs on mediolateral postural stability during gait initiation. Changes in body weight distribution were induced under experimental conditions by modifying the frontal plane distribution of an external load located at the participants’ waists. Fifteen healthy adults performed a gait initiation series at a similar speed under three conditions: with the overload evenly distributed over both legs; with the overload strictly distributed over the swing-limb side; and with the overload strictly distributed over the stance-leg side. Our results showed that the mediolateral location of center-of-mass (CoM) during the initial upright posture differed between the experimental conditions, indicating modifications in the initial distribution of body weight between the legs according to the load distribution. While the parameters related to the forward progression remained unchanged, the alterations in body weight distribution elicited adaptive changes in the amplitude of APA in the mediolateral direction (i.e., maximal mediolateral shift of the center of pressure (CoP)), without variation in their duration. Specifically, it was observed that the amplitude of APA was modulated in such a way that mediolateral dynamic stability at swing foot-contact, quantified by the margin of stability (i.e., the distance between the base of support boundary and the extrapolated CoM position), did not vary between the conditions. These findings suggest that APA seem to be scaled as a function of the initial body weight distribution between both legs so as to maintain optimal conditions of stability during gait initiation. PMID:28396629

Focus for Impact: The PacifiCorp Foundation for Learning's Early Childhood Literacy Initiative. Principles for Effective Education Grantmaking. Case Study Number 5

ERIC Educational Resources Information Center

King, Caroline

2006-01-01

The PacifiCorp Foundation for Learning was at a turning point in August 2006. It had been five years since the corporate foundation had shifted its focus from general-purpose grantmaking to supporting individual and community learning, and its flagship Early Childhood Literacy Initiative--launched in 2003 to raise literacy rates in the communities…

"I Did Think It Was a Bit Strange Taking Outdoor Education Online": Exploration of Initial Teacher Education Students' Online Learning Experiences in a Tertiary Outdoor Education Unit

ERIC Educational Resources Information Center

Dyment, Janet; Downing, Jillian; Hill, Allen; Smith, Heidi

2018-01-01

With a view to attracting more students and offering flexible learning opportunities, online teaching and learning is becoming increasingly wide-spread across the higher education sector. This research reports on the experiences of eight initial teacher education students who studied an outdoor education unit in the online space. Using a…

A Proficiency-Based Cost Estimate of Surface Warfare Officer On-the-Job Training

DTIC Science & Technology

2011-12-01

established later in the chapter. 30 b. Proficiency Gained at Initial Training Formal training learning outcomes contribute the most to the initial...different billets call for different levels of training. Additionally, BST learning outcomes are not necessarily based on SWO PQS, and therefore...process. Without knowing BDOC learning outcomes , it is difficult to quantify proficiency-based OJT cost reductions. However, it is certain that

Use of Simulation in Nursing Education: Initial Experiences on a European Union Lifelong Learning Programme--Leonardo Da Vinci Project

ERIC Educational Resources Information Center

Terzioglu, Fusun; Tuna, Zahide; Duygulu, Sergul; Boztepe, Handan; Kapucu, Sevgisun; Ozdemir, Leyla; Akdemir, Nuran; Kocoglu, Deniz; Alinier, Guillaume; Festini, Filippo

2013-01-01

Aim: The aim of this paper is to share the initial experiences on a European Union (EU) Lifelong Learning Programme Leonardo Da Vinci Transfer of Innovation Project related to the use of simulation-based learning with nursing students from Turkey. The project started at the end of the 2010 involving 7 partners from 3 different countries including…

Large-scale coupling dynamics of instructed reversal learning.

PubMed

Mohr, Holger; Wolfensteller, Uta; Ruge, Hannes

2018-02-15

The ability to rapidly learn from others by instruction is an important characteristic of human cognition. A recent study found that the rapid transfer from initial instructions to fluid behavior is supported by changes of functional connectivity between and within several large-scale brain networks, and particularly by the coupling of the dorsal attention network (DAN) with the cingulo-opercular network (CON). In the present study, we extended this approach to investigate how these brain networks interact when stimulus-response mappings are altered by novel instructions. We hypothesized that residual stimulus-response associations from initial practice might negatively impact the ability to implement novel instructions. Using functional imaging and large-scale connectivity analysis, we found that functional coupling between the CON and DAN was generally at a higher level during initial than reversal learning. Examining the learning-related connectivity dynamics between the CON and DAN in more detail by means of multivariate patterns analyses, we identified a specific subset of connections which showed a particularly high increase in connectivity during initial learning compared to reversal learning. This finding suggests that the CON-DAN connections can be separated into two functionally dissociable yet spatially intertwined subsystems supporting different aspects of short-term task automatization. Copyright © 2017 Elsevier Inc. All rights reserved.

Verbal learning changes in older adults across 18 months.

PubMed

Zimprich, Daniel; Rast, Philippe

2009-07-01

The major aim of this study was to investigate individual changes in verbal learning across a period of 18 months. Individual differences in verbal learning have largely been neglected in the last years and, even more so, individual differences in change in verbal learning. The sample for this study comes from the Zurich Longitudinal Study on Cognitive Aging (ZULU; Zimprich et al., 2008a) and comprised 336 older adults in the age range of 65-80 years at first measurement occasion. In order to address change in verbal learning we used a latent change model of structured latent growth curves to account for the non-linearity of the verbal learning data. The individual learning trajectories were captured by a hyperbolic function which yielded three psychologically distinct parameters: initial performance, learning rate, and asymptotic performance. We found that average performance increased with respect to initial performance, but not in learning rate or in asymptotic performance. Further, variances and covariances remained stable across both measurement occasions, indicating that the amount of individual differences in the three parameters remained stable, as did the relationships among them. Moreover, older adults differed reliably in their amount of change in initial performance and asymptotic performance. Eventually, changes in asymptotic performance and learning rate were strongly negatively correlated. It thus appears as if change in verbal learning in old age is a constrained process: an increase in total learning capacity implies that it takes longer to learn. Together, these results point to the significance of individual differences in change of verbal learning in the elderly.

Improving resident well-being and clinical learning environment through academic initiatives.

PubMed

Lee, Nathaniel; Appelbaum, Nital; Amendola, Michael; Dodson, Kelley; Kaplan, Brian

2017-07-01

Organizational effects on job satisfaction, burnout, work-life balance, and perceived support have not been studied in the context of the clinical learning environment. We evaluated the relationship between academic resources and resident well-being, the clinical learning environment, and in-service examination performance of surgical residents. Residents of general surgery and surgical specialty programs were recruited from March 2016 through June 2016 across the Southeast, Mid-Atlantic, and Northeast regions. Program directors were asked to allow distribution of a paper survey or to forward an electronic survey link onto residents. Five dichotomous questions were asked regarding access to academic resources. Validated measures were obtained assessing resident well-being and perceived clinical learning environment. Data were analyzed through t-tests and chi-squared test of independence. We received 276 respondents across 50 programs. Residents perceiving adequate support to succeed had less burnout (P = 0.008), better resilience (P = 0.009), better job satisfaction (P < 0.001), less work/life strain (P = 0.001), better workplace climate (P < 0.001), better organizational support (P < 0.001), and were more likely to have high performance on the in-service examination (P = 0.001). Specific resources including educational stipends, review questions, in-service board prep, and support for poor performers correlated with improved well-being and perceived clinical learning environment. Provision of academic resources has implications beyond in-service examination performance, correlating with improved resident well-being and perceptions of the clinical learning environment. Copyright © 2017 Elsevier Inc. All rights reserved.

Creating the Future: Changing Culture Through Leadership Capacity Development

NASA Astrophysics Data System (ADS)

Lefoe, Geraldine

Leadership for change is key to universities finding new ways to meet the needs of their future students. This chapter describes an innovative framework for leadership capacity development which has been implemented in a number of Australian universities. The framework, underpinned by a distributive approach to leadership, prepares a new generation of leaders for formal positions of leadership in all aspects of teaching and learning. The faculty scholars implemented projects, including a number of them using innovative technologies, to establish strategic change within their faculties. They shared their outcomes annually through national roundtables, which focussed on methods for improving assessment practice. Five critical factors for success are discussed including implemenation of strategic faculty-based projects; formal leadership training and related activities; opportunities for dialog about leadership practice and experiences; and activities that expanded current professional networks. The model can be adapted to have a specific focus on leadership for e-Learning, and some examples of faculty based strategic initiatives are described.

Strategies to improve learning of all students in a class

NASA Astrophysics Data System (ADS)

Suraishkumar, G. K.

2018-05-01

The statistical distribution of the student learning abilities in a typical undergraduate engineering class poses a significant challenge to simultaneously improve the learning of all the students in the class. With traditional instruction styles, the students with significantly high learning abilities are not satisfied due to a feeling of unfulfilled potential, and the students with significantly low learning abilities feel lost. To address the challenge in an undergraduate core/required course on 'transport phenomena in biological systems', a combination of learning strategies such as active learning including co-operative group learning, challenge exercises, and others were employed in a pro-advising context. The short-term and long-term impacts were evaluated through student course performances and input, respectively. The results show that it is possible to effectively address the challenge posed by the distribution of student learning abilities in a class.

Distributed Learning and Institutional Restructuring.

ERIC Educational Resources Information Center

Hawkins, Brian L.

1999-01-01

Discusses the following challenges institutions must consider as they enter the new marketplace of distributed learning: library access, faculty workload, faculty incentives, faculty-support structures, intellectual property, articulation agreements, financial aid, pricing, cross-subsidization of programs, institutional loyalty and philanthropy,…

Cinemedicine: Using movies to improve students' understanding of psychosocial aspects of medicine.

PubMed

Kadivar, Maliheh; Mafinejad, Mahboobeh Khabaz; Bazzaz, Javad Tavakkoly; Mirzazadeh, Azim; Jannat, Zeinab

2018-04-01

There are rising concerns about how to teach psychosocial aspects of medicine to students. The aim of the study was the use of "cinemedicine" as a tool and technique in teaching psychosocial aspects of medicine to medical students at Tehran University of Medical Sciences (TUMS). This was an educational study with quantitative and qualitative data analysis. Two hundred seventy medical students participated in this study. Nine sessions were held to teach psychosocial subjects in medicine using movies. Each session began with an initial explanation of the program objectives. After the show, medicine related points of the movie were discussed and analyzed by experts and students. In the end, questionnaires were distributed to assess the students' perceptions. The results of our study show that most of the students (84%) stated that teaching these subjects through movies was a nice event comparing to usual lectures. 56.5% of the students agreed with the application of points learned in the events in professional performance. The majority of the students (72.8%) agreed that participating in those events was useful for them as a physician and they would advise other students to attend to later sessions. Content analysis of the students' notes uncovered three categories of cinemedicine: "learning by observation", "creation of a supportive and tangible learning" and "motivation for learning". Cinemedicine provides the opportunity for medical students to learn psychosocial subjects related to medicine through observing and reflecting on movies.

Perceptual grouping enhances visual plasticity.

