An approach for investigation of secure access processes at a combined e-learning environment

NASA Astrophysics Data System (ADS)

Romansky, Radi; Noninska, Irina

2017-12-01

The article discuses an approach to investigate processes for regulation the security and privacy control at a heterogenous e-learning environment realized as a combination of traditional and cloud means and tools. Authors' proposal for combined architecture of e-learning system is presented and main subsystems and procedures are discussed. A formalization of the processes for using different types resources (public, private internal and private external) is proposed. The apparatus of Markovian chains (MC) is used for modeling and analytical investigation of the secure access to the resources is used and some assessments are presented.

Identifying Consistent Variables in a Heterogeneous Data Set: Evaluation of a Web-Based Pre-Course in Mathematics

ERIC Educational Resources Information Center

Derr, Katja

2017-01-01

E-learning has made course evaluation easier in many ways, as a multitude of learner data can be collected and related to student performance. At the same time, open learning environments can be a difficult field for evaluation, with a large variance in participants' knowledge level, learner behaviour, and commitment. In this study the…

The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

PubMed

Jeste, Shafali S; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F N; Johnson, Scott P

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function. © 2014 John Wiley & Sons Ltd.

Voices from Networked Classrooms.

ERIC Educational Resources Information Center

Brownlee-Conyers, Jean; Kraber, Brenda

1996-01-01

In 1994, the Glenview (Illinois) Public Schools created three technology-rich educational environments (TREEs) that use alternative teaching and learning methods through networked communication technologies. Each setting consists of three teachers and about 75 heterogeneously grouped students (ages 9-12) who work collaboratively to solve problems…

Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

NASA Astrophysics Data System (ADS)

Johnson, Nicholas E.; Bonczak, Bartosz; Kontokosta, Constantine E.

2018-07-01

The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36-0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68-0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors.

Law of Large Numbers: The Theory, Applications and Technology-Based Education

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas; Gould, Robert

2009-01-01

Modern approaches for technology-based blended education utilize a variety of recently developed novel pedagogical, computational and network resources. Such attempts employ technology to deliver integrated, dynamically-linked, interactive-content and heterogeneous learning environments, which may improve student comprehension and information…

Rejecting salient distractors: Generalization from experience.

PubMed

Vatterott, Daniel B; Mozer, Michael C; Vecera, Shaun P

2018-02-01

Distraction impairs performance of many important, everyday tasks. Attentional control limits distraction by preferentially selecting important items for limited-capacity cognitive operations. Research in attentional control has typically investigated the degree to which selection of items is stimulus-driven versus goal-driven. Recent work finds that when observers initially learn a task, the selection is based on stimulus-driven factors, but through experience, goal-driven factors have an increasing influence. The modulation of selection by goals has been studied within the paradigm of learned distractor rejection, in which experience over a sequence of trials enables individuals eventually to ignore a perceptually salient distractor. The experiments presented examine whether observers can generalize learned distractor rejection to novel distractors. Observers searched for a target and ignored a salient color-singleton distractor that appeared in half of the trials. In Experiment 1, observers who learned distractor rejection in a variable environment rejected a novel distractor more effectively than observers who learned distractor rejection in a less variable, homogeneous environment, demonstrating that variable, heterogeneous stimulus environments encourage generalizable learned distractor rejection. Experiments 2 and 3 investigated the time course of learned distractor rejection across the experiment and found that after experiencing four color-singleton distractors in different blocks, observers could effectively reject subsequent novel color-singleton distractors. These results suggest that the optimization of attentional control to the task environment can be interpreted as a form of learning, demonstrating experience's critical role in attentional control.

The effect of homogeneous and heterogeneous review pairs on student achievement and attitude when utilizing computer-assisted instruction in middle-level Earth science classes

NASA Astrophysics Data System (ADS)

Lyon, Ellen Beth

1998-09-01

This research project investigated the influence of homogeneous (like-ability) review pairs coupled with heterogeneous (mixed-ability) cooperative learning groups using computer-assisted instruction (CAI) on academic achievement and attitude toward science in eighth grade Earth science students. Subjects were placed into academic quartiles (Hi, Med-Hi, Med-Lo, and Lo) based on achievement. Cooperative learning groups of four (one student from each academic quartile) were formed in all classes, within which students completed CAI through a software package entitled Geoscience Education Through Interactive Technology, or GETITspTM. Each day, when computer activities were completed, students in the experimental classes were divided into homogeneous review pairs to review their work. The students in the control classes were divided into heterogeneous review pairs to review their work. The effects of the experimental treatment were measured by pretest, posttest, and delayed posttest measures, by pre- and post-student attitude scales, and by evaluation of amendments students made to their work during the time spent in review pairs. Results showed that student achievement was not significantly influenced by placement in homogeneous or heterogeneous review pairs, regardless of academic quartile assignment. Student attitude toward science as a school subject did not change significantly due to experimental treatment. Achievement retention of students in experimental and control groups within each quartile showed no significant difference. Notebook amendment patterns showed some significant differences in a few categories. For the Hi quartile, there were significant differences in numbers of deletion amendments and substitution amendments between the experimental and the control group. In both cases, subjects in the experimental group (homogeneous review pairs) made greater number of amendments then those in the control group (heterogeneous review pairs). For the Lo quartile, there was a significant difference in the number of grammar/usage/mechanics (GUM) amendments between the experimental and control groups. The experimental group made far more GUM amendments than the control group. This research highlights the fact that many factors may influence a successful learning environment in which CAI is successfully implemented. Educational research projects should be designed and used to help teachers create learning environments in which CAI is maximized.

Video-Enhanced Training to Support Professional Development in Elementary Science Teaching: A Beginning Teacher's Experience

ERIC Educational Resources Information Center

Hamel, Christine; Viau-Guay, Anabelle; Ria, Luc; Dion-Routhier, Justine

2018-01-01

Elementary teachers are expected to teach complex and authentic lessons and integrating multiple disciplines. In so doing, they must take many elements into account, such as disciplinary content, learning standards, and pedagogical knowledge, in an ever more complex environment, including pupils' increasingly heterogeneous characteristics. Our…

Collaborative Technology. An Examination of Adults' Concurrent Use of Technology and Collaboration.

ERIC Educational Resources Information Center

Hill, Janice J.

A qualitative study examined what happens to the learning environment when a heterogeneous group of male adults uses technology and collaborative strategies to improve their writing skills. During the 14-week study, the teacher modeled the use of technology when introducing units in a writing course and used the abilities and strengths of the…

Learning from Data with Heterogeneous Noise using SGD

PubMed Central

Song, Shuang; Chaudhuri, Kamalika; Sarwate, Anand D.

2015-01-01

We consider learning from data of variable quality that may be obtained from different heterogeneous sources. Addressing learning from heterogenous data in its full generality is a challenging problem. In this paper, we adopt instead a model in which data is observed through heterogeneous noise, where the noise level reflects the quality of the data source. We study how to use stochastic gradient algorithms to learn in this model. Our study is motivated by two concrete examples where this problem arises naturally: learning with local differential privacy based on data from multiple sources with different privacy requirements, and learning from data with labels of variable quality. The main contribution of this paper is to identify how heterogeneous noise impacts performance. We show that given two datasets with heterogeneous noise, the order in which to use them in standard SGD depends on the learning rate. We propose a method for changing the learning rate as a function of the heterogeneity, and prove new regret bounds for our method in two cases of interest. Experiments on real data show that our method performs better than using a single learning rate and using only the less noisy of the two datasets when the noise level is low to moderate. PMID:26705435

Can personality predict individual differences in brook trout spatial learning ability?

USGS Publications Warehouse

White, S.L.; Wagner, Tyler; Gowan, C.; Braithwaite, V.A.

2017-01-01

While differences in individual personality are common in animal populations, understanding the ecological significance of variation has not yet been resolved. Evidence suggests that personality may influence learning and memory; a finding that could improve our understanding of the evolutionary processes that produce and maintain intraspecific behavioural heterogeneity. Here, we tested whether boldness, the most studied personality trait in fish, could predict learning ability in brook trout. After quantifying boldness, fish were trained to find a hidden food patch in a maze environment. Stable landmark cues were provided to indicate the location of food and, at the conclusion of training, cues were rearranged to test for learning. There was a negative relationship between boldness and learning as shy fish were increasingly more successful at navigating the maze and locating food during training trials compared to bold fish. In the altered testing environment, only shy fish continued using cues to search for food. Overall, the learning rate of bold fish was found to be lower than that of shy fish for several metrics suggesting that personality could have widespread effects on behaviour. Because learning can increase plasticity to environmental change, these results have significant implications for fish conservation.

Education in a Multicultural Environment: Equity Issues in Teaching and Learning in the School System in England

ERIC Educational Resources Information Center

Boyle, Bill; Charles, Marie

2011-01-01

The paper focuses on the auditing and accountancy paradigm that has dominated educational measurement of pupil performance for the last 20 years in England. The advocates of this minimum competency paradigm do not take account of the results of its dominance. These results include ignoring the heterogeneous complexity of groups within societies…

The Impact of Homogeneous vs. Heterogeneous Collaborative Learning Groups in Multicultural Classes on the Achievement and Attitudes of Nine Graders towards Learning Science

ERIC Educational Resources Information Center

Faris, Ahmed O.

2009-01-01

The current study aims at investigating the impact of homogeneous versus heterogeneous collaborative learning grouping in multicultural classes on the students' achievements and attitudes towards learning science. In the present study, heterogeneity was unpacked through two dimensions: the cultural background, represented by the different…

Can personality predict individual differences in brook trout spatial learning ability?

PubMed

White, S L; Wagner, T; Gowan, C; Braithwaite, V A

2017-08-01

While differences in individual personality are common in animal populations, understanding the ecological significance of variation has not yet been resolved. Evidence suggests that personality may influence learning and memory; a finding that could improve our understanding of the evolutionary processes that produce and maintain intraspecific behavioural heterogeneity. Here, we tested whether boldness, the most studied personality trait in fish, could predict learning ability in brook trout. After quantifying boldness, fish were trained to find a hidden food patch in a maze environment. Stable landmark cues were provided to indicate the location of food and, at the conclusion of training, cues were rearranged to test for learning. There was a negative relationship between boldness and learning as shy fish were increasingly more successful at navigating the maze and locating food during training trials compared to bold fish. In the altered testing environment, only shy fish continued using cues to search for food. Overall, the learning rate of bold fish was found to be lower than that of shy fish for several metrics suggesting that personality could have widespread effects on behaviour. Because learning can increase plasticity to environmental change, these results have significant implications for fish conservation. Copyright © 2016 Elsevier B.V. All rights reserved.

Examining the Impact of Critical Multicultural Education Training on the Multicultural Attitudes, Awareness, and Practices of Nurse Educators.

PubMed

Beard, Kenya V

Some nurse educators lack training in the educational methods that facilitate learning among underrepresented groups. Limited awareness of equitable pedagogical practices could threaten the academic achievement of underrepresented groups and hinder efforts to make the nursing profession more heterogeneous. Training in multicultural education could strengthen the capacity of educators to create culturally responsive learning environments. This quasi-experimental study examined the impact that training in critical multicultural education had on the multicultural attitudes, awareness, and practices of 37 nurse educators. A pre-posttest design without a control group found that the training was an effective way to strengthen the multicultural awareness and attitudes of nurse educators, although there was little impact on the multicultural practices. The nation's capacity to improve the quality of health care hinges upon educators who can create inclusive learning environments and graduate diverse nurses. The findings could inform policies seeking to promote diversity and inclusion in nursing education. Copyright © 2016 Elsevier Inc. All rights reserved.

Adult educators' core competences

NASA Astrophysics Data System (ADS)

Wahlgren, Bjarne

2016-06-01

Which competences do professional adult educators need? This research note discusses the topic from a comparative perspective, finding that adult educators' required competences are wide-ranging, heterogeneous and complex. They are subject to context in terms of national and cultural environment as well as the kind of adult education concerned (e.g. basic education, work-related education etc.). However, it seems that it is possible to identify certain competence requirements which transcend national, cultural and functional boundaries. This research note summarises these common or "core" requirements, organising them into four thematic subcategories: (1) communicating subject knowledge; (2) taking students' prior learning into account; (3) supporting a learning environment; and (4) the adult educator's reflection on his or her own performance. At the end of his analysis of different competence profiles, the author notes that adult educators' ability to train adult learners in a way which then enables them to apply and use what they have learned in practice (thus performing knowledge transfer) still seems to be overlooked.

Abilities in tactile discrimination of textures in adult rats exposed to enriched or impoverished environments.

PubMed

Bourgeon, Stéphanie; Xerri, Christian; Coq, Jacques-Olivier

2004-08-12

In previous studies, we have shown that housing in enriched environment for about 3 months after weaning improved the topographic organization and decreased the size of the receptive fields (RFs) located on the glabrous skin surfaces in the forepaw maps of the primary somatosensory cortex (SI) in rats [Exp. Brain Res. 121 (1998) 191]. In contrast, housing in impoverished environment induced a degradation of the SI forepaw representation, characterized by topographic disruptions, a reduction of the cutaneous forepaw area and an enlargement of the glabrous RFs [Exp. Brain Res. 129 (1999) 518]. Based on these two studies, we postulated that these representational alterations could underlie changes in haptic perception. Therefore, the present study was aimed at determining the influence of housing conditions on the rat's abilities in tactile texture discrimination. After a 2-month exposure to enriched or impoverished environments, rats were trained to perform a discrimination task during locomotion on floorboards of different roughness. At the end of every daily behavioral session, rats were replaced in their respective housing environment. Rats had to discriminate homogeneous (low roughness) from heterogeneous floorboards (combination of two different roughness levels). To determine the maximum performance in texture discrimination, the roughness contrast of the heterogeneous texture was gradually reduced, so that homogeneous and heterogeneous floorboards became harder to differentiate. We found that the enriched rats learned the first steps of the behavioral task faster than the impoverished rats, whereas both groups exhibited similar performances in texture discrimination. An individual "predilection" for either homogeneous or heterogeneous floorboards, presumably reflecting a behavioral strategy, seemed to account for the absence of differences in haptic discrimination between groups. The sensory experience depending on the rewarded texture discrimination task seems to have a greater influence on individual texture discrimination abilities than the sensorimotor experience related to housing conditions.

Meeting People's Needs in a Fully Interoperable Domotic Environment

PubMed Central

Miori, Vittorio; Russo, Dario; Concordia, Cesare

2012-01-01

The key idea underlying many Ambient Intelligence (AmI) projects and applications is context awareness, which is based mainly on their capacity to identify users and their locations. The actual computing capacity should remain in the background, in the periphery of our awareness, and should only move to the center if and when necessary. Computing thus becomes ‘invisible’, as it is embedded in the environment and everyday objects. The research project described herein aims to realize an Ambient Intelligence-based environment able to improve users' quality of life by learning their habits and anticipating their needs. This environment is part of an adaptive, context-aware framework designed to make today's incompatible heterogeneous domotic systems fully interoperable, not only for connecting sensors and actuators, but for providing comprehensive connections of devices to users. The solution is a middleware architecture based on open and widely recognized standards capable of abstracting the peculiarities of underlying heterogeneous technologies and enabling them to co-exist and interwork, without however eliminating their differences. At the highest level of this infrastructure, the Ambient Intelligence framework, integrated with the domotic sensors, can enable the system to recognize any unusual or dangerous situations and anticipate health problems or special user needs in a technological living environment, such as a house or a public space. PMID:22969322

Meeting people's needs in a fully interoperable domotic environment.

PubMed

Miori, Vittorio; Russo, Dario; Concordia, Cesare

2012-01-01

The key idea underlying many Ambient Intelligence (AmI) projects and applications is context awareness, which is based mainly on their capacity to identify users and their locations. The actual computing capacity should remain in the background, in the periphery of our awareness, and should only move to the center if and when necessary. Computing thus becomes 'invisible', as it is embedded in the environment and everyday objects. The research project described herein aims to realize an Ambient Intelligence-based environment able to improve users' quality of life by learning their habits and anticipating their needs. This environment is part of an adaptive, context-aware framework designed to make today's incompatible heterogeneous domotic systems fully interoperable, not only for connecting sensors and actuators, but for providing comprehensive connections of devices to users. The solution is a middleware architecture based on open and widely recognized standards capable of abstracting the peculiarities of underlying heterogeneous technologies and enabling them to co-exist and interwork, without however eliminating their differences. At the highest level of this infrastructure, the Ambient Intelligence framework, integrated with the domotic sensors, can enable the system to recognize any unusual or dangerous situations and anticipate health problems or special user needs in a technological living environment, such as a house or a public space.

Improving Video Game Development: Facilitating Heterogeneous Team Collaboration through Flexible Software Processes

NASA Astrophysics Data System (ADS)

Musil, Juergen; Schweda, Angelika; Winkler, Dietmar; Biffl, Stefan

Based on our observations of Austrian video game software development (VGSD) practices we identified a lack of systematic processes/method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. VGSD includes heterogeneous disciplines, e.g. creative arts, game/content design, and software. Nevertheless, improving team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process/method support and (b) suggests a candidate flexible process approach based on Scrum to improve VGSD and team collaboration. Results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.

A broadband multimedia TeleLearning system

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

Wang, Ruiping; Karmouch, A.

1996-12-31

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

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

PubMed

Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

2017-08-10

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

[Digital learning and teaching in medical education : Already there or still at the beginning?

PubMed

Kuhn, Sebastian; Frankenhauser, Susanne; Tolks, Daniel

2018-02-01

The current choice of digital teaching and learning formats in medicine is very heterogeneous. In addition to the widely used classical static formats, social communication tools, audio/video-based media, interactive formats, and electronic testing systems enrich the learning environment.For medical students, the private use of digital media is not necessarily linked to their meaningful use in the study. Many gain their experience of digital learning in the sense of "assessment drives learning", especially by taking online exams in a passive, consuming role. About half of all medical students can be referred to as "e-examinees" whose handling of digital learning is primarily focused on online exam preparation. Essentially, they do not actively influence their digital environment. Only a quarter can be identified as a "digital all-rounder", who compiles their individual learning portfolio from the broad range of digital media.At present, the use of digital media is not yet an integral and comprehensive component of the teaching framework of medical studies in Germany, but is rather used in the sense of a punctual teaching enrichment. Current trends in digital teaching and learning offerings are mobile, interactive, and personalized platforms as well as increasing the relevance of learning platforms. Furthermore, didactical concepts targeting the changed learning habits of the students are more successful regarding the acceptance and learning outcomes. In addition, digitalization is currently gaining importance as a component in the medical school curricula.

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

PubMed

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

2001-03-01

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

With you or against you: social orientation dependent learning signals guide actions made for others.

PubMed

Christopoulos, George I; King-Casas, Brooks

2015-01-01

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

With you or against you: Social orientation dependent learning signals guide actions made for others

PubMed Central

Christopoulos, George I.; King-Casas, Brooks

2014-01-01

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

The Influence of Student Characteristics on the Use of Adaptive E-Learning Material

ERIC Educational Resources Information Center

van Seters, J. R.; Ossevoort, M. A.; Tramper, J.; Goedhart, M. J.

2012-01-01

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting…

Does Environmental Knowledge Inhibit Hominin Dispersal?

PubMed

Wren, Colin D; Costopoulos, Andre

2015-07-01

We investigated the relationship between the dispersal potential of a hominin population, its local-scale foraging strategies, and the characteristics of the resource environment using an agent-based modeling approach. In previous work we demonstrated that natural selection can favor a relatively low capacity for assessing and predicting the quality of the resource environment, especially when the distribution of resources is highly clustered. That work also suggested that the more knowledge foraging populations had about their environment, the less likely they were to abandon the landscape they know and disperse into novel territory. The present study gives agents new individual and social strategies for learning about their environment. For both individual and social learning, natural selection favors decreased levels of environmental knowledge, particularly in low-heterogeneity environments. Social acquisition of detailed environmental knowledge results in crowding of agents, which reduces available reproductive space and relative fitness. Agents with less environmental knowledge move away from resource clusters and into areas with more space available for reproduction. These results suggest that, rather than being a requirement for successful dispersal, environmental knowledge strengthens the ties to particular locations and significantly reduces the dispersal potential as a result. The evolved level of environmental knowledge in a population depends on the characteristics of the resource environment and affects the dispersal capacity of the population.

Simultaneous Local Binary Feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition.

PubMed

Lu, Jiwen; Erin Liong, Venice; Zhou, Jie

2017-08-09

In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which usually require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which automatically learns face representation from raw pixels. Unlike existing binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact binary face descriptor (CBFD) which use a two-stage feature extraction procedure, our SLBFLE jointly learns binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local binary feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogeneous face matching. Unlike most existing coupled feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets are presented to demonstrate the effectiveness of the proposed method.

Differentiated Teaching & Learning in Heterogeneous Classrooms: Strategies for Meeting the Needs of All Students.

ERIC Educational Resources Information Center

Kronberg, Robi; York-Barr, Jennifer; Arnold, Kathy; Gombos, Shawn; Truex, Sharon; Vallejo, Barb; Stevenson, Jane

This guide provides conceptual as well as practical information for meeting the needs of all learners in heterogeneous classrooms. The first six sections discuss the growing heterogeneity in today's classrooms, the rationale for differentiated teaching and learning, the changing roles of teachers and students, the importance of creating classroom…

Heterogeneous Embedded Real-Time Systems Environment

DTIC Science & Technology

2003-12-01

AFRL-IF-RS-TR-2003-290 Final Technical Report December 2003 HETEROGENEOUS EMBEDDED REAL - TIME SYSTEMS ENVIRONMENT Integrated...HETEROGENEOUS EMBEDDED REAL - TIME SYSTEMS ENVIRONMENT 6. AUTHOR(S) Cosmo Castellano and James Graham 5. FUNDING NUMBERS C - F30602-97-C-0259

A "Mixed" Strategy for Collaborative Group Formation and Its Learning Outcomes

ERIC Educational Resources Information Center

Acharya, Anal; Sinha, Devadatta

2018-01-01

This study uses homogeneity in personal learning styles and heterogeneity in subject knowledge for collaborative learning group decomposition indicating that groups are "mixed" in nature. Homogeneity within groups was formed using K-means clustering and greedy search, whereas heterogeneity imbibed using agenda-driven search. For checking…

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

PubMed

Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D

2017-01-01

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Carrying capacity in a heterogeneous environment with habitat connectivity.

PubMed

Zhang, Bo; Kula, Alex; Mack, Keenan M L; Zhai, Lu; Ryce, Arrix L; Ni, Wei-Ming; DeAngelis, Donald L; Van Dyken, J David

2017-09-01

A large body of theory predicts that populations diffusing in heterogeneous environments reach higher total size than if non-diffusing, and, paradoxically, higher size than in a corresponding homogeneous environment. However, this theory and its assumptions have not been rigorously tested. Here, we extended previous theory to include exploitable resources, proving qualitatively novel results, which we tested experimentally using spatially diffusing laboratory populations of yeast. Consistent with previous theory, we predicted and experimentally observed that spatial diffusion increased total equilibrium population abundance in heterogeneous environments, with the effect size depending on the relationship between r and K. Refuting previous theory, however, we discovered that homogeneously distributed resources support higher total carrying capacity than heterogeneously distributed resources, even with species diffusion. Our results provide rigorous experimental tests of new and old theory, demonstrating how the traditional notion of carrying capacity is ambiguous for populations diffusing in spatially heterogeneous environments. © 2017 John Wiley & Sons Ltd/CNRS.

Carrying capacity in a heterogeneous environment with habitat connectivity

USGS Publications Warehouse

Zhang, Bo; Kula, Alex; Mack, Keenan M.L.; Zhai, Lu; Ryce, Arrix L.; Ni, Wei-Ming; DeAngelis, Donald L.; Van Dyken, J. David

2017-01-01

A large body of theory predicts that populations diffusing in heterogeneous environments reach higher total size than if non-diffusing, and, paradoxically, higher size than in a corresponding homogeneous environment. However, this theory and its assumptions have not been rigorously tested. Here, we extended previous theory to include exploitable resources, proving qualitatively novel results, which we tested experimentally using spatially diffusing laboratory populations of yeast. Consistent with previous theory, we predicted and experimentally observed that spatial diffusion increased total equilibrium population abundance in heterogeneous environments, with the effect size depending on the relationship between r and K. Refuting previous theory, however, we discovered that homogeneously distributed resources support higher total carrying capacity than heterogeneously distributed resources, even with species diffusion. Our results provide rigorous experimental tests of new and old theory, demonstrating how the traditional notion of carrying capacity is ambiguous for populations diffusing in spatially heterogeneous environments.

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.

Phenotypically heterogeneous populations in spatially heterogeneous environments

NASA Astrophysics Data System (ADS)

Patra, Pintu; Klumpp, Stefan

2014-03-01

The spatial expansion of a population in a nonuniform environment may benefit from phenotypic heterogeneity with interconverting subpopulations using different survival strategies. We analyze the crossing of an antibiotic-containing environment by a bacterial population consisting of rapidly growing normal cells and slow-growing, but antibiotic-tolerant persister cells. The dynamics of crossing is characterized by mean first arrival times and is found to be surprisingly complex. It displays three distinct regimes with different scaling behavior that can be understood based on an analytical approximation. Our results suggest that a phenotypically heterogeneous population has a fitness advantage in nonuniform environments and can spread more rapidly than a homogeneous population.

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

PubMed Central

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-01-01

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514

"For most of us Africans, we don't just speak": a qualitative investigation into collaborative heterogeneous PBL group learning.

PubMed

Singaram, Veena S; van der Vleuten, Cees P M; Stevens, Fred; Dolmans, Diana H J M

2011-08-01

Collaborative approaches such as Problem Based Learning (PBL) may provide the opportunity to bring together diverse students but their efficacy in practice and the complications that arise due to the mixed ethnicity needs further investigation. This study explores the key advantages and problems of heterogeneous PBL groups from the students' and teachers' opinions. Focus groups were conducted with a stratified sample of second year medical students and their PBL teachers. We found that students working in heterogeneous groupings interact with students with whom they don't normally interact with, learn a lot more from each other because of their differences in language and academic preparedness and become better prepared for their future professions in multicultural societies. On the other hand we found students segregating in the tutorials along racial lines and that status factors disempowered students and subsequently their productivity. Among the challenges was also that academic and language diversity hindered student learning. In light of these the recommendations were that teachers need special diversity training to deal with heterogeneous groups and the tensions that arise. Attention should be given to create 'the right mix' for group learning in diverse student populations. The findings demonstrate that collaborative heterogeneous learning has two sides that need to be balanced. On the positive end we have the 'ideology' behind mixing diverse students and on the negative the 'practice' behind mixing students. More research is needed to explore these variations and their efficacy in more detail.

Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

PubMed

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-11-14

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

NASA Astrophysics Data System (ADS)

Li, Yung-Hui; Zheng, Bo-Ren; Ji, Dai-Yan; Tien, Chung-Hao; Liu, Po-Tsun

2014-09-01

Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

Heterogeneity in Learning Style in Asperger Syndrome and High-Functioning Autism

ERIC Educational Resources Information Center

Tsatsanis, Katharine D.

2004-01-01

Although children and adolescents with high-functioning autism and Asperger syndrome present with some similar clinical features and challenges, heterogeneity of learning style coupled with the predominance of specific "packages" of materials and methods tends to understate the need for individualization when designing an educational and/or a…

Higher rates of sex evolve in spatially heterogeneous environments.

PubMed

Becks, Lutz; Agrawal, Aneil F

2010-11-04

The evolution and maintenance of sexual reproduction has puzzled biologists for decades. Although this field is rich in hypotheses, experimental evidence is scarce. Some important experiments have demonstrated differences in evolutionary rates between sexual and asexual populations; other experiments have documented evolutionary changes in phenomena related to genetic mixing, such as recombination and selfing. However, direct experiments of the evolution of sex within populations are extremely rare (but see ref. 12). Here we use the rotifer, Brachionus calyciflorus, which is capable of both sexual and asexual reproduction, to test recent theory predicting that there is more opportunity for sex to evolve in spatially heterogeneous environments. Replicated experimental populations of rotifers were maintained in homogeneous environments, composed of either high- or low-quality food habitats, or in heterogeneous environments that consisted of a mix of the two habitats. For populations maintained in either type of homogeneous environment, the rate of sex evolves rapidly towards zero. In contrast, higher rates of sex evolve in populations experiencing spatially heterogeneous environments. The data indicate that the higher level of sex observed under heterogeneity is not due to sex being less costly or selection against sex being less efficient; rather sex is sufficiently advantageous in heterogeneous environments to overwhelm its inherent costs. Counter to some alternative theories for the evolution of sex, there is no evidence that genetic drift plays any part in the evolution of sex in these populations.