PubMed

Mastropasqua, Tommaso; Turatto, Massimo

2013-01-01

Visual perceptual learning, a manifestation of neural plasticity, refers to improvements in performance on a visual task achieved by training. Attention is known to play an important role in perceptual learning, given that the observer's discriminative ability improves only for those stimulus feature that are attended. However, the distribution of attention can be severely constrained by perceptual grouping, a process whereby the visual system organizes the initial retinal input into candidate objects. Taken together, these two pieces of evidence suggest the interesting possibility that perceptual grouping might also affect perceptual learning, either directly or via attentional mechanisms. To address this issue, we conducted two experiments. During the training phase, participants attended to the contrast of the task-relevant stimulus (oriented grating), while two similar task-irrelevant stimuli were presented in the adjacent positions. One of the two flanking stimuli was perceptually grouped with the attended stimulus as a consequence of its similar orientation (Experiment 1) or because it was part of the same perceptual object (Experiment 2). A test phase followed the training phase at each location. Compared to the task-irrelevant no-grouping stimulus, orientation discrimination improved at the attended location. Critically, a perceptual learning effect equivalent to the one observed for the attended location also emerged for the task-irrelevant grouping stimulus, indicating that perceptual grouping induced a transfer of learning to the stimulus (or feature) being perceptually grouped with the task-relevant one. Our findings indicate that no voluntary effort to direct attention to the grouping stimulus or feature is necessary to enhance visual plasticity.

Emulation for probabilistic weather forecasting

NASA Astrophysics Data System (ADS)

Cornford, Dan; Barillec, Remi

2010-05-01

Numerical weather prediction models are typically very expensive to run due to their complexity and resolution. Characterising the sensitivity of the model to its initial condition and/or to its parameters requires numerous runs of the model, which is impractical for all but the simplest models. To produce probabilistic forecasts requires knowledge of the distribution of the model outputs, given the distribution over the inputs, where the inputs include the initial conditions, boundary conditions and model parameters. Such uncertainty analysis for complex weather prediction models seems a long way off, given current computing power, with ensembles providing only a partial answer. One possible way forward that we develop in this work is the use of statistical emulators. Emulators provide an efficient statistical approximation to the model (or simulator) while quantifying the uncertainty introduced. In the emulator framework, a Gaussian process is fitted to the simulator response as a function of the simulator inputs using some training data. The emulator is essentially an interpolator of the simulator output and the response in unobserved areas is dictated by the choice of covariance structure and parameters in the Gaussian process. Suitable parameters are inferred from the data in a maximum likelihood, or Bayesian framework. Once trained, the emulator allows operations such as sensitivity analysis or uncertainty analysis to be performed at a much lower computational cost. The efficiency of emulators can be further improved by exploiting the redundancy in the simulator output through appropriate dimension reduction techniques. We demonstrate this using both Principal Component Analysis on the model output and a new reduced-rank emulator in which an optimal linear projection operator is estimated jointly with other parameters, in the context of simple low order models, such as the Lorenz 40D system. We present the application of emulators to probabilistic weather forecasting, where the construction of the emulator training set replaces the traditional ensemble model runs. Thus the actual forecast distributions are computed using the emulator conditioned on the ‘ensemble runs' which are chosen to explore the plausible input space using relatively crude experimental design methods. One benefit here is that the ensemble does not need to be a sample from the true distribution of the input space, rather it should cover that input space in some sense. The probabilistic forecasts are computed using Monte Carlo methods sampling from the input distribution and using the emulator to produce the output distribution. Finally we discuss the limitations of this approach and briefly mention how we might use similar methods to learn the model error within a framework that incorporates a data assimilation like aspect, using emulators and learning complex model error representations. We suggest future directions for research in the area that will be necessary to apply the method to more realistic numerical weather prediction models.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

2014-01-01

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

A Collaborative Framework for Distributed Privacy-Preserving Support Vector Machine Learning

PubMed Central

Que, Jialan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates “privacy-insensitive” intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner. PMID:23304414

The Differences across Distributed Leadership Practices by School Position According to the Comprehensive Assessment of Leadership for Learning (CALL)

ERIC Educational Resources Information Center

Blitz, Mark H.; Modeste, Marsha

2015-01-01

The Comprehensive Assessment of Leadership for Learning (CALL) is a multi-source assessment of distributed instructional leadership. As part of the validation of CALL, researchers examined differences between teacher and leader ratings in assessing distributed leadership practices. The authors utilized a t-test for equality of means for the…

The Virginia Generalist Initiative: Lessons Learned in a Statewide Consortium.

ERIC Educational Resources Information Center

Morse, R. Michael; Plungas, Gay S.; Duke, Debra; Rollins, Lisa K.; Barnes, H. Verdain; Brinson, Betsy K.; Martindale, James R.; Marsland, David W.

1999-01-01

To increase supply of generalist physicians, three state-supported Virginia medical schools formed a partnership with governmental stakeholders in the Generalist Physician Initiative. Lessons learned concerning stakeholder participation in planning, shared philosophical commitment, support for risk-taking, attitudes toward change, and trust are…

Hybrid E-Learning Tool TransLearning: Video Storytelling to Foster Vicarious Learning within Multi-Stakeholder Collaboration Networks

ERIC Educational Resources Information Center

van der Meij, Marjoleine G.; Kupper, Frank; Beers, Pieter J.; Broerse, Jacqueline E. W.

2016-01-01

E-learning and storytelling approaches can support informal vicarious learning within geographically widely distributed multi-stakeholder collaboration networks. This case study evaluates hybrid e-learning and video-storytelling approach "TransLearning" by investigation into how its storytelling e-tool supported informal vicarious…

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning.

PubMed

Fernandez-Gauna, Borja; Etxeberria-Agiriano, Ismael; Graña, Manuel

2015-01-01

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Designing Professional Learning Communities through Understanding the Beliefs of Learning

ERIC Educational Resources Information Center

Ke, Jie; Kang, Rui; Liu, Di

2016-01-01

This study was designed to initiate the process of building professional development learning communities for pre-service math teachers through revealing those teachers' conceptions/beliefs of students' learning and their own learning in China. It examines Chinese pre-service math teachers' conceptions of student learning and their related…

How learning might strengthen existing visual object representations in human object-selective cortex.

PubMed

Brants, Marijke; Bulthé, Jessica; Daniels, Nicky; Wagemans, Johan; Op de Beeck, Hans P

2016-02-15

Visual object perception is an important function in primates which can be fine-tuned by experience, even in adults. Which factors determine the regions and the neurons that are modified by learning is still unclear. Recently, it was proposed that the exact cortical focus and distribution of learning effects might depend upon the pre-learning mapping of relevant functional properties and how this mapping determines the informativeness of neural units for the stimuli and the task to be learned. From this hypothesis we would expect that visual experience would strengthen the pre-learning distributed functional map of the relevant distinctive object properties. Here we present a first test of this prediction in twelve human subjects who were trained in object categorization and differentiation, preceded and followed by a functional magnetic resonance imaging session. Specifically, training increased the distributed multi-voxel pattern information for trained object distinctions in object-selective cortex, resulting in a generalization from pre-training multi-voxel activity patterns to after-training activity patterns. Simulations show that the increased selectivity combined with the inter-session generalization is consistent with a training-induced strengthening of a pre-existing selectivity map. No training-related neural changes were detected in other regions. In sum, training to categorize or individuate objects strengthened pre-existing representations in human object-selective cortex, providing a first indication that the neuroanatomical distribution of learning effects depends upon the pre-learning mapping of visual object properties. Copyright © 2015 Elsevier Inc. All rights reserved.

Imbalanced learning for pattern recognition: an empirical study

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Man, Hong; Desai, Sachi; Quoraishee, Shafik

2010-10-01

The imbalanced learning problem (learning from imbalanced data) presents a significant new challenge to the pattern recognition and machine learning society because in most instances real-world data is imbalanced. When considering military applications, the imbalanced learning problem becomes much more critical because such skewed distributions normally carry the most interesting and critical information. This critical information is necessary to support the decision-making process in battlefield scenarios, such as anomaly or intrusion detection. The fundamental issue with imbalanced learning is the ability of imbalanced data to compromise the performance of standard learning algorithms, which assume balanced class distributions or equal misclassification penalty costs. Therefore, when presented with complex imbalanced data sets these algorithms may not be able to properly represent the distributive characteristics of the data. In this paper we present an empirical study of several popular imbalanced learning algorithms on an army relevant data set. Specifically we will conduct various experiments with SMOTE (Synthetic Minority Over-Sampling Technique), ADASYN (Adaptive Synthetic Sampling), SMOTEBoost (Synthetic Minority Over-Sampling in Boosting), and AdaCost (Misclassification Cost-Sensitive Boosting method) schemes. Detailed experimental settings and simulation results are presented in this work, and a brief discussion of future research opportunities/challenges is also presented.

Unifying practice schedules in the timescales of motor learning and performance.

PubMed

Verhoeven, F Martijn; Newell, Karl M

2018-06-01

In this article, we elaborate from a multiple time scales model of motor learning to examine the independent and integrated effects of massed and distributed practice schedules within- and between-sessions on the persistent (learning) and transient (warm-up, fatigue) processes of performance change. The timescales framework reveals the influence of practice distribution on four learning-related processes: the persistent processes of learning and forgetting, and the transient processes of warm-up decrement and fatigue. The superposition of the different processes of practice leads to a unified set of effects for massed and distributed practice within- and between-sessions in learning motor tasks. This analysis of the interaction between the duration of the interval of practice trials or sessions and parameters of the introduced time scale model captures the unified influence of the between trial and session scheduling of practice on learning and performance. It provides a starting point for new theoretically based hypotheses, and the scheduling of practice that minimizes the negative effects of warm-up decrement, fatigue and forgetting while exploiting the positive effects of learning and retention. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Evaluation of the sustained implementation of a mental health learning initiative in long-term care.

PubMed

McAiney, Carrie A; Stolee, Paul; Hillier, Loretta M; Harris, Diane; Hamilton, Pam; Kessler, Linda; Madsen, Victoria; Le Clair, J Kenneth

2007-10-01

This paper describes an innovative education program for the management of mental health problems in long-term care (LTC) homes and the evaluation of its longer-term sustainability. Since 1998, the "Putting the P.I.E.C.E.S. Together" learning initiative has been providing education sessions and related learning strategies aimed at developing the knowledge and skills of health professionals who care for older persons with complex physical and mental health needs and associated behaviors, in Ontario, Canada. A major focus of this province-wide initiative was the development of in-house Psychogeriatric Resource Persons (PRPs). Evaluation of this initiative included the completion of pre- and post-education questionnaires (over three data collection time periods) assessing learner confidence (N = 1,024 and 792, for pre- and post-education, respectively) and session evaluation questionnaires gathering feedback on the session (N = 2,029 across all sessions). A survey of LTC homes in Ontario (N = 439, 79% of the homes in the province) was conducted to assess longer-term sustainability. Ratings of the sessions indicated that they were relevant to learners' clinical practice. There were significant increases in ratings of ability to recognize and understand challenging behaviors and mental health problems, and in ability to use a variety of assessment tools. Few homes (15%) do not have a PRP; over 50% of the staff who completed the first session in 1999 continue to serve as a PRP and to apply learned skills. A learning initiative with supportive and reinforcing strategies can develop in-house PRPs to enhance the care of the elderly in LTC. Incorporation of PRP functions into job descriptions and management support contributed to the success of this initiative. This study highlights the importance of work environments that support and reinforce the use of learned skills to the success of continuing education and quality improvement initiatives in LTC.

Distributed synaptic weights in a LIF neural network and learning rules

NASA Astrophysics Data System (ADS)

Perthame, Benoît; Salort, Delphine; Wainrib, Gilles

2017-09-01

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.

Support groups: an empowering, experiential strategy.

PubMed

Heinrich, K T; Robinson, C M; Scales, M E

1998-01-01

The authors describe a student-facilitated support group experience initiated at student request and designed for RN-BSN students. Students report they emerged enlightened about group theory, empowered to share their knowledge of groups, and energized to initiate groups in their work settings. If educators make the learning experience safe, practice letting go and being vigilant, and celebrate group successes, students learn how to initiate, facilitate, and terminate small groups.

Understanding Diversity and the Teacher's Role in Supporting Learning in Diverse Classrooms: Scaffolding Early Childhood Preservice Teacher's Growth in Initial Placements with Technology

ERIC Educational Resources Information Center

Solvie, Pamela A.

2013-01-01

This research project sought to examine the ways in which early childhood preservice teachers develop an understanding of diversity and the teacher's role in supporting learning in diverse classrooms. Preservice teachers in their initial foundations course and in their initial placements in early childhood settings were participants in the…

Derivatives of logarithmic stationary distributions for policy gradient reinforcement learning.

PubMed

Morimura, Tetsuro; Uchibe, Eiji; Yoshimoto, Junichiro; Peters, Jan; Doya, Kenji

2010-02-01

Most conventional policy gradient reinforcement learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the policy parameter. That term involves the derivative of the stationary state distribution that corresponds to the sensitivity of its distribution to changes in the policy parameter. Although the bias introduced by this omission can be reduced by setting the forgetting rate gamma for the value functions close to 1, these algorithms do not permit gamma to be set exactly at gamma = 1. In this article, we propose a method for estimating the log stationary state distribution derivative (LSD) as a useful form of the derivative of the stationary state distribution through backward Markov chain formulation and a temporal difference learning framework. A new policy gradient (PG) framework with an LSD is also proposed, in which the average reward gradient can be estimated by setting gamma = 0, so it becomes unnecessary to learn the value functions. We also test the performance of the proposed algorithms using simple benchmark tasks and show that these can improve the performances of existing PG methods.

Virtual water maze learning in human increases functional connectivity between posterior hippocampus and dorsal caudate.

PubMed

Woolley, Daniel G; Mantini, Dante; Coxon, James P; D'Hooge, Rudi; Swinnen, Stephan P; Wenderoth, Nicole

2015-04-01

Recent work has demonstrated that functional connectivity between remote brain regions can be modulated by task learning or the performance of an already well-learned task. Here, we investigated the extent to which initial learning and stable performance of a spatial navigation task modulates functional connectivity between subregions of hippocampus and striatum. Subjects actively navigated through a virtual water maze environment and used visual cues to learn the position of a fixed spatial location. Resting-state functional magnetic resonance imaging scans were collected before and after virtual water maze navigation in two scan sessions conducted 1 week apart, with a behavior-only training session in between. There was a large significant reduction in the time taken to intercept the target location during scan session 1 and a small significant reduction during the behavior-only training session. No further reduction was observed during scan session 2. This indicates that scan session 1 represented initial learning and scan session 2 represented stable performance. We observed an increase in functional connectivity between left posterior hippocampus and left dorsal caudate that was specific to scan session 1. Importantly, the magnitude of the increase in functional connectivity was correlated with offline gains in task performance. Our findings suggest cooperative interaction occurs between posterior hippocampus and dorsal caudate during awake rest following the initial phase of spatial navigation learning. Furthermore, we speculate that the increase in functional connectivity observed during awake rest after initial learning might reflect consolidation-related processing. © 2014 Wiley Periodicals, Inc.

Reflections on the Ready to Learn Initiative 2010 to 2015: How a Federal Program in Partnership with Public Media Supported Young Children's Equitable Learning during a Time of Great Change

ERIC Educational Resources Information Center

Pasnik, Shelley; Llorente, Carlin; Hupert, Naomi; Moorthy, Savitha

2016-01-01

"Reflections on the Ready to Learn Initiative, 2010 to 2015," draws upon interviews with 26 prominent children's media researchers, producers, and thought leaders and a review of scholarly articles and reports to provide a big picture view of the status and future directions of children's media. In this illuminating report, EDC and SRI…

Listen, Live and Learn: A Review of the Application Process, Aiming to Enhance Diversity within the Listen, Live and Learn Senior Student Housing Initiative at Stellenbosch University

ERIC Educational Resources Information Center

Smorenburg, Mathew; Dunn, Munita

2014-01-01

The Listen, Live and Learn (LLL) initiative at Stellenbosch University (SU) is a senior student housing model with the aim of providing an experiential opportunity for students to make contact with "the other". It is posited on the social contact theory assumption that if people of different genders, races, ethnicities, and/or religions…

The Role of Teacher's Initiation in Online Pedagogy

ERIC Educational Resources Information Center

Tsai, Chia-Wen

2012-01-01

Purpose: The author redesigned a course titled "Applied Information Technology: Networking" and applied online collaborative learning (CL) with initiation and self-regulated learning (SRL) to improve students' involvement in this course in an environment that is full of free online games, shopping websites, and social networking…

Flipping Engineering Courses: A School Wide Initiative

ERIC Educational Resources Information Center

Clark, Renee M.; Besterfield-Sacre, Mary; Budny, Daniel; Bursic, Karen M.; Clark, William W.; Norman, Bryan A.; Parker, Robert S.; Patzer, John F., II; Slaughter, William S.

2016-01-01

In the 2013-2014 school year, we implemented the "flipped classroom" as part of an initiative to drive active learning, student engagement and enhanced learning in our school. The flipped courses consisted of freshman through senior engineering classes in introductory programming, statics/mechanics, mechanical design, bio-thermodynamics,…

College Student Activism: An Exploration of Learning Outcomes

ERIC Educational Resources Information Center

Rosas, Marisela

2010-01-01

Researchers, politicians, and the public have criticized colleges and universities for not effectively preparing college students to be active participants in their communities and within a democratic society. Institutional initiatives on civic engagement have focused on community service and service-learning initiatives to meet this demand. The…

Towards a lifelong learning society through reading promotion: Opportunities and challenges for libraries and community learning centres in Viet Nam

NASA Astrophysics Data System (ADS)

Hossain, Zakir

2016-04-01

The government of Viet Nam has made a commitment to build a Lifelong Learning Society by 2020. A range of related initiatives have been launched, including the Southeast Asian Ministers of Education Organization Centre for Lifelong Learning (SEAMEO CELLL) and "Book Day" - a day aimed at encouraging reading and raising awareness of its importance for the development of knowledge and skills. Viet Nam also aims to implement lifelong learning (LLL) activities in libraries, museums, cultural centres and clubs. The government of Viet Nam currently operates more than 11,900 Community Learning Centres (CLCs) and is in the process of both renovating and innovating public libraries and museums throughout the country. In addition to the work undertaken by the Viet Nam government, a number of enterprises have been initiated by non-governmental organisations and non-profit organisations to promote literacy and lifelong learning. This paper investigates some government initiatives focused on libraries and CLCs and their impact on reading promotion. Proposing a way forward, the paper confirms that Viet Nam's libraries and CLCs play an essential role in promoting reading and building a LLL Society.

Mechanisms and time course of vocal learning and consolidation in the adult songbird.

PubMed

Warren, Timothy L; Tumer, Evren C; Charlesworth, Jonathan D; Brainard, Michael S

2011-10-01

In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry.

Mechanisms and time course of vocal learning and consolidation in the adult songbird

PubMed Central

Tumer, Evren C.; Charlesworth, Jonathan D.; Brainard, Michael S.

2011-01-01

In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry. PMID:21734110

Action Learning in Undergraduate Engineering Thesis Supervision

ERIC Educational Resources Information Center

Stappenbelt, Brad

2017-01-01

In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative investigated the influence of the action learning environment on student approaches…

Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

PubMed

Ernst, Benjamin; Steinhauser, Marco

2012-06-01

Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

Adaptation Criteria for the Personalised Delivery of Learning Materials: A Multi-Stage Empirical Investigation

ERIC Educational Resources Information Center

Thalmann, Stefan

2014-01-01

Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of…

Construction of a Digital Learning Environment Based on Cloud Computing

ERIC Educational Resources Information Center

Ding, Jihong; Xiong, Caiping; Liu, Huazhong

2015-01-01

Constructing the digital learning environment for ubiquitous learning and asynchronous distributed learning has opened up immense amounts of concrete research. However, current digital learning environments do not fully fulfill the expectations on supporting interactive group learning, shared understanding and social construction of knowledge.…

Learning Systems in Post-Statutory Education

ERIC Educational Resources Information Center

Catherall, Paul

2008-01-01

This article examines the broad scope of systemised learning (e-learning) in post-statutory education. Issues for discussion include the origins and forms of learning systems, including technical and educational concepts and approaches, such as distributed and collaborative learning. The VLE (Virtual Learning Environment) is defined as the…

Efficient Delivery and Visualization of Long Time-Series Datasets Using Das2 Tools

NASA Astrophysics Data System (ADS)

Piker, C.; Granroth, L.; Faden, J.; Kurth, W. S.

2017-12-01

For over 14 years the University of Iowa Radio and Plasma Wave Group has utilized a network transparent data streaming and visualization system for most daily data review and collaboration activities. This system, called Das2, was originally designed in support of the Cassini Radio and Plasma Wave Science (RPWS) investigation, but is now relied on for daily review and analysis of Voyager, Polar, Cluster, Mars Express, Juno and other mission results. In light of current efforts to promote automatic data distribution in space physics it seems prudent to provide an overview of our open source Das2 programs and interface definitions to the wider community and to recount lessons learned. This submission will provide an overview of interfaces that define the system, describe the relationship between the Das2 effort and Autoplot and will examine handling Cassini RPWS Wideband waveforms and dynamic spectra as examples of dealing with long time-series data sets. In addition, the advantages and limitations of the current Das2 tool set will be discussed, as well as lessons learned that are applicable to other data sharing initiatives. Finally, plans for future developments including improved catalogs to support 'no-software' data sources and redundant multi-server fail over, as well as new adapters for CSV (Comma Separated Values) and JSON (Javascript Object Notation) output to support Cassini closeout and the HAPI (Heliophysics Application Programming Interface) initiative are outlined.

The Play Experience Scale: development and validation of a measure of play.

PubMed

Pavlas, Davin; Jentsch, Florian; Salas, Eduardo; Fiore, Stephen M; Sims, Valerie

2012-04-01

A measure of play experience in video games was developed through literature review and two empirical validation studies. Despite the considerable attention given to games in the behavioral sciences, play experience remains empirically underexamined. One reason for this gap is the absence of a scale that measures play experience. In Study 1, the initial Play Experience Scale (PES) was tested through an online validation that featured three different games (N = 203). In Study 2, a revised PES was assessed with a serious game in the laboratory (N = 77). Through principal component analysis of the Study 1 data, the initial 20-item PES was revised, resulting in the 16-item PES-16. Study 2 showed the PES-16 to be a robust instrument with the same patterns of correlations as in Study 1 via (a) internal consistency estimates, (b) correlations with established scales of motivation, (c) distributions of PES-16 scores in different game conditions, and (d) examination of the average variance extracted of the PES and the Intrinsic Motivation Scale. We suggest that the PES is appropriate for use in further validation studies. Additional examinations of the scale are required to determine its applicability to other contexts and its relationship with other constructs. The PES is potentially relevant to human factors undertakings involving video games, including basic research into play, games, and learning; prototype testing; and exploratory learning studies.