Interventions for Learning Disorders

MedlinePlus

... about any treatment you are considering. Children and Learning Disabilities Here are some points to keep in mind about learning disabilities. Children with learning disabilities are a very heterogeneous ...

A learning theory account of depression.

PubMed

Ramnerö, Jonas; Folke, Fredrik; Kanter, Jonathan W

2015-06-11

Learning theory provides a foundation for understanding and deriving treatment principles for impacting a spectrum of functional processes relevant to the construct of depression. While behavioral interventions have been commonplace in the cognitive behavioral tradition, most often conceptualized within a cognitive theoretical framework, recent years have seen renewed interest in more purely behavioral models. These modern learning theory accounts of depression focus on the interchange between behavior and the environment, mainly in terms of lack of reinforcement, extinction of instrumental behavior, and excesses of aversive control, and include a conceptualization of relevant cognitive and emotional variables. These positions, drawn from extensive basic and applied research, cohere with biological theories on reduced reward learning and reward responsiveness and views of depression as a heterogeneous, complex set of disorders. Treatment techniques based on learning theory, often labeled Behavioral Activation (BA) focus on activating the individual in directions that increase contact with potential reinforcers, as defined ideographically with the client. BA is considered an empirically well-established treatment that generalizes well across diverse contexts and populations. The learning theory account is discussed in terms of being a parsimonious model and ground for treatments highly suitable for large scale dissemination. © 2015 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Health promotion in medical education: lessons from a major undergraduate curriculum implementation.

PubMed

Wylie, Ann; Leedham-Green, Kathleen

2017-11-01

Despite the economic, environmental and patient-related imperatives to prepare medical students to become health promoting doctors, health promotion remains relatively deprioritised in medical curricula. This paper uses an in-depth case study of a health promotion curriculum implementation at a large UK medical school to provide insights into the experiences of teachers and learners across a range of topics, pedagogies, and teaching & assessment modalities. Topics included smoking cessation, behavioural change approaches to obesity, exercise prescribing, social prescribing, maternal and child health, public and global health; with pedagogies ranging from e-learning to practice-based project work. Qualitative methods including focus groups, analysis of reflective learning submissions, and evaluation data are used to illuminate motivations, frustrations, practicalities, successes and limiting factors. Over this three year implementation, a range of challenges have been highlighted including: how adequately to prepare and support clinical teachers; the need to establish relevance and importance to strategic learners; the need for experiential learning in clinical environments to support classroom-based activities; and the need to rebalance competing aspects of the curriculum. Conclusions are drawn about heterogeneous deep learning over standardised surface learning, and the impacts, both positive and negative, of different assessment modalities on these types of learning.

Computational and experimental single cell biology techniques for the definition of cell type heterogeneity, interplay and intracellular dynamics.

PubMed

de Vargas Roditi, Laura; Claassen, Manfred

2015-08-01

Novel technological developments enable single cell population profiling with respect to their spatial and molecular setup. These include single cell sequencing, flow cytometry and multiparametric imaging approaches and open unprecedented possibilities to learn about the heterogeneity, dynamics and interplay of the different cell types which constitute tissues and multicellular organisms. Statistical and dynamic systems theory approaches have been applied to quantitatively describe a variety of cellular processes, such as transcription and cell signaling. Machine learning approaches have been developed to define cell types, their mutual relationships, and differentiation hierarchies shaping heterogeneous cell populations, yielding insights into topics such as, for example, immune cell differentiation and tumor cell type composition. This combination of experimental and computational advances has opened perspectives towards learning predictive multi-scale models of heterogeneous cell populations. Copyright © 2014 Elsevier Ltd. All rights reserved.

High fidelity wireless network evaluation for heterogeneous cognitive radio networks

NASA Astrophysics Data System (ADS)

Ding, Lei; Sagduyu, Yalin; Yackoski, Justin; Azimi-Sadjadi, Babak; Li, Jason; Levy, Renato; Melodia, Tammaso

2012-06-01

We present a high fidelity cognitive radio (CR) network emulation platform for wireless system tests, measure- ments, and validation. This versatile platform provides the configurable functionalities to control and repeat realistic physical channel effects in integrated space, air, and ground networks. We combine the advantages of scalable simulation environment with reliable hardware performance for high fidelity and repeatable evaluation of heterogeneous CR networks. This approach extends CR design only at device (software-defined-radio) or lower-level protocol (dynamic spectrum access) level to end-to-end cognitive networking, and facilitates low-cost deployment, development, and experimentation of new wireless network protocols and applications on frequency- agile programmable radios. Going beyond the channel emulator paradigm for point-to-point communications, we can support simultaneous transmissions by network-level emulation that allows realistic physical-layer inter- actions between diverse user classes, including secondary users, primary users, and adversarial jammers in CR networks. In particular, we can replay field tests in a lab environment with real radios perceiving and learning the dynamic environment thereby adapting for end-to-end goals over distributed spectrum coordination channels that replace the common control channel as a single point of failure. CR networks offer several dimensions of tunable actions including channel, power, rate, and route selection. The proposed network evaluation platform is fully programmable and can reliably evaluate the necessary cross-layer design solutions with configurable op- timization space by leveraging the hardware experiments to represent the realistic effects of physical channel, topology, mobility, and jamming on spectrum agility, situational awareness, and network resiliency. We also provide the flexibility to scale up the test environment by introducing virtual radios and establishing seamless signal-level interactions with real radios. This holistic wireless evaluation approach supports a large-scale, het- erogeneous, and dynamic CR network architecture and allows developing cross-layer network protocols under high fidelity, repeatable, and scalable wireless test scenarios suitable for heterogeneous space, air, and ground networks.

Structural white matter differences underlying heterogeneous learning abilities after TBI.

PubMed

Chiou, Kathy S; Genova, Helen M; Chiaravalloti, Nancy D

2016-12-01

The existence of learning deficits after traumatic brain injury (TBI) is generally accepted; however, our understanding of the structural brain mechanisms underlying learning impairment after TBI is limited. Furthermore, our understanding of learning after TBI is often at risk for overgeneralization, as research often overlooks within sample heterogeneity in learning abilities. The present study examined differences in white matter integrity in a sample of adults with moderate to severe TBI who differed in learning abilities. Adults with moderate to severe TBI were grouped into learners and non-learners based upon achievement of the learning criterion of the open-trial Selective Reminding Test (SRT). Diffusion tensor imaging (DTI) was used to identify white matter differences between the learners and non-learners. Adults with TBI who were able to meet the learning criterion had greater white matter integrity (as indicated by higher fractional anisotropy [FA] values) in the right anterior thalamic radiation, forceps minor, inferior fronto-occipital fasciculus, and forceps minor than non-learners. The results of the study suggest that differences in white matter integrity may explain the observed heterogeneity in learning ability after moderate to severe TBI. This also supports emerging evidence for the involvement of the thalamus in higher order cognition, and the role of thalamo-cortical tracts in connecting functional networks associated with learning.

Mission Planning for Heterogeneous UxVs Operating in a Post-Disaster Urban Environment

DTIC Science & Technology

2017-09-01

FOR HETEROGENEOUS UxVs OPERATING IN A POST -DISASTER URBAN ENVIRONMENT by Choon Seng Leon Mark Tan September 2017 Thesis Advisor: Oleg...September 2017 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE MISSION PLANNING FOR HETEROGENEOUS UxVs OPERATING IN A POST ...UxVs OPERATING IN A POST -DISASTER URBAN ENVIRONMENT Choon Seng Leon Mark Tan Civilian Engineer, ST Aerospace Ltd., Singapore B. Eng (Hons

From inter-specific behavioural interactions to species distribution patterns along gradients of habitat heterogeneity.

PubMed

Laiolo, Paola

2013-01-01

The strength of the behavioural processes associated with competitor coexistence may vary when different physical environments, and their biotic communities, come into contact, although empirical evidence of how interference varies across gradients of environmental complexity is still scarce in vertebrates. Here, I analyse how behavioural interactions and habitat selection regulate the local distribution of steppeland larks (Alaudidae) in a gradient from simple to heterogeneous agricultural landscapes in Spain, using crested lark Galerida cristata and Thekla lark G. theklae as study models. Galerida larks significantly partitioned by habitat but frequently co-occurred in heterogeneous environments. Irrespective of habitat divergence, however, the local densities of the two larks were negatively correlated, and the mechanisms beyond this pattern were investigated by means of playback experiments. When simulating the intrusion of the congener by broadcasting the species territorial calls, both larks responded with an aggressive response as intense with respect to warning and approach behaviour as when responding to the intrusion of a conspecific. However, birds promptly responded to playbacks only when congener territories were nearby, a phenomenon that points to learning as the mechanisms through which individuals finely tune their aggressive responses to the local competition levels. Heterospecifics occurred in closer proximity in diverse agro-ecosystems, possibly because of more abundant or diverse resources, and here engage in antagonistic interactions. The drop of species diversity associated with agricultural homogenisation is therefore likely to also bring about the disappearance of the behavioural repertoires associated with species interactions.

Effects of the duration and inorganic nitrogen composition of a nutrient-rich patch on soil exploration by the roots of Lolium perenne in a heterogeneous environment.

PubMed

Nakamura, Ryoji; Kachi, N; Suzuki, J-I

2010-05-01

We investigated the growth of and soil exploration by Lolium perenne under a heterogeneous environment before its roots reached a nutrient-rich patch. Temporal changes in the distribution of inorganic nitrogen, i.e., NO(3)(-)-N and NH(4)(+)-N, in the heterogeneous environment during the experimental period were also examined. The results showed that roots randomly explored soil, irrespective of the patchy distribution of inorganic nitrogen and differences in the chemical composition of inorganic nitrogen distribution between heterogeneous and homogeneous environments. We have also elucidated the potential effects of patch duration and inorganic nitrogen distribution on soil exploration by roots and thus on plant growth.

Group Composition of Cooperative Learning: Does Heterogeneous Grouping Work in Asian Classrooms?

ERIC Educational Resources Information Center

Thanh, Pham Thi Hong; Gillies, Robyn

2010-01-01

Constructing an appropriate group is important to teamwork success. Although, heterogeneous grouping is widely recommended in Western countries, this method of grouping is questioned in Asian classrooms because Asian and Western students have different cultures of learning. Unfortunately, this issue has not been addressed in any research to date.…

When high achievers and low achievers work in the same group: the roles of group heterogeneity and processes in project-based learning.

PubMed

Cheng, Rebecca Wing-yi; Lam, Shui-fong; Chan, Joanne Chung-yan

2008-06-01

There has been an ongoing debate about the inconsistent effects of heterogeneous ability grouping on students in small group work such as project-based learning. The present research investigated the roles of group heterogeneity and processes in project-based learning. At the student level, we examined the interaction effect between students' within-group achievement and group processes on their self- and collective efficacy. At the group level, we examined how group heterogeneity was associated with the average self- and collective efficacy reported by the groups. The participants were 1,921 Hong Kong secondary students in 367 project-based learning groups. Student achievement was determined by school examination marks. Group processes, self-efficacy and collective efficacy were measured by a student-report questionnaire. Hierarchical linear modelling was used to analyse the nested data. When individual students in each group were taken as the unit of analysis, results indicated an interaction effect of group processes and students' within-group achievement on the discrepancy between collective- and self-efficacy. When compared with low achievers, high achievers reported lower collective efficacy than self-efficacy when group processes were of low quality. However, both low and high achievers reported higher collective efficacy than self-efficacy when group processes were of high quality. With 367 groups taken as the unit of analysis, the results showed that group heterogeneity, group gender composition and group size were not related to the discrepancy between collective- and self-efficacy reported by the students. Group heterogeneity was not a determinant factor in students' learning efficacy. Instead, the quality of group processes played a pivotal role because both high and low achievers were able to benefit when group processes were of high quality.

What do students actually do on an internal medicine clerkship? A log diary study.

PubMed

Murray, E; Alderman, P; Coppola, W; Grol, R; Bouhuijs, P; van der Vleuten, C

2001-12-01

There are limited data on the amount of time students spend on teaching and learning while on internal medicine clerkships, and existing data suggest a wide international variation. Community-based teaching of internal medicine is now widespread; but its strengths and weaknesses compared to traditional hospital based teaching are still unclear. To determine the proportion of time students spend on different activities on an internal medicine clerkship, and to determine whether this differs in general practice and in hospital. In addition we aimed to determine students' views on the educational value and enjoyment of various activities. Prospective completion of log diaries recording student activities. Each student was asked to complete the diary for two separate weeks of their internal medicine clerkship: one week of general practice-based teaching and one week of hospital-based teaching. The response rate was 68% (88/130). Students spent approximately 5.5 h per day on teaching and learning activities in both environments, with more time (50 min vs. 30 min, P = 0.007) on unsupervised interaction with patients in hospital than in general practice, and more time (53 min vs. 21 min, P < 0.001) undergoingassessment in general practice than in hospital. Standard deviations were wide, demonstrating the heterogeneous nature of the data. Students perceived supervised interaction with patients and teaching by doctors as the most educational activities in both environments, but found it even more educationally valuable and enjoyable in general practice than in hospital (mean score for educational value: 4.27 in general practice, 3.88 in hospital, P = 0.048; mean score for enjoyment 4.13 in general practice, 3.66 in hospital, P = 0.03). Students greatly value interactions with patients, perceiving these as both educational and enjoyable. Curriculum planners must continue to place patient-based learning at the centre of undergraduate medical education. The heterogeneity of the data suggests that individual students have very different experiences, despite apparently similar timetables.

Heterogeneous Systems for Information-Variable Environments (HIVE)

DTIC Science & Technology

2017-05-01

ARL-TR-8027 ● May 2017 US Army Research Laboratory Heterogeneous Systems for Information - Variable Environments (HIVE) by Amar...not return it to the originator. ARL-TR-8027 ● May 2017 US Army Research Laboratory Heterogeneous Systems for Information ...Computational and Information Sciences Directorate, ARL Approved for public release; distribution is unlimited. ii REPORT

Heterogeneity in Health Care Computing Environments

PubMed Central

Sengupta, Soumitra

1989-01-01

This paper discusses issues of heterogeneity in computer systems, networks, databases, and presentation techniques, and the problems it creates in developing integrated medical information systems. The need for institutional, comprehensive goals are emphasized. Using the Columbia-Presbyterian Medical Center's computing environment as the case study, various steps to solve the heterogeneity problem are presented.

Heterogeneous variances in multi-environment yield trials for corn hybrids

USDA-ARS?s Scientific Manuscript database

Recent developments in statistics and computing have enabled much greater levels of complexity in statistical models of multi-environment yield trial data. One particular feature of interest to breeders is simultaneously modeling heterogeneity of variances among environments and cultivars. Our obj...

Arcade: A Web-Java Based Framework for Distributed Computing

NASA Technical Reports Server (NTRS)

Chen, Zhikai; Maly, Kurt; Mehrotra, Piyush; Zubair, Mohammad; Bushnell, Dennis M. (Technical Monitor)

2000-01-01

Distributed heterogeneous environments are being increasingly used to execute a variety of large size simulations and computational problems. We are developing Arcade, a web-based environment to design, execute, monitor, and control distributed applications. These targeted applications consist of independent heterogeneous modules which can be executed on a distributed heterogeneous environment. In this paper we describe the overall design of the system and discuss the prototype implementation of the core functionalities required to support such a framework.

Meta-Analysis of Fluid Intelligence Tests of Children from the Chinese Mainland with Learning Difficulties

PubMed Central

Tong, Fang; Fu, Tong

2013-01-01

Objective To evaluate the differences in fluid intelligence tests between normal children and children with learning difficulties in China. Method PubMed, MD Consult, and other Chinese Journal Database were searched from their establishment to November 2012. After finding comparative studies of Raven measurements of normal children and children with learning difficulties, full Intelligent Quotation (FIQ) values and the original values of the sub-measurement were extracted. The corresponding effect model was selected based on the results of heterogeneity and parallel sub-group analysis was performed. Results Twelve documents were included in the meta-analysis, and the studies were all performed in mainland of China. Among these, two studies were performed at child health clinics, the other ten sites were schools and control children were schoolmates or classmates. FIQ was evaluated using a random effects model. WMD was −13.18 (95% CI: −16.50–−9.85). Children with learning difficulties showed significantly lower FIQ scores than controls (P<0.00001); Type of learning difficulty and gender differences were evaluated using a fixed-effects model (I2 = 0%). The sites and purposes of the studies evaluated here were taken into account, but the reasons of heterogeneity could not be eliminated; The sum IQ of all the subgroups showed considerable heterogeneity (I2 = 76.5%). The sub-measurement score of document A showed moderate heterogeneity among all documents, and AB, B, and E showed considerable heterogeneity, which was used in a random effect model. Individuals with learning difficulties showed heterogeneity as well. There was a moderate delay in the first three items (−0.5 to −0.9), and a much more pronounced delay in the latter three items (−1.4 to −1.6). Conclusion In the Chinese mainland, the level of fluid intelligence of children with learning difficulties was lower than that of normal children. Delayed development in sub-items of C, D, and E was more obvious. PMID:24236016

"The Coat Traps All Your Body Heat": Heterogeneity as Fundamental to Learning

ERIC Educational Resources Information Center

Rosebery, Ann S.; Ogonowski, Mark; DiSchino, Mary; Warren, Beth

2010-01-01

This article explores heterogeneity as fundamental to learning. Inspired by Bakhtin's notion of heteroglossia, a design team consisting of an experienced classroom teacher and 2 researchers investigated how a class of 3rd and 4th graders came to understand disciplinary points of view on heat, heat transfer, and the particulate nature of matter.…

Cooperative Learning: Homogeneous and Heterogeneous Grouping of Iranian EFL Learners in a Writing Context

ERIC Educational Resources Information Center

Zamani, Mona

2016-01-01

One of the important aspects of learning and teaching through cooperation is the group composition or grouping "who with whom". An unresolved issue is that of the superiority of heterogeneity or homogeneity in the structure of the groups. The present study was an attempt to investigate the impact that homogeneous and heterogeneous…

Diversity of devices along with diversity of data formats as a new challenge in global teaching and learning system

NASA Astrophysics Data System (ADS)

Sultana, Razia; Christ, Andreas; Meyrueis, Patrick

2014-07-01

The popularity of mobile communication devices is increasing day by day among students, especially for e-learning activities. "Always-ready-to-use" feature of mobile devices is a key motivation for students to use it even in a short break for a short time. This leads to new requirements regarding learning content presentation, user interfaces, and system architecture for heterogeneous devices. To support diverse devices is not enough to establish global teaching and learning system, it is equally important to support various formats of data along with different sort of devices having different capabilities in terms of processing power, display size, supported data formats, operating system, access method of data etc. Not only the existing data formats but also upcoming data formats, such as due to research results in the area of optics and photonics, virtual reality etc should be considered. This paper discusses the importance, risk and challenges of supporting heterogeneous devices to provide heterogeneous data as a learning content to make global teaching and learning system literally come true at anytime and anywhere. We proposed and implemented a sustainable architecture to support device and data format independent learning system.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

PubMed Central

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization. PMID:28786986

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PubMed

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Learning Building Layouts with Non-geometric Visual Information: The Effects of Visual Impairment and Age

PubMed Central

Kalia, Amy A.; Legge, Gordon E.; Giudice, Nicholas A.

2009-01-01

Previous studies suggest that humans rely on geometric visual information (hallway structure) rather than non-geometric visual information (e.g., doors, signs and lighting) for acquiring cognitive maps of novel indoor layouts. This study asked whether visual impairment and age affect reliance on non-geometric visual information for layout learning. We tested three groups of participants—younger (< 50 years) normally sighted, older (50–70 years) normally sighted, and low vision (people with heterogeneous forms of visual impairment ranging in age from 18–67). Participants learned target locations in building layouts using four presentation modes: a desktop virtual environment (VE) displaying only geometric cues (Sparse VE), a VE displaying both geometric and non-geometric cues (Photorealistic VE), a Map, and a Real building. Layout knowledge was assessed by map drawing and by asking participants to walk to specified targets in the real space. Results indicate that low-vision and older normally-sighted participants relied on additional non-geometric information to accurately learn layouts. In conclusion, visual impairment and age may result in reduced perceptual and/or memory processing that makes it difficult to learn layouts without non-geometric visual information. PMID:19189732

Interoperability of GADU in using heterogeneous Grid resources for bioinformatics applications.

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

Sulakhe, D.; Rodriguez, A.; Wilde, M.

2008-03-01

Bioinformatics tools used for efficient and computationally intensive analysis of genetic sequences require large-scale computational resources to accommodate the growing data. Grid computational resources such as the Open Science Grid and TeraGrid have proved useful for scientific discovery. The genome analysis and database update system (GADU) is a high-throughput computational system developed to automate the steps involved in accessing the Grid resources for running bioinformatics applications. This paper describes the requirements for building an automated scalable system such as GADU that can run jobs on different Grids. The paper describes the resource-independent configuration of GADU using the Pegasus-based virtual datamore » system that makes high-throughput computational tools interoperable on heterogeneous Grid resources. The paper also highlights the features implemented to make GADU a gateway to computationally intensive bioinformatics applications on the Grid. The paper will not go into the details of problems involved or the lessons learned in using individual Grid resources as it has already been published in our paper on genome analysis research environment (GNARE) and will focus primarily on the architecture that makes GADU resource independent and interoperable across heterogeneous Grid resources.« less

Associations between Academic and Motor Performance in a Heterogeneous Sample of Children with Learning Disabilities

ERIC Educational Resources Information Center

Vuijk, Pieter Jelle; Hartman, Esther; Mombarg, Remo; Scherder, Erik; Visscher, Chris

2011-01-01

A heterogeneous sample of 137 school-aged children with learning disabilities (IQ greater than 80) attending special needs schools was examined on the "Movement Assessment Battery for Children" (MABC). The results show that compared to the available norm scores, 52.6% of the children tested performed below the 15th percentile on manual…

Speciation reversal and biodiversity dynamics with hybridization in changing environments.

PubMed

Seehausen, Ole; Takimoto, Gaku; Roy, Denis; Jokela, Jukka

2008-01-01

A considerable fraction of the world's biodiversity is of recent evolutionary origin and has evolved as a by-product of, and is maintained by, divergent adaptation in heterogeneous environments. Conservationists have paid attention to genetic homogenization caused by human-induced translocations (e.g. biological invasions and stocking), and to the importance of environmental heterogeneity for the ecological coexistence of species. However, far less attention has been paid to the consequences of loss of environmental heterogeneity to the genetic coexistence of sympatric species. Our review of empirical observations and our theoretical considerations on the causes and consequences of interspecific hybridization suggest that a loss of environmental heterogeneity causes a loss of biodiversity through increased genetic admixture, effectively reversing speciation. Loss of heterogeneity relaxes divergent selection and removes ecological barriers to gene flow between divergently adapted species, promoting interspecific introgressive hybridization. Since heterogeneity of natural environments is rapidly deteriorating in most biomes, the evolutionary ecology of speciation reversal ought to be fully integrated into conservation biology.

Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memory

PubMed Central

Uematsu, Akira; Tan, Bao Zhen

2015-01-01

Noradrenergic neurons in the locus coeruleus (LC) play a critical role in many functions including learning and memory. This relatively small population of cells sends widespread projections throughout the brain including to a number of regions such as the amygdala which is involved in emotional associative learning and the medial prefrontal cortex which is important for facilitating flexibility when learning rules change. LC noradrenergic cells participate in both of these functions, but it is not clear how this small population of neurons modulates these partially distinct processes. Here we review anatomical, behavioral, and electrophysiological studies to assess how LC noradrenergic neurons regulate these different aspects of learning and memory. Previous work has demonstrated that subpopulations of LC noradrenergic cells innervate specific brain regions suggesting heterogeneity of function in LC neurons. Furthermore, noradrenaline in mPFC and amygdala has distinct effects on emotional learning and cognitive flexibility. Finally, neural recording data show that LC neurons respond during associative learning and when previously learned task contingencies change. Together, these studies suggest a working model in which distinct and potentially opposing subsets of LC neurons modulate particular learning functions through restricted efferent connectivity with amygdala or mPFC. This type of model may provide a general framework for understanding other neuromodulatory systems, which also exhibit cell type heterogeneity and projection specificity. PMID:26330494

Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound.

PubMed

Sheet, Debdoot; Karamalis, Athanasios; Eslami, Abouzar; Noël, Peter; Chatterjee, Jyotirmoy; Ray, Ajoy K; Laine, Andrew F; Carlier, Stephane G; Navab, Nassir; Katouzian, Amin

2014-01-01

Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications. Each of these tissues backscatter ultrasonic pulses and are associated with a characteristic intensity in B-mode IVUS image. However, clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode IVUS image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess plaque vulnerability. In this paper, we propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated. Folded cross-validation experiments have been performed with 53 plaques indicating high concurrence with traditional tissue histology. On the wider horizon, this framework enables learning of tissue-energy interaction statistical physics and can be leveraged for promising clinical applications requiring tissue characterization beyond the application demonstrated in this paper. Copyright © 2013 Elsevier B.V. All rights reserved.

A Modular Environment for Geophysical Inversion and Run-time Autotuning using Heterogeneous Computing Systems

NASA Astrophysics Data System (ADS)

Myre, Joseph M.

Heterogeneous computing systems have recently come to the forefront of the High-Performance Computing (HPC) community's interest. HPC computer systems that incorporate special purpose accelerators, such as Graphics Processing Units (GPUs), are said to be heterogeneous. Large scale heterogeneous computing systems have consistently ranked highly on the Top500 list since the beginning of the heterogeneous computing trend. By using heterogeneous computing systems that consist of both general purpose processors and special- purpose accelerators, the speed and problem size of many simulations could be dramatically increased. Ultimately this results in enhanced simulation capabilities that allows, in some cases for the first time, the execution of parameter space and uncertainty analyses, model optimizations, and other inverse modeling techniques that are critical for scientific discovery and engineering analysis. However, simplifying the usage and optimization of codes for heterogeneous computing systems remains a challenge. This is particularly true for scientists and engineers for whom understanding HPC architectures and undertaking performance analysis may not be primary research objectives. To enable scientists and engineers to remain focused on their primary research objectives, a modular environment for geophysical inversion and run-time autotuning on heterogeneous computing systems is presented. This environment is composed of three major components: 1) CUSH---a framework for reducing the complexity of programming heterogeneous computer systems, 2) geophysical inversion routines which can be used to characterize physical systems, and 3) run-time autotuning routines designed to determine configurations of heterogeneous computing systems in an attempt to maximize the performance of scientific and engineering codes. Using three case studies, a lattice-Boltzmann method, a non-negative least squares inversion, and a finite-difference fluid flow method, it is shown that this environment provides scientists and engineers with means to reduce the programmatic complexity of their applications, to perform geophysical inversions for characterizing physical systems, and to determine high-performing run-time configurations of heterogeneous computing systems using a run-time autotuner.

Field Studies—Essential Cognitive Foundations for Geoscience Expertise

NASA Astrophysics Data System (ADS)

Goodwin, C.; Mogk, D. W.

2010-12-01

Learning in the field has traditionally been one of the fundamental components of the geoscience curriculum. Field experiences have been attributed to having positive impacts on cognitive, affective, metacognitive, mastery of skills and social components of learning geoscience. The development of geoscience thinking, and of geoscience expertise, encompasses a number of learned behaviors that contribute to the progress of Science and the development of scientists. By getting out into Nature, students necessarily engage active and experiential learning. The open, dynamic, heterogeneous and complex Earth system provides ample opportunities to learn by inquiry and discovery. Learning in this environment requires that students make informed decisions and to think critically about what is important to observe, and what should be excluded in the complex overload of information provided by Nature. Students must learn to employ the full range of cognitive skills that include observation, description, interpretation, analysis and synthesis that lead to “deep learning”. They must be able to integrate and rationalize observations of Nature with modern experimental, analytical, theoretical, and modeling approaches to studying the Earth system, and they must be able to iterate between what is known and what is yet to be discovered. Immersion in the field setting provides students with a sense of spatial and temporal scales of natural phenomena that can not be derived in other learning environments. The field setting provides strong sensory inputs that stimulate cognition and memories that will be available for future application. The field environment also stimulates strong affective responses related to motivation, curiosity, a sense of “ownership” of field projects, and inclusion in shared experiences that carry on throughout professional careers. The nature of field work also contains a strong metacognitive component, as students learn to be aware of what and how they are learning in the field, regulate and modify their activities, and plan for future work.Embodied practice in the field shows students how to explore and interrogate nature, and how to interact and learn from other scientists. Learning geoscience is a social enterprise, requiring a long apprenticeship through which newcomers learn about Nature by working with competent senior practitioners in the settings where relevant nature is systematically studied. Learned social practices include the ability to enhance understanding of natural phenomena by constructing appropriate representations (inscriptions), knowing how to select and use appropriate tools, engaging the accepted community of practice, adopting professional standards and values, and the ability to contribute to geoscience discourse about the complex world. Both tools and the ability to locate perspicuous sites in the environment must be mastered so that representations can be made of structures in the landscape that cannot actually be seen from any single point of view to obtain a holistic and integrated interpretation of Earth history and processes. Sustained development of these cognitive strategies and skills is essential to the professional development of all geoscientists.