Joint reconstruction of the initial pressure and speed of sound distributions from combined photoacoustic and ultrasound tomography measurements

NASA Astrophysics Data System (ADS)

Matthews, Thomas P.; Anastasio, Mark A.

2017-12-01

The initial pressure and speed of sound (SOS) distributions cannot both be stably recovered from photoacoustic computed tomography (PACT) measurements alone. Adjunct ultrasound computed tomography (USCT) measurements can be employed to estimate the SOS distribution. Under the conventional image reconstruction approach for combined PACT/USCT systems, the SOS is estimated from the USCT measurements alone and the initial pressure is estimated from the PACT measurements by use of the previously estimated SOS. This approach ignores the acoustic information in the PACT measurements and may require many USCT measurements to accurately reconstruct the SOS. In this work, a joint reconstruction method where the SOS and initial pressure distributions are simultaneously estimated from combined PACT/USCT measurements is proposed. This approach allows accurate estimation of both the initial pressure distribution and the SOS distribution while requiring few USCT measurements.

On the importance of an accurate representation of the initial state of the system in classical dynamics simulations

NASA Astrophysics Data System (ADS)

García-Vela, A.

2000-05-01

A definition of a quantum-type phase-space distribution is proposed in order to represent the initial state of the system in a classical dynamics simulation. The central idea is to define an initial quantum phase-space state of the system as the direct product of the coordinate and momentum representations of the quantum initial state. The phase-space distribution is then obtained as the square modulus of this phase-space state. The resulting phase-space distribution closely resembles the quantum nature of the system initial state. The initial conditions are sampled with the distribution, using a grid technique in phase space. With this type of sampling the distribution of initial conditions reproduces more faithfully the shape of the original phase-space distribution. The method is applied to generate initial conditions describing the three-dimensional state of the Ar-HCl cluster prepared by ultraviolet excitation. The photodissociation dynamics is simulated by classical trajectories, and the results are compared with those of a wave packet calculation. The classical and quantum descriptions are found in good agreement for those dynamical events less subject to quantum effects. The classical result fails to reproduce the quantum mechanical one for the more strongly quantum features of the dynamics. The properties and applicability of the phase-space distribution and the sampling technique proposed are discussed.

BCR CDR3 length distributions differ between blood and spleen and between old and young patients, and TCR distributions can be used to detect myelodysplastic syndrome

NASA Astrophysics Data System (ADS)

Pickman, Yishai; Dunn-Walters, Deborah; Mehr, Ramit

2013-10-01

Complementarity-determining region 3 (CDR3) is the most hyper-variable region in B cell receptor (BCR) and T cell receptor (TCR) genes, and the most critical structure in antigen recognition and thereby in determining the fates of developing and responding lymphocytes. There are millions of different TCR Vβ chain or BCR heavy chain CDR3 sequences in human blood. Even now, when high-throughput sequencing becomes widely used, CDR3 length distributions (also called spectratypes) are still a much quicker and cheaper method of assessing repertoire diversity. However, distribution complexity and the large amount of information per sample (e.g. 32 distributions of the TCRα chain, and 24 of TCRβ) calls for the use of machine learning tools for full exploration. We have examined the ability of supervised machine learning, which uses computational models to find hidden patterns in predefined biological groups, to analyze CDR3 length distributions from various sources, and distinguish between experimental groups. We found that (a) splenic BCR CDR3 length distributions are characterized by low standard deviations and few local maxima, compared to peripheral blood distributions; (b) healthy elderly people's BCR CDR3 length distributions can be distinguished from those of the young; and (c) a machine learning model based on TCR CDR3 distribution features can detect myelodysplastic syndrome with approximately 93% accuracy. Overall, we demonstrate that using supervised machine learning methods can contribute to our understanding of lymphocyte repertoire diversity.

The relationship between novel word learning and anomia treatment success in adults with chronic aphasia.

PubMed

Dignam, Jade; Copland, David; Rawlings, Alicia; O'Brien, Kate; Burfein, Penni; Rodriguez, Amy D

2016-01-29

Learning capacity may influence an individual's response to aphasia rehabilitation. However, investigations into the relationship between novel word learning ability and response to anomia therapy are lacking. The aim of the present study was to evaluate the novel word learning ability in post-stroke aphasia and to establish the relationship between learning ability and anomia treatment outcomes. We also explored the influence of locus of language breakdown on novel word learning ability and anomia treatment response. 30 adults (6F; 24M) with chronic, post-stroke aphasia were recruited to the study. Prior to treatment, participants underwent an assessment of language, which included the Comprehensive Aphasia Test and three baseline confrontation naming probes in order to develop sets of treated and untreated items. We also administered the novel word learning paradigm, in which participants learnt novel names associated with unfamiliar objects and were immediately tested on recall (expressive) and recognition (receptive) tasks. Participants completed 48 h of Aphasia Language Impairment and Functioning Therapy (Aphasia LIFT) over a 3 week (intensive) or 8 week (distributed) schedule. Therapy primarily targeted the remediation of word retrieval deficits, so naming of treated and untreated items immediately post-therapy and at 1 month follow-up was used to determine therapeutic response. Performance on recall and recognition tasks demonstrated that participants were able to learn novel words; however, performance was variable and was influenced by participants' aphasia severity, lexical-semantic processing and locus of language breakdown. Novel word learning performance was significantly correlated with participants' response to therapy for treated items at post-therapy. In contrast, participants' novel word learning performance was not correlated with therapy gains for treated items at 1 month follow-up or for untreated items at either time point. Therapy intensity did not influence treatment outcomes. This is the first group study to directly examine the relationship between novel word learning and therapy outcomes for anomia rehabilitation in adults with aphasia. Importantly, we found that novel word learning performance was correlated with therapy outcomes. We propose that novel word learning ability may contribute to the initial acquisition of treatment gains in anomia rehabilitation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Racial stereotypes impair flexibility of emotional learning

PubMed Central

Kubota, Jennifer T.; Li, Jian; Coelho, Cesar A.O.; Phelps, Elizabeth A.

2016-01-01

Flexibility of associative learning can be revealed by establishing and then reversing cue-outcome discriminations. Here, we used functional MRI to examine whether neurobehavioral correlates of reversal-learning are impaired in White and Asian volunteers when initial learning involves fear-conditioning to a racial out-group. For one group, the picture of a Black male was initially paired with shock (threat) and a White male was unpaired (safe). For another group, the White male was a threat and the Black male was safe. These associations reversed midway through the task. Both groups initially discriminated threat from safety, as expressed through skin conductance responses (SCR) and activity in the insula, thalamus, midbrain and striatum. After reversal, the group initially conditioned to a Black male exhibited impaired reversal of SCRs to the new threat stimulus (White male), and impaired reversals in the striatum, anterior cingulate cortex, midbrain and thalamus. In contrast, the group initially conditioned to a White male showed successful reversal of SCRs and successful reversal in these brain regions toward the new threat. These findings provide new evidence that an aversive experience with a racial out-group member impairs the ability to flexibly and appropriately adjust fear expression towards a new threat in the environment. PMID:27107298

Distributing vs. Blocking Learning Questions in a Web-Based Learning Environment

ERIC Educational Resources Information Center

Kapp, Felix; Proske, Antje; Narciss, Susanne; Körndle, Hermann

2015-01-01

Effective studying in web-based learning environments (web-LEs) requires cognitive engagement and demands learners to regulate their learning activities. One way to support learners in web-LEs is to provide interactive learning questions within the learning environment. Even though research on learning questions has a long tradition, there are…

Testing Prepares Students to Learn Better: The Forward Effect of Testing in Category Learning

ERIC Educational Resources Information Center

Lee, Hee Seung; Ahn, Dahwi

2018-01-01

The forward effect of testing occurs when testing on previously studied information facilitates subsequent learning. The present research investigated whether interim testing on initially studied materials enhances the learning of new materials in category learning and examined the metacognitive judgments of such learning. Across the 4…

Connected Learning: Harnessing the Information Age to Make Learning More Powerful

ERIC Educational Resources Information Center

Roc, Martens

2014-01-01

This report introduces connected learning, a promising educational approach supported by the MacArthur Foundation and the Digital Learning Media (DLM) initiative that schools and out-of-school sites are adopting to enhance student learning and outcomes by connecting their education to their interests. Connected learning uses digital media to…

Intrinsic and Extrinsic Motivation Learning Processes: Why Japanese Can't Speak English.

ERIC Educational Resources Information Center

Kamada, Laurel Diane

Motivation towards English learning in Japanese schools today is analyzed according to John Condry and James Chambers' process-of-learning paradigm. The four stages of learning (initial engagement, process, disengagement, and re-engagement) are shown to emit different processes of learning in students based on whether learning is intrinsically or…

Fostering Collaborative Teaching and Learning Scholarship through an International Writing Group Initiative

ERIC Educational Resources Information Center

Marquis, Elizabeth; Healey, Mick; Vine, Michelle

2016-01-01

The research presented here explored the experiences of participants in an international collaborative writing group (ICWG) initiative that ran in conjunction with the 2012 International Society for the Scholarship of Teaching and Learning (ISSoTL) conference. The ICWG sought to cultivate collaborative pedagogical scholarship by bringing together…

Child-Initiated Learning, the Outdoor Environment and the "Underachieving" Child

ERIC Educational Resources Information Center

Maynard, Trisha; Waters, Jane; Clement, Jennifer

2013-01-01

The Foundation Phase for Wales advocates an experiential, play-based approach to learning for children aged three to seven years that includes child-initiated activity within the outdoor environment. In previous research, Foundation Phase practitioners maintained that children perceived to be "underachieving" within the classroom came…

Mixed-Initiative Clustering

ERIC Educational Resources Information Center

Huang, Yifen

2010-01-01

Mixed-initiative clustering is a task where a user and a machine work collaboratively to analyze a large set of documents. We hypothesize that a user and a machine can both learn better clustering models through enriched communication and interactive learning from each other. The first contribution or this thesis is providing a framework of…

The Intent and Processes of a Professional Learning Initiative Seeking to Foster Discussion around Innovative Approaches to Teaching

ERIC Educational Resources Information Center

Sullivan, Peter; Holmes, Marilyn; Ingram, Naomi; Linsell, Chris; Livy, Sharyn; McCormack, Melody

2016-01-01

The following outlines the rationale and structure of a professional learning initiative that seeks to explore teachers' ways of engaging students more actively in building mathematical connections for themselves. An example of one of the suggested experiences is presented.

Evaluation of the Massachusetts Expanded Learning Time (ELT) Initiative: Final Study Findings

ERIC Educational Resources Information Center

Checkoway, Amy; Gamse, Beth; Velez, Melissa; Linkow, Tamara

2013-01-01

The Massachusetts Expanded Learning Time (ELT) initiative provides grants to selected schools to redesign their schedules by adding 300-plus instructional hours to the school year to improve outcomes, broaden enrichment opportunities, and provide teachers with more planning and professional development time. The Massachusetts Department of…

Designing Learning Object Repositories as Systems for Managing Educational Communities Knowledge

ERIC Educational Resources Information Center

Sampson, Demetrios G.; Zervas, Panagiotis

2013-01-01

Over the past years, a number of international initiatives that recognize the importance of sharing and reusing digital educational resources among educational communities through the use of Learning Object Repositories (LORs) have emerged. Typically, these initiatives focus on collecting digital educational resources that are offered by their…

The Open Learning Initiative: New Directions for Higher Education.