Immersive communication intervention for speaking and non-speaking children with intellectual disabilities.

PubMed

van der Schuit, Margje; Segers, Eliane; van Balkom, Hans; Stoep, Judith; Verhoeven, Ludo

2010-09-01

The current study demonstrates the effectiveness of an intervention that addresses both home care and day care for children with intellectual disabilities while also taking the large individual differences between the children into account. The KLINc Studio intervention was designed to improve the language development, communication skills, and emergent literacy of 10 children with complex communication needs. The focus of the anchor-based intervention program was on the stimulation of vocabulary learning via the incorporation of AAC into the learning environment in the most natural manner possible. While all of the children showed significant progress across the intervention period of 2 years, the group of speaking children showed greater development in the domains of receptive language and productive syntax than the group of non-speaking children. For heterogeneous groups of children with disabilities, the use of a combined intervention such as that described here appears to be promising.

Spatial heterogeneity lowers rather than increases host-parasite specialization.

PubMed

Hesse, E; Best, A; Boots, M; Hall, A R; Buckling, A

2015-09-01

Abiotic environmental heterogeneity can promote the evolution of diverse resource specialists, which in turn may increase the degree of host-parasite specialization. We coevolved Pseudomonas fluorescens and lytic phage ϕ2 in spatially structured populations, each consisting of two interconnected subpopulations evolving in the same or different nutrient media (homogeneous and heterogeneous environments, respectively). Counter to the normal expectation, host-parasite specialization was significantly lower in heterogeneous compared with homogeneous environments. This result could not be explained by dispersal homogenizing populations, as this would have resulted in the heterogeneous treatments having levels of specialization equal to or greater than that of the homogeneous environments. We argue that selection for costly generalists is greatest when the coevolving species are exposed to diverse environmental conditions and that this can provide an explanation for our results. A simple coevolutionary model of this process suggests that this can be a general mechanism by which environmental heterogeneity can reduce rather than increase host-parasite specialization. © 2015 The Authors. J. EVOL. BIOL. Journal of Evolutionary Biology Published by John Wiley & Sons Ltd on Behalf of European Society for Evolutionary Biology.

Graph Partitioning for Parallel Applications in Heterogeneous Grid Environments

NASA Technical Reports Server (NTRS)

Bisws, Rupak; Kumar, Shailendra; Das, Sajal K.; Biegel, Bryan (Technical Monitor)

2002-01-01

The problem of partitioning irregular graphs and meshes for parallel computations on homogeneous systems has been extensively studied. However, these partitioning schemes fail when the target system architecture exhibits heterogeneity in resource characteristics. With the emergence of technologies such as the Grid, it is imperative to study the partitioning problem taking into consideration the differing capabilities of such distributed heterogeneous systems. In our model, the heterogeneous system consists of processors with varying processing power and an underlying non-uniform communication network. We present in this paper a novel multilevel partitioning scheme for irregular graphs and meshes, that takes into account issues pertinent to Grid computing environments. Our partitioning algorithm, called MiniMax, generates and maps partitions onto a heterogeneous system with the objective of minimizing the maximum execution time of the parallel distributed application. For experimental performance study, we have considered both a realistic mesh problem from NASA as well as synthetic workloads. Simulation results demonstrate that MiniMax generates high quality partitions for various classes of applications targeted for parallel execution in a distributed heterogeneous environment.

Heterogeneous environments shape invader impacts: integrating environmental, structural and functional effects by isoscapes and remote sensing.

PubMed

Hellmann, Christine; Große-Stoltenberg, André; Thiele, Jan; Oldeland, Jens; Werner, Christiane

2017-06-23

Spatial heterogeneity of ecosystems crucially influences plant performance, while in return plant feedbacks on their environment may increase heterogeneous patterns. This is of particular relevance for exotic plant invaders that transform native ecosystems, yet, approaches integrating geospatial information of environmental heterogeneity and plant-plant interaction are lacking. Here, we combined remotely sensed information of site topography and vegetation cover with a functional tracer of the N cycle, δ 15 N. Based on the case study of the invasion of an N 2 -fixing acacia in a nutrient-poor dune ecosystem, we present the first model that can successfully predict (R 2  = 0.6) small-scale spatial variation of foliar δ 15 N in a non-fixing native species from observed geospatial data. Thereby, the generalized additive mixed model revealed modulating effects of heterogeneous environments on invader impacts. Hence, linking remote sensing techniques with tracers of biological processes will advance our understanding of the dynamics and functioning of spatially structured heterogeneous systems from small to large spatial scales.

How does informational heterogeneity affect the quality of forecasts?

NASA Astrophysics Data System (ADS)

Gualdi, S.; De Martino, A.

2010-01-01

We investigate a toy model of inductive interacting agents aiming to forecast a continuous, exogenous random variable E. Private information on E is spread heterogeneously across agents. Herding turns out to be the preferred forecasting mechanism when heterogeneity is maximal. However in such conditions aggregating information efficiently is hard even in the presence of learning, as the herding ratio rises significantly above the efficient market expectation of 1 and remarkably close to the empirically observed values. We also study how different parameters (interaction range, learning rate, cost of information and score memory) may affect this scenario and improve efficiency in the hard phase.

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

PubMed

Dinov, Ivo D; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W; Price, Nathan D; Van Horn, John D; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M; Dauer, William; Toga, Arthur W

2016-01-01

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson's disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson's disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer's, Huntington's, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Brief Report: Simulations Suggest Heterogeneous Category Learning and Generalization in Children with Autism is a Result of Idiosyncratic Perceptual Transformations.

PubMed

Mercado, Eduardo; Church, Barbara A

2016-08-01

Children with autism spectrum disorder (ASD) sometimes have difficulties learning categories. Past computational work suggests that such deficits may result from atypical representations in cortical maps. Here we use neural networks to show that idiosyncratic transformations of inputs can result in the formation of feature maps that impair category learning for some inputs, but not for other closely related inputs. These simulations suggest that large inter- and intra-individual variations in learning capacities shown by children with ASD across similar categorization tasks may similarly result from idiosyncratic perceptual encoding that is resistant to experience-dependent changes. If so, then both feedback- and exposure-based category learning should lead to heterogeneous, stimulus-dependent deficits in children with ASD.

Rethinking the evolution of specialization: A model for the evolution of phenotypic heterogeneity.

PubMed

Rubin, Ilan N; Doebeli, Michael

2017-12-21

Phenotypic heterogeneity refers to genetically identical individuals that express different phenotypes, even when in the same environment. Traditionally, "bet-hedging" in fluctuating environments is offered as the explanation for the evolution of phenotypic heterogeneity. However, there are an increasing number of examples of microbial populations that display phenotypic heterogeneity in stable environments. Here we present an evolutionary model of phenotypic heterogeneity of microbial metabolism and a resultant theory for the evolution of phenotypic versus genetic specialization. We use two-dimensional adaptive dynamics to track the evolution of the population phenotype distribution of the expression of two metabolic processes with a concave trade-off. Rather than assume a Gaussian phenotype distribution, we use a Beta distribution that is capable of describing genotypes that manifest as individuals with two distinct phenotypes. Doing so, we find that environmental variation is not a necessary condition for the evolution of phenotypic heterogeneity, which can evolve as a form of specialization in a stable environment. There are two competing pressures driving the evolution of specialization: directional selection toward the evolution of phenotypic heterogeneity and disruptive selection toward genetically determined specialists. Because of the lack of a singular point in the two-dimensional adaptive dynamics and the fact that directional selection is a first order process, while disruptive selection is of second order, the evolution of phenotypic heterogeneity dominates and often precludes speciation. We find that branching, and therefore genetic specialization, occurs mainly under two conditions: the presence of a cost to maintaining a high phenotypic variance or when the effect of mutations is large. A cost to high phenotypic variance dampens the strength of selection toward phenotypic heterogeneity and, when sufficiently large, introduces a singular point into the evolutionary dynamics, effectively guaranteeing eventual branching. Large mutations allow the second order disruptive selection to dominate the first order selection toward phenotypic heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Population dynamics on heterogeneous bacterial substrates

NASA Astrophysics Data System (ADS)

Mobius, Wolfram; Murray, Andrew W.; Nelson, David R.

2012-02-01

How species invade new territories and how these range expansions influence the population's genotypes are important questions in the field of population genetics. The majority of work addressing these questions focuses on homogeneous environments. Much less is known about the population dynamics and population genetics when the environmental conditions are heterogeneous in space. To better understand range expansions in two-dimensional heterogeneous environments, we employ a system of bacteria and bacteriophage, the viruses of bacteria. Thereby, the bacteria constitute the environment in which a population of bacteriophages expands. The spread of phage constitutes itself in lysis of bacteria and thus formation of clear regions on bacterial lawns, called plaques. We study the population dynamics and genetics of the expanding page for various patterns of environments.

Effects of Algal Diversity on the Production of Biomass in Homogeneous and Heterogeneous Nutrient Environments: A Microcosm Experiment

PubMed Central

Weis, Jerome J.; Madrigal, Daniel S.; Cardinale, Bradley J.

2008-01-01

Background One of the most common questions addressed by ecologists over the past decade has been-how does species richness impact the production of community biomass? Recent summaries of experiments have shown that species richness tends to enhance the production of biomass across a wide range of trophic groups and ecosystems; however, the biomass of diverse polycultures only rarely exceeds that of the single most productive species in a community (a phenomenon called ‘transgressive overyielding’). Some have hypothesized that the lack of transgressive overyielding is because experiments have generally been performed in overly-simplified, homogeneous environments where species have little opportunity to express the niche differences that lead to ‘complementary’ use of resources that can enhance biomass production. We tested this hypothesis in a laboratory experiment where we manipulated the richness of freshwater algae in homogeneous and heterogeneous nutrient environments. Methodology/Principal Findings Experimental units were comprised of patches containing either homogeneous nutrient ratios (16∶1 nitrogen to phosphorus (N∶P) in all patches) or heterogeneous nutrient ratios (ranging from 4∶1 to 64∶1 N∶P across patches). After allowing 6–10 generations of algal growth, we found that algal species richness had similar impacts on biomass production in both homo- and heterogeneous environments. Although four of the five algal species showed a strong response to nutrient heterogeneity, a single species dominated algal communities in both types of environments. As a result, a ‘selection effect’–where diversity maximizes the chance that a competitively superior species will be included in, and dominate the biomass of a community–was the primary mechanism by which richness influenced biomass in both homo- and heterogeneous environments. Conclusions/Significance Our study suggests that spatial heterogeneity, by itself, is not sufficient to generate strong effects of biodiversity on productivity. Rather, heterogeneity must be coupled with variation in the relative fitness of species across patches in order for spatial niche differentiation to generate complementary resource use. PMID:18665221

The ontogeny of the homing pigeon navigational map: evidence for a sensitive learning period.

PubMed Central

Gagliardo, A; Ioalè, P; Odetti, F; Bingman, V P

2001-01-01

Homing pigeons can learn a navigational map by relying on the heterogeneous distribution of atmospheric odours in the environment. To test whether there might be a sensitive period for learning an olfactory-based navigational map, we maintained a group of young pigeons in an aviary screened from the winds until the age of three to four months post-fledging. Subsequently, the screens were removed and the pigeons were exposed to the winds and the environmental odours they carry for three months. One control group of pigeons was held in a similar aviary but exposed to the winds immediately upon Hedging, while another control group of pigeons was allowed free-flight. When the pigeons from the three groups were released from two distant release sites at about six months of age post-fledging, the two control groups were found to be equally good at orientating and returning home, while the experimental pigeons held in the shielded aviary for the first three months post-fledging were unable to orientate homeward and they were generally unsuccessful in returning home. This result supports the hypothesis that environmental experience during the first three months post-fledging is critical for some aspect of navigational map learning and that navigational map learning displays sensitive period-like properties. PMID:11209891

The ontogeny of the homing pigeon navigational map: evidence for a sensitive learning period.

PubMed

Gagliardo, A; Ioalè, P; Odetti, F; Bingman, V P

2001-01-22

Homing pigeons can learn a navigational map by relying on the heterogeneous distribution of atmospheric odours in the environment. To test whether there might be a sensitive period for learning an olfactory-based navigational map, we maintained a group of young pigeons in an aviary screened from the winds until the age of three to four months post-fledging. Subsequently, the screens were removed and the pigeons were exposed to the winds and the environmental odours they carry for three months. One control group of pigeons was held in a similar aviary but exposed to the winds immediately upon Hedging, while another control group of pigeons was allowed free-flight. When the pigeons from the three groups were released from two distant release sites at about six months of age post-fledging, the two control groups were found to be equally good at orientating and returning home, while the experimental pigeons held in the shielded aviary for the first three months post-fledging were unable to orientate homeward and they were generally unsuccessful in returning home. This result supports the hypothesis that environmental experience during the first three months post-fledging is critical for some aspect of navigational map learning and that navigational map learning displays sensitive period-like properties.

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices

PubMed Central

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B.

2018-01-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support. PMID:29629431

RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices.

PubMed

Alzantot, Moustafa; Wang, Yingnan; Ren, Zhengshuang; Srivastava, Mani B

2017-06-01

Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to act as intelligent assistants in many tasks rather than a mere way of making phone calls. However, popular and commonly used tools and frameworks for machine intelligence are still lacking the ability to make proper use of the available heterogeneous computing resources on mobile devices. In this paper, we study the benefits of utilizing the heterogeneous (CPU and GPU) computing resources available on commodity android devices while running deep learning models. We leveraged the heterogeneous computing framework RenderScript to accelerate the execution of deep learning models on commodity Android devices. Our system is implemented as an extension to the popular open-source framework TensorFlow. By integrating our acceleration framework tightly into TensorFlow, machine learning engineers can now easily make benefit of the heterogeneous computing resources on mobile devices without the need of any extra tools. We evaluate our system on different android phones models to study the trade-offs of running different neural network operations on the GPU. We also compare the performance of running different models architectures such as convolutional and recurrent neural networks on CPU only vs using heterogeneous computing resources. Our result shows that although GPUs on the phones are capable of offering substantial performance gain in matrix multiplication on mobile devices. Therefore, models that involve multiplication of large matrices can run much faster (approx. 3 times faster in our experiments) due to GPU support.

Covariation of learning and "reasoning" abilities in mice: evolutionary conservation of the operations of intelligence.

PubMed

Wass, Christopher; Denman-Brice, Alexander; Rios, Chris; Light, Kenneth R; Kolata, Stefan; Smith, Andrew M; Matzel, Louis D

2012-04-01

Contemporary descriptions of human intelligence hold that this trait influences a broad range of cognitive abilities, including learning, attention, and reasoning. Like humans, individual genetically heterogeneous mice express a "general" cognitive trait that influences performance across a diverse array of learning and attentional tasks, and it has been suggested that this trait is qualitatively and structurally analogous to general intelligence in humans. However, the hallmark of human intelligence is the ability to use various forms of "reasoning" to support solutions to novel problems. Here, we find that genetically heterogeneous mice are capable of solving problems that are nominally indicative of inductive and deductive forms of reasoning, and that individuals' capacity for reasoning covaries with more general learning abilities. Mice were characterized for their general learning ability as determined by their aggregate performance (derived from principal component analysis) across a battery of five diverse learning tasks. These animals were then assessed on prototypic tests indicative of deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping") and inductive reasoning (execution of an efficient search strategy in a binary decision tree). The animals exhibited systematic abilities on each of these nominal reasoning tasks that were predicted by their aggregate performance on the battery of learning tasks. These results suggest that the coregulation of reasoning and general learning performance in genetically heterogeneous mice form a core cognitive trait that is analogous to human intelligence. (c) 2012 APA, all rights reserved.

Agent-Based Models in Social Physics

NASA Astrophysics Data System (ADS)

Quang, Le Anh; Jung, Nam; Cho, Eun Sung; Choi, Jae Han; Lee, Jae Woo

2018-06-01

We review the agent-based models (ABM) on social physics including econophysics. The ABM consists of agent, system space, and external environment. The agent is autonomous and decides his/her behavior by interacting with the neighbors or the external environment with the rules of behavior. Agents are irrational because they have only limited information when they make decisions. They adapt using learning from past memories. Agents have various attributes and are heterogeneous. ABM is a non-equilibrium complex system that exhibits various emergence phenomena. The social complexity ABM describes human behavioral characteristics. In ABMs of econophysics, we introduce the Sugarscape model and the artificial market models. We review minority games and majority games in ABMs of game theory. Social flow ABM introduces crowding, evacuation, traffic congestion, and pedestrian dynamics. We also review ABM for opinion dynamics and voter model. We discuss features and advantages and disadvantages of Netlogo, Repast, Swarm, and Mason, which are representative platforms for implementing ABM.

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

NASA Astrophysics Data System (ADS)

Paradis, Daniel; Lefebvre, René; Gloaguen, Erwan; Rivera, Alfonso

2015-01-01

The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.

A "Simple Query Interface" Adapter for the Discovery and Exchange of Learning Resources

ERIC Educational Resources Information Center

Massart, David

2006-01-01

Developed as part of CEN/ISSS Workshop on Learning Technology efforts to improve interoperability between learning resource repositories, the Simple Query Interface (SQI) is an Application Program Interface (API) for querying heterogeneous repositories of learning resource metadata. In the context of the ProLearn Network of Excellence, SQI is used…

Grouped to Achieve: Are There Benefits to Assigning Students to Heterogeneous Cooperative Learning Groups Based on Pre-Test Scores?

NASA Astrophysics Data System (ADS)

Werth, Arman Karl

Cooperative learning has been one of the most widely used instructional practices around the world since the early 1980's. Small learning groups have been in existence since the beginning of the human race. These groups have grown in their variance and complexity overtime. Classrooms are getting more diverse every year and instructors need a way to take advantage of this diversity to improve learning. The purpose of this study was to see if heterogeneous cooperative learning groups based on student achievement can be used as a differentiated instructional strategy to increase students' ability to demonstrate knowledge of science concepts and ability to do engineering design. This study includes two different groups made up of two different middle school science classrooms of 25-30 students. These students were given an engineering design problem to solve within cooperative learning groups. One class was put into heterogeneous cooperative learning groups based on student's pre-test scores. The other class was grouped based on random assignment. The study measured the difference between each class's pre-post gains, student's responses to a group interaction form and interview questions addressing their perceptions of the makeup of their groups. The findings of the study were that there was no significant difference between learning gains for the treatment and comparison groups. There was a significant difference between the treatment and comparison groups in student perceptions of their group's ability to stay on task and manage their time efficiently. Both the comparison and treatment groups had a positive perception of the composition of their cooperative learning groups.

Individual differences in learning predict the return of fear.

PubMed

Gershman, Samuel J; Hartley, Catherine A

2015-09-01

Using a laboratory analogue of learned fear (Pavlovian fear conditioning), we show that there is substantial heterogeneity across individuals in spontaneous recovery of fear following extinction training. We propose that this heterogeneity might stem from qualitative individual differences in the nature of extinction learning. Whereas some individuals tend to form a new memory during extinction, leaving their fear memory intact, others update the original threat association with new safety information, effectively unlearning the fear memory. We formalize this account in a computational model of fear learning and show that individuals who, according to the model, are more likely to form new extinction memories tend to show greater spontaneous recovery compared to individuals who appear to only update a single memory. This qualitative variation in fear and extinction learning may have important implications for understanding vulnerability and resilience to fear-related psychiatric disorders.

Metacognitive components in smart learning environment

NASA Astrophysics Data System (ADS)

Sumadyo, M.; Santoso, H. B.; Sensuse, D. I.

2018-03-01

Metacognitive ability in digital-based learning process helps students in achieving learning goals. So that digital-based learning environment should make the metacognitive component as a facility that must be equipped. Smart Learning Environment is the concept of a learning environment that certainly has more advanced components than just a digital learning environment. This study examines the metacognitive component of the smart learning environment to support the learning process. A review of the metacognitive literature was conducted to examine the components involved in metacognitive learning strategies. Review is also conducted on the results of study smart learning environment, ranging from design to context in building smart learning. Metacognitive learning strategies certainly require the support of adaptable, responsive and personalize learning environments in accordance with the principles of smart learning. The current study proposed the role of metacognitive component in smart learning environment, which is useful as the basis of research in building environment in smart learning.

A Hybrid Density Functional Theory/Molecular Mechanics Approach for Linear Response Properties in Heterogeneous Environments.

PubMed

Rinkevicius, Zilvinas; Li, Xin; Sandberg, Jaime A R; Mikkelsen, Kurt V; Ågren, Hans

2014-03-11

We introduce a density functional theory/molecular mechanical approach for computation of linear response properties of molecules in heterogeneous environments, such as metal surfaces or nanoparticles embedded in solvents. The heterogeneous embedding environment, consisting from metallic and nonmetallic parts, is described by combined force fields, where conventional force fields are used for the nonmetallic part and capacitance-polarization-based force fields are used for the metallic part. The presented approach enables studies of properties and spectra of systems embedded in or placed at arbitrary shaped metallic surfaces, clusters, or nanoparticles. The capability and performance of the proposed approach is illustrated by sample calculations of optical absorption spectra of thymidine absorbed on gold surfaces in an aqueous environment, where we study how different organizations of the gold surface and how the combined, nonadditive effect of the two environments is reflected in the optical absorption spectrum.

Psychosocial Functioning of Learning-Disabled Children: Replicability of Statistically Derived Subtypes.

ERIC Educational Resources Information Center

Fuerst, Darren R.; And Others

1989-01-01

Investigated Personality Inventory for Children scores of 132 learning-disabled children between ages of 6 and 12 years. Results indicated that learning-disabled children comprised heterogeneous population in terms of psychosocial functioning and that subtypes of learning-disabled children with similar patterns of socioemotional adjustment can be…

A Learning Module for BA Students to Develop ICT Skills for Their Learning Activities

ERIC Educational Resources Information Center

Platteaux, Hervé; Hoein, Sergio

2015-01-01

This case illustrates the process of developing a learning module to support BA students in their use of ICT (Information and Communication Technology) tools in their learning. At the university where this case occurred, the skill level of ICT use among students in a learning context was very heterogeneous. The E-learning Competency Centre, or…

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

PubMed

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

Unifying Heterogeneous E-Learning Modalities in a Single Platform: CADI, a Case Study

ERIC Educational Resources Information Center

Cabrera-Lozoya, Andres; Cerdan, Fernando; Cano, Maria-Dolores; Garcia-Sanchez, Diego; Lujan, Sergio

2012-01-01

Current e-learning forms are commonly based on improving the learning process through the enhancement of certain skills in students, such as collaborative, competitive or problem-based learning. However, it seems that there is still no e-learning "formula" that gathers the implementation of a number of more generic educational principles in a…

The E-Learning Setting Circle: First Steps toward Theory Development in E-Learning Research

ERIC Educational Resources Information Center

Rüth, Marco; Kaspar, Kai

2017-01-01

E-learning projects and related research generate an increasing amount of evidence within and across various disciplines and contexts. The field is very heterogeneous as e-learning approaches are often characterized by rather unique combinations of situational factors that guide the design and realization of e-learning in a bottom-up fashion.…

Domain-Specific and Domain-General Learning Factors are Expressed in Genetically Heterogeneous CD-1 mice

PubMed Central

Kolata, Stefan; Light, Kenneth; Matzel, Louis D.

2008-01-01

It has been established that both domain-specific (e.g. spatial) as well as domain-general (general intelligence) factors influence human cognition. However, the separation of these processes has rarely been attempted in studies using laboratory animals. Previously, we have found that the performances of outbred mice across a wide range of learning tasks correlate in such a way that a single factor can explain 30– 44% of the variance between animals. This general learning factor is in some ways qualitatively and quantitatively analogous to general intelligence in humans. The complete structure of cognition in mice, however, has not been explored due to the limited sample sizes of our previous analyses. Here we report a combined analysis from 241 CD-1 mice tested in five primary learning tasks, and a subset of mice tested in two additional learning tasks. At least two (possibly three) of the seven learning tasks placed explicit demands on spatial and/or hippocampus-dependent processing abilities. Consistent with previous findings, we report a robust general factor influencing learning in mice that accounted for 38% of the variance across tasks. In addition, a domain-specific factor was found to account for performance on that subset of tasks that shared a dependence on hippocampal and/or spatial processing. These results provide further evidence for a general learning/cognitive factor in genetically heterogeneous mice. Furthermore (and similar to human cognitive performance), these results suggest a hierarchical structure to cognitive processes in this genetically heterogeneous species. PMID:19129932

Accounting for Heterogeneous-Phase Chemistry in Air Quality Models - Research Needs and Applications

EPA Science Inventory

Understanding the extent to which heterogeneous chemical reactions affect the burden and distribution of atmospheric pollutants is important because heterogeneous surfaces are ubiquitous throughout our environment. They include materials such as aerosol particles, clouds and fog,...

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Learning to Reflect and to Attribute Constructively as Basic Components of Self-Regulated Learning

ERIC Educational Resources Information Center

Masui, Chris; De Corte, Erik

2005-01-01

Background: Higher education is facing a number of problems: adjusting to larger and more heterogeneous student populations, increasing the number of graduating students, and preparing for lifelong learning. Improving learning competence can make a substantial contribution to solving each of these major concerns. The growing knowledge base on…

Social Software for Life-Long Learning

ERIC Educational Resources Information Center

Klamma, Ralf; Chatti, Mohamed Amine; Duval, Erik; Hummel, Hans; Hvannberg, Ebba Thora; Kravcik, Milos; Law, Effie; Naeve, Ambjorn; Scott, Peter

2007-01-01

Life-long learning is a key issue for our knowledge society. With social software systems new heterogeneous kinds of technology enhanced informal learning are now available to the life-long learner. Learners outside of learning institutions now have access to powerful social communities of experts and peers who are together forging a new web 2.0.…

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

ERIC Educational Resources Information Center

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

Learning Disabilities: Implications for Policy regarding Research and Practice--A Report by the National Joint Committee on Learning Disabilities, March 2011

ERIC Educational Resources Information Center

Learning Disabilities: A Multidisciplinary Journal, 2012

2012-01-01

The National Joint Committee on Learning Disabilities (NJCLD) affirms that the construct of learning disabilities represents a valid, unique, and heterogeneous group of disorders, and that recognition of this construct is essential for sound policy and practice. An extensive body of scientific research on learning disabilities continues to support…

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

PubMed Central

Dinov, Ivo D.; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W.; Price, Nathan D.; Van Horn, John D.; Ames, Joseph; Clark, Kristi; Hood, Leroy; Hampstead, Benjamin M.; Dauer, William; Toga, Arthur W.

2016-01-01

Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data. Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting. Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications. PMID:27494614

Environmental metabolomics with data science for investigating ecosystem homeostasis.

PubMed

Kikuchi, Jun; Ito, Kengo; Date, Yasuhiro

2018-02-01

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches. Copyright © 2017. Published by Elsevier B.V.

Crop classification and mapping based on Sentinel missions data in cloud environment

NASA Astrophysics Data System (ADS)

Lavreniuk, M. S.; Kussul, N.; Shelestov, A.; Vasiliev, V.

2017-12-01

Availability of high resolution satellite imagery (Sentinel-1/2/3, Landsat) over large territories opens new opportunities in agricultural monitoring. In particular, it becomes feasible to solve crop classification and crop mapping task at country and regional scale using time series of heterogenous satellite imagery. But in this case, we face with the problem of Big Data. Dealing with time series of high resolution (10 m) multispectral imagery we need to download huge volumes of data and then process them. The solution is to move "processing chain" closer to data itself to drastically shorten time for data transfer. One more advantage of such approach is the possibility to parallelize data processing workflow and efficiently implement machine learning algorithms. This could be done with cloud platform where Sentinel imagery are stored. In this study, we investigate usability and efficiency of two different cloud platforms Amazon and Google for crop classification and crop mapping problems. Two pilot areas were investigated - Ukraine and England. Google provides user friendly environment Google Earth Engine for Earth observation applications with a lot of data processing and machine learning tools already deployed. At the same time with Amazon one gets much more flexibility in implementation of his own workflow. Detailed analysis of pros and cons will be done in the presentation.