ERIC Educational Resources Information Center

King, Bruce

This paper describes the Australian Open Learning Initiative (OLI), a program to facilitate access to postsecondary education. The program will provide off-campus or distance education courses for which there is evident high demand. Program features include an independent brokering agency, coordination by a university or group of universities,…

Reduced Interference from Memory Testing: A Postretrieval Monitoring Account

ERIC Educational Resources Information Center

Pierce, Benton H.; Gallo, David A.; McCain, Jason L.

2017-01-01

Initial learning can interfere with subsequent learning (proactive interference [PI]), but recent work indicates initial testing can reduce PI. Here, we tested 2 alternative hypotheses of this effect: Does testing reduce PI by constraining retrieval to the target list, or by facilitating a postretrieval monitoring process? Participants first…

Promoting Experiential Learning in Pre-Service Teacher Education

ERIC Educational Resources Information Center

Gao, Xuesong

2015-01-01

This report introduces the experiential learning initiative at a major university in Hong Kong that prepares pre-service teachers with experience of engaging with social and cultural issues in teaching. It calls on teacher educators in different contexts to work together on similar initiatives that help pre-service teachers grow professionally…

Framing Innovation: Do Professional Learning Communities Influence Acceptance of Large-Scale Technology Initiatives?

ERIC Educational Resources Information Center

Nolin, Anna P.

2014-01-01

This study explored the role of professional learning communities for district leadership implementing large-scale technology initiatives such as 1:1 implementations (one computing device for every student). The existing literature regarding technology leadership is limited, as is literature on how districts use existing collaborative structures…

Libraries as Facilitators of Coding for All

ERIC Educational Resources Information Center

Martin, Crystle

2017-01-01

Learning to code has been an increasingly frequent topic of conversation both in academic circles and popular media. Learning to code recently received renewed attention with the announcement of the White House's Computer Science for All initiative (Smith 2016). This initiative intends "to empower all American students from kindergarten…

Integrating Technology to Maximize Learning

ERIC Educational Resources Information Center

Jones, Eric

2007-01-01

Such initiatives as one-to-one computing, laptop learning, and technology immersion are gaining momentum in middle level and high schools, but the key to their success is more than cutting-edge technology. Henrico County Public Schools, a pioneer in educational technology in Virginia, launched a one-to-one computing initiative in 2001. The…

A Spatially Constrained Multi-autoencoder Approach for Multivariate Geochemical Anomaly Recognition

NASA Astrophysics Data System (ADS)

Lirong, C.; Qingfeng, G.; Renguang, Z.; Yihui, X.

2017-12-01

Separating and recognizing geochemical anomalies from the geochemical background is one of the key tasks in geochemical exploration. Many methods have been developed, such as calculating the mean ±2 standard deviation, and fractal/multifractal models. In recent years, deep autoencoder, a deep learning approach, have been used for multivariate geochemical anomaly recognition. While being able to deal with the non-normal distributions of geochemical concentrations and the non-linear relationships among them, this self-supervised learning method does not take into account the spatial heterogeneity of geochemical background and the uncertainty induced by the randomly initialized weights of neurons, leading to ineffective recognition of weak anomalies. In this paper, we introduce a spatially constrained multi-autoencoder (SCMA) approach for multivariate geochemical anomaly recognition, which includes two steps: spatial partitioning and anomaly score computation. The first step divides the study area into multiple sub-regions to segregate the geochemical background, by grouping the geochemical samples through K-means clustering, spatial filtering, and spatial constraining rules. In the second step, for each sub-region, a group of autoencoder neural networks are constructed with an identical structure but different initial weights on neurons. Each autoencoder is trained using the geochemical samples within the corresponding sub-region to learn the sub-regional geochemical background. The best autoencoder of a group is chosen as the final model for the corresponding sub-region. The anomaly score at each location can then be calculated as the euclidean distance between the observed concentrations and reconstructed concentrations of geochemical elements.The experiments using the geochemical data and Fe deposits in the southwestern Fujian province of China showed that our SCMA approach greatly improved the recognition of weak anomalies, achieving the AUC of 0.89, compared with the AUC of 0.77 using a single deep autoencoder approach.

Brownfields City of Cleveland: Deconstruction Lessons Learned Report

EPA Pesticide Factsheets

This technical memorandum presents an overview of Cleveland’s current deconstruction initiative goals and lessons learned (in the Cleveland area) and potential strategies for addressing lessons learned.

Learning stochastic reward distributions in a speeded pointing task.

PubMed

Seydell, Anna; McCann, Brian C; Trommershäuser, Julia; Knill, David C

2008-04-23

Recent studies have shown that humans effectively take into account task variance caused by intrinsic motor noise when planning fast hand movements. However, previous evidence suggests that humans have greater difficulty accounting for arbitrary forms of stochasticity in their environment, both in economic decision making and sensorimotor tasks. We hypothesized that humans can learn to optimize movement strategies when environmental randomness can be experienced and thus implicitly learned over several trials, especially if it mimics the kinds of randomness for which subjects might have generative models. We tested the hypothesis using a task in which subjects had to rapidly point at a target region partly covered by three stochastic penalty regions introduced as "defenders." At movement completion, each defender jumped to a new position drawn randomly from fixed probability distributions. Subjects earned points when they hit the target, unblocked by a defender, and lost points otherwise. Results indicate that after approximately 600 trials, subjects approached optimal behavior. We further tested whether subjects simply learned a set of stimulus-contingent motor plans or the statistics of defenders' movements by training subjects with one penalty distribution and then testing them on a new penalty distribution. Subjects immediately changed their strategy to achieve the same average reward as subjects who had trained with the second penalty distribution. These results indicate that subjects learned the parameters of the defenders' jump distributions and used this knowledge to optimally plan their hand movements under conditions involving stochastic rewards and penalties.

South Africa's protracted struggle for equal distribution and equitable access - still not there.

PubMed

van Rensburg, Hendrik C J

2014-05-08

The purpose of this contribution is to analyse and explain the South African HRH case, its historical evolution, and post-apartheid reform initiatives aimed at addressing deficiencies and shortfalls. HRH in South Africa not only mirrors the nature and diversity of challenges globally, but also the strategies pursued by countries to address these challenges. Although South Africa has strongly developed health professions, large numbers of professional and mid-level workers, and also well-established training institutions, it is experiencing serious workforce shortages and access constraints. This results from the unequal distribution of health workers between the well-resourced private sector over the poorly-resourced public sector, as well as from distributional disparities between urban and rural areas. During colonial and apartheid times, disparities were aggravated by policies of racial segregation and exclusion, remnants of which are today still visible in health-professional backlogs, unequal provincial HRH distribution, and differential access to health services for specific race and class groups. Since 1994, South Africa's transition to democracy deeply transformed the health system, health professions and HRH establishments. The introduction of free-health policies, the district health system and the prioritisation of PHC ensured more equal distribution of the workforce, as well as greater access to services for deprived groups. However, the HIV/AIDS epidemic brought about huge demands for care and massive patient loads in the public-sector. The emigration of health professionals to developed countries and to the private sector also undermines the strength and effectiveness of the public health sector. For the poor, access to care thus remains constrained and in perpetual shortfall. The post-1994 government has introduced several HRH-specific strategies to recruit, distribute, motivate and retain health professionals to strengthen the public sector and to expand access and coverage. Of great significance among these is the NHI Plan that aims to bridge the structural divide and to redistribute material and human resources more equally. Its success largely hinges on HRH and the balanced deployment of the national workforce.Low- and middle-income countries have much to learn from South African HRH experiences. In turn, South Africa has much to learn from other countries, as this case study shows.

Transforming Initial Entry Training to Produce the Objective Force Soldier

DTIC Science & Technology

2003-04-23

and memory as function of age Learning how to learn Andragogy Self-directed learning Socialization Social participation Associationalism Conversation...San Francisco: Jossey-Bass, Number 89, Spring 2001. Merriam, Sharan B. “ Andragogy and Self-Directed Learning: Pillars of Adult Learning Theory,” in New

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…

Innovation in Open & Distance Learning: Successful Development of Online and Web-Based Learning.

ERIC Educational Resources Information Center

Lockwood, Fred, Ed.; Gooley, Anne, Ed.

This book contains 19 papers examining innovation in open and distance learning through development of online and World Wide Web-based learning. The following papers are included: "Innovation in Distributed Learning: Creating the Environment" (Fred Lockwood); "Innovation in Open and Distance Learning: Some Lessons from Experience…

New Definitions for New Higher Education Institutions

ERIC Educational Resources Information Center

Meyer, Katrina A.

2009-01-01

New terms were exploding early in the development of distance learning and virtual universities. Distance learning, online learning, e-learning, and distributed learning were applied to the various new forms of learning using online or Web-based materials and processes. However, largely thanks to the immediate popularity of the Western Governors'…

Final Report on the Study of the Impact of the Statewide Systemic Initiatives. Lessons Learned about Designing, Implementing, and Evaluating Statewide Systemic Reform. WCER Working Paper No. 2003-12

ERIC Educational Resources Information Center

Heck, Daniel J.; Weiss, Iris R.; Boyd, Sally E.; Howard, Michael N.; Supovitz, Jonathan A.

2003-01-01

This document represents the first of two volumes presented in "Study of the Impact of the Statewide Systemic Initiatives Program" (Norman L. Webb and Iris R. Weiss). In an effort to evaluate the impact of the Statewide Systemic Initiatives (SSIs) on student achievement and the lessons that could be learned from the National Science…

2012 Context Study of the Use of Technology and PBS KIDS Transmedia in the Home Environment: A Report to the CPB-PBS "Ready to Learn Initiative"

ERIC Educational Resources Information Center

Pasnik, Shelley; Llorente, Carlin

2012-01-01

The CPB-PBS Ready To Learn initiative, funded by the U. S. Department of Education, brings engaging, high-quality media to young children who may be at risk for academic difficulties due to economic and social disadvantages. The initiative aims to deliver early mathematics and literacy resources on new and emerging digital platforms such as tablet…

A comparative study of deep learning models for medical image classification

NASA Astrophysics Data System (ADS)

Dutta, Suvajit; Manideep, B. C. S.; Rai, Shalva; Vijayarajan, V.

2017-11-01

Deep Learning(DL) techniques are conquering over the prevailing traditional approaches of neural network, when it comes to the huge amount of dataset, applications requiring complex functions demanding increase accuracy with lower time complexities. Neurosciences has already exploited DL techniques, thus portrayed itself as an inspirational source for researchers exploring the domain of Machine learning. DL enthusiasts cover the areas of vision, speech recognition, motion planning and NLP as well, moving back and forth among fields. This concerns with building models that can successfully solve variety of tasks requiring intelligence and distributed representation. The accessibility to faster CPUs, introduction of GPUs-performing complex vector and matrix computations, supported agile connectivity to network. Enhanced software infrastructures for distributed computing worked in strengthening the thought that made researchers suffice DL methodologies. The paper emphases on the following DL procedures to traditional approaches which are performed manually for classifying medical images. The medical images are used for the study Diabetic Retinopathy(DR) and computed tomography (CT) emphysema data. Both DR and CT data diagnosis is difficult task for normal image classification methods. The initial work was carried out with basic image processing along with K-means clustering for identification of image severity levels. After determining image severity levels ANN has been applied on the data to get the basic classification result, then it is compared with the result of DNNs (Deep Neural Networks), which performed efficiently because of its multiple hidden layer features basically which increases accuracy factors, but the problem of vanishing gradient in DNNs made to consider Convolution Neural Networks (CNNs) as well for better results. The CNNs are found to be providing better outcomes when compared to other learning models aimed at classification of images. CNNs are favoured as they provide better visual processing models successfully classifying the noisy data as well. The work centres on the detection on Diabetic Retinopathy-loss in vision and recognition of computed tomography (CT) emphysema data measuring the severity levels for both cases. The paper discovers how various Machine Learning algorithms can be implemented ensuing a supervised approach, so as to get accurate results with less complexity possible.

Exploration and practice in-class practice teaching mode

NASA Astrophysics Data System (ADS)

Zang, Xue-Ping; Wu, Wei-Feng

2017-08-01

According to the opto-electronic information science and engineering professional course characteristics and cultivate students' learning initiative, raised the teaching of photoelectric professional course introduce In-class practice teaching mode. By designing different In-class practice teaching content, the students' learning interest and learning initiative are improved, deepen students' understanding of course content and enhanced students' team cooperation ability. In-class practice teaching mode in the course of the opto-electronic professional teaching practice, the teaching effect is remarkable.

Distributed Learning. CAUSE Professional Paper Series, No. 14.

ERIC Educational Resources Information Center

Oblinger, Diana G.; Maruyama, Mark K.

This paper synthesizes current thought about the role of networking technologies in instruction and addresses the need for higher education to create affordable and flexible student-centered "distributed learning environments" employing networking technologies. First, relevant trends are identified in the areas of information volume, technology…

Transforming Distance Education Curricula through Distributive Leadership

ERIC Educational Resources Information Center

Keppell, Mike; O'Dwyer, Carolyn; Lyon, Betsy; Childs, Merilyn

2011-01-01

This paper examines a core leadership strategy for transforming learning and teaching in distance education through flexible and blended learning. It focuses on a project centred on distributive leadership that involves collaboration, shared purpose, responsibility and recognition of leadership irrespective of role or position within an…

Transforming Distance Education Curricula through Distributive Leadership

ERIC Educational Resources Information Center

Keppell, Mike; O'Dwyer, Carolyn; Lyon, Betsy; Childs, Merilyn

2010-01-01

This paper examines a core leadership strategy for transforming learning and teaching in distance education through flexible and blended learning. It focuses on a project centred on distributive leadership that involves collaboration, shared purpose, responsibility and recognition of leadership irrespective of role or position within an…

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

PubMed

Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

2017-10-01

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

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

Stewart, Emma M.; Hendrix, Val; Chertkov, Michael

This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper wemore » consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop.« less

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

A Statewide Service Learning Network Ignites Teachers and Students.

ERIC Educational Resources Information Center

Monsour, Florence

Service learning, curriculum-linked community service, has proved remarkably effective in igniting students' desire to learn. In 1997, the Wisconsin Partnership in Service Learning was initiated as a cross-disciplinary, cross-institutional endeavor. Supported by a grant from Learn and Serve America, the partnership created a network throughout…

An Examination of Learning Profiles in Physical Education

ERIC Educational Resources Information Center

Shen, Bo; Chen, Ang

2007-01-01

Using the model of domain learning as a theoretical framework, the study was designed to examine the extent to which learners' initial learning profiles based on previously acquired knowledge, learning strategy application, and interest-based motivation were distinctive in learning softball. Participants were 177 sixth-graders from three middle…

Toward an Instructionally Oriented Theory of Example-Based Learning

ERIC Educational Resources Information Center

Renkl, Alexander

2014-01-01

Learning from examples is a very effective means of initial cognitive skill acquisition. There is an enormous body of research on the specifics of this learning method. This article presents an instructionally oriented theory of example-based learning that integrates theoretical assumptions and findings from three research areas: learning from…

Seizing the Moment: State Lessons for Transforming Professional Learning

ERIC Educational Resources Information Center

Learning Forward, 2013

2013-01-01

Explore this first look at lessons learned through Learning Forward's ongoing initiative to develop a comprehensive system of professional learning that spans the distance from the statehouse to the classroom. This policy brief underscores the importance of a coordinated state professional learning strategy, the adoption of professional learning…

Team Learning and Team Composition in Nursing

ERIC Educational Resources Information Center

Timmermans, Olaf; Van Linge, Roland; Van Petegem, Peter; Elseviers, Monique; Denekens, Joke

2011-01-01

Purpose: This study aims to explore team learning activities in nursing teams and to test the effect of team composition on team learning to extend conceptually an initial model of team learning and to examine empirically a new model of ambidextrous team learning in nursing. Design/methodology/approach: Quantitative research utilising exploratory…

The Motivational Effects of the Classroom Environment in Facilitating Self-Regulated Learning

ERIC Educational Resources Information Center

Young, Mark R.

2005-01-01

Students can be proactive and engaged or, alternatively, lack initiative and responsibility for their learning. Self-regulated learning involves learning strategies and mental processes that learners deliberately engage to help themselves learn and perform better academically. The results of this study provide empirical support for the theoretical…

Are They Learning? Are We? Learning Outcomes and the Academic Library

ERIC Educational Resources Information Center

Oakleaf, Megan

2011-01-01

Since the 1990s, the assessment of learning outcomes in academic libraries has accelerated rapidly, and librarians have come to recognize the necessity of articulating and assessing student learning outcomes. Initially, librarians developed tools and instruments to assess information literacy student learning outcomes. Now, academic librarians are…

[Connectionist models of social learning: a case of learning by observing a simple task].

PubMed

Paignon, A; Desrichard, O; Bollon, T

2004-03-01

This article proposes a connectionist model of the social learning theory developed by Bandura (1977). The theory posits that an individual in an interactive situation is capable of learning new behaviours merely by observing them in others. Such learning is acquired through an initial phase in which the individual memorizes what he has observed (observation phase), followed by a second phase where he puts the recorded observations to use as a guide for adjusting his own behaviour (reproduction phase). We shall refer to the two above-mentioned phases to demonstrate that it is conceivable to simulate learning by observation otherwise than through the recording of perceived information using symbolic representation. To this end we shall rely on the formalism of ecological neuron networks (Parisi, Cecconi, & Nolfi, 1990) to implement an agent provided with the major processes identified as essential to learning through observation. The connectionist model so designed shall implement an agent capable of recording perceptive information and producing motor behaviours. The learning situation we selected associates an agent demonstrating goal-achievement behaviour and an observer agent learning the same behaviour by observation. Throughout the acquisition phase, the demonstrator supervises the observer's learning process based on association between spatial information (input) and behavioural information (output). Representation thus constructed then serves as an adjustment guide during the production phase, involving production by the observer of a sequence of actions which he compares to the representation stored in distributed form as constructed through observation. An initial simulation validates model architecture by confirming the requirement for both phases identified in the literature (Bandura, 1977) to simulate learning through observation. The representation constructed over the observation phase evidences acquisition of observed behaviours, although this phase alone is not sufficient to ensure accurate reproduction and must be made functional through the production phase (Deakin & Proteau, 2000). Results obtained through a second simulation replicate those produced by Bandura & Jeffery (1973), who observed that the individual tested following the retention phase recalled recorded information better than he realized in the production phase. The outcome of a third simulation shows that, when performing the transfer task, agents performed the task all the more effectively when they were required to learn a simple path which facilitated knowledge transfer to an adjacent situation. New explanatory assumptions of the mechanics of learning through observation may be produced through OLEANNet. Thus, observed deterioration between memorization and production is caused by successive approximations which occur in the acquisition phase then in the production phase. Further, depending on the type of learning undergone by agents, use of representation as a production guide induces a more or less stringent constraint in the approximation of actual behaviour. This results, during the transfer task, in the ability to effectively generalize acquired knowledge where such knowledge is not specifically related to the task at hand. In conclusion, connectionist model architecture appears valid for modeling learning through observation as defined by Bandura (1977). However, certain limitations appear during implementation, especially in terms of the observed behaviour's availability and the planning of produced behaviours that future developments are liable to counter.

Learning to read aloud: A neural network approach using sparse distributed memory

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

A Large-scale Distributed Indexed Learning Framework for Data that Cannot Fit into Memory

DTIC Science & Technology

2015-03-27

learn a classifier. Integrating three learning techniques (online, semi-supervised and active learning ) together with a selective sampling with minimum communication between the server and the clients solved this problem.

Flow behavior in the Wright Brothers Facility

NASA Technical Reports Server (NTRS)

Genn, S.

1984-01-01

It has become increasingly apparent that a reexamination of the flow characteristics in the low speed Wright Brothers Facility (WBF) is of some importance in view of recent improvements in the precision of the data acquisition system. In particular, the existence of local regions of separation, if any, in back portions of the circuit, and possible related unsteadiness, are of interest. Observations from that initial experiment did indicate some unsteady air flow problems in the cross leg, and thereafter the test region (Section A) was calibrated quantitatively. The intent was to learn something about the effect of upstream intermittent behavior flow on the test section flow, as well as to provide an extensive calibration as a standard for the effects induced by future alteration of the tunnel. Distributions of total pressure coefficients were measured first at one cross-section plane of the test section, namely the model station. Data were obtained for several tunnel speeds. The reduced data yielded an unexpected distribution involving larger pressures along the inside wall.

Progress on water data integration and distribution: a summary of select U.S. Geological Survey data systems

USGS Publications Warehouse

Blodgett, David L.; Lucido, Jessica M.; Kreft, James M.

2016-01-01

Critical water-resources issues ranging from flood response to water scarcity make access to integrated water information, services, tools, and models essential. Since 1995 when the first water data web pages went online, the U.S. Geological Survey has been at the forefront of water data distribution and integration. Today, real-time and historical streamflow observations are available via web pages and a variety of web service interfaces. The Survey has built partnerships with Federal and State agencies to integrate hydrologic data providing continuous observations of surface and groundwater, temporally discrete water quality data, groundwater well logs, aquatic biology data, water availability and use information, and tools to help characterize the landscape for modeling. In this paper, we summarize the status and design patterns implemented for selected data systems. We describe how these systems contribute to a U.S. Federal Open Water Data Initiative and present some gaps and lessons learned that apply to global hydroinformatics data infrastructure.

MODIS Land Data Products: Generation, Quality Assurance and Validation

NASA Technical Reports Server (NTRS)

Masuoka, Edward; Wolfe, Robert; Morisette, Jeffery; Sinno, Scott; Teague, Michael; Saleous, Nazmi; Devadiga, Sadashiva; Justice, Christopher; Nickeson, Jaime

2008-01-01

The Moderate Resolution Imaging Spectrometer (MODIS) on-board NASA's Earth Observing System (EOS) Terra and Aqua Satellites are key instruments for providing data on global land, atmosphere, and ocean dynamics. Derived MODIS land, atmosphere and ocean products are central to NASA's mission to monitor and understand the Earth system. NASA has developed and generated on a systematic basis a suite of MODIS products starting with the first Terra MODIS data sensed February 22, 2000 and continuing with the first MODIS-Aqua data sensed July 2, 2002. The MODIS Land products are divided into three product suites: radiation budget products, ecosystem products, and land cover characterization products. The production and distribution of the MODIS Land products are described, from initial software delivery by the MODIS Land Science Team, to operational product generation and quality assurance, delivery to EOS archival and distribution centers, and product accuracy assessment and validation. Progress and lessons learned since the first MODIS data were in early 2000 are described.