Effects of congruence between preferred and perceived learning environments in nursing education in Taiwan: a cross-sectional study.

PubMed

Yeh, Ting-Kuang; Huang, Hsiu-Mei; Chan, Wing P; Chang, Chun-Yen

2016-05-20

To investigate the effects of congruence between preferred and perceived learning environments on learning outcomes of nursing students. A nursing course at a university in central Taiwan. 124 Taiwanese nursing students enrolled in a 13-week problem-based Fundamental Nursing curriculum. Students' preferred learning environment, perceptions about the learning environment and learning outcomes (knowledge, self-efficacy and attitudes) were assessed. On the basis of test scores measuring their preferred and perceived learning environments, students were assigned to one of two groups: a 'preferred environment aligned with perceived learning environment' group and a 'preferred environment discordant with perceived learning environment' group. Learning outcomes were analysed by group. Most participants preferred learning in a classroom environment that combined problem-based and lecture-based instruction. However, a mismatch of problem-based instruction with students' perceptions occurred. Learning outcomes were significantly better when students' perceptions of their instructional activities were congruent with their preferred learning environment. As problem-based learning becomes a focus of educational reform in nursing, teachers need to be aware of students' preferences and perceptions of the learning environment. Teachers may also need to improve the match between an individual student's perception and a teacher's intention in the learning environment, and between the student's preferred and actual perceptions of the learning environment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning.

PubMed

Shteingart, Hanan; Loewenstein, Yonatan

2016-01-01

There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants' choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the "random" sequences.

Cognitive Impairment in Euthymic Pediatric Bipolar Disorder: A Systematic Review and Meta-Analysis.

PubMed

Elias, Liana R; Miskowiak, Kamilla W; Vale, Antônio M O; Köhler, Cristiano A; Kjærstad, Hanne L; Stubbs, Brendon; Kessing, Lars V; Vieta, Eduard; Maes, Michael; Goldstein, Benjamin I; Carvalho, André F

2017-04-01

To perform a systematic review and meta-analysis of studies investigating neurocognition in euthymic youths with bipolar disorder (BD) compared to healthy controls (HCs). A systematic literature search was conducted in the PubMed/MEDLINE, PsycINFO, and EMBASE databases from inception up until March 23, 2016, for original peer-reviewed articles that investigated neurocognition in euthymic youths with BD compared to HCs. Effect sizes (ES) for individual tests were extracted. In addition, results were grouped according to cognitive domain. This review complied with the PRISMA statement guidelines. A total of 24 studies met inclusion criteria (N = 1,146; 510 with BD). Overall, euthymic youths with BD were significantly impaired in verbal learning, verbal memory, working memory, visual learning, and visual memory, with moderate to large ESs (Hedge's g 0.76-0.99); significant impairments were not observed for attention/vigilance, reasoning and problem solving, and/or processing speed. Heterogeneity was moderate to large (I 2  ≥ 50%) for most ES estimates. Differences in the definition of euthymia across studies explained the heterogeneity in the ES estimate for verbal learning and memory. We also found evidence for other potential sources of heterogeneity in several ES estimates including co-occurring attention-deficit/hyperactivity disorder (ADHD) and anxiety disorders, and the use of medications. In addition, the use of different neuropsychological tests appeared to contribute to heterogeneity of some estimates (e.g., attention/vigilance domain). Euthymic youths with BD exhibit significant cognitive dysfunction encompassing verbal learning and memory, working memory, and/or visual learning and memory domains. These data indicate that for a subset of individuals with BD, neurodevelopmental factors may contribute to cognitive dysfunction. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles.

PubMed

Zhang, Duona; Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-03-20

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.

PubMed

Erraguntla, Madhav; Zapletal, Josef; Lawley, Mark

2017-12-01

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

PubMed Central

Ding, Wenrui; Zhang, Baochang; Xie, Chunyu; Li, Hongguang; Liu, Chunhui; Han, Jungong

2018-01-01

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition and remains challenging for traditional methods due to complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include the following: (1) a convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and (3) experimental results demonstrate that HDMF is very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network. PMID:29558434

Functional divergence in nitrogen uptake rates explains diversity-productivity relationship in microalgal communities

DOE PAGES

Mandal, Shovon; Shurin, Jonathan B.; Efroymson, Rebecca A.; ...

2018-05-23

The relationship between biodiversity and productivity has emerged as a central theme in ecology. Mechanistic explanations for this relationship suggest that the role organisms play in the ecosystem (i.e., niches or functional traits) is a better predictor of ecosystem stability and productivity than taxonomic richness. Here, we tested the capacity of functional diversity in nitrogen uptake in experimental microalgal communities to predict the complementarity effect (CE) and selection effect (SE) of biodiversity on productivity. We grew five algal species as monocultures and as polycultures in pairwise combinations in homogeneous (ammonium, nitrate, or urea alone) and heterogeneous nitrogen (mixed nitrogen) environmentsmore » to determine whether complementarity between species may be enhanced in heterogeneous environments. We show that the positive diversity effects on productivity in heterogeneous environments resulted from complementarity effects with no positive contribution by species–specific SEs. Positive biodiversity effects in homogeneous environments, when present (nitrate and urea treatments but not ammonium), were driven both by CE and SE. Our results suggest that functional diversity increases species complementarity and productivity mainly in heterogeneous resource environments. Furthermore, these results provide evidence that the positive effect of functional diversity on community productivity depends on the diversity of resources present in the environment.« less

Functional divergence in nitrogen uptake rates explains diversity-productivity relationship in microalgal communities

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

Mandal, Shovon; Shurin, Jonathan B.; Efroymson, Rebecca A.

The relationship between biodiversity and productivity has emerged as a central theme in ecology. Mechanistic explanations for this relationship suggest that the role organisms play in the ecosystem (i.e., niches or functional traits) is a better predictor of ecosystem stability and productivity than taxonomic richness. Here, we tested the capacity of functional diversity in nitrogen uptake in experimental microalgal communities to predict the complementarity effect (CE) and selection effect (SE) of biodiversity on productivity. We grew five algal species as monocultures and as polycultures in pairwise combinations in homogeneous (ammonium, nitrate, or urea alone) and heterogeneous nitrogen (mixed nitrogen) environmentsmore » to determine whether complementarity between species may be enhanced in heterogeneous environments. We show that the positive diversity effects on productivity in heterogeneous environments resulted from complementarity effects with no positive contribution by species–specific SEs. Positive biodiversity effects in homogeneous environments, when present (nitrate and urea treatments but not ammonium), were driven both by CE and SE. Our results suggest that functional diversity increases species complementarity and productivity mainly in heterogeneous resource environments. Furthermore, these results provide evidence that the positive effect of functional diversity on community productivity depends on the diversity of resources present in the environment.« less

Effects of congruence between preferred and perceived learning environments in nursing education in Taiwan: a cross-sectional study

PubMed Central

Yeh, Ting-Kuang; Huang, Hsiu-Mei; Chan, Wing P; Chang, Chun-Yen

2016-01-01

Objective To investigate the effects of congruence between preferred and perceived learning environments on learning outcomes of nursing students. Setting A nursing course at a university in central Taiwan. Participants 124 Taiwanese nursing students enrolled in a 13-week problem-based Fundamental Nursing curriculum. Design and methods Students' preferred learning environment, perceptions about the learning environment and learning outcomes (knowledge, self-efficacy and attitudes) were assessed. On the basis of test scores measuring their preferred and perceived learning environments, students were assigned to one of two groups: a ‘preferred environment aligned with perceived learning environment’ group and a ‘preferred environment discordant with perceived learning environment’ group. Learning outcomes were analysed by group. Outcome measures Most participants preferred learning in a classroom environment that combined problem-based and lecture-based instruction. However, a mismatch of problem-based instruction with students' perceptions occurred. Learning outcomes were significantly better when students' perceptions of their instructional activities were congruent with their preferred learning environment. Conclusions As problem-based learning becomes a focus of educational reform in nursing, teachers need to be aware of students' preferences and perceptions of the learning environment. Teachers may also need to improve the match between an individual student's perception and a teacher's intention in the learning environment, and between the student's preferred and actual perceptions of the learning environment. PMID:27207620

A Classroom Learning Cycle: Using Diagrams to Classify Matter.

ERIC Educational Resources Information Center

James, Helen J.; Nelson, Samuel L.

1981-01-01

A learning cycle involves the active participation of students in exploration, invention, and application phases. Describes one such learning cycle dealing with classification of matter and designed to provide students with an understanding of the terms: atom, molecule, element, compound, solution, and heterogeneous matter. (Author/JN)

Computers and Cooperative Learning. Tech Use Guide: Using Computer Technology.

ERIC Educational Resources Information Center

Council for Exceptional Children, Reston, VA. Center for Special Education Technology.

This guide focuses on the use of computers and cooperative learning techniques in classrooms that include students with disabilities. The guide outlines the characteristics of cooperative learning such as goal interdependence, individual accountability, and heterogeneous groups, emphasizing the value of each group member. Several cooperative…

Science Learning Outcomes in Alignment with Learning Environment Preferences

NASA Astrophysics Data System (ADS)

Chang, Chun-Yen; Hsiao, Chien-Hua; Chang, Yueh-Hsia

2011-04-01

This study investigated students' learning environment preferences and compared the relative effectiveness of instructional approaches on students' learning outcomes in achievement and attitude among 10th grade earth science classes in Taiwan. Data collection instruments include the Earth Science Classroom Learning Environment Inventory and Earth Science Learning Outcomes Inventory. The results showed that most students preferred learning in a classroom environment where student-centered and teacher-centered instructional approaches coexisted over a teacher-centered learning environment. A multivariate analysis of covariance also revealed that the STBIM students' cognitive achievement and attitude toward earth science were enhanced when the learning environment was congruent with their learning environment preference.

Heterogeneous Distributed Computing for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Sunderam, Vaidy S.

1998-01-01

The research supported under this award focuses on heterogeneous distributed computing for high-performance applications, with particular emphasis on computational aerosciences. The overall goal of this project was to and investigate issues in, and develop solutions to, efficient execution of computational aeroscience codes in heterogeneous concurrent computing environments. In particular, we worked in the context of the PVM[1] system and, subsequent to detailed conversion efforts and performance benchmarking, devising novel techniques to increase the efficacy of heterogeneous networked environments for computational aerosciences. Our work has been based upon the NAS Parallel Benchmark suite, but has also recently expanded in scope to include the NAS I/O benchmarks as specified in the NHT-1 document. In this report we summarize our research accomplishments under the auspices of the grant.

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

NASA Astrophysics Data System (ADS)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Intelligent autonomy for unmanned naval systems

NASA Astrophysics Data System (ADS)

Steinberg, Marc

2006-05-01

This paper provides an overview of the development and demonstration of intelligent autonomy technologies for control of heterogeneous unmanned naval air and sea vehicles and describes some of the current limitations of such technologies. The focus is on modular technologies that support highly automated retasking and fully autonomous dynamic replanning for up to ten heterogeneous unmanned systems based on high-level mission objectives, priorities, constraints, and Rules-of-Engagement. A key aspect of the demonstrations is incorporating frequent naval operator evaluations in order to gain better understanding of the integrated man/machine system and its tactical utility. These evaluations help ensure that the automation can provide information to the user in a meaningful way and that the user has a sufficient level of control and situation awareness to task the system as needed to complete complex mission tasks. Another important aspect of the program is examination of the interactions of higher-level autonomy algorithms with other relevant components that would be needed within the decision-making and control loops. Examples of these are vision and other sensor processing algorithms, sensor fusion, obstacle avoidance, and other lower level vehicle autonomous navigation, guidance, and control functions. Initial experiments have been completed using medium and high-fidelity vehicle simulations in a virtual warfare environment and inexpensive surrogate vehicles in flight and in-water demonstrations. Simulation experiments included integration of multi-vehicle task allocation, dynamic replanning under constraints, lower level autonomous vehicle control, automatic assessment of the impact of contingencies on plans, management of situation awareness data, operator alert management, and a mixed-initiative operator interface. In-water demonstrations of a maritime situation awareness capability were completed in both a river and a harbor environment using unmanned surface vehicles and a buoy as surrogate platforms. In addition, a multiple heterogeneous vehicle demonstration was performed using five different types of small unmanned air and ground vehicles. This provided some initial experimentation with specifying tasking for high-level mission objectives and then mapping those objectives onto heterogeneous unmanned vehicles that each have different lower-level autonomy software. Finally, this paper will discuss lessons learned.

Invasive alien plants benefit more from clonal integration in heterogeneous environments than natives.

PubMed

Wang, Yong-Jian; Müller-Schärer, Heinz; van Kleunen, Mark; Cai, Ai-Ming; Zhang, Ping; Yan, Rong; Dong, Bi-Cheng; Yu, Fei-Hai

2017-12-01

What confers invasive alien plants a competitive advantage over native plants remains open to debate. Many of the world's worst invasive alien plants are clonal and able to share resources within clones (clonal integration), particularly in heterogeneous environments. Here, we tested the hypothesis that clonal integration benefits invasive clonal plants more than natives and thus confers invasives a competitive advantage. We selected five congeneric and naturally co-occurring pairs of invasive alien and native clonal plants in China, and grew pairs of connected and disconnected ramets under heterogeneous light, soil nutrient and water conditions that are commonly encountered by alien plants during their invasion into new areas. Clonal integration increased biomass of all plants in all three heterogeneous resource environments. However, invasive plants benefited more from clonal integration than natives. Consequently, invasive plants produced more biomass than natives. Our results indicate that clonal integration may confer invasive alien clonal plants a competitive advantage over natives. Therefore, differences in the ability of clonal integration could potentially explain, at least partly, the invasion success of alien clonal plants in areas where resources are heterogeneously distributed. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

How Heterogeneity Affects the Design of Hadoop MapReduce Schedulers: A State-of-the-Art Survey and Challenges.

PubMed

Pandey, Vaibhav; Saini, Poonam

2018-06-01

MapReduce (MR) computing paradigm and its open source implementation Hadoop have become a de facto standard to process big data in a distributed environment. Initially, the Hadoop system was homogeneous in three significant aspects, namely, user, workload, and cluster (hardware). However, with growing variety of MR jobs and inclusion of different configurations of nodes in the existing cluster, heterogeneity has become an essential part of Hadoop systems. The heterogeneity factors adversely affect the performance of a Hadoop scheduler and limit the overall throughput of the system. To overcome this problem, various heterogeneous Hadoop schedulers have been proposed in the literature. Existing survey works in this area mostly cover homogeneous schedulers and classify them on the basis of quality of service parameters they optimize. Hence, there is a need to study the heterogeneous Hadoop schedulers on the basis of various heterogeneity factors considered by them. In this survey article, we first discuss different heterogeneity factors that typically exist in a Hadoop system and then explore various challenges that arise while designing the schedulers in the presence of such heterogeneity. Afterward, we present the comparative study of heterogeneous scheduling algorithms available in the literature and classify them by the previously said heterogeneity factors. Lastly, we investigate different methods and environment used for evaluation of discussed Hadoop schedulers.

Improving Collaborative Learning in the Classroom: Text Mining Based Grouping and Representing

ERIC Educational Resources Information Center

Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich

2016-01-01

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…

Toward a critical approach to the study of learning environments in science classrooms

NASA Astrophysics Data System (ADS)

Lorsbach, Anthony; Tobin, Kenneth

1995-03-01

Traditional learning environment research in science classrooms has been built on survey methods meant to measure students' and teachers' perceptions of variables used to define the learning environment. This research has led mainly to descriptions of learning environments. We argue that learning environment research should play a transformative role in science classrooms; that learning environment research should take into account contemporary post-positivist ways of thinking about learning and teaching to assist students and teachers to construct a more emancipatory learning environment. In particular, we argue that a critical perspective could lead to research playing a larger role in the transformation of science classroom learning environments. This argument is supplemented with an example from a middle school science classroom.

An Examination through Conjoint Analysis of the Preferences of Students Concerning Online Learning Environments According to Their Learning Styles

ERIC Educational Resources Information Center

Daghan, Gökhan; Akkoyunlu, Buket

2012-01-01

This study examines learning styles of students receiving education via online learning environments, and their preferences concerning the online learning environment. Maggie McVay Lynch Learning Style Inventory was used to determine learning styles of the students. The preferences of students concerning online learning environments were detected…

RapidIO as a multi-purpose interconnect

NASA Astrophysics Data System (ADS)

Baymani, Simaolhoda; Alexopoulos, Konstantinos; Valat, Sébastien

2017-10-01

RapidIO (http://rapidio.org/) technology is a packet-switched high-performance fabric, which has been under active development since 1997. Originally meant to be a front side bus, it developed into a system level interconnect which is today used in all 4G/LTE base stations world wide. RapidIO is often used in embedded systems that require high reliability, low latency and scalability in a heterogeneous environment - features that are highly interesting for several use cases, such as data analytics and data acquisition (DAQ) networks. We will present the results of evaluating RapidIO in a data analytics environment, from setup to benchmark. Specifically, we will share the experience of running ROOT and Hadoop on top of RapidIO. To demonstrate the multi-purpose characteristics of RapidIO, we will also present the results of investigating RapidIO as a technology for high-speed DAQ networks using a generic multi-protocol event-building emulation tool. In addition we will present lessons learned from implementing native ports of CERN applications to RapidIO.

Mapping of Coral Reef Environment in the Arabian Gulf Using Multispectral Remote Sensing

NASA Astrophysics Data System (ADS)

Ben-Romdhane, H.; Marpu, P. R.; Ghedira, H.; Ouarda, T. B. M. J.

2016-06-01

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

Discovering spatio-temporal models of the spread of West Nile virus.

PubMed

Orme-Zavaleta, Jennifer; Jorgensen, Jane; D'Ambrosio, Bruce; Altendorf, Eric; Rossignol, Philippe A

2006-04-01

Emerging infectious diseases are characterized by complex interactions among disease agents, vectors, wildlife, humans, and the environment. Since the appearance of West Nile virus (WNV) in New York City in 1999, it has infected over 8,000 people in the United States, resulting in several hundred deaths in 46 contiguous states. The virus is transmitted by mosquitoes and maintained in various bird reservoir hosts. Its unexpected introduction, high morbidity, and rapid spread have left public health agencies facing severe time constraints in a theory-poor environment, dependent largely on observational data collected by independent survey efforts and much uncertainty. Current knowledge may be expressed as a priori constraints on models learned from data. Accordingly, we applied a Bayesian probabilistic relational approach to generate spatially and temporally linked models from heterogeneous data sources. Using data collected from multiple independent sources in Maryland, we discovered the integrated context in which infected birds are plausible indicators for positive mosquito pools and human cases for 2001 and 2002.

A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment

PubMed Central

Gao, Ying; Wkram, Chris Hadri; Duan, Jiajie; Chou, Jarong

2015-01-01

In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network’s running and the degree of candidate nodes’ effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime. PMID:26690440

Promoting Student Collaboration in a Detracked, Heterogeneous Secondary Mathematics Classroom

ERIC Educational Resources Information Center

Staples, Megan E.

2008-01-01

Detracking and heterogeneous groupwork are two educational practices that have been shown to have promise for affording all students needed learning opportunities to develop mathematical proficiency. However, teachers face significant pedagogical challenges in organizing productive groupwork in these settings. This study offers an analysis of one…

The application of heterogeneous cluster grouping to reflective writing for medical humanities literature study to enhance students' empathy, critical thinking, and reflective writing.

PubMed

Liao, Hung-Chang; Wang, Ya-Huei

2016-09-02

To facilitate interdisciplinary collaboration and to make connections between patients' diseases and their social/cultural contexts, the study examined whether the use of heterogeneous cluster grouping in reflective writing for medical humanities literature acquisition could have positive effects on medical university students in terms of empathy, critical thinking, and reflective writing. A 15-week quasi-experimental design was conducted to investigate the learning outcomes. After conducting cluster algorithms, heterogeneous learning clusters (experimental group; n = 43) and non-heterogeneous learning clusters (control group; n = 43) were derived for a medical humanities literature study. Before and after the intervention, an Empathy Scale in Patient Care (ES-PC), a critical thinking disposition assessment (CTDA-R), and a reflective writing test were administered to both groups. The findings showed that on the empathy scale, significant differences in the "behavioral empathy," "affective empathy," and overall sections existed between the post-test mean scores of the experimental group and those of the control group, but such differences did not exist in "intelligent empathy." Regarding critical thinking, there were significant differences in "systematicity and analyticity," "skepticism and well-informed," "maturity and skepticism," and overall sections. As for reflective writing, significant differences existed in "ideas," "voice and point of view," "critical thinking and representation," "depth of reflection on personal growth," and overall sections, but not in "focus and context structure" and "language and conventions." This study outlined an alternative for using heterogeneous cluster grouping in reflective writing about medical humanities literature to facilitate interdisciplinary cooperation to provide more humanizing medical care.

Pre-Service English Teachers in Blended Learning Environment in Respect to Their Learning Approaches

ERIC Educational Resources Information Center

Yilmaz, M. Betul; Orhan, Feza

2010-01-01

Blended learning environment (BLE) is increasingly used in the world, especially in university degrees and it is based on integrating web-based learning and face-to-face (FTF) learning environments. Besides integrating different learning environments, BLE also addresses to students with different learning approaches. The "learning…

Group Formation Based on Learning Styles: Can It Improve Students' Teamwork?

ERIC Educational Resources Information Center

Kyprianidou, Maria; Demetriadis, Stavros; Tsiatsos, Thrasyvoulos; Pombortsis, Andreas

2012-01-01

This work explores the impact of teacher-led heterogeneous group formation on students' teamwork, based on students' learning styles. Fifty senior university students participated in a project-based course with two key organizational features: first, a web system (PEGASUS) was developed to help students identify their learning styles and…

Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

ERIC Educational Resources Information Center

Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

2015-01-01

Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead…

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…

Differential Recruitment of Distinct Amygdalar Nuclei across Appetitive Associative Learning

ERIC Educational Resources Information Center

Cole, Sindy; Powell, Daniel J.; Petrovich, Gorica D.

2013-01-01

The amygdala is important for reward-associated learning, but how distinct cell groups within this heterogeneous structure are recruited during appetitive learning is unclear. Here we used Fos induction to map the functional amygdalar circuitry recruited during early and late training sessions of Pavlovian appetitive conditioning. We found that a…

An Ontology Infrastructure for an E-Learning Scenario

ERIC Educational Resources Information Center

Guo, Wen-Ying; Chen, De-Ren

2007-01-01

Selecting appropriate learning services for a learner from a large number of heterogeneous knowledge sources is a complex and challenging task. This article illustrates and discusses how Semantic Web technologies such as RDF [resource description framework] and ontology can be applied to e-learning systems to help the learner in selecting an…

Toward a New Motivation to Learn Framework for Older Adult Learners

ERIC Educational Resources Information Center

Lin, Yi-Yin; Sandmann, Lorilee R.

2012-01-01

Although existing literature addresses adults' motivation to learn, and some specifically focuses on older adults, it is now recognized that older adults are more heterogeneous and complex than other age groups. Therefore, this study seeks to provide an alternative theoretical framework to investigate motivation to learn for older adult learners…

Unpacking "Active Learning": A Combination of Flipped Classroom and Collaboration Support Is More Effective but Collaboration Support Alone Is Not

ERIC Educational Resources Information Center

Rau, Martina A.; Kennedy, Kristopher; Oxtoby, Lucas; Bollom, Mark; Moore, John W.

2017-01-01

Much evidence shows that instruction that actively engages students with learning materials is more effective than traditional, lecture-centric instruction. These "active learning" models comprise an extremely heterogeneous set of instructional methods: they often include collaborative activities, flipped classrooms, or a combination of…

Group learning versus local learning: Which is prefer for public cooperation?

NASA Astrophysics Data System (ADS)

Yang, Shi-Han; Song, Qi-Qing

2018-01-01

We study the evolution of cooperation in public goods games on various graphs, focusing on the effects that are brought by different kinds of strategy donors. This highlights a basic feature of a public good game, for which there exists a remarkable difference between the interactive players and the players who are imitated. A player can learn from all the groups where the player is a member or from the typically local nearest neighbors, and the results show that the group learning rules have better performance in promoting cooperation on many networks than the local learning rules. The heterogeneity of networks' degree may be an effective mechanism for harvesting the cooperation expectation in many cases, however, we find that heterogeneity does not definitely mean the high frequency of cooperators in a population under group learning rules. It was shown that cooperators always hardly evolve whenever the interaction and the replacement do not coincide for evolutionary pairwise dilemmas on graphs, while for PG games we find that breaking the symmetry is conducive to the survival of cooperators.

Using Unsupervised Learning to Unlock the Potential of Hydrologic Similarity

NASA Astrophysics Data System (ADS)

Chaney, N.; Newman, A. J.

2017-12-01

By clustering environmental data into representative hydrologic response units (HRUs), hydrologic similarity aims to harness the covariance between a system's physical environment and its hydrologic response to create reduced-order models. This is the primary approach through which sub-grid hydrologic processes are represented in large-scale models (e.g., Earth System Models). Although the possibilities of hydrologic similarity are extensive, its practical implementations have been limited to 1-d bins of oversimplistic metrics of hydrologic response (e.g., topographic index)—this is a missed opportunity. In this presentation we will show how unsupervised learning is unlocking the potential of hydrologic similarity; clustering methods enable generalized frameworks to effectively and efficiently harness the petabytes of global environmental data to robustly characterize sub-grid heterogeneity in large-scale models. To illustrate the potential that unsupervised learning has towards advancing hydrologic similarity, we introduce a hierarchical clustering algorithm (HCA) that clusters very high resolution (30-100 meters) elevation, soil, climate, and land cover data to assemble a domain's representative HRUs. These HRUs are then used to parameterize the sub-grid heterogeneity in land surface models; for this study we use the GFDL LM4 model—the land component of the GFDL Earth System Model. To explore HCA and its impacts on the hydrologic system we use a ¼ grid cell in southeastern California as a test site. HCA is used to construct an ensemble of 9 different HRU configurations—each configuration has a different number of HRUs; for each ensemble member LM4 is run between 2002 and 2014 with a 26 year spinup. The analysis of the ensemble of model simulations show that: 1) clustering the high-dimensional environmental data space leads to a robust representation of the role of the physical environment in the coupled water, energy, and carbon cycles at a relatively low number of HRUs; 2) the reduced-order model with around 300 HRUs effectively reproduces the fully distributed model simulation (30 meters) with less than 1/1000 of computational expense; 3) assigning each grid cell of the fully distributed grid to an HRU via HCA enables novel visualization methods for large-scale models—this has significant implications for how these models are applied and evaluated. We will conclude by outlining the potential that this work has within operational prediction systems including numerical weather prediction, Earth System models, and Early Warning systems.

Bridging Social Capital in Online Communities: Heterogeneity and Social Tolerance of Online Game Players in Japan

ERIC Educational Resources Information Center

Kobayashi, Tetsuro

2010-01-01

This article examines the democratic potential of online communities by investigating the influence of network heterogeneity on social tolerance in an online gaming environment. Online game communities are potential sources of bridging social capital because they tend to be relatively heterogeneous. Causal analyses are conducted using structural…

Design and implementation of a VoIP broadcasting service over embedded systems in a heterogeneous network environment.

PubMed

Leu, Jenq-Shiou; Lin, Wei-Hsiang; Hsieh, Wen-Bin; Lo, Chien-Chih

2014-01-01

As the digitization is integrated into daily life, media including video and audio are heavily transferred over the Internet nowadays. Voice-over-Internet Protocol (VoIP), the most popular and mature technology, becomes the focus attracting many researches and investments. However, most of the existing studies focused on a one-to-one communication model in a homogeneous network, instead of one-to-many broadcasting model among diverse embedded devices in a heterogeneous network. In this paper, we present the implementation of a VoIP broadcasting service on the open source-Linphone-in a heterogeneous network environment, including WiFi, 3G, and LAN networks. The proposed system featuring VoIP broadcasting over heterogeneous networks can be integrated with heterogeneous agile devices, such as embedded devices or mobile phones. VoIP broadcasting over heterogeneous networks can be integrated into modern smartphones or other embedded devices; thus when users run in a traditional AM/FM signal unreachable area, they still can receive the broadcast voice through the IP network. Also, comprehensive evaluations are conducted to verify the effectiveness of the proposed implementation.