Correlation between the Hurst exponent and the maximal Lyapunov exponent: Examining some low-dimensional conservative maps

NASA Astrophysics Data System (ADS)

Tarnopolski, Mariusz

2018-01-01

The Chirikov standard map and the 2D Froeschlé map are investigated. A few thousand values of the Hurst exponent (HE) and the maximal Lyapunov exponent (mLE) are plotted in a mixed space of the nonlinear parameter versus the initial condition. Both characteristic exponents reveal remarkably similar structures in this space. A tight correlation between the HEs and mLEs is found, with the Spearman rank ρ = 0 . 83 and ρ = 0 . 75 for the Chirikov and 2D Froeschlé maps, respectively. Based on this relation, a machine learning (ML) procedure, using the nearest neighbor algorithm, is performed to reproduce the HE distribution based on the mLE distribution alone. A few thousand HE and mLE values from the mixed spaces were used for training, and then using 2 - 2 . 4 × 105 mLEs, the HEs were retrieved. The ML procedure allowed to reproduce the structure of the mixed spaces in great detail.

Mentoring in a Distributed Learning Social Work Program

ERIC Educational Resources Information Center

Jensen, Donna

2017-01-01

Students in alternative education programs often experience differential access to faculty, advisors, university support systems, and the supportive culture established by being on campus. This study is a descriptive-exploratory program evaluation of the distributed learning social work mentoring program at California State University, Chico. The…

Innovative Socio-Technical Environments in Support of Distributed Intelligence and Lifelong Learning

ERIC Educational Resources Information Center

Fischer, G; Konomi, S.

2007-01-01

Individual, unaided human abilities are constrained. Media have helped us to transcend boundaries in thinking, working, learning and collaborating by supporting "distributed intelligence". Wireless and mobile technologies provide new opportunities for creating novel socio-technical environments and thereby empowering humans, but not without…

Distributed Systems of Generalizing as the Basis of Workplace Learning

ERIC Educational Resources Information Center

Virkkunen, Jaakko; Pihlaja, Juha

2004-01-01

This article proposes a new way of conceptualizing workplace learning as distributed systems of appropriation, development and the use of practice-relevant generalizations fixed within mediational artifacts. This article maintains that these systems change historically as technology and increasingly sophisticated forms of production develop.…

Organisational Learning as an Emerging Process: The Generative Role of Digital Tools in Informal Learning Practices

ERIC Educational Resources Information Center

Za, Stefano; Spagnoletti, Paolo; North-Samardzic, Andrea

2014-01-01

Increasing attention is paid to organisational learning, with the success of contemporary organisations strongly contingent on their ability to learn and grow. Importantly, informal learning is argued to be even more significant than formal learning initiatives. Given the widespread use of digital technologies in the workplace, what requires…

Toward Mobile Assisted Language Learning Apps for Professionals That Integrate Learning into the Daily Routine

ERIC Educational Resources Information Center

Pareja-Lora, Antonio; Arús-Hita, Jorge; Read, Timothy; Rodríguez-Arancón, Pilar; Calle-Martínez, Cristina; Pomposo, Lourdes; Martín-Monje, Elena; Bárcena, Elena

2013-01-01

In this short paper, we present some initial work on Mobile Assisted Language Learning (MALL) undertaken by the ATLAS research group. ATLAS embraced this multidisciplinary field cutting across Mobile Learning and Computer Assisted Language Learning (CALL) as a natural step in their quest to find learning formulas for professional English that…

In Search of Social Movement Learning: The Growing Jobs for Living Project. NALL Working Paper.

ERIC Educational Resources Information Center

Clover, Darlene E.; Hall, Budd L.

The New Approaches to Lifelong Learning (NALL) project is a Canada-wide 5-year research initiative during which more than 70 academic and community members are working collaboratively within a framework of informal learning to address the following issues: informal computer-based learning, recognition of prior learning, informal learning in a…

Associative learning in baboons (Papio papio) and humans (Homo sapiens): species differences in learned attention to visual features.

PubMed

Fagot, J; Kruschke, J K; Dépy, D; Vauclair, J

1998-10-01

We examined attention shifting in baboons and humans during the learning of visual categories. Within a conditional matching-to-sample task, participants of the two species sequentially learned two two-feature categories which shared a common feature. Results showed that humans encoded both features of the initially learned category, but predominantly only the distinctive feature of the subsequently learned category. Although baboons initially encoded both features of the first category, they ultimately retained only the distinctive features of each category. Empirical data from the two species were analyzed with the 1996 ADIT connectionist model of Kruschke. ADIT fits the baboon data when the attentional shift rate is zero, and the human data when the attentional shift rate is not zero. These empirical and modeling results suggest species differences in learned attention to visual features.

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Global polio eradication initiative: lessons learned and legacy.

PubMed

Cochi, Stephen L; Freeman, Andrew; Guirguis, Sherine; Jafari, Hamid; Aylward, Bruce

2014-11-01

The world is on the verge of achieving global polio eradication. During >25 years of operations, the Global Polio Eradication Initiative (GPEI) has mobilized and trained millions of volunteers, social mobilizers, and health workers; accessed households untouched by other health initiatives; mapped and brought health interventions to chronically neglected and underserved communities; and established a standardized, real-time global surveillance and response capacity. It is important to document the lessons learned from polio eradication, especially because it is one of the largest ever global health initiatives. The health community has an obligation to ensure that these lessons and the knowledge generated are shared and contribute to real, sustained changes in our approach to global health. We have summarized what we believe are 10 leading lessons learned from the polio eradication initiative. We have the opportunity and obligation to build a better future by applying the lessons learned from GPEI and its infrastructure and unique functions to other global health priorities and initiatives. In so doing, we can extend the global public good gained by ending for all time one of the world's most devastating diseases by also ensuring that these investments provide public health dividends and benefits for years to come. Published by Oxford University Press on behalf of the Infectious Diseases Society of America 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Emergent Learning and Interactive Media Artworks: Parameters of Interaction for Novice Groups

ERIC Educational Resources Information Center

Kawka, Marta; Larkin, Kevin; Danaher, P. A.

2011-01-01

Emergent learning describes learning that occurs when participants interact and distribute knowledge, where learning is self-directed, and where the learning destination of the participants is largely unpredictable (Williams, Karousou, & Mackness, 2011). These notions of learning arise from the topologies of social networks and can be applied to…

ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics

ERIC Educational Resources Information Center

Graesser, Arthur C.; Hu, Xiangen; Nye, Benjamin D.; VanLehn, Kurt; Kumar, Rohit; Heffernan, Cristina; Heffernan, Neil; Woolf, Beverly; Olney, Andrew M.; Rus, Vasile; Andrasik, Frank; Pavlik, Philip; Cai, Zhiqiang; Wetzel, Jon; Morgan, Brent; Hampton, Andrew J.; Lippert, Anne M.; Wang, Lijia; Cheng, Qinyu; Vinson, Joseph E.; Kelly, Craig N.; McGlown, Cadarrius; Majmudar, Charvi A.; Morshed, Bashir; Baer, Whitney

2018-01-01

Background: The Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics,…

From Schools to Community Learning Centers: A Program Evaluation of a School Reform Process

ERIC Educational Resources Information Center

Magolda, Peter; Ebben, Kelsey

2007-01-01

This manuscript reports on a program evaluation of a school reform initiative conducted in an Ohio city. The paper describes, interprets, and evaluates this reform process aimed at transforming schools into community learning centers. The manuscript also describes and analyzes the initiative's program evaluation process. Elliot Eisner's [(1998).…

Ethical Practice in Learning through Participation: Showcasing and Evaluating the PACE Ethical Practice Module

ERIC Educational Resources Information Center

Baker, Michaela; Beale, Alison; Hammersley, Laura; Lloyd, Kate; Semple, Anne-Louise; White, Karolyn

2013-01-01

In 2008, Macquarie University instituted the Participation and Community Engagement (PACE) initiative. This initiative embeds units in the curriculum that involve learning through participation (LTP) that is mutually beneficial to the student, the University and the organisation or community in which student participation activities take place.…

Initial Perceptions of Open Higher Education Students with Learner Management Systems

ERIC Educational Resources Information Center

Altunoglu, Asu

2017-01-01

Learner management systems (LMS) are used in open education as a means of managing and recording e-learning facilities as well as improving student engagement. Students benefit from them to become active participants in the decision-making process of their own learning. This study aims to investigate the initial perceptions of students…

Overview: Measuring Early Learning Quality and Outcomes (MELQO)

ERIC Educational Resources Information Center

Brookings Institution, 2017

2017-01-01

The Measuring Early Learning Quality and Outcomes (MELQO) initiative began in 2014 in anticipation of a new global emphasis on early childhood development (ECD). Led by UNESCO, the World Bank, the Center for Universal Education at the Brookings Institution, and UNICEF, the initiative aims to promote feasible, accurate and useful measurement of…

Initial Understandings of Fraction Concepts Evidenced by Students with Mathematics Learning Disabilities and Difficulties: A Framework

ERIC Educational Resources Information Center

Hunt, Jessica H.; Welch-Ptak, Jasmine J.; Silva, Juanita M.

2016-01-01

Documenting how students with learning disabilities (LD) initially conceive of fractional quantities, and how their understandings may align with or differ from students with mathematics difficulties, is necessary to guide development of assessments and interventions that attach to unique ways of thinking or inherent difficulties these students…

Follow-Up Study of Reading Achievement in Learning Disabled Children.

ERIC Educational Resources Information Center

Gottesman, Ruth L.

Forty-three learning disabled children referred initially between ages 7 and 14 years to a medical outpatient clinic for developmentally disabled children were evaluated and followed for a period of 5 to 7 years after which their level of academic achievement was reassessed. Initial evaluation included pediatric, neurological and developmental…

The Seeds to Success Modified Field Test: Findings from the Impact and Implementation Studies

ERIC Educational Resources Information Center

Boller, Kimberly; Del Grosso, Patricia; Blair, Randall; Jolly, Yumiko; Fortson, Ken; Paulsell, Diane; Lundquist, Eric; Hallgren, Kristin; Kovac, Martha

2010-01-01

In 2006, the Bill & Melinda Gates Foundation launched the Early Learning Initiative (ELI) to improve the school readiness of Washington State's children through three main strategies: (1) development of high-quality, community-wide early learning initiatives in two communities; (2) enhancement of statewide systems that support early…

Is a Laptop Initiative in Your Future? Policy Brief

ERIC Educational Resources Information Center

Pitler, Howard; Flynn, Kathleen; Gaddy, Barbara

2004-01-01

Research indicates that thoughtful technology use can positively influence learning process inside and outside the classroom, and one-to-one computing has been gaining popularity. Although some view such initiatives as passing, others look at the mounting research and see opportunities to reshape the nature of instruction and learning. This brief…

Draft version 1.0 final report : evaluation methods and lessons learned from the Minnesota Department of Transportation intelligent vehicle initiative field operational test

DOT National Transportation Integrated Search

2003-09-26

This report on the Evaluation Methods and Lessons Learned for the Mn/DOT Intelligent Vehicle Initiative (IVI) Field Operational Test (FOT) documents the goals and objectives, research approach, methods, and findings of a program to measure the feasib...

Cooperative Learning and Unity: The Perspectives of Faculty, Students, and TA's.