Design and Implementation of a VoIP Broadcasting Service over Embedded Systems in a Heterogeneous Network Environment

PubMed Central

Lin, Wei-Hsiang; Hsieh, Wen-Bin; Lo, Chien-Chih

2014-01-01

As the digitization is integrated into daily life, media including video and audio are heavily transferred over the Internet nowadays. Voice-over-Internet Protocol (VoIP), the most popular and mature technology, becomes the focus attracting many researches and investments. However, most of the existing studies focused on a one-to-one communication model in a homogeneous network, instead of one-to-many broadcasting model among diverse embedded devices in a heterogeneous network. In this paper, we present the implementation of a VoIP broadcasting service on the open source—Linphone—in a heterogeneous network environment, including WiFi, 3G, and LAN networks. The proposed system featuring VoIP broadcasting over heterogeneous networks can be integrated with heterogeneous agile devices, such as embedded devices or mobile phones. VoIP broadcasting over heterogeneous networks can be integrated into modern smartphones or other embedded devices; thus when users run in a traditional AM/FM signal unreachable area, they still can receive the broadcast voice through the IP network. Also, comprehensive evaluations are conducted to verify the effectiveness of the proposed implementation. PMID:25300280

Revisiting the Stability of Spatially Heterogeneous Predator-Prey Systems Under Eutrophication.

PubMed

Farkas, J Z; Morozov, A Yu; Arashkevich, E G; Nikishina, A

2015-10-01

We employ partial integro-differential equations to model trophic interaction in a spatially extended heterogeneous environment. Compared to classical reaction-diffusion models, this framework allows us to more realistically describe the situation where movement of individuals occurs on a faster time scale than on the demographic (population) time scale, and we cannot determine population growth based on local density. However, most of the results reported so far for such systems have only been verified numerically and for a particular choice of model functions, which obviously casts doubts about these findings. In this paper, we analyse a class of integro-differential predator-prey models with a highly mobile predator in a heterogeneous environment, and we reveal the main factors stabilizing such systems. In particular, we explore an ecologically relevant case of interactions in a highly eutrophic environment, where the prey carrying capacity can be formally set to 'infinity'. We investigate two main scenarios: (1) the spatial gradient of the growth rate is due to abiotic factors only, and (2) the local growth rate depends on the global density distribution across the environment (e.g. due to non-local self-shading). For an arbitrary spatial gradient of the prey growth rate, we analytically investigate the possibility of the predator-prey equilibrium in such systems and we explore the conditions of stability of this equilibrium. In particular, we demonstrate that for a Holling type I (linear) functional response, the predator can stabilize the system at low prey density even for an 'unlimited' carrying capacity. We conclude that the interplay between spatial heterogeneity in the prey growth and fast displacement of the predator across the habitat works as an efficient stabilizing mechanism. These results highlight the generality of the stabilization mechanisms we find in spatially structured predator-prey ecological systems in a heterogeneous environment.

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments

ERIC Educational Resources Information Center

Rowe, Jonathan P.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C.

2011-01-01

A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to…

The effect of environmental heterogeneity on RPW8-mediated resistance to powdery mildews in Arabidopsis thaliana.

PubMed

Jorgensen, Tove H

2012-03-01

The biotic and abiotic environment of interacting hosts and parasites may vary considerably over small spatial and temporal scales. It is essential to understand how different environments affect host disease resistance because this determines frequency of disease and, importantly, heterogeneous environments can retard direct selection and potentially maintain genetic variation for resistance in natural populations. The effect of different temperatures and soil nutrient conditions on the outcome of infection by a pathogen was quantified in Arabidopsis thaliana. Expression levels of a gene conferring resistance to powdery mildews, RPW8, were compared with levels of disease to test a possible mechanism behind variation in resistance. Most host genotypes changed from susceptible to resistant across environments with the ranking of genotypes differing between treatments. Transcription levels of RPW8 increased after infection and varied between environments, but there was no tight association between transcription and resistance levels. There is a strong potential for a heterogeneous environment to change the resistance capacity of A. thaliana genotypes and hence the direction and magnitude of selection in the presence of the pathogen. Possible causative links between resistance gene expression and disease resistance are discussed in light of the present results on RPW8.

Factors Influencing Learning Environments in an Integrated Experiential Program

NASA Astrophysics Data System (ADS)

Koci, Peter

The research conducted for this dissertation examined the learning environment of a specific high school program that delivered the explicit curriculum through an integrated experiential manner, which utilized field and outdoor experiences. The program ran over one semester (five months) and it integrated the grade 10 British Columbian curriculum in five subjects. A mixed methods approach was employed to identify the students' perceptions and provide richer descriptions of their experiences related to their unique learning environment. Quantitative instruments were used to assess changes in students' perspectives of their learning environment, as well as other supporting factors including students' mindfulness, and behaviours towards the environment. Qualitative data collection included observations, open-ended questions, and impromptu interviews with the teacher. The qualitative data describe the factors and processes that influenced the learning environment and give a richer, deeper interpretation which complements the quantitative findings. The research results showed positive scores on all the quantitative measures conducted, and the qualitative data provided further insight into descriptions of learning environment constructs that the students perceived as most important. A major finding was that the group cohesion measure was perceived by students as the most important attribute of their preferred learning environment. A flow chart was developed to help the researcher conceptualize how the learning environment, learning process, and outcomes relate to one another in the studied program. This research attempts to explain through the consideration of this case study: how learning environments can influence behavioural change and how an interconnectedness among several factors in the learning process is influenced by the type of learning environment facilitated. Considerably more research is needed in this area to understand fully the complexity learning environments and how they influence learning and behaviour. Keywords: learning environments; integrated experiential programs; environmental education.

Homogenization of a Directed Dispersal Model for Animal Movement in a Heterogeneous Environment.

PubMed

Yurk, Brian P

2016-10-01

The dispersal patterns of animals moving through heterogeneous environments have important ecological and epidemiological consequences. In this work, we apply the method of homogenization to analyze an advection-diffusion (AD) model of directed movement in a one-dimensional environment in which the scale of the heterogeneity is small relative to the spatial scale of interest. We show that the large (slow) scale behavior is described by a constant-coefficient diffusion equation under certain assumptions about the fast-scale advection velocity, and we determine a formula for the slow-scale diffusion coefficient in terms of the fast-scale parameters. We extend the homogenization result to predict invasion speeds for an advection-diffusion-reaction (ADR) model with directed dispersal. For periodic environments, the homogenization approximation of the solution of the AD model compares favorably with numerical simulations. Invasion speed approximations for the ADR model also compare favorably with numerical simulations when the spatial period is sufficiently small.

Experimental observation of complete and anticipation synchronization of heterogeneous oscillators using a common dynamical environment

NASA Astrophysics Data System (ADS)

Singla, Tanu; Chandrasekhar, E.; Singh, B. P.; Parmananda, P.

2014-12-01

Complete and anticipation synchronization of nonlinear oscillators from different origins is attempted experimentally. This involves coupling these heterogeneous oscillators to a common dynamical environment. Initially, this phenomenon was studied using two parameter mismatched Chua circuits. Subsequently, three different timeseries: a) x variable of the Lorenz oscillator b) the X-component of Earth's magnetic field and c) per-day temperature variation of the Region Santa Cruz in Mumbai, India are environmentally coupled, under the master-slave scenario, with a Chua circuit. Our results indicate that environmental coupling is a potent tool to provoke complete and anticipation synchronization of heterogeneous oscillators from distinct origins.

Bi-level Multi-Source Learning for Heterogeneous Block-wise Missing Data

PubMed Central

Xiang, Shuo; Yuan, Lei; Fan, Wei; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer’s Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified “bi-level” learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches. PMID:23988272

Bi-level multi-source learning for heterogeneous block-wise missing data.

PubMed

Xiang, Shuo; Yuan, Lei; Fan, Wei; Wang, Yalin; Thompson, Paul M; Ye, Jieping

2014-11-15

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified "bi-level" learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches. © 2013 Elsevier Inc. All rights reserved.

Hypermedia in Vocational Learning: A Hypermedia Learning Environment for Training Management Skills

ERIC Educational Resources Information Center

Konradt, Udo

2004-01-01

A learning environment is defined as an arrangement of issues, methods, techniques, and media in a given domain. Besides temporal and spatial features a learning environment considers the social situation in which learning takes place. In (hypermedia) learning environments the concept of exploration and the active role of the learner is…

Using Wikis as a Support and Assessment Tool in Collaborative Digital Game-Based Learning Environments

ERIC Educational Resources Information Center

Samur, Yavuz

2011-01-01

In computer-supported collaborative learning (CSCL) environments, there are many researches done on collaborative learning activities; however, in game-based learning environments, more research and literature on collaborative learning activities are required. Actually, both game-based learning environments and wikis enable us to use new chances…

Assessing culturally sensitive factors in the learning environment of science classrooms

NASA Astrophysics Data System (ADS)

Fisher, Darrell L.; Waldrip, Bruce G.

1997-03-01

As schools are becoming increasingly diverse in their scope and clientele, any examination of the interaction of culturally sensitive factors of students' learning environments with learning science assumes critical importance. The purpose of this exploratory study was to develop an instrument to assess learning environment factors that are culturally sensitive, to provide initial validation information on the instrument and to examine associations between students' perceptions of their learning environments and their attitudes towards science and achievement of enquiry skills. A measure of these factors of science student's learning environment, namely the Cultural Learning Environment Questionnaire (CLEQ), was developed from past learning environment instruments and influenced by Hofstede's four dimensions of culture (Power Distance, Uncertainty Avoidance, Individualism, and Masculinity/Femininity). The reliability and discriminant validity for each scale were obtained and associations between learning environment, attitude to science and enquiry skills achievement were found.

Collegial Activity Learning between Heterogeneous Sensors.

PubMed

Feuz, Kyle D; Cook, Diane J

2017-11-01

Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.

Provably Secure Heterogeneous Access Control Scheme for Wireless Body Area Network.

PubMed

Omala, Anyembe Andrew; Mbandu, Angolo Shem; Mutiria, Kamenyi Domenic; Jin, Chunhua; Li, Fagen

2018-04-28

Wireless body area network (WBAN) provides a medium through which physiological information could be harvested and transmitted to application provider (AP) in real time. Integrating WBAN in a heterogeneous Internet of Things (IoT) ecosystem would enable an AP to monitor patients from anywhere and at anytime. However, the IoT roadmap of interconnected 'Things' is still faced with many challenges. One of the challenges in healthcare is security and privacy of streamed medical data from heterogeneously networked devices. In this paper, we first propose a heterogeneous signcryption scheme where a sender is in a certificateless cryptographic (CLC) environment while a receiver is in identity-based cryptographic (IBC) environment. We then use this scheme to design a heterogeneous access control protocol. Formal security proof for indistinguishability against adaptive chosen ciphertext attack and unforgeability against adaptive chosen message attack in random oracle model is presented. In comparison with some of the existing access control schemes, our scheme has lower computation and communication cost.

The Role of Edaphic Environment and Climate in Structuring Phylogenetic Pattern in Seasonally Dry Tropical Plant Communities

PubMed Central

Moro, Marcelo Freire; Silva, Igor Aurélio; de Araújo, Francisca Soares; Nic Lughadha, Eimear; Meagher, Thomas R.; Martins, Fernando Roberto

2015-01-01

Seasonally dry tropical plant formations (SDTF) are likely to exhibit phylogenetic clustering owing to niche conservatism driven by a strong environmental filter (water stress), but heterogeneous edaphic environments and life histories may result in heterogeneity in degree of phylogenetic clustering. We investigated phylogenetic patterns across ecological gradients related to water availability (edaphic environment and climate) in the Caatinga, a SDTF in Brazil. Caatinga is characterized by semiarid climate and three distinct edaphic environments – sedimentary, crystalline, and inselberg –representing a decreasing gradient in soil water availability. We used two measures of phylogenetic diversity: Net Relatedness Index based on the entire phylogeny among species present in a site, reflecting long-term diversification; and Nearest Taxon Index based on the tips of the phylogeny, reflecting more recent diversification. We also evaluated woody species in contrast to herbaceous species. The main climatic variable influencing phylogenetic pattern was precipitation in the driest quarter, particularly for herbaceous species, suggesting that environmental filtering related to minimal periods of precipitation is an important driver of Caatinga biodiversity, as one might expect for a SDTF. Woody species tended to show phylogenetic clustering whereas herbaceous species tended towards phylogenetic overdispersion. We also found phylogenetic clustering in two edaphic environments (sedimentary and crystalline) in contrast to phylogenetic overdispersion in the third (inselberg). We conclude that while niche conservatism is evident in phylogenetic clustering in the Caatinga, this is not a universal pattern likely due to heterogeneity in the degree of realized environmental filtering across edaphic environments. Thus, SDTF, in spite of a strong shared environmental filter, are potentially heterogeneous in phylogenetic structuring. Our results support the need for scientifically informed conservation strategies in the Caatinga and other SDTF regions that have not previously been prioritized for conservation in order to take into account this heterogeneity. PMID:25798584

Experiences of a student-run clinic in primary care: a mixed-method study with students, patients and supervisors.

PubMed

Fröberg, Maria; Leanderson, Charlotte; Fläckman, Birgitta; Hedman-Lagerlöf, Erik; Björklund, Karin; Nilsson, Gunnar H; Stenfors, Terese

2018-03-01

To explore how a student-run clinic (SRC) in primary health care (PHC) was perceived by students, patients and supervisors. A mixed methods study. Clinical learning environment, supervision and nurse teacher evaluation scale (CLES + T) assessed student satisfaction. Client satisfaction questionnaire-8 (CSQ-8) assessed patient satisfaction. Semi-structured interviews were conducted with supervisors. Gustavsberg PHC Center, Stockholm County, Sweden. Students in medicine, nursing, physiotherapy, occupational therapy and psychology and their patients filled in questionnaires. Supervisors in medicine, nursing and physiotherapy were interviewed. Mean values and medians of CLES + T and CSQ-8 were calculated. Interviews were analyzed using content analysis. A majority of 199 out of 227 student respondents reported satisfaction with the pedagogical atmosphere and the supervisory relationship. Most of the 938 patient respondents reported satisfaction with the care given. Interviews with 35 supervisors showed that the organization of the SRC provided time and support to focus on the tutorial assignment. Also, the pedagogical role became more visible and targeted toward the student's individual needs. However, balancing the student's level of autonomy and the own control over care was described as a challenge. Many expressed the need for further pedagogical education. High student and patient satisfaction reported from five disciplines indicate that a SRC in PHC can be adapted for heterogeneous student groups. Supervisors experienced that the SRC facilitated and clarified their pedagogical role. Simultaneously their need for continuous pedagogical education was highlighted. The SRC model has the potential to enhance student-centered tuition in PHC. Key Points Knowledge of student-run clinics (SRCs) as learning environments within standard primary health care (PHC) is limited. We report experiences from the perspectives of students, their patients and supervisors, representing five healthcare disciplines. Students particularly valued the pedagogical atmosphere and the supervisory relationship. Patients expressed high satisfaction with the care provided. Supervisors expressed that the structure of the SRC supported the pedagogical assignment and facilitated student-centered tuition - simultaneously the altered learning environment highlighted the need for further pedagogical education. Student-run clinics in primary health care have great potential for student-regulated learning.

The relationship between urban environment and the inflammatory bowel diseases: a systematic review and meta-analysis.

PubMed

Soon, Ing Shian; Molodecky, Natalie A; Rabi, Doreen M; Ghali, William A; Barkema, Herman W; Kaplan, Gilaad G

2012-05-24

The objective of this study was to conduct a systematic review with meta-analysis of studies assessing the association between living in an urban environment and the development of the Crohn's disease (CD) or ulcerative colitis (UC). A systematic literature search of MEDLINE (1950-Oct. 2009) and EMBASE (1980-Oct. 2009) was conducted to identify studies investigating the relationship between urban environment and IBD. Cohort and case-control studies were analyzed using incidence rate ratio (IRR) or odds ratio (OR) with 95 % confidence intervals (CIs), respectively. Stratified and sensitivity analyses were performed to explore heterogeneity between studies and assess effects of study quality. The search strategy retrieved 6940 unique citations and 40 studies were selected for inclusion. Of these, 25 investigated the relationship between urban environment and UC and 30 investigated this relationship with CD. Included in our analysis were 7 case-control UC studies, 9 case-control CD studies, 18 cohort UC studies and 21 cohort CD studies. Based on a random effects model, the pooled IRRs for urban compared to rural environment for UC and CD studies were 1.17 (1.03, 1.32) and 1.42 (1.26, 1.60), respectively. These associations persisted across multiple stratified and sensitivity analyses exploring clinical and study quality factors. Heterogeneity was observed in the cohort studies for both UC and CD, whereas statistically significant heterogeneity was not observed for the case-control studies. A positive association between urban environment and both CD and UC was found. Heterogeneity may be explained by differences in study design and quality factors.

The Effects of Inquiry-Based Computer Simulation with Cooperative Learning on Scientific Thinking and Conceptual Understanding of Gas Laws

ERIC Educational Resources Information Center

Abdullah, Sopiah; Shariff, Adilah

2008-01-01

The purpose of the study was to investigate the effects of inquiry-based computer simulation with heterogeneous-ability cooperative learning (HACL) and inquiry-based computer simulation with friendship cooperative learning (FCL) on (a) scientific reasoning (SR) and (b) conceptual understanding (CU) among Form Four students in Malaysian Smart…

A SCORM Thin Client Architecture for E-Learning Systems Based on Web Services

ERIC Educational Resources Information Center

Casella, Giovanni; Costagliola, Gennaro; Ferrucci, Filomena; Polese, Giuseppe; Scanniello, Giuseppe

2007-01-01

In this paper we propose an architecture of e-learning systems characterized by the use of Web services and a suitable middleware component. These technical infrastructures allow us to extend the system with new services as well as to integrate and reuse heterogeneous software e-learning components. Moreover, they let us better support the…

Relationship between learning environment characteristics and academic engagement.

PubMed

Opdenakker, Marie-Christine; Minnaert, Alexander

2011-08-01

The relationship between learning environment characteristics and academic engagement of 777 Grade 6 children located in 41 learning environments was explored. Questionnaires were used to tap learning environment perceptions of children, their academic engagement, and their ethnic-cultural background. The basis of the learning environment questionnaire was the International System for Teacher Observation and Feedback (ISTOF). Factor analysis indicated three factors: the teacher as a helpful and good instructor (having good instructional skills, clear instruction), the teacher as promoter of active learning and differentiation, and the teacher as manager and organizer of classroom activities. Multilevel analysis indicated that about 12% of the differences in engagement between children was related to the learning environment. All the mentioned learning environment characteristics mattered, but the teacher as a helpful, good instructor was most important followed by the teacher as promoter of active learning and differentiation.

Nigerian Physiotherapy Clinical Students' Perception of Their Learning Environment Measured by the Dundee Ready Education Environment Measure Inventory

ERIC Educational Resources Information Center

Odole, Adesola C.; Oyewole, Olufemi O.; Ogunmola, Oluwasolape T.

2014-01-01

The identification of the learning environment and the understanding of how students learn will help teacher to facilitate learning and plan a curriculum to achieve the learning outcomes. The purpose of this study was to investigate undergraduate physiotherapy clinical students' perception of University of Ibadan's learning environment. Using the…

Does Everyone's Motivational Beliefs about Physical Science Decline in Secondary School?: Heterogeneity of Adolescents' Achievement Motivation Trajectories in Physics and Chemistry.

PubMed

Wang, Ming-Te; Chow, Angela; Degol, Jessica Lauren; Eccles, Jacquelynne Sue

2017-08-01

Students' motivational beliefs about learning physical science are critical for achieving positive educational outcomes. In this study, we incorporated expectancy-value theory to capture the heterogeneity of adolescents' motivational trajectories in physics and chemistry from seventh to twelfth grade and linked these trajectories to science-related outcomes. We used a cross-sequential design based on three different cohorts of adolescents (N = 699; 51.5 % female; 95 % European American; M ages for youngest, middle, and oldest cohorts at the first wave = 13.2, 14.1, and 15.3 years) coming from ten public secondary schools. Although many studies claim that physical science motivation declines on average over time, we identified seven differential motivational trajectories of ability self-concept and task values, and found associations of these trajectories with science achievement, advanced science course taking, and science career aspirations. Adolescents' ability self-concept and task values in physics and chemistry were also positively related and interlinked over time. Examining how students' motivational beliefs about physical science develop in secondary school offers insight into the capacity of different groups of students to successfully adapt to their changing educational environments.

Supporting cognitive engagement in a learning-by-doing learning environment: Case studies of participant engagement and social configurations in Kitchen Science Investigators

NASA Astrophysics Data System (ADS)

Gardner, Christina M.

Learning-by-doing learning environments support a wealth of physical engagement in activities. However, there is also a lot of variability in what participants learn in each enactment of these types of environments. Therefore, it is not always clear how participants are learning in these environments. In order to design technologies to support learning in these environments, we must have a greater understanding of how participants engage in learning activities, their goals for their engagement, and the types of help they need to cognitively engage in learning activities. To gain a greater understanding of participant engagement and factors and circumstances that promote and inhibit engagement, this dissertation explores and answers several questions: What are the types of interactions and experiences that promote and /or inhibit learning and engagement in learning-by-doing learning environments? What are the types of configurations that afford or inhibit these interactions and experiences in learning-by-doing learning environments? I explore answers to these questions through the context of two enactments of Kitchen Science Investigators (KSI), a learning-by-doing learning environment where middle-school aged children learn science through cooking from customizing recipes to their own taste and texture preferences. In small groups, they investigate effects of ingredients through the design of cooking and science experiments, through which they experience and learn about chemical, biological, and physical science phenomena and concepts (Clegg, Gardner, Williams, & Kolodner, 2006). The research reported in this dissertation sheds light on the different ways participant engagement promotes and/or inhibits cognitive engagement in by learning-by-doing learning environments through two case studies. It also provides detailed descriptions of the circumstances (social, material, and physical configurations) that promote and/or inhibit participant engagement in these learning environments through cross-case analyses of these cases. Finally, it offers suggestions about structuring activities, selecting materials and resources, and designing facilitation and software-realized scaffolding in the design of these types of learning environments. These design implications focus on affording participant engagement in science content and practices learning. Overall, the case studies, cross-case analyses, and empirically-based design implications begin to bridge the gap between theory and practice in the design and implementation of these learning environments. This is demonstrated by providing detailed and explanatory examples and factors that affect how participants take up the affordances of the learning opportunities designed into these learning environments.

Solvent fluctuations and nuclear quantum effects modulate the molecular hyperpolarizability of water

NASA Astrophysics Data System (ADS)

Liang, Chungwen; Tocci, Gabriele; Wilkins, David M.; Grisafi, Andrea; Roke, Sylvie; Ceriotti, Michele

2017-07-01

Second-harmonic scattering (SHS) experiments provide a unique approach to probe noncentrosymmetric environments in aqueous media, from bulk solutions to interfaces, living cells, and tissue. A central assumption made in analyzing SHS experiments is that each molecule scatters light according to a constant molecular hyperpolarizability tensor β(2 ). Here, we investigate the dependence of the molecular hyperpolarizability of water on its environment and internal geometric distortions, in order to test the hypothesis of constant β(2 ). We use quantum chemistry calculations of the hyperpolarizability of a molecule embedded in point-charge environments obtained from simulations of bulk water. We demonstrate that both the heterogeneity of the solvent configurations and the quantum mechanical fluctuations of the molecular geometry introduce large variations in the nonlinear optical response of water. This finding has the potential to change the way SHS experiments are interpreted: In particular, isotopic differences between H2O and D2O could explain recent SHS observations. Finally, we show that a machine-learning framework can predict accurately the fluctuations of the molecular hyperpolarizability. This model accounts for the microscopic inhomogeneity of the solvent and represents a step towards quantitative modeling of SHS experiments.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity.

PubMed

Majumder, Biswanath; Baraneedharan, Ulaganathan; Thiyagarajan, Saravanan; Radhakrishnan, Padhma; Narasimhan, Harikrishna; Dhandapani, Muthu; Brijwani, Nilesh; Pinto, Dency D; Prasath, Arun; Shanthappa, Basavaraja U; Thayakumar, Allen; Surendran, Rajagopalan; Babu, Govind K; Shenoy, Ashok M; Kuriakose, Moni A; Bergthold, Guillaume; Horowitz, Peleg; Loda, Massimo; Beroukhim, Rameen; Agarwal, Shivani; Sengupta, Shiladitya; Sundaram, Mallikarjun; Majumder, Pradip K

2015-02-27

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

Intelligent control and cooperation for mobile robots

NASA Astrophysics Data System (ADS)

Stingu, Petru Emanuel

The topic discussed in this work addresses the current research being conducted at the Automation & Robotics Research Institute in the areas of UAV quadrotor control and heterogenous multi-vehicle cooperation. Autonomy can be successfully achieved by a robot under the following conditions: the robot has to be able to acquire knowledge about the environment and itself, and it also has to be able to reason under uncertainty. The control system must react quickly to immediate challenges, but also has to slowly adapt and improve based on accumulated knowledge. The major contribution of this work is the transfer of the ADP algorithms from the purely theoretical environment to the complex real-world robotic platforms that work in real-time and in uncontrolled environments. Many solutions are adopted from those present in nature because they have been proven to be close to optimal in very different settings. For the control of a single platform, reinforcement learning algorithms are used to design suboptimal controllers for a class of complex systems that can be conceptually split in local loops with simpler dynamics and relatively weak coupling to the rest of the system. Optimality is enforced by having a global critic but the curse of dimensionality is avoided by using local actors and intelligent pre-processing of the information used for learning the optimal controllers. The system model is used for constructing the structure of the control system, but on top of that the adaptive neural networks that form the actors use the knowledge acquired during normal operation to get closer to optimal control. In real-world experiments, efficient learning is a strong requirement for success. This is accomplished by using an approximation of the system model to focus the learning for equivalent configurations of the state space. Due to the availability of only local data for training, neural networks with local activation functions are implemented. For the control of a formation of robots subjected to dynamic communication constraints, game theory is used in addition to reinforcement learning. The nodes maintain an extra set of state variables about all the other nodes that they can communicate to. The more important are trust and predictability. They are a way to incorporate knowledge acquired in the past into the control decisions taken by each node. The trust variable provides a simple mechanism for the implementation of reinforcement learning. For robot formations, potential field based control algorithms are used to generate the control commands. The formation structure changes due to the environment and due to the decisions of the nodes. It is a problem of building a graph and coalitions by having distributed decisions but still reaching an optimal behavior globally.

How People Learn in an Asynchronous Online Learning Environment: The Relationships between Graduate Students' Learning Strategies and Learning Satisfaction

ERIC Educational Resources Information Center

Choi, Beomkyu

2016-01-01

The purpose of this study was to examine the relationships between learners' learning strategies and learning satisfaction in an asynchronous online learning environment. In an attempt to shed some light on how people learn in an online learning environment, one hundred and sixteen graduate students who were taking online learning courses…

Scaffolding in Connectivist Mobile Learning Environment

ERIC Educational Resources Information Center

Ozan, Ozlem

2013-01-01

Social networks and mobile technologies are transforming learning ecology. In this changing learning environment, we find a variety of new learner needs. The aim of this study is to investigate how to provide scaffolding to the learners in connectivist mobile learning environment: (1) to learn in a networked environment; (2) to manage their…

Online Resource-Based Learning Environment: Case Studies in Primary Classrooms

ERIC Educational Resources Information Center

So, Winnie Wing Mui; Ching, Fiona Ngai Ying

2012-01-01

This paper discusses the creation of learning environments with online resources by three primary school teachers for pupil's learning of science-related topics with reference to the resource-based e-learning environments (RBeLEs) framework. Teachers' choice of contexts, resources, tools, and scaffolds in designing the learning environments are…

The Predicaments of Language Learners in Traditional Learning Environments

ERIC Educational Resources Information Center

Shafie, Latisha Asmaak; Mansor, Mahani

2009-01-01

Some public universities in developing countries have traditional language learning environments such as classrooms with only blackboards and furniture which do not provide conducive learning environments. These traditional environments are unable to cater for digital learners who need to learn with learning technologies. In order to create…

The Integration of Personal Learning Environments & Open Network Learning Environments

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; Sujo-Montes, Laura; Yen, Cherng-Jyh; Chan, Junn-Yih; Blocher, Michael

2012-01-01

Learning management systems traditionally provide structures to guide online learners to achieve their learning goals. Web 2.0 technology empowers learners to create, share, and organize their personal learning environments in open network environments; and allows learners to engage in social networking and collaborating activities. Advanced…

Experiential Learning and Learning Environments: The Case of Active Listening Skills

ERIC Educational Resources Information Center

Huerta-Wong, Juan Enrique; Schoech, Richard

2010-01-01

Social work education research frequently has suggested an interaction between teaching techniques and learning environments. However, this interaction has never been tested. This study compared virtual and face-to-face learning environments and included active listening concepts to test whether the effectiveness of learning environments depends…

Losing Wallets, Retaining Trust? The Relationship Between Ethnic Heterogeneity and Trusting Coethnic and Non-coethnic Neighbours and Non-neighbours to Return a Lost Wallet.

PubMed

Tolsma, J; van der Meer, T W G

2017-01-01

The constrict claim that ethnic heterogeneity drives down social trust has been empirically tested across the globe. Meta-analyses suggest that neighbourhood ethnic heterogeneity generally undermines ties within the neighbourhood (such as trust in neighbours), but concurrently has an inconsistent or even positive effect on interethnic ties (such as outgroup trust). While the composition of the living environment thus often seems to matter, when and where remain unclear. We contribute to the literature by: (1) scrutinizing the extent to which ethnic heterogeneity drives down trust in coethnic neighbours, non-coethnic neighbours, unknown neighbours and unknown non-neighbours similarly; (2) comparing effects of heterogeneity aggregated to geographical areas that vary in scale and type of boundary; and (3) assessing whether the impact of heterogeneity of the local area depends on the wider geographic context. We test our hypotheses on the Religion in Dutch Society 2011-2012 dataset, supplemented with uniquely detailed GIS-data of Statistics Netherlands. Our dependent variables are four different so-called wallet-items, which we model through spatial and multilevel regression techniques. We demonstrate that both trust in non-coethnic and coethnic neighbours are lower in heterogeneous environments. Trust in people outside the neighbourhood is not affected by local heterogeneity. Measures of heterogeneity aggregated to relatively large scales, such as, administrative municipalities and egohoods with a 4000 m radius, demonstrate the strongest negative relationships with our trust indicators.

Uranium (VI) transport in saturated heterogeneous media: Influence of kaolinite and humic acid.

PubMed

Chen, Chong; Zhao, Kang; Shang, Jianying; Liu, Chongxuan; Wang, Jin; Yan, Zhifeng; Liu, Kesi; Wu, Wenliang

2018-05-07

Natural aquifers typically exhibit a variety of structural heterogeneities. However, the effect of mineral colloids and natural organic matter on the transport behavior of uranium (U) in saturated heterogeneous media are not totally understood. In this study, heterogeneous column experiments were conducted, and the constructed columns contained a fast-flow domain (FFD) and a slow-flow domain (SFD). The effect of kaolinite, humic acid (HA), and kaolinite/HA mixture on U(VI) retention and release in saturated heterogeneous media was examined. Media heterogeneity significantly influenced U fate and transport behavior in saturated subsurface environment. The presence of kaolinite, HA, and kaolinite/HA enhanced the mobility of U in heterogeneous media, and the mobility of U was the highest in the presence of kaolinite/HA and the lowest in the presence of kaolinite. In the presence of kaolinite, there was no difference in the amount of U released from the FFD and SFD. However, in the presence of HA and kaolinite/HA, a higher amount of U was released from the FFD. The findings in this study showed that medium structure and mineral colloids, as well as natural organic matter in the aqueous phase had significant effects on U transport and fate in subsurface environment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Habitat heterogeneity hypothesis and edge effects in model metacommunities.

PubMed

Hamm, Michaela; Drossel, Barbara

2017-08-07

Spatial heterogeneity is an inherent property of any living environment and is expected to favour biodiversity due to a broader niche space. Furthermore, edges between different habitats can provide additional possibilities for species coexistence. Using computer simulations, this study examines metacommunities consisting of several trophic levels in heterogeneous environments in order to explore the above hypotheses on a community level. We model heterogeneous landscapes by using two different sized resource pools and evaluate the combined effect of dispersal and heterogeneity on local and regional species diversity. This diversity is obtained by running population dynamics and evaluating the robustness (i.e., the fraction of surviving species). The main results for regional robustness are in agreement with the habitat heterogeneity hypothesis, as the largest robustness is found in heterogeneous systems with intermediate dispersal rates. This robustness is larger than in homogeneous systems with the same total amount of resources. We study the edge effect by arranging the two types of resources in two homogeneous blocks. Different edge responses in diversity are observed, depending on dispersal strength. Local robustness is highest for edge habitats that contain the smaller amount of resource in combination with intermediate dispersal. The results show that dispersal is relevant to correctly identify edge responses on community level. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Role of the Constructivist Learning Theory and Collaborative Learning Environment on Wiki Classroom, and the Relationship between Them

ERIC Educational Resources Information Center

Alzahrani, Ibraheem; Woollard, John

2013-01-01

This paper seeks to discover the relationship between both the social constructivist learning theory and the collaborative learning environment. This relationship can be identified by giving an example of the learning environment. Due to wiki characteristics, Wiki technology is one of the most famous learning environments that can show the…

Practice education learning environments: the mismatch between perceived and preferred expectations of undergraduate health science students.

PubMed

Brown, Ted; Williams, Brett; McKenna, Lisa; Palermo, Claire; McCall, Louise; Roller, Louis; Hewitt, Lesley; Molloy, Liz; Baird, Marilyn; Aldabah, Ligal

2011-11-01

Practical hands-on learning opportunities are viewed as a vital component of the education of health science students, but there is a critical shortage of fieldwork placement experiences. It is therefore important that these clinical learning environments are well suited to students' perceptions and expectations. To investigate how undergraduate students enrolled in health-related education programs view their clinical learning environments and specifically to compare students' perception of their 'actual' clinical learning environment to that of their 'preferred/ideal' clinical learning environment. The Clinical Learning Environment Inventory (CLEI) was used to collect data from 548 undergraduate students (55% response rate) enrolled in all year levels of paramedics, midwifery, radiography and medical imaging, occupational therapy, pharmacy, nutrition and dietetics, physiotherapy and social work at Monash University via convenience sampling. Students were asked to rate their perception of the clinical learning environment at the completion of their placements using the CLEI. Satisfaction of the students enrolled in the health-related disciplines was closely linked with the five constructs measured by the CLEI: Personalization, Student Involvement, Task Orientation, Innovation, and Individualization. Significant differences were found between the student's perception of their 'actual' clinical learning environment and their 'ideal' clinical learning environment. The study highlights the importance of a supportive clinical learning environment that places emphasis on effective two-way communication. A thorough understanding of students' perceptions of their clinical learning environments is essential. Copyright © 2010 Elsevier Ltd. All rights reserved.

Stroke Rehabilitation using Virtual Environments

PubMed Central

Fu, Michael J.; Knutson, Jayme; Chae, John

2015-01-01

Synopsis This review covers the rationale, mechanisms, and availability of commercially available virtual environment-based interventions for stroke rehabilitation. It describes interventions for motor, speech, cognitive, and sensory dysfunction. Also discussed are the important features and mechanisms that allow virtual environments to facilitate motor relearning. A common challenge facing the field is inability to translate success in small trials to efficacy in larger populations. The heterogeneity of stroke pathophysiology has been blamed and experts advocate for the study of multimodal approaches. Therefore, this article also introduces a framework to help define new therapy combinations that may be necessary to address stroke heterogeneity. PMID:26522910

Attention deficits and hyperactivity–impulsivity: What have we learned, what next?

PubMed Central

NIGG, JOEL T.

2015-01-01

The domains of self-regulation, self-control, executive function, inattention, and impulsivity cut across broad swathes of normal and abnormal development. Attention-deficit/hyperactivity disorder is a common syndrome that encompasses a portion of these domains. In the past 25 years research on attention-deficit/hyperactivity disorder has been characterized by dramatic advances in genetic, neural, and neuropsychological description of the syndrome as well as clarification of its multidimensional phenotypic structure. The limited clinical applicability of these research findings poses the primary challenge for the next generation. It is likely that clinical breakthroughs will require further refinement in describing heterogeneity or clinical/biological subgroups, renewed focus on the environment in the form of etiological events as well as psychosocial contexts of development, and integration of both with biological understanding. PMID:24342852

Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

ERIC Educational Resources Information Center

Fratamico, Lauren; Conati, Cristina; Kardan, Samad; Roll, Ido

2017-01-01

Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a…

A Simultaneous Mobile E-Learning Environment and Application

ERIC Educational Resources Information Center

Karal, Hasan; Bahcekapili, Ekrem; Yildiz, Adil

2010-01-01

The purpose of the present study was to design a mobile learning environment that enables the use of a teleconference application used in simultaneous e-learning with mobile devices and to evaluate this mobile learning environment based on students' views. With the mobile learning environment developed in the study, the students are able to follow…

Using Scenarios to Design Complex Technology-Enhanced Learning Environments

ERIC Educational Resources Information Center

de Jong, Ton; Weinberger, Armin; Girault, Isabelle; Kluge, Anders; Lazonder, Ard W.; Pedaste, Margus; Ludvigsen, Sten; Ney, Muriel; Wasson, Barbara; Wichmann, Astrid; Geraedts, Caspar; Giemza, Adam; Hovardas, Tasos; Julien, Rachel; van Joolingen, Wouter R.; Lejeune, Anne; Manoli, Constantinos C.; Matteman, Yuri; Sarapuu, Tago; Verkade, Alex; Vold, Vibeke; Zacharia, Zacharias C.

2012-01-01

Science Created by You (SCY) learning environments are computer-based environments in which students learn about science topics in the context of addressing a socio-scientific problem. Along their way to a solution for this problem students produce many types of intermediate products or learning objects. SCY learning environments center the entire…

Heterogeneous structure and solvation dynamics of DME/water binary mixtures: A combined spectroscopic and simulation investigation

NASA Astrophysics Data System (ADS)

Das Mahanta, Debasish; Rana, Debkumar; Patra, Animesh; Mukherjee, Biswaroop; Mitra, Rajib Kumar

2018-05-01

Water is often found in (micro)-heterogeneous environments and therefore it is necessary to understand their H-bonded network structure in such altered environments. We explore the structure and dynamics of water in its binary mixture with relatively less polar small biocompatible amphiphilic molecule 1,2-Dimethoxyethane (DME) by a combined spectroscopic and molecular dynamics (MD) simulation study. Picosecond (ps) resolved fluorescence spectroscopy using coumarin 500 as the fluorophore establishes a non-monotonic behaviour of the mixture. Simulation studies also explore the various possible H-bond formations between water and DME. The relative abundance of such different water species manifests the heterogeneity in the mixture.

[Self-directed learning and academic background of 2010 to 2014 cohorts of medical students].

PubMed

Pérez-Villalobos, Cristhian E; Fasce-Henry, Eduardo A; Ortega-Bastidas, Javiera A; Ortiz-Moreira, Liliana E; Bastías-Vega, Nancy; Bustamante-Durán, Carolina E; Ibáñez-Gracia, Pilar; Márquez-Urrizola, Carolina G; Delgado-Rivera, Macarena; Glaría-López, Rocío

2017-07-01

The widespread growth of higher education is increasing the heterogeneity of university students in terms of socioeconomic characteristics, academic story and cultural background. Medical schools are not an exception of this phenomenon. To compare the academic background and self-directed learning behavior of students who entered to a public medial school between 2010 and 2014. A non-probabilistic sample of 527 medical students aged between 17 and 29 years (60% men), was studied. Their academic information was collected from the University data base; they answered the Self-directed learning readiness scale of Fisher. Students from the 2014 cohort had higher high school grades than their counterparts. The scores in mathematics of the Scholarship Aptitude Test (SAT) were higher in the cohorts of 2010 and 2011. Those of the sciences test were superior in the 2013 cohort. The 2014 cohort had the lower general score of self-directed learning behaviors. The lower SAT and self-directed learning scores of the students entering medical school in 2014, indicate the progressive increase in the heterogeneity of Medical students.

Learning Environments Designed According to Learning Styles and Its Effects on Mathematics Achievement

ERIC Educational Resources Information Center

Özerem, Aysen; Akkoyunlu, Buket

2015-01-01

Problem Statement: While designing a learning environment it is vital to think about learner characteristics (learning styles, approaches, motivation, interests… etc.) in order to promote effective learning. The learning environment and learning process should be designed not to enable students to learn in the same manner and at the same level,…

Heterogeneous Deformable Modeling of Bio-Tissues and Haptic Force Rendering for Bio-Object Modeling

NASA Astrophysics Data System (ADS)

Lin, Shiyong; Lee, Yuan-Shin; Narayan, Roger J.

This paper presents a novel technique for modeling soft biological tissues as well as the development of an innovative interface for bio-manufacturing and medical applications. Heterogeneous deformable models may be used to represent the actual internal structures of deformable biological objects, which possess multiple components and nonuniform material properties. Both heterogeneous deformable object modeling and accurate haptic rendering can greatly enhance the realism and fidelity of virtual reality environments. In this paper, a tri-ray node snapping algorithm is proposed to generate a volumetric heterogeneous deformable model from a set of object interface surfaces between different materials. A constrained local static integration method is presented for simulating deformation and accurate force feedback based on the material properties of a heterogeneous structure. Biological soft tissue modeling is used as an example to demonstrate the proposed techniques. By integrating the heterogeneous deformable model into a virtual environment, users can both observe different materials inside a deformable object as well as interact with it by touching the deformable object using a haptic device. The presented techniques can be used for surgical simulation, bio-product design, bio-manufacturing, and medical applications.

Flexible Grouping as a Means for Classroom Management in a Heterogeneous Classroom

ERIC Educational Resources Information Center

Rytivaara, Anna

2011-01-01

This article concerns issues of classroom management in heterogeneous classrooms. Although research in the field of learning styles has yielded mixed results, there is a call for information about how they could be used to individualize instruction, especially in primary schools. This article is part of an ethnographic study aiming to examine…

Reward from bugs to bipeds: a comparative approach to understanding how reward circuits function

PubMed Central

Scaplen, Kristin M.; Kaun, Karla R.

2016-01-01

Abstract In a complex environment, animals learn from their responses to stimuli and events. Appropriate response to reward and punishment can promote survival, reproduction and increase evolutionary fitness. Interestingly, the neural processes underlying these responses are remarkably similar across phyla. In all species, dopamine is central to encoding reward and directing motivated behaviors, however, a comprehensive understanding of how circuits encode reward and direct motivated behaviors is still lacking. In part, this is a result of the sheer diversity of neurons, the heterogeneity of their responses and the complexity of neural circuits within which they are found. We argue that general features of reward circuitry are common across model organisms, and thus principles learned from invertebrate model organisms can inform research across species. In particular, we discuss circuit motifs that appear to be functionally equivalent from flies to primates. We argue that a comparative approach to studying and understanding reward circuit function provides a more comprehensive understanding of reward circuitry, and informs disorders that affect the brain’s reward circuitry. PMID:27328845

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.

PubMed

Abuassba, Adnan O M; Zhang, Dezheng; Luo, Xiong; Shaheryar, Ahmad; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L 2 -norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

PubMed Central

Abuassba, Adnan O. M.; Ali, Hazrat

2017-01-01

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets. PMID:28546808

[E-learning and the continuing professional development in medicine].

PubMed

De Fiore, Luca

2010-06-01

E-learning is widely used in continuing medical education but three main problems still face health decision makers: the substantial heterogeneity among the characteristics of the web-based educational projects; the concerns about the e-learning effectiveness; the variety of outcomes used to evaluate the effectiveness. Systematic reviews suggest e-learning has effectiveness similar to traditional educational methods.The attention should now be given to how and when can we use e-learning to improve the health workers' performance and better healthcare.

Tumor Heterogenity Research Interactive Visualization Environment (THRIVE) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

A platform for quantitative evaluation of intratumoral spatial heterogeneity in multiplexed immunofluorescence images, via characterization of the spatial interactions between different cellular phenotypes and non-cellular constituents in the tumor microenvironment.

Integrating heterogeneous databases in clustered medic care environments using object-oriented technology

NASA Astrophysics Data System (ADS)

Thakore, Arun K.; Sauer, Frank

1994-05-01

The organization of modern medical care environments into disease-related clusters, such as a cancer center, a diabetes clinic, etc., has the side-effect of introducing multiple heterogeneous databases, often containing similar information, within the same organization. This heterogeneity fosters incompatibility and prevents the effective sharing of data amongst applications at different sites. Although integration of heterogeneous databases is now feasible, in the medical arena this is often an ad hoc process, not founded on proven database technology or formal methods. In this paper we illustrate the use of a high-level object- oriented semantic association method to model information found in different databases into an integrated conceptual global model that integrates the databases. We provide examples from the medical domain to illustrate an integration approach resulting in a consistent global view, without attacking the autonomy of the underlying databases.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

Integrating XQuery-Enabled SCORM XML Metadata Repositories into an RDF-Based E-Learning P2P Network

ERIC Educational Resources Information Center

Qu, Changtao; Nejdl, Wolfgang

2004-01-01

Edutella is an RDF-based E-Learning P2P network that is aimed to accommodate heterogeneous learning resource metadata repositories in a P2P manner and further facilitate the exchange of metadata between these repositories based on RDF. Whereas Edutella provides RDF metadata repositories with a quite natural integration approach, XML metadata…

Dispersal patterns, active behaviour, and flow environment during early life history of coastal cold water fishes.

PubMed

Stanley, Ryan; Snelgrove, Paul V R; Deyoung, Brad; Gregory, Robert S

2012-01-01

During the pelagic larval phase, fish dispersal may be influenced passively by surface currents or actively determined by swimming behaviour. In situ observations of larval swimming are few given the constraints of field sampling. Active behaviour is therefore often inferred from spatial patterns in the field, laboratory studies, or hydrodynamic theory, but rarely are these approaches considered in concert. Ichthyoplankton survey data collected during 2004 and 2006 from coastal Newfoundland show that changes in spatial heterogeneity for multiple species do not conform to predictions based on passive transport. We evaluated the interaction of individual larvae with their environment by calculating Reynolds number as a function of ontogeny. Typically, larvae hatch into a viscous environment in which swimming is inefficient, and later grow into more efficient intermediate and inertial swimming environments. Swimming is therefore closely related to length, not only because of swimming capacity but also in how larvae experience viscosity. Six of eight species sampled demonstrated consistent changes in spatial patchiness and concomitant increases in spatial heterogeneity as they transitioned into more favourable hydrodynamic swimming environments, suggesting an active behavioural element to dispersal. We propose the tandem assessment of spatial heterogeneity and hydrodynamic environment as a potential approach to understand and predict the onset of ecologically significant swimming behaviour of larval fishes in the field.

The effect of environmental heterogeneity on RPW8-mediated resistance to powdery mildews in Arabidopsis thaliana

PubMed Central

Jorgensen, Tove H.

2012-01-01

Background and Aims The biotic and abiotic environment of interacting hosts and parasites may vary considerably over small spatial and temporal scales. It is essential to understand how different environments affect host disease resistance because this determines frequency of disease and, importantly, heterogeneous environments can retard direct selection and potentially maintain genetic variation for resistance in natural populations. Methods The effect of different temperatures and soil nutrient conditions on the outcome of infection by a pathogen was quantified in Arabidopsis thaliana. Expression levels of a gene conferring resistance to powdery mildews, RPW8, were compared with levels of disease to test a possible mechanism behind variation in resistance. Key Results Most host genotypes changed from susceptible to resistant across environments with the ranking of genotypes differing between treatments. Transcription levels of RPW8 increased after infection and varied between environments, but there was no tight association between transcription and resistance levels. Conclusions There is a strong potential for a heterogeneous environment to change the resistance capacity of A. thaliana genotypes and hence the direction and magnitude of selection in the presence of the pathogen. Possible causative links between resistance gene expression and disease resistance are discussed in light of the present results on RPW8. PMID:22234559

An overview of learning disabilities: psychoeducational perspectives.

PubMed

Johnson, D J

1995-01-01

In general, people with learning disabilities are a heterogeneous population that require a multidisciplinary evaluation and careful, well-planned intervention. Despite this heterogeneity, patterns of problems often co-occur. Therefore, diagnosticians and educators should look beyond single areas of achievement such as reading or arithmetic. In addition, problems in one area of learning typically have secondary impacts on higher levels of learning. That is, comprehension problems typically interfere with expression. Every effort should be made to examine patterns of problems and to avoid fragmentation of services so that each area of underachievement is not treated separately. Although learning disabilities usually interfere with school performance, they are not simply academic handicaps. They interfere with certain social activities as well as occupational pursuits. In many instances, they impact on mental health and self-esteem. Therefore, students need multiple services. And, as emphasized throughout this journal issue, learning disabled individuals may have comorbid conditions such as attention deficit disorder, depression, and neurologic problems. Furthermore, the problems may change over time. Children may first be identified because of language comprehension problems but later have reading or mathematics difficulty. With intervention, oral expressive problems may be alleviated but may be manifested later in written language.

Web-Based Learning Environment Based on Students’ Needs

NASA Astrophysics Data System (ADS)

Hamzah, N.; Ariffin, A.; Hamid, H.

2017-08-01

Traditional learning needs to be improved since it does not involve active learning among students. Therefore, in the twenty-first century, the development of internet technology in the learning environment has become the main needs of each student. One of the learning environments to meet the needs of the teaching and learning process is a web-based learning environment. This study aims to identify the characteristics of a web-based learning environment that supports students’ learning needs. The study involved 542 students from fifteen faculties in a public higher education institution in Malaysia. A quantitative method was used to collect the data via a questionnaire survey by randomly. The findings indicate that the characteristics of a web-based learning environment that support students’ needs in the process of learning are online discussion forum, lecture notes, assignments, portfolio, and chat. In conclusion, the students overwhelmingly agreed that online discussion forum is the highest requirement because the tool can provide a space for students and teachers to share knowledge and experiences related to teaching and learning.

Grid heterogeneity in in-silico experiments: an exploration of drug screening using DOCK on cloud environments.

PubMed

Yim, Wen-Wai; Chien, Shu; Kusumoto, Yasuyuki; Date, Susumu; Haga, Jason

2010-01-01

Large-scale in-silico screening is a necessary part of drug discovery and Grid computing is one answer to this demand. A disadvantage of using Grid computing is the heterogeneous computational environments characteristic of a Grid. In our study, we have found that for the molecular docking simulation program DOCK, different clusters within a Grid organization can yield inconsistent results. Because DOCK in-silico virtual screening (VS) is currently used to help select chemical compounds to test with in-vitro experiments, such differences have little effect on the validity of using virtual screening before subsequent steps in the drug discovery process. However, it is difficult to predict whether the accumulation of these discrepancies over sequentially repeated VS experiments will significantly alter the results if VS is used as the primary means for identifying potential drugs. Moreover, such discrepancies may be unacceptable for other applications requiring more stringent thresholds. This highlights the need for establishing a more complete solution to provide the best scientific accuracy when executing an application across Grids. One possible solution to platform heterogeneity in DOCK performance explored in our study involved the use of virtual machines as a layer of abstraction. This study investigated the feasibility and practicality of using virtual machine and recent cloud computing technologies in a biological research application. We examined the differences and variations of DOCK VS variables, across a Grid environment composed of different clusters, with and without virtualization. The uniform computer environment provided by virtual machines eliminated inconsistent DOCK VS results caused by heterogeneous clusters, however, the execution time for the DOCK VS increased. In our particular experiments, overhead costs were found to be an average of 41% and 2% in execution time for two different clusters, while the actual magnitudes of the execution time costs were minimal. Despite the increase in overhead, virtual clusters are an ideal solution for Grid heterogeneity. With greater development of virtual cluster technology in Grid environments, the problem of platform heterogeneity may be eliminated through virtualization, allowing greater usage of VS, and will benefit all Grid applications in general.

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…

Learning with Collaborative Inquiry: A Science Learning Environment for Secondary Students

ERIC Educational Resources Information Center

Sun, Daner; Looi, Chee-Kit; Xie, Wenting

2017-01-01

When inquiry-based learning is designed for a collaborative context, the interactions that arise in the learning environment can become fairly complex. While the learning effectiveness of such learning environments has been reported in the literature, there have been fewer studies on the students' learning processes. To address this, the article…

Learning in a u-Museum: Developing a Context-Aware Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Chia-Chen; Huang, Tien-Chi

2012-01-01

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the…

Interoperability in Personalized Adaptive Learning

ERIC Educational Resources Information Center

Aroyo, Lora; Dolog, Peter; Houben, Geert-Jan; Kravcik, Milos; Naeve, Ambjorn; Nilsson, Mikael; Wild, Fridolin

2006-01-01

Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the…

How has problem based learning fared in Pakistan?

PubMed

Mahmud, Waqas; Hyder, Omar

2012-10-01

To conduct a systematic review of primary research in undergraduate medical education in Pakistan in order to evaluate PBL programs, examine outcomes and competencies influenced by PBL, and compare them with conventional learning (lecture based learning, LBL). Qualitative content analysis. Rawalpindi Medical College, Rawalpindi, from June 2010 - February 2011. Literature was searched using online resources. Studies evaluating outcomes influenced by PBL, or comparing PBL with lecture based learning (LBL) were selected. Due to heterogeneity, a qualitative content analysis was performed in which studies were classified according to the methods of assessment; results were then summarized by outcome and frequencies were calculated. Eleven studies were included. Apart from knowledge acquisition, students gave high ratings to PBL in selected outcomes, alone, and in comparison with LBL. There was a disagreement among results of studies that evaluated knowledge acquisition alone. Based on student perceptions, PBL has many advantages. However, the results of this review are limited due to heterogeneity and methodological weakness of studies, specially the studies that compared exam scores to assess knowledge acquisition.

Assessing the Impact of Student Learning Style Preferences

NASA Astrophysics Data System (ADS)

Davis, Stacey M.; Franklin, Scott V.

2004-09-01

Students express a wide range of preferences for learning environments. We are trying to measure the manifestation of learning styles in various learning environments. In particular, we are interested in performance in an environment that disagrees with the expressed learning style preference, paying close attention to social (group vs. individual) and auditory (those who prefer to learn by listening) environments. These are particularly relevant to activity-based curricula which typically emphasize group-work and de-emphasize lectures. Our methods include multiple-choice assessments, individual student interviews, and a study in which we attempt to isolate the learning environment.

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.…

A Well Designed School Environment Facilitates Brain Learning.

ERIC Educational Resources Information Center

Chan, Tak Cheung; Petrie, Garth

2000-01-01

Examines how school design facilitates learning by complementing how the brain learns. How the brain learns is discussed and how an artistic environment, spaciousness in the learning areas, color and lighting, and optimal thermal and acoustical environments aid student learning. School design suggestions conclude the article. (GR)

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.

Geologic Controls on the Growth of Petroleum Reserves

USGS Publications Warehouse

Fishman, Neil S.; Turner, Christine E.; Peterson, Fred; Dyman, Thaddeus S.; Cook, Troy

2008-01-01

The geologic characteristics of selected siliciclastic (largely sandstone) and carbonate (limestone and dolomite) reservoirs in North America (largely the continental United States) were investigated to improve our understanding of the role of geology in the growth of petroleum reserves. Reservoirs studied were deposited in (1) eolian environments (Jurassic Norphlet Formation of the Gulf Coast and Pennsylvanian-Permian Minnelusa Formation of the Powder River Basin), (2) interconnected fluvial, deltaic, and shallow marine environments (Oligocene Frio Formation of the Gulf Coast and the Pennsylvanian Morrow Formation of the Anadarko and Denver Basins), (3) deeper marine environments (Mississippian Barnett Shale of the Fort Worth Basin and Devonian-Mississippian Bakken Formation of the Williston Basin), (4) marine carbonate environments (Ordovician Ellenburger Group of the Permian Basin and Jurassic Smackover Formation of the Gulf of Mexico Basin), (5) a submarine fan environment (Permian Spraberry Formation of the Midland Basin), and (6) a fluvial environment (Paleocene-Eocene Wasatch Formation of the Uinta-Piceance Basin). The connection between an oil reservoir's production history and geology was also evaluated by studying production histories of wells in disparate reservoir categories and wells in a single formation containing two reservoir categories. This effort was undertaken to determine, in general, if different reservoir production heterogeneities could be quantified on the basis of gross geologic differences. It appears that reserve growth in existing fields is most predictable for those in which reservoir heterogeneity is low and thus production differs little between wells, probably owing to relatively homogeneous fluid flow. In fields in which reservoirs are highly heterogeneous, prediction of future growth from infill drilling is notably more difficult. In any case, success at linking heterogeneity to reserve growth depends on factors in addition to geology, such as engineering and technological advances and political or cultural or economic influences.

Concentrative nitrogen allocation to sun-lit branches and the effects on whole-plant growth under heterogeneous light environments.