ERIC Educational Resources Information Center

Hagedorn, Linda Serra; Moon, Hye Sun; Buchanan, Donald; Shockman, Eric; Jackson, Michael

A program designed to encourage university faculty and teaching assistants (TAs) to use cooperative learning in undergraduate classrooms was evaluated through the perspectives of faculty, TAs and students. The program was part of an initiative called DiverSCity, and the evaluation focused on the initial climate and culture of the college and…

Social Partnerships: Practices, Paradoxes and Prospects of Local Learning Networks

ERIC Educational Resources Information Center

Seddon, Terri; Clemans, Allie; Billett, Stephen

2005-01-01

This paper discusses the formation, character and contradictions of social partnerships. We report on a specific initiative, the Local Learning and Employment Networks (LLEN) established by the Victorian Government in Australia in 2001, documenting the nature of this initiative and how it is playing out. We draw attention to some of the tensions…

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

PubMed

Hsu, Anne; Griffiths, Thomas L

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning

PubMed Central

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning. PMID:27310576

Inter-individual differences in the initial 80 minutes of motor learning of handrim wheelchair propulsion.

PubMed

Vegter, Riemer J K; Lamoth, Claudine J; de Groot, Sonja; Veeger, Dirkjan H E J; van der Woude, Lucas H V

2014-01-01

Handrim wheelchair propulsion is a cyclic skill that needs to be learned during rehabilitation. Yet it is unclear how inter-individual differences in motor learning impact wheelchair propulsion practice. Therefore we studied how early-identified motor learning styles in novice able-bodied participants impact the outcome of a low-intensity wheelchair-practice intervention. Over a 12-minute pre-test, 39 participants were split in two groups based on a relative 10% increase in mechanical efficiency. Following the pretest the participants continued one of four different low-intensity wheelchair practice interventions, yet all performed in the same trial-setup with a total 80-minute dose at 1.11 m/s at 0.20 W/kg. Instead of focusing on the effect of the different interventions, we focused on differences in motor learning between participants over the intervention. Twenty-six participants started the pretest with a lower mechanical efficiency and a less optimal propulsion technique, but showed a fast improvement during the first 12 minutes and this effect continued over the 80 minutes of practice. Eventually these initially fast improvers benefitted more from the given practice indicated by a better propulsion technique (like reduced frequency and increased stroke angle) and a higher mechanical efficiency. The initially fast improvers also had a higher intra-individual variability in the pre and posttest, which possibly relates to the increased motor learning of the initially fast improvers. Further exploration of the common characteristics of different types of learners will help to better tailor rehabilitation to the needs of wheelchair-dependent persons and improve our understanding of cyclic motor learning processes.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

PubMed

Arguello Casteleiro, Mercedes; Maseda Fernandez, Diego; Demetriou, George; Read, Warren; Fernandez Prieto, Maria Jesus; Des Diz, Julio; Nenadic, Goran; Keane, John; Stevens, Robert

2017-01-01

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

Implementation of a tobacco-free workplace program at a local mental health authority.

PubMed

Correa-Fernández, Virmarie; Wilson, William T; Shedrick, Deborah A; Kyburz, Bryce; L Samaha, Hannah; Stacey, Timothy; Williams, Teresa; Lam, Cho Y; Reitzel, Lorraine R

2017-06-01

Tobacco-free workplace policies that incorporate evidence-based practices can increase the reach and effectiveness of tobacco dependence treatment among underserved populations but may be underutilized due to limited knowledge about implementation processes. This paper describes the implementation of a comprehensive tobacco-free workplace program at a behavioral healthcare community center in Texas. The center participated in a tobacco-free workplace program implementation project that provided guidance and resources and allowed center autonomy in implementation. Six employee-based subcommittees guided implementation of program components including consumer and staff surveys, policy development, signage, tobacco use assessments, communication, and nicotine replacement distribution. Timeline development, successes, challenges, lessons learned, and sustainability initiatives are delineated. Concerns about the tobacco-free workplace policy from the center's staff and consumers were gradually replaced by strong support for the initiative. Program success was enabled by consistent support from the center's leadership, publicity of program efforts, and educational campaigns. The center surpassed the program expectations when it adopted a tobacco-free hiring policy, which was not an initial program goal. This center's path to a tobacco-free workplace provides an implementation and sustainability model for other behavioral health community centers and other organizations to become tobacco free.

Out of Africa: Uganda and UNAIDS advance a bold experiment.

PubMed

Zuniga, J

1999-10-01

The UNAIDS HIV Drug Access Initiative was launched in 1997 to aid four resource-limited countries: Chile, Cote d'Ivoire, Uganda, and Viet Nam. This multipronged initiative between pharmaceutical companies and government officials aims to expand access to HIV-related drugs on a small, sustainable scale in developing countries. Uganda's experience in the implementation of the UNAIDS HIV Drug Access Initiative is presented. Ugandan President Yoweri Museveni was struck by the reality of AIDS in 1986 when he learned that up to 25 percent of Ugandan troops might be HIV-infected. The overall Ugandan incidence of AIDS has been reduced since that time from 30 percent to about 14.5 percent due, in part, to cooperation between government and international institutions. Various charts are included, indicating cost estimates for the delivery of HIV care, and a flow chart diagrams drug procurement from six pharmaceutical companies for distribution to Ugandans living with HIV/AIDS. Minister of Health Crispus Kiyonga appointed a 15-member National Advisory Board in 1998 that established subcommittees on Drug Policy and Financing, Care and Practice, and Vertical Transmission to implement and oversee responsibilities. The establishment of Uganda's antiretroviral (ARV) treatment guidelines, standards, and educational and treatment efforts are discussed.

The trilayer approach of teaching physiology, pathophysiology, and pharmacology concepts in a first-year pharmacy course: the TLAT model.

PubMed

Islam, Mohammed A; Sabnis, Gauri; Farris, Fred

2017-09-01

This paper describes the development, implementation, and students' perceptions of a new trilayer approach of teaching (TLAT). The TLAT model involved blending lecture, in-class group activities, and out-of-class assignments on selected content areas and was implemented initially in a first-year integrated pharmacy course. Course contents were either delivered by traditional lectures or by the TLAT. A survey instrument was distributed by SurveyMonkey to determine students' perceptions of the TLAT model. Descriptive statistics were used for data analysis. Students' performance in a total of 225 examination and quiz questions was analyzed to evaluate whether the TLAT model improved students' learning. Students' ( n = 98) performance scores for TLAT-based and lecture-based questions were 83.3 ± 10.2 and 79.5 ± 14.0, respectively ( P < 0.05). Ninety-three percent of students believed that in-class group activities enhanced conceptual understanding of course materials, helped them take responsibility of their own learning, and enhanced their overall learning experiences. More than 80% of respondents felt that solving cases and developing concept maps helped them sharpen creative and critical thinking skills. In addition, 90% of the respondents indicated that the homework throughout the semester helped them stay up to date and focused with the progress of the course. The use of the TLAT model led to an improvement in student learning of complex concepts. Moreover, the results suggest that this model improves students' self-reliance and attitudes toward learning. Our findings should serve as an impetus for inclusion of diverse active learning strategies in pharmacy education. Copyright © 2017 the American Physiological Society.

Emotion blocks the path to learning under stereotype threat

PubMed Central

Good, Catherine; Whiteman, Ronald C.; Maniscalco, Brian; Dweck, Carol S.

2012-01-01

Gender-based stereotypes undermine females’ performance on challenging math tests, but how do they influence their ability to learn from the errors they make? Females under stereotype threat or non-threat were presented with accuracy feedback after each problem on a GRE-like math test, followed by an optional interactive tutorial that provided step-wise problem-solving instruction. Event-related potentials tracked the initial detection of the negative feedback following errors [feedback related negativity (FRN), P3a], as well as any subsequent sustained attention/arousal to that information [late positive potential (LPP)]. Learning was defined as success in applying tutorial information to correction of initial test errors on a surprise retest 24-h later. Under non-threat conditions, emotional responses to negative feedback did not curtail exploration of the tutor, and the amount of tutor exploration predicted learning success. In the stereotype threat condition, however, greater initial salience of the failure (FRN) predicted less exploration of the tutor, and sustained attention to the negative feedback (LPP) predicted poor learning from what was explored. Thus, under stereotype threat, emotional responses to negative feedback predicted both disengagement from learning and interference with learning attempts. We discuss the importance of emotion regulation in successful rebound from failure for stigmatized groups in stereotype-salient environments. PMID:21252312

Emotion blocks the path to learning under stereotype threat.

PubMed

Mangels, Jennifer A; Good, Catherine; Whiteman, Ronald C; Maniscalco, Brian; Dweck, Carol S

2012-02-01

Gender-based stereotypes undermine females' performance on challenging math tests, but how do they influence their ability to learn from the errors they make? Females under stereotype threat or non-threat were presented with accuracy feedback after each problem on a GRE-like math test, followed by an optional interactive tutorial that provided step-wise problem-solving instruction. Event-related potentials tracked the initial detection of the negative feedback following errors [feedback related negativity (FRN), P3a], as well as any subsequent sustained attention/arousal to that information [late positive potential (LPP)]. Learning was defined as success in applying tutorial information to correction of initial test errors on a surprise retest 24-h later. Under non-threat conditions, emotional responses to negative feedback did not curtail exploration of the tutor, and the amount of tutor exploration predicted learning success. In the stereotype threat condition, however, greater initial salience of the failure (FRN) predicted less exploration of the tutor, and sustained attention to the negative feedback (LPP) predicted poor learning from what was explored. Thus, under stereotype threat, emotional responses to negative feedback predicted both disengagement from learning and interference with learning attempts. We discuss the importance of emotion regulation in successful rebound from failure for stigmatized groups in stereotype-salient environments.

A Space-Based Learning Service for Schools Worldwide

NASA Astrophysics Data System (ADS)

White, Norman A.; Gibson, Alan

2002-01-01

This paper outlines a scheme for international collaboration to enrich the use of space in school education, to improve students' learning about science and related subjects and to enhance the continuity of science-related studies after the age of 16. Guidelines are presented for the design of an on-line learning service to provide schools worldwide with:- interactive curriculum-related learning resources for teaching about space and through - access to a purpose-designed education satellite or satellites; - opportunities for hands-on work by students in out-of-school hours; - news about space developments to attract, widen and deepen initial interest among teachers - support services to enable teachers to make effective use of the learning service. The Learning Service is the product of almost twenty years of experience by a significant number of UK schools in experimenting with, and in using, satellites and space to aid learning; and over four years of study and development by the SpaceLink Learning Foundation - a private-sector, not- for-profit UK registered charity, which is dedicated to help in increasing both the supply of scientists and engineers and the public understanding of science. This initiative provides scope for, and could benefit from, the involvement of relevant/interested organisations drawn from different countries. The Foundation would be ready, from its UK base, to be among such a group of initiating organisations.

Linking Infants' Distributional Learning Abilities to Natural Language Acquisition

ERIC Educational Resources Information Center

van Heugten, Marieke; Johnson, Elizabeth K.

2010-01-01

This study examines the link between distributional patterns in the input and infants' acquisition of non-adjacent dependencies. In two Headturn Preference experiments, Dutch-learning 24-month-olds (but not 17-month-olds) were found to track the remote dependency between the definite article "het" and the diminutive suffix…

Distributed Revisiting: An Analytic for Retention of Coherent Science Learning

ERIC Educational Resources Information Center

Svihla, Vanessa; Wester, Michael J.; Linn, Marcia C.

2015-01-01

Designing learning experiences that support the development of coherent understanding of complex scientific phenomena is challenging. We sought to identify analytics that can also guide such designs to support retention of coherent understanding. Based on prior research that distributing study of material over time supports retention, we explored…