PubMed

Sugiura, D; Tateno, M

2013-08-01

We investigated the nitrogen and carbohydrate allocation patterns of trees under heterogeneous light environments using saplings of the devil maple tree (Acer diabolicum) with Y-shaped branches. Different branch groups were created: all branches of a sapling exposed to full light (L-branches), all branches exposed to full shade (S-branches), and half of the branches of a sapling exposed to light (HL-branches) and the other half exposed to shade (HS-branches). Throughout the growth period, nitrogen was preferentially allocated to HL-branches, whereas nitrogen allocation to HS-branches was suppressed compared to L- and S-branches. HL-branches with the highest leaf nitrogen content (N(area)) also had the highest rates of growth, and HS-branches with the lowest N(area) had the lowest observed growth rates. In addition, net nitrogen assimilation, estimated using a photosynthesis model, was strongly correlated with branch growth and whole-plant growth. In contrast, patterns of photosynthate allocation to branches and roots were not affected by the light conditions of the other branch. These observations suggest that tree canopies develop as a result of resource allocation patterns, where the growth of sun-lit branches is favoured over shaded branches, which leads to enhanced whole-plant growth in heterogeneous light environments. Our results indicate that whole-plant growth is enhanced by the resource allocation patterns created for saplings in heterogeneous light environments.

Utility functions and resource management in an oversubscribed heterogeneous computing environment

DOE PAGES

Khemka, Bhavesh; Friese, Ryan; Briceno, Luis Diego; ...

2014-09-26

We model an oversubscribed heterogeneous computing system where tasks arrive dynamically and a scheduler maps the tasks to machines for execution. The environment and workloads are based on those being investigated by the Extreme Scale Systems Center at Oak Ridge National Laboratory. Utility functions that are designed based on specifications from the system owner and users are used to create a metric for the performance of resource allocation heuristics. Each task has a time-varying utility (importance) that the enterprise will earn based on when the task successfully completes execution. We design multiple heuristics, which include a technique to drop lowmore » utility-earning tasks, to maximize the total utility that can be earned by completing tasks. The heuristics are evaluated using simulation experiments with two levels of oversubscription. The results show the benefit of having fast heuristics that account for the importance of a task and the heterogeneity of the environment when making allocation decisions in an oversubscribed environment. Furthermore, the ability to drop low utility-earning tasks allow the heuristics to tolerate the high oversubscription as well as earn significant utility.« less

Personal Learning Environments: A Solution for Self-Directed Learners

ERIC Educational Resources Information Center

Haworth, Ryan

2016-01-01

In this paper I discuss "personal learning environments" and their diverse benefits, uses, and implications for life-long learning. Personal Learning Environments (PLEs) are Web 2.0 and social media technologies that enable individual learners the ability to manage their own learning. Self-directed learning is explored as a foundation…

Ubiquitous Learning Environments in Higher Education: A Scoping Literature Review

ERIC Educational Resources Information Center

Virtanen, Mari Aulikki; Haavisto, Elina; Liikanen, Eeva; Kääriäinen, Maria

2018-01-01

Ubiquitous learning and the use of ubiquitous learning environments heralds a new era in higher education. Ubiquitous learning environments enhance context-aware and seamless learning experiences available from any location at any time. They support smooth interaction between authentic and digital learning resources and provide personalized…

Co-Regulation of Learning in Computer-Supported Collaborative Learning Environments: A Discussion

ERIC Educational Resources Information Center

Chan, Carol K. K.

2012-01-01

This discussion paper for this special issue examines co-regulation of learning in computer-supported collaborative learning (CSCL) environments extending research on self-regulated learning in computer-based environments. The discussion employs a socio-cognitive perspective focusing on social and collective views of learning to examine how…

Trajectories of the home learning environment across the first 5 years: associations with children's vocabulary and literacy skills at prekindergarten.

PubMed

Rodriguez, Eileen T; Tamis-LeMonda, Catherine S

2011-01-01

Children's home learning environments were examined in a low-income sample of 1,852 children and families when children were 15, 25, 37, and 63 months. During home visits, children's participation in literacy activities, the quality of mothers' engagements with their children, and the availability of learning materials were assessed, yielding a total learning environment score at each age. At 63 months, children's vocabulary and literacy skills were assessed. Six learning environment trajectories were identified, including environments that were consistently low, environments that were consistently high, and environments characterized by varying patterns of change. The skills of children at the extremes of learning environment trajectories differed by more than 1 SD and the timing of learning experiences related to specific emerging skills. © 2011 The Authors. Child Development © 2011 Society for Research in Child Development, Inc.

HeNCE: A Heterogeneous Network Computing Environment

DOE PAGES

Beguelin, Adam; Dongarra, Jack J.; Geist, George Al; ...

1994-01-01

Network computing seeks to utilize the aggregate resources of many networked computers to solve a single problem. In so doing it is often possible to obtain supercomputer performance from an inexpensive local area network. The drawback is that network computing is complicated and error prone when done by hand, especially if the computers have different operating systems and data formats and are thus heterogeneous. The heterogeneous network computing environment (HeNCE) is an integrated graphical environment for creating and running parallel programs over a heterogeneous collection of computers. It is built on a lower level package called parallel virtual machine (PVM).more » The HeNCE philosophy of parallel programming is to have the programmer graphically specify the parallelism of a computation and to automate, as much as possible, the tasks of writing, compiling, executing, debugging, and tracing the network computation. Key to HeNCE is a graphical language based on directed graphs that describe the parallelism and data dependencies of an application. Nodes in the graphs represent conventional Fortran or C subroutines and the arcs represent data and control flow. This article describes the present state of HeNCE, its capabilities, limitations, and areas of future research.« less

Study of selected phenotype switching strategies in time varying environment

NASA Astrophysics Data System (ADS)

Horvath, Denis; Brutovsky, Branislav

2016-03-01

Population heterogeneity plays an important role across many research, as well as the real-world, problems. The population heterogeneity relates to the ability of a population to cope with an environment change (or uncertainty) preventing its extinction. However, this ability is not always desirable as can be exemplified by an intratumor heterogeneity which positively correlates with the development of resistance to therapy. Causation of population heterogeneity is therefore in biology and medicine an intensively studied topic. In this paper the evolution of a specific strategy of population diversification, the phenotype switching, is studied at a conceptual level. The presented simulation model studies evolution of a large population of asexual organisms in a time-varying environment represented by a stochastic Markov process. Each organism disposes with a stochastic or nonlinear deterministic switching strategy realized by discrete-time models with evolvable parameters. We demonstrate that under rapidly varying exogenous conditions organisms operate in the vicinity of the bet-hedging strategy, while the deterministic patterns become relevant as the environmental variations are less frequent. Statistical characterization of the steady state regimes of the populations is done using the Hellinger and Kullback-Leibler functional distances and the Hamming distance.

The Interplay of Perceptions of the Learning Environment, Personality and Learning Strategies: A Study amongst International Business Studies Students

ERIC Educational Resources Information Center

Nijhuis, Jan; Segers, Mien; Gijselaers, Wim

2007-01-01

Previous research on students' learning strategies has examined the relationships between either perceptions of the learning environment or personality and learning strategies. The focus of this study was on the joint relationships between the students' perceptions of the learning environment, their personality, and the learning strategies they…

CLEW: A Cooperative Learning Environment for the Web.

ERIC Educational Resources Information Center

Ribeiro, Marcelo Blois; Noya, Ricardo Choren; Fuks, Hugo

This paper outlines CLEW (collaborative learning environment for the Web). The project combines MUD (Multi-User Dimension), workflow, VRML (Virtual Reality Modeling Language) and educational concepts like constructivism in a learning environment where students actively participate in the learning process. The MUD shapes the environment structure.…

Evaluating and Implementing Learning Environments: A United Kingdom Experience.

ERIC Educational Resources Information Center

Ingraham, Bruce; Watson, Barbara; McDowell, Liz; Brockett, Adrian; Fitzpatrick, Simon

2002-01-01

Reports on ongoing work at five universities in northeastern England that have been evaluating and implementing online learning environments known as virtual learning environments (VLEs) or managed learning environments (MLEs). Discusses do-it-yourself versus commercial systems; transferability; Web-based versus client-server; integration with…

Cognitive Clusters in Specific Learning Disorder

ERIC Educational Resources Information Center

Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

2018-01-01

The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the "Diagnostic and Statistical Manual of Mental Disorders." The…

Proteus: A Lecturer-Friendly Adaptive Tutoring System

ERIC Educational Resources Information Center

Sessink, Olivier D. T.; Beeftink, Hendrik H.; Tramper, Johannes; Hartog, Rob J. M.

2007-01-01

Effectively targeting a heterogeneous student population is a common challenge in academic courses. Most traditional learning material targets the "average student," and is suboptimal for students who lack certain prior knowledge, or students who have already attained some of the course objectives. Student-activating learning material supports…

A systematic review of service-learning in medical education: 1998-2012.

PubMed

Stewart, Trae; Wubbena, Zane C

2015-01-01

PHENOMENON: In the United States, the Affordable Care Act has increased the need for community-centered pedagogy for medical education such as service-learning, wherein students connect academic curriculum and reflections to address a community need. Yet heterogeneity among service-learning programs suggests the need for a framework to understand variations among service-learning programs in medical education. A qualitative systematic review of literature on service-learning and medical education was conducted for the period between 1998 and 2012. A two-stage inclusion criteria process resulted in articles (n = 32) on service-learning and Doctor of Medicine or Doctor of Osteopathic Medicine being included for both coding and analysis. Focused and selective coding were employed to identify recurring themes and subthemes from the literature. The findings of the qualitative thematic analysis of service-learning variation in medical education identified a total of seven themes with subthemes. The themes identified from the analysis were (a) geographic location and setting, (b) program design, (c) funding, (d) participation, (e) program implementation, (f) assessment, and (g) student outcomes. Insights: This systematic review of literature confirmed the existence of program heterogeneity among service-learning program in medical education. However, the findings of this study provide key insights into the nature of service-learning in medical education building a framework for which to organize differences among service-learning programs. A list of recommendations for future areas of inquiry is provided to guide future research.

Group Modeling in Social Learning Environments

ERIC Educational Resources Information Center

Stankov, Slavomir; Glavinic, Vlado; Krpan, Divna

2012-01-01

Students' collaboration while learning could provide better learning environments. Collaboration assumes social interactions which occur in student groups. Social theories emphasize positive influence of such interactions on learning. In order to create an appropriate learning environment that enables social interactions, it is important to…

The clinical learning environment in nursing education: a concept analysis.

PubMed

Flott, Elizabeth A; Linden, Lois

2016-03-01

The aim of this study was to report an analysis of the clinical learning environment concept. Nursing students are evaluated in clinical learning environments where skills and knowledge are applied to patient care. These environments affect achievement of learning outcomes, and have an impact on preparation for practice and student satisfaction with the nursing profession. Providing clarity of this concept for nursing education will assist in identifying antecedents, attributes and consequences affecting student transition to practice. The clinical learning environment was investigated using Walker and Avant's concept analysis method. A literature search was conducted using WorldCat, MEDLINE and CINAHL databases using the keywords clinical learning environment, clinical environment and clinical education. Articles reviewed were written in English and published in peer-reviewed journals between 1995-2014. All data were analysed for recurring themes and terms to determine possible antecedents, attributes and consequences of this concept. The clinical learning environment contains four attribute characteristics affecting student learning experiences. These include: (1) the physical space; (2) psychosocial and interaction factors; (3) the organizational culture and (4) teaching and learning components. These attributes often determine achievement of learning outcomes and student self-confidence. With better understanding of attributes comprising the clinical learning environment, nursing education programmes and healthcare agencies can collaborate to create meaningful clinical experiences and enhance student preparation for the professional nurse role. © 2015 John Wiley & Sons Ltd.

Combined effects of waggle dance communication and landscape heterogeneity on nectar and pollen uptake in honey bee colonies.

PubMed

Nürnberger, Fabian; Steffan-Dewenter, Ingolf; Härtel, Stephan

2017-01-01

The instructive component of waggle dance communication has been shown to increase resource uptake of Apis mellifera colonies in highly heterogeneous resource environments, but an assessment of its relevance in temperate landscapes with different levels of resource heterogeneity is currently lacking. We hypothesized that the advertisement of resource locations via dance communication would be most relevant in highly heterogeneous landscapes with large spatial variation of floral resources. To test our hypothesis, we placed 24 Apis mellifera colonies with either disrupted or unimpaired instructive component of dance communication in eight Central European agricultural landscapes that differed in heterogeneity and resource availability. We monitored colony weight change and pollen harvest as measure of foraging success. Dance disruption did not significantly alter colony weight change, but decreased pollen harvest compared to the communicating colonies by 40%. There was no general effect of resource availability on nectar or pollen foraging success, but the effect of landscape heterogeneity on nectar uptake was stronger when resource availability was high. In contrast to our hypothesis, the effects of disrupted bee communication on nectar and pollen foraging success were not stronger in landscapes with heterogeneous compared to homogenous resource environments. Our results indicate that in temperate regions intra-colonial communication of resource locations benefits pollen foraging more than nectar foraging, irrespective of landscape heterogeneity. We conclude that the so far largely unexplored role of dance communication in pollen foraging requires further consideration as pollen is a crucial resource for colony development and health.

Educational environment and approaches to learning of undergraduate nursing students in an Indonesian school of nursing.

PubMed

Rochmawati, Erna; Rahayu, Gandes Retno; Kumara, Amitya

2014-11-01

The aims of this study were to assess students' perceptions of their educational environment and approaches to learning, and determine if perceptions of learning environment associates with approaches to learning. A survey was conducted to collect data from a regional private university in Indonesia. A total of 232 nursing students completed two questionnaires that measured their perceptions of educational environment and approaches to learning. The measurement was based on Dundee Ready Education Environment Measurement (DREEM) and Approaches and Study Skills Inventory for Students (ASSIST). Five learning environments dimensions and three learning approaches dimensions from two measures were measured. The overall score of DREEM was 131.03/200 (SD 17.04), it was in the range considered to be favourable. The overall score is different significantly between years of study (p value = 0.01). This study indicated that the majority of undergraduate nursing students' adopt strategic approach (n = 139. 59.9%). The finding showed that perceived educational environment significantly associated with approaches to learning. This study implicated the need to maintain conducive learning environment. There is also a need to improve the management of learning activities that reflect the use of student-centered learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sharing e-Learning Experiences: A Personalised Approach

NASA Astrophysics Data System (ADS)

Clematis, Andrea; Forcheri, Paola; Ierardi, Maria Grazia; Quarati, Alfonso

A two-tier architecture is presented, based on hybrid peer-to-peer technology, aimed at providing personalized access to heterogeneous learning sources. The architecture deploys a conceptual model that is superimposed over logically and physically separated repositories. The model is based on the interactions between users and learning resources, described by means of coments. To support users to find out material satisfying their needs, mechanisms for ranking resources and for extracting personalized views of the learning space are provided.

Steps towards incorporating heterogeneities into program theory: A case study of a data-driven approach.

PubMed

Sridharan, Sanjeev; Jones, Bobby; Caudill, Barry; Nakaima, April

2016-10-01

This paper describes a framework that can help refine program theory through data explorations and stakeholder dialogue. The framework incorporates the following steps: a recognition that program implementation might need to be multi-phased for a number of interventions, the need to take stock of program theory, the application of pattern recognition methods to help identify heterogeneous program mechanisms, and stakeholder dialogue to refine the program. As part of the data exploration, a method known as developmental trajectories is implemented to learn about heterogeneous trajectories of outcomes in longitudinal evaluations. This method identifies trajectory clusters and also can estimate different treatment impacts for the various groups. The framework is highlighted with data collected in an evaluation of an alcohol risk-reduction program delivered in a college fraternity setting. The framework discussed in the paper is informed by a realist focus on "what works for whom under what contexts." The utility of the framework in contributing to a dialogue on heterogeneous mechanism and subsequent implementation is described. The connection of the ideas in paper to a 'learning through principled discovery' approach is also described. Copyright © 2016. Published by Elsevier Ltd.

Learning with Hypertext Learning Environments: Theory, Design, and Research.

ERIC Educational Resources Information Center

Jacobson, Michael J.; And Others

1996-01-01

Studied 69 undergraduates who used conceptually-indexed hypertext learning environments with differently structured thematic criss-crossing (TCC) treatments: guided and learner selected. Found that students need explicit modeling and scaffolding support to learn complex knowledge from these learning environments, and considers implications for…

Interpretable Categorization of Heterogeneous Time Series Data

NASA Technical Reports Server (NTRS)

Lee, Ritchie; Kochenderfer, Mykel J.; Mengshoel, Ole J.; Silbermann, Joshua

2017-01-01

We analyze data from simulated aircraft encounters to validate and inform the development of a prototype aircraft collision avoidance system. The high-dimensional and heterogeneous time series dataset is analyzed to discover properties of near mid-air collisions (NMACs) and categorize the NMAC encounters. Domain experts use these properties to better organize and understand NMAC occurrences. Existing solutions either are not capable of handling high-dimensional and heterogeneous time series datasets or do not provide explanations that are interpretable by a domain expert. The latter is critical to the acceptance and deployment of safety-critical systems. To address this gap, we propose grammar-based decision trees along with a learning algorithm. Our approach extends decision trees with a grammar framework for classifying heterogeneous time series data. A context-free grammar is used to derive decision expressions that are interpretable, application-specific, and support heterogeneous data types. In addition to classification, we show how grammar-based decision trees can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply grammar-based decision trees to a simulated aircraft encounter dataset and evaluate the performance of four variants of our learning algorithm. The best algorithm is used to analyze and categorize near mid-air collisions in the aircraft encounter dataset. We describe each discovered category in detail and discuss its relevance to aircraft collision avoidance.

Facilitative and obstructive factors in the clinical learning environment: Experiences of pupil enrolled nurses.

PubMed

Lekalakala-Mokgele, Eucebious; Caka, Ernestine M

2015-03-31

The clinical learning environment is a complex social entity that influences student learning outcomes in the clinical setting. Students can experience the clinical learning environment as being both facilitative and obstructive to their learning. The clinical environment may be a source of stress, creating feelings of fear and anxiety which in turn affect the students' responses to learning. Equally, the environment can enhance learning if experienced positively. This study described pupil enrolled nurses' experiences of facilitative and obstructive factors in military and public health clinical learning settings. Using a qualitative, contextual, exploratory descriptive design, three focus group interviews were conducted until data saturation was reached amongst pupil enrolled nurses in a military School of Nursing. Data analysed provided evidence that acceptance by clinical staff and affordance of self-directed learning facilitated learning. Students felt safe to practise when they were supported by the clinical staff. They felt a sense of belonging when the staff showed an interest in and welcomed them. Learning was obstructed when students were met with condescending comments. Wearing of a military uniform in the public hospital and horizontal violence obstructed learning in the clinical learning environment. Students cannot have effective clinical preparation if the environment is not conducive to and supportive of clinical learning, The study shows that military nursing students experience unique challenges as they are trained in two professions that are hierarchical in nature. The students experienced both facilitating and obstructing factors to their learning during their clinical practice. Clinical staff should be made aware of factors which can impact on students' learning. Policies need to be developed for supporting students in the clinical learning environment.

First passage time: Connecting random walks to functional responses in heterogeneous environments (Invited)

NASA Astrophysics Data System (ADS)

Lewis, M. A.; McKenzie, H.; Merrill, E.

2010-12-01

In this talk I will outline first passage time analysis for animals undertaking complex movement patterns, and will demonstrate how first passage time can be used to derive functional responses in predator prey systems. The result is a new approach to understanding type III functional responses based on a random walk model. I will extend the analysis to heterogeneous environments to assess the effects of linear features on functional responses in wolves and elk using GPS tracking data.

Spatial radiation environment in a heterogeneous oak woodland using a three-dimensional radiative transfer model and multiple constraints from observations

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Ryu, Y.; Ustin, S.; Baldocchi, D. D.

2009-12-01

B15: Remote Characterization of Vegetation Structure: Including Research to Inform the Planned NASA DESDynI and ESA BIOMASS Missions Title: Spatial radiation environment in a heterogeneous oak woodland using a three-dimensional radiative transfer model and multiple constraints from observations Hideki Kobayashi, Youngryel Ryu, Susan Ustin, and Dennis Baldocchi Abstract Accurate evaluations of radiation environments of visible, near infrared, and thermal infrared wavebands in forest canopies are important to estimate energy, water, and carbon fluxes. Californian oak woodlands are sparse and highly clumped so that radiation environments are extremely heterogeneous spatially. The heterogeneity of radiation environments also varies with wavebands which depend on scattering and emission properties. So far, most of modeling studies have been performed in one dimensional radiative transfer models with (or without) clumping effect in the forest canopies. While some studies have been performed by using three dimensional radiative transfer models, several issues are still unresolved. For example, some 3D models calculate the radiation field with individual tree basis, and radiation interactions among trees are not considered. This interaction could be important in the highly scattering waveband such as near infrared. The objective of this study is to quantify the radiation field in the oak woodland. We developed a three dimensional radiative transfer model, which includes the thermal waveband. Soil/canopy energy balances and canopy physiology models, CANOAK, are incorporated in the radiative transfer model to simulate the diurnal patterns of thermal radiation fields and canopy physiology. Airborne LiDAR and canopy gap data measured by the several methods (digital photographs and plant canopy analyzer) were used to constrain the forest structures such as tree positions, crown sizes and leaf area density. Modeling results were tested by a traversing radiometer system that measured incoming photosynthetically active radiation and net radiation at forest floor and spatial variations in canopy reflectances taken by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). In this study, we show how the model with available measurements can reproduce the spatially heterogeneous radiation environments in the oak woodland.

Observational Learning of Academic and Social Behaviors during Small-Group Direct Instruction

ERIC Educational Resources Information Center

Ledford, Jennifer R.; Wolery, Mark

2015-01-01

Many studies have shown that small-group direct instruction is effective and efficient for teaching students with and without disabilities, although relatively few studies have been conducted with heterogeneous groups of preschool participants. In addition, previous studies have primarily assessed whether observational learning occurred for…

Arithmetic Facts Storage Deficit: The Hypersensitivity-to-Interference in Memory Hypothesis

ERIC Educational Resources Information Center

De Visscher, Alice; Noël, Marie-Pascale

2014-01-01

Dyscalculia, or mathematics learning disorders, is currently known to be heterogeneous (Wilson & Dehaene, 2007). While various profiles of dyscalculia coexist, a general and persistent hallmark of this math learning disability is the difficulty in memorizing arithmetic facts (Geary, Hoard & Hamson, 1999; Jordan & Montani, 1997; Slade…

Prescriptions for Success in Heterogeneous Classrooms.

ERIC Educational Resources Information Center

Schurr, Sandra L.

This handbook details 28 specific learning strategies for diverse groups of middle school students, each cast as a prescription applicable for students whose diagnosis reveals certain "conditions" such as particular learning styles or high or low reading skills. Reproducible pages accompany most of the strategies. Following are the activities: (1)…

Large-Scale Modeling of Wordform Learning and Representation

ERIC Educational Resources Information Center

Sibley, Daragh E.; Kello, Christopher T.; Plaut, David C.; Elman, Jeffrey L.

2008-01-01

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed the "sequence encoder" is used to learn…

Cooperative Learning versus Competition: Which Is Better?

ERIC Educational Resources Information Center

Ediger, Marlow

Most educators advocate cooperative learning in the curriculum. Heterogeneous grouping is also recommended so that students with mixed achievement levels work in a committee setting. Cooperative endeavors stress democracy as a way of life, according to many educators, as compared to competition in the classroom. This paper examines the philosophy…

RuleML-Based Learning Object Interoperability on the Semantic Web

ERIC Educational Resources Information Center

Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

Experience(s) in Creating Distance Learning Texts

ERIC Educational Resources Information Center

Todd, Peter A.

2007-01-01

A combination of factors including longer working hours, emphasis on professional development, and increased access to the Internet have fuelled the current high demand for distance learning options in tertiary biology. Distance learner students come from a heterogeneous pool of ages, backgrounds and abilities and they require choice in how they…

Science Learning Outcomes in Alignment with Learning Environment Preferences

ERIC Educational Resources Information Center

Chang, Chun-Yen; Hsiao, Chien-Hua; Chang, Yueh-Hsia

2011-01-01

This study investigated students' learning environment preferences and compared the relative effectiveness of instructional approaches on students' learning outcomes in achievement and attitude among 10th grade earth science classes in Taiwan. Data collection instruments include the Earth Science Classroom Learning Environment Inventory and Earth…

Exploring Collaborative Learning Effect in Blended Learning Environments

ERIC Educational Resources Information Center

Sun, Z.; Liu, R.; Luo, L.; Wu, M.; Shi, C.

2017-01-01

The use of new technology encouraged exploration of the effectiveness and difference of collaborative learning in blended learning environments. This study investigated the social interactive network of students, level of knowledge building and perception level on usefulness in online and mobile collaborative learning environments in higher…

A Preliminary Investigation of Self-Directed Learning Activities in a Non-Formal Blended Learning Environment

ERIC Educational Resources Information Center

Schwier, Richard A.; Morrison, Dirk; Daniel, Ben K.

2009-01-01

This research considers how professional participants in a non-formal self-directed learning environment (NFSDL) made use of self-directed learning activities in a blended face-to-face and on line learning professional development course. The learning environment for the study was a professional development seminar on teaching in higher education…

Students' Reflections on the Relationships between Safe Learning Environments, Learning Challenge and Positive Experiences of Learning in a Simulated GP Clinic

ERIC Educational Resources Information Center

Young, J. E.; Williamson, M. I.; Egan, T. G.

2016-01-01

Learning environments are a significant determinant of student behaviour, achievement and satisfaction. In this article we use students' reflective essays to identify key features of the learning environment that contributed to positive and transformative learning experiences. We explore the relationships between these features, the students'…

Personal Learning Environments in the Workplace: An Exploratory Study into the Key Business Decision Factors

ERIC Educational Resources Information Center

Chatterjee, Arunangsu; Law, Effie Lai-Chong; Mikroyannidis, Alexander; Owen, Glyn; Velasco, Karen

2013-01-01

Personal Learning Environments (PLEs) have emerged as a solution to the need of learners for open and easily customisable learning environments. PLEs essentially hand complete control over the learning process to the learner. However, this learning model is not fully compatible with learning in the workplace, which is influenced by certain…

Heterogeneous continuous-time random walks

NASA Astrophysics Data System (ADS)

Grebenkov, Denis S.; Tupikina, Liubov

2018-01-01

We introduce a heterogeneous continuous-time random walk (HCTRW) model as a versatile analytical formalism for studying and modeling diffusion processes in heterogeneous structures, such as porous or disordered media, multiscale or crowded environments, weighted graphs or networks. We derive the exact form of the propagator and investigate the effects of spatiotemporal heterogeneities onto the diffusive dynamics via the spectral properties of the generalized transition matrix. In particular, we show how the distribution of first-passage times changes due to local and global heterogeneities of the medium. The HCTRW formalism offers a unified mathematical language to address various diffusion-reaction problems, with numerous applications in material sciences, physics, chemistry, biology, and social sciences.

Argumentative Knowledge Construction in Online Learning Environments in and across Different Cultures: A Collaboration Script Perspective

ERIC Educational Resources Information Center

Weinberger, A.; Clark, D. B.; Haekkinen, P.; Tamura, Y.; Fischer, F.

2007-01-01

In recent years, information and communication technology has established new opportunities to participate in online learning environments around the globe. These opportunities include the dissemination of specific online learning environments as well as opportunities for learners to connect to online learning environments in distant locations.…

Turkish High School Student's Perceptions of Learning Environment in Biology Classrooms and Their Attitudes toward Biology.

ERIC Educational Resources Information Center

Cakiroglu, Jale; Telli, Sibel; Cakiroglu, Erdinc

The purpose of this study was to examine Turkish high school students' perceptions of learning environment in biology classrooms and to investigate relationships between learning environment and students' attitudes toward biology. Secondly, the study aimed to investigate the differences in students' perceptions of learning environments in biology…

Does academic performance or personal growth share a stronger association with learning environment perception?

PubMed

Colbert-Getz, Jorie M; Tackett, Sean; Wright, Scott M; Shochet, Robert S

2016-08-28

This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R(2)Δ of 0.09, F(1,175) = 14.99,  p < .001] and year 3 [R(2)Δ of 0.28, F(1,169) = 76.80, p < .001]. Learning environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception.

Does academic performance or personal growth share a stronger association with learning environment perception?

PubMed Central

Tackett, Sean; Wright, Scott M.; Shochet, Robert S.

2016-01-01

Objectives This study was conducted to characterize the relative strength of associations of learning environment perception with academic performance and with personal growth. Methods In 2012-2014 second and third year students at Johns Hopkins University School of Medicine completed a learning environment survey and personal growth scale. Hierarchical linear regression analysis was employed to determine if the proportion of variance in learning environment scores accounted for by personal growth was significantly larger than the proportion accounted for by academic performance (course/clerkship grades). Results The proportion of variance in learning environment scores accounted for by personal growth was larger than the proportion accounted for by academic performance in year 2 [R2Δ of 0.09, F(1,175) = 14.99,  p < .001] and year 3 [R2Δ of 0.28, F(1,169) = 76.80, p < .001]. Learning environment scores shared a small amount of variance with academic performance in years 2 and 3.  The amount of variance between learning environment scores and personal growth was small in year 2 and large in year 3. Conclusions Since supportive learning environments are essential for medical education, future work must determine if enhancing personal growth prior to and during the clerkship year will increase learning environment perception. PMID:27570912

Integration of heterogeneous features for remote sensing scene classification

NASA Astrophysics Data System (ADS)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

Developing Learning Theory by Refining Conjectures Embodied in Educational Designs

ERIC Educational Resources Information Center

Sandoval, William A.

2004-01-01

Designed learning environments embody conjectures about learning and instruction, and the empirical study of learning environments allows such conjectures to be refined over time. The construct of embodied conjecture is introduced as a way to demonstrate the theoretical nature of learning environment design and to frame methodological issues in…

Virtual Learning Environment for Interactive Engagement with Advanced Quantum Mechanics

ERIC Educational Resources Information Center

Pedersen, Mads Kock; Skyum, Birk; Heck, Robert; Müller, Romain; Bason, Mark; Lieberoth, Andreas; Sherson, Jacob F.

2016-01-01

A virtual learning environment can engage university students in the learning process in ways that the traditional lectures and lab formats cannot. We present our virtual learning environment "StudentResearcher," which incorporates simulations, multiple-choice quizzes, video lectures, and gamification into a learning path for quantum…

Issues of Learning Games: From Virtual to Real

ERIC Educational Resources Information Center

Carron, Thibault; Pernelle, Philippe; Talbot, Stéphane

2013-01-01

Our research work deals with the development of new learning environments, and we are particularly interested in studying the different aspects linked to users' collaboration in these environments. We believe that Game-based Learning can significantly enhance learning. That is why we have developed learning environments grounded on graphical…

Agent-Based Learning Environments as a Research Tool for Investigating Teaching and Learning.

ERIC Educational Resources Information Center

Baylor, Amy L.

2002-01-01

Discusses intelligent learning environments for computer-based learning, such as agent-based learning environments, and their advantages over human-based instruction. Considers the effects of multiple agents; agents and research design; the use of Multiple Intelligent Mentors Instructing Collaboratively (MIMIC) for instructional design for…

Student-Centred Learning Environments: An Investigation into Student Teachers' Instructional Preferences and Approaches to Learning

ERIC Educational Resources Information Center

Baeten, Marlies; Dochy, Filip; Struyven, Katrien; Parmentier, Emmeline; Vanderbruggen, Anne

2016-01-01

The use of student-centred learning environments in education has increased. This study investigated student teachers' instructional preferences for these learning environments and how these preferences are related to their approaches to learning. Participants were professional Bachelor students in teacher education. Instructional preferences and…

Active Learning Environment with Lenses in Geometric Optics

ERIC Educational Resources Information Center

Tural, Güner

2015-01-01

Geometric optics is one of the difficult topics for students within physics discipline. Students learn better via student-centered active learning environments than the teacher-centered learning environments. So this study aimed to present a guide for middle school teachers to teach lenses in geometric optics via active learning environment…

Practical Applications and Experiences in K-20 Blended Learning Environments

ERIC Educational Resources Information Center

Kyei-Blankson, Lydia, Ed.; Ntuli, Esther, Ed.

2014-01-01

Learning environments continue to change considerably and is no longer confined to the face-to-face classroom setting. As learning options have evolved, educators must adopt a variety of pedagogical strategies and innovative technologies to enable learning. "Practical Applications and Experiences in K-20 Blended Learning Environments"…

An Overview of MSHN: The Management System for Heterogeneous Networks

DTIC Science & Technology

1999-04-01

An Overview of MSHN: The Management System for Heterogeneous Networks Debra A. Hensgen†, Taylor Kidd†, David St. John§, Matthew C . Schnaidt†, Howard...ABSTRACT UU 18. NUMBER OF PAGES 15 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c . THIS PAGE...Alhusaini, V. K. Prasanna, and C . S. Raghavendra, “A unified resource scheduling framework for heterogeneous computing environments,” Proc. 8th IEEE

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

The effects of different learning environments on students' motivation for learning and their achievement.

PubMed

Baeten, Marlies; Dochy, Filip; Struyven, Katrien

2013-09-01

Research in higher education on the effects of student-centred versus lecture-based learning environments generally does not take into account the psychological need support provided in these learning environments. From a self-determination theory perspective, need support is important to study because it has been associated with benefits such as autonomous motivation and achievement. The purpose of the study is to investigate the effects of different learning environments on students' motivation for learning and achievement, while taking into account the perceived need support. First-year student teachers (N= 1,098) studying a child development course completed questionnaires assessing motivation and perceived need support. In addition, a prior knowledge test and case-based assessment were administered. A quasi-experimental pre-test/post-test design was set up consisting of four learning environments: (1) lectures, (2) case-based learning (CBL), (3) alternation of lectures and CBL, and (4) gradual implementation with lectures making way for CBL. Autonomous motivation and achievement were higher in the gradually implemented CBL environment, compared to the CBL environment. Concerning achievement, two additional effects were found; students in the lecture-based learning environment scored higher than students in the CBL environment, and students in the gradually implemented CBL environment scored higher than students in the alternated learning environment. Additionally, perceived need support was positively related to autonomous motivation, and negatively to controlled motivation. The study shows the importance of gradually introducing students to CBL, in terms of their autonomous motivation and achievement. Moreover, the study emphasizes the importance of perceived need support for students' motivation. © 2012 The British Psychological Society.

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects.

PubMed

Feder, Stephan; Sundermann, Benedikt; Wersching, Heike; Teuber, Anja; Kugel, Harald; Teismann, Henning; Heindel, Walter; Berger, Klaus; Pfleiderer, Bettina

2017-11-01

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification. We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained. Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level. It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data. Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity. Copyright © 2017 Elsevier B.V. All rights reserved.

Dispersal Patterns, Active Behaviour, and Flow Environment during Early Life History of Coastal Cold Water Fishes

PubMed Central

Stanley, Ryan; Snelgrove, Paul V. R.; deYoung, Brad; Gregory, Robert S.

2012-01-01

During the pelagic larval phase, fish dispersal may be influenced passively by surface currents or actively determined by swimming behaviour. In situ observations of larval swimming are few given the constraints of field sampling. Active behaviour is therefore often inferred from spatial patterns in the field, laboratory studies, or hydrodynamic theory, but rarely are these approaches considered in concert. Ichthyoplankton survey data collected during 2004 and 2006 from coastal Newfoundland show that changes in spatial heterogeneity for multiple species do not conform to predictions based on passive transport. We evaluated the interaction of individual larvae with their environment by calculating Reynolds number as a function of ontogeny. Typically, larvae hatch into a viscous environment in which swimming is inefficient, and later grow into more efficient intermediate and inertial swimming environments. Swimming is therefore closely related to length, not only because of swimming capacity but also in how larvae experience viscosity. Six of eight species sampled demonstrated consistent changes in spatial patchiness and concomitant increases in spatial heterogeneity as they transitioned into more favourable hydrodynamic swimming environments, suggesting an active behavioural element to dispersal. We propose the tandem assessment of spatial heterogeneity and hydrodynamic environment as a potential approach to understand and predict the onset of ecologically significant swimming behaviour of larval fishes in the field. PMID:23029455

Data-driven heterogeneity in mathematical learning disabilities based on the triple code model.

PubMed

Peake, Christian; Jiménez, Juan E; Rodríguez, Cristina

2017-12-01

Many classifications of heterogeneity in mathematical learning disabilities (MLD) have been proposed over the past four decades, however no empirical research has been conducted until recently, and none of the classifications are derived from Triple Code Model (TCM) postulates. The TCM proposes MLD as a heterogeneous disorder, with two distinguishable profiles: a representational subtype and a verbal subtype. A sample of elementary school 3rd to 6th graders was divided into two age cohorts (3rd - 4th grades, and 5th - 6th grades). Using data-driven strategies, based on the cognitive classification variables predicted by the TCM, our sample of children with MLD clustered as expected: a group with representational deficits and a group with number-fact retrieval deficits. In the younger group, a spatial subtype also emerged, while in both cohorts a non-specific cluster was produced whose profile could not be explained by this theoretical approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

ARIANNA: A research environment for neuroimaging studies in autism spectrum disorders.

PubMed

Retico, Alessandra; Arezzini, Silvia; Bosco, Paolo; Calderoni, Sara; Ciampa, Alberto; Coscetti, Simone; Cuomo, Stefano; De Santis, Luca; Fabiani, Dario; Fantacci, Maria Evelina; Giuliano, Alessia; Mazzoni, Enrico; Mercatali, Pietro; Miscali, Giovanni; Pardini, Massimiliano; Prosperi, Margherita; Romano, Francesco; Tamburini, Elena; Tosetti, Michela; Muratori, Filippo

2017-08-01

The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. The ARIANNA project has developed a collaborative interdisciplinary research environment that is easily accessible to the community of researchers working on ASD (https://arianna.pi.infn.it). The main goals of the project are: to analyze neuroimaging data acquired in multiple sites with multivariate approaches based on machine learning; to detect structural and functional brain characteristics that allow the distinguishing of individuals with ASD from control subjects; to identify neuroimaging-based criteria to stratify the population with ASD to support the future development of personalized treatments. Secure data handling and storage are guaranteed within the project, as well as the access to fast grid/cloud-based computational resources. This paper outlines the web-based architecture, the computing infrastructure and the collaborative analysis workflows at the basis of the ARIANNA interdisciplinary working environment. It also demonstrates the full functionality of the research platform. The availability of this innovative working environment for analyzing clinical and neuroimaging information of individuals with ASD is expected to support researchers in disentangling complex data thus facilitating their interpretation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Understanding teacher responses to constructivist learning environments: Challenges and resolutions

NASA Astrophysics Data System (ADS)

Rosenfeld, Melodie; Rosenfeld, Sherman

2006-05-01

The research literature is just beginning to uncover factors involved in sustaining constructivist learning environments, such as Project-Based Learning (PBL). Our case study investigates teacher responses to the challenges of constructivist environments, since teachers can play strong roles in supporting or undermining even the best constructivist environments or materials. We were invited to work as mediators with a middle-school science staff that was experiencing conflicts regarding two learning environments, PBL (which was the school's politically correc learning environment) and traditional. With mediated group workshops, teachers were sensitized to their own and colleagues' individual learning differences (ILDs), as measured by two styles inventories (the LSI - Kolb, 1976; and the LCI - Johnston & Dainton, 1997). Using these inventories, a learning-environment questionnaire, field notes, and delayed interviews a year later, we found that there was a relationship between teachers' preferred styles, epistemological beliefs, and their preferred teaching environment. Moreover, when the participating teachers, including early-adopters and nonvolunteers to PBL, became more sensitive to their colleagues' preferences, many staff conflicts were resolved and some mismatched teachers expressed more openness to PBL. We argue that having teachers understand their own ILDs and related responses to constructivist learning environments can contribute to resolving staff conflicts and sustaining such environments. We present a cognitive model and a strategy which illustrate this argument.

Interactive effects of genotype x environment on the live weight of GIFT Nile tilapias.

PubMed

Oliveira, Sheila N DE; Ribeiro, Ricardo P; Oliveira, Carlos A L DE; Alexandre, Luiz; Oliveira, Aline M S; Lopera-Barrero, Nelson M; Santander, Victor F A; Santana, Renan A C

2017-01-01

In this paper, the existence of a genotype x environment interaction for the average daily weight in GIFT Nile tilapia (Oreochromis niloticus) in different regions in the state of Paraná (Brazil) was analyzed. The heritability results were high in the uni-characteristic analysis: 0.71, 0.72 and 0.67 for the cities of Palotina (PL), Floriano (FL) and Diamond North (DN), respectively. Genetic correlations estimated in bivariate analyzes were weak with values between 0.12 for PL-FL, 0.06 for PL and 0.23 for DN-FL-DN. The Spearman correlation values were low, which indicated a change in ranking in the selection of animals in different environments in the study. There was heterogeneity in the phenotypic variance among the three regions and heterogeneity in the residual variance between PL and DN. The direct genetic gain was greater for the region with a DN value gain of 198.24 g/generation, followed by FL (98.73 g/generation) and finally PL (98.73 g/generation). The indirect genetic gains were lower than 0.37 and greater than 0.02 g/generation. The evidence of the genotype x environment interaction was verified, which indicated the phenotypic heterogeneity of the variances among the three regions, weak genetic correlation and modified rankings in the different environments.

Quorum Sensing in Populations of Spatially Extended Chaotic Oscillators Coupled Indirectly via a Heterogeneous Environment

NASA Astrophysics Data System (ADS)

Li, Bing-Wei; Cao, Xiao-Zhi; Fu, Chenbo

2017-12-01

Many biological and chemical systems could be modeled by a population of oscillators coupled indirectly via a dynamical environment. Essentially, the environment by which the individual element communicates with each other is heterogeneous. Nevertheless, most of previous works considered the homogeneous case only. Here we investigated the dynamical behaviors in a population of spatially distributed chaotic oscillators immersed in a heterogeneous environment. Various dynamical synchronization states (such as oscillation death, phase synchronization, and complete synchronized oscillation) as well as their transitions were explored. In particular, we uncovered a non-traditional quorum sensing transition: increasing the population density leaded to a transition from oscillation death to synchronized oscillation at first, but further increasing the density resulted in degeneration from complete synchronization to phase synchronization or even from phase synchronization to desynchronization. The underlying mechanism of this finding was attributed to the dual roles played by the population density. What's more, by treating the environment as another component of the oscillator, the full system was then effectively equivalent to a locally coupled system. This fact allowed us to utilize the master stability functions approach to predict the occurrence of complete synchronization oscillation, which agreed with that from the direct numerical integration of the system. The potential candidates for the experimental realization of our model were also discussed.

Use of Heuristics to Facilitate Scientific Discovery Learning in a Simulation Learning Environment in a Physics Domain

ERIC Educational Resources Information Center

Veermans, Koen; van Joolingen, Wouter; de Jong, Ton

2006-01-01

This article describes a study into the role of heuristic support in facilitating discovery learning through simulation-based learning. The study compares the use of two such learning environments in the physics domain of collisions. In one learning environment (implicit heuristics) heuristics are only used to provide the learner with guidance…

The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

ERIC Educational Resources Information Center

Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

2015-01-01

An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

Students' Conception of Learning Environment and Their Approach to Learning and Its Implication on Quality Education

ERIC Educational Resources Information Center

Belaineh, Matheas Shemelis

2017-01-01

Quality of education in higher institutions can be affected by different factors. It partly rests on the learning environment created by teachers and the learning approach students are employing during their learning. The main purpose of this study is to examine the learning environment at Mizan Tepi University from students' perspective and their…

Review of Opinions of Math Teachers Concerning the Learning Environment That They Design

ERIC Educational Resources Information Center

Aydin, Bünyamin; Yavuz, Ayse

2016-01-01

Design of appropriate learning environment has a significant importance in creation of aims of the math teaching. In the design of learning environments, teachers play a significant role. The aim of this study is determination of opinions of the math teachers concerning the learning environment that they design. In accordance with this aim, an…

An Effect of the Learning Environment Management System toward Student Quality of Thai Secondary School

ERIC Educational Resources Information Center

Wirussawa, Seatuch; Tesaputa, Kowat; Duangpaeng, Amporn

2016-01-01

This study aimed at 1) investigating the element of the learning environment management system in the secondary schools, 2) exploring the current states and problems of the system on the learning environment management in the secondary schools, 3) designing the learning environment management system for the secondary schools, and 4) identifying…

Authoring Adaptive 3D Virtual Learning Environments

ERIC Educational Resources Information Center

Ewais, Ahmed; De Troyer, Olga

2014-01-01

The use of 3D and Virtual Reality is gaining interest in the context of academic discussions on E-learning technologies. However, the use of 3D for learning environments also has drawbacks. One way to overcome these drawbacks is by having an adaptive learning environment, i.e., an environment that dynamically adapts to the learner and the…

Assessing the Quality of Learning Environments in Swedish Schools: Development and Analysis of a Theory-Based Instrument

ERIC Educational Resources Information Center

Westling Allodi, Mara

2007-01-01

The Goals, Attitudes and Values in School (GAVIS) questionnaire was developed on the basis of theoretical frameworks concerning learning environments, universal human values and studies of students' experience of learning environments. The theory hypothesises that learning environments can be described and structured in a circumplex model using…

Students' Perceptions of Computer-Based Learning Environments, Their Attitude towards Business Statistics, and Their Academic Achievement: Implications from a UK University

ERIC Educational Resources Information Center

Nguyen, ThuyUyen H.; Charity, Ian; Robson, Andrew

2016-01-01

This study investigates students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement in higher education. Guided by learning environments concepts and attitudinal theory, a theoretical model was proposed with two instruments, one for measuring the learning environment and…

Blackboard as an Online Learning Environment: What Do Teacher Education Students and Staff Think?

ERIC Educational Resources Information Center

Heirdsfield, Ann; Walker, Susan; Tambyah, Mallihai; Beutel, Denise

2011-01-01

As online learning environments now have an established presence in higher education we need to ask the question: How effective are these environments for student learning? Online environments can provide a different type of learning experience than traditional face-to-face contexts (for on-campus students) or print-based materials (for distance…

Effects of the Physical Environment on Cognitive Load and Learning: Towards a New Model of Cognitive Load

ERIC Educational Resources Information Center

Choi, Hwan-Hee; van Merriënboer, Jeroen J. G.; Paas, Fred

2014-01-01

Although the theoretical framework of cognitive load theory has acknowledged a role for the learning environment, the specific characteristics of the physical learning environment that could affect cognitive load have never been considered, neither theoretically nor empirically. In this article, we argue that the physical learning environment, and…

Context Aware Ubiquitous Learning Environments for Peer-to-Peer Collaborative Learning

ERIC Educational Resources Information Center

Yang, Stephen J. H.

2006-01-01

A ubiquitous learning environment provides an interoperable, pervasive, and seamless learning architecture to connect, integrate, and share three major dimensions of learning resources: learning collaborators, learning contents, and learning services. Ubiquitous learning is characterized by providing intuitive ways for identifying right learning…

Challenges in reproducibility of genetic association studies: lessons learned from the obesity field.

PubMed

Li, A; Meyre, D

2013-04-01

A robust replication of initial genetic association findings has proved to be difficult in human complex diseases and more specifically in the obesity field. An obvious cause of non-replication in genetic association studies is the initial report of a false positive result, which can be explained by a non-heritable phenotype, insufficient sample size, improper correction for multiple testing, population stratification, technical biases, insufficient quality control or inappropriate statistical analyses. Replication may, however, be challenging even when the original study describes a true positive association. The reasons include underpowered replication samples, gene × gene, gene × environment interactions, genetic and phenotypic heterogeneity and subjective interpretation of data. In this review, we address classic pitfalls in genetic association studies and provide guidelines for proper discovery and replication genetic association studies with a specific focus on obesity.

Object-oriented analysis and design: a methodology for modeling the computer-based patient record.

PubMed

Egyhazy, C J; Eyestone, S M; Martino, J; Hodgson, C L

1998-08-01

The article highlights the importance of an object-oriented analysis and design (OOAD) methodology for the computer-based patient record (CPR) in the military environment. Many OOAD methodologies do not adequately scale up, allow for efficient reuse of their products, or accommodate legacy systems. A methodology that addresses these issues is formulated and used to demonstrate its applicability in a large-scale health care service system. During a period of 6 months, a team of object modelers and domain experts formulated an OOAD methodology tailored to the Department of Defense Military Health System and used it to produce components of an object model for simple order processing. This methodology and the lessons learned during its implementation are described. This approach is necessary to achieve broad interoperability among heterogeneous automated information systems.

XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

[The use of virtual learning environment in teaching basic and advanced life support].

PubMed

Cogo, Ana Luísa Petersen; Silveira, Denise Tolfo; Lírio, Aline de Morais; Severo, Carolina Lopes

2003-12-01

The present paper is the result of an experiment conducted as part of the Nursing: basic and advanced life support course, which was offered as a semi-online course using the virtual learning environment called Learning Space. The virtual learning environment optimizes classroom dynamics, since in the classroom setting, practical activities may be privileged; besides, learning is customized as students may access the environment whenever and wherever they wish.

Extinction and the associative structure of heterogeneous instrumental chains.

PubMed

Thrailkill, Eric A; Bouton, Mark E

2016-09-01

Drug abuse, overeating, and smoking are all examples of instrumental behaviors that often involve chains or sequences of behavior. A behavior chain is minimally composed of a procurement response that is required in order for a subsequent consumption response to be reinforced. Despite the translational importance of behavior chains, few studies have attempted to understand what binds them together and takes them apart. This article surveys the development of the heterogeneous instrumental chain method and introduces recent findings that have used extinction to analyze the associative content of (what is learned in) the chain. Chained responses that are occasion-set by their own discriminative stimuli may be directly associated; extinction of the procurement response weakens its associated consumption response, and extinction of the consumption response weakens its associated procurement response. Extinction itself involves learning to inhibit the response. Extinguished chained responses are subject to renewal when they are tested either back in the acquisition context or in a new context. In addition, a consumption response that is extinguished outside its chain is renewed when returned to the context of the preceding response in the chain. Research on heterogeneous behavior chains can provide important insights into an important but often overlooked aspect of instrumental learning. Copyright © 2016 Elsevier Inc. All rights reserved.

Characterizing physical properties and heterogeneous chemistry of single particles in air using optical trapping-Raman spectroscopy

NASA Astrophysics Data System (ADS)

Gong, Z.; Wang, C.; Pan, Y. L.; Videen, G.

2017-12-01

Heterogeneous reactions of solid particles in a gaseous environment are of increasing interest; however, most of the heterogeneous chemistry studies of airborne solids were conducted on particle ensembles. A close examination on the heterogeneous chemistry between single particles and gaseous-environment species is the key to elucidate the fundamental mechanisms of hydroscopic growth, cloud nuclei condensation, secondary aerosol formation, etc., and reduce the uncertainty of models in radiative forcing, climate change, and atmospheric chemistry. We demonstrate an optical trapping-Raman spectroscopy (OT-RS) system to study the heterogeneous chemistry of the solid particles in air at single-particle level. Compared to other single-particle techniques, optical trapping offers a non-invasive, flexible, and stable method to isolate single solid particle from substrates. Benefited from two counter-propagating hollow beams, the optical trapping configuration is adaptive to trap a variety of particles with different materials from inorganic substitution (carbon nanotubes, silica, etc.) to organic, dye-doped polymers and bioaerosols (spores, pollen, etc.), with different optical properties from transparent to strongly absorbing, with different sizes from sub-micrometers to tens of microns, or with distinct morphologies from loosely packed nanotubes to microspheres and irregular pollen grains. The particles in the optical trap may stay unchanged, surface degraded, or optically fragmented according to different laser intensity, and their physical and chemical properties are characterized by the Raman spectra and imaging system simultaneously. The Raman spectra is able to distinguish the chemical compositions of different particles, while the synchronized imaging system can resolve their physical properties (sizes, shapes, morphologies, etc.). The temporal behavior of the trapped particles also can be monitored by the OT-RS system at an indefinite time with a resolution from 10 ms to 5 min, which can be further applied to monitor the dynamics of heterogeneous reactions. The OT-RS system provides a flexible method to characterize and monitor the physical properties and heterogeneous chemistry of optically trapped solid particles in gaseous environment at single-particle level.

Smile: Student Modification in Learning Environments. Establishing Congruence between Actual and Preferred Classroom Learning Environment.

ERIC Educational Resources Information Center

Yarrow, Allan; Millwater, Jan

1995-01-01

This study investigated whether classroom psychosocial environment, as perceived by student teachers, could be improved to their preferred level. Students completed the College and University Classroom Environment Inventory, discussed interventions, then completed it again. Significant deficiencies surfaced in the learning environment early in the…

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam

2017-01-01

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…

A Comparison of Participation Patterns in Selected Formal, Non-Formal, and Informal Online Learning Environments

ERIC Educational Resources Information Center

Schwier, Richard A.; Seaton, J. X.

2013-01-01

Does learner participation vary depending on the learning context? Are there characteristic features of participation evident in formal, non-formal, and informal online learning environments? Six online learning environments were chosen as epitomes of formal, non-formal, and informal learning contexts and compared. Transcripts of online…

Student-Teachers' Approaches to Learning, Academic Performance and Teaching Efficacy

ERIC Educational Resources Information Center

Swee-Choo, Pauline Goh; Kung-Teck, Wong; Osman, Rosma

2012-01-01

Purpose: It is argued that the approaches to learning of students undergoing teacher training are likely to be related to their teaching and learning environment, especially as they move from a more regimented, structured learning environment in school to a tertiary learning environment that encourages more independent thinking and perhaps…

Visits to Cultural Learning Places in the Early Childhood

ERIC Educational Resources Information Center

Mudiappa, Michael; Kluczniok, Katharina

2015-01-01

Studies show the important role of the home learning environment in early childhood for later school success. This article focuses on a particular aspect of the home learning environment: visits to cultural learning places (e.g. museums) as a component of the quality of the home learning environment. Therefore the educational concept of…

Investigating Learning through Work: Learning Environment Scale & User Guide to the Provider. Support Document

ERIC Educational Resources Information Center

Hawke, Geof; Chappell, Clive

2008-01-01

This Support Document was produced by the authors based on their research for the report, "Investigating Learning through Work: The Development of the 'Provider Learning Environment Scale'" (ED503392). It provides readers with a complete copy of the "Provider Learning Environment Scale" (version 2.0); and an accompanying user…

Assessing and Monitoring Student Progress in an E-Learning Personnel Preparation Environment.

ERIC Educational Resources Information Center

Meyen, Edward L.; Aust, Ronald J.; Bui, Yvonne N.; Isaacson, Robert

2002-01-01

Discussion of e-learning in special education personnel preparation focuses on student assessment in e-learning environments. It includes a review of the literature, lessons learned by the authors from assessing student performance in e-learning environments, a literature perspective on electronic portfolios in monitoring student progress, and the…

A Context-Adaptive Teacher Training Model in a Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Min; Chiang, Feng Kuang; Jiang, Ya Na; Yu, Sheng Quan

2017-01-01

In view of the discrepancies in teacher training and teaching practice, this paper put forward a context-adaptive teacher training model in a ubiquitous learning (u-learning) environment. The innovative model provides teachers of different subjects with adaptive and personalized learning content in a u-learning environment, implements intra- and…

A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion

NASA Astrophysics Data System (ADS)

Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em

2017-09-01

Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in exascale simulations.

Enabling Flexible and Continuous Capability Invocation in Mobile Prosumer Environments

PubMed Central

Alcarria, Ramon; Robles, Tomas; Morales, Augusto; López-de-Ipiña, Diego; Aguilera, Unai

2012-01-01

Mobile prosumer environments require the communication with heterogeneous devices during the execution of mobile services. These environments integrate sensors, actuators and smart devices, whose availability continuously changes. The aim of this paper is to design a reference architecture for implementing a model for continuous service execution and access to capabilities, i.e., the functionalities provided by these devices. The defined architecture follows a set of software engineering patterns and includes some communication paradigms to cope with the heterogeneity of sensors, actuators, controllers and other devices in the environment. In addition, we stress the importance of the flexibility in capability invocation by allowing the communication middleware to select the access technology and change the communication paradigm when dealing with smart devices, and by describing and evaluating two algorithms for resource access management. PMID:23012526

Effects of Spatial Patch Arrangement and Scale of Covarying Resources on Growth and Intraspecific Competition of a Clonal Plant

PubMed Central

Wang, Yong-Jian; Shi, Xue-Ping; Meng, Xue-Feng; Wu, Xiao-Jing; Luo, Fang-Li; Yu, Fei-Hai

2016-01-01

Spatial heterogeneity in two co-variable resources such as light and water availability is common and can affect the growth of clonal plants. Several studies have tested effects of spatial heterogeneity in the supply of a single resource on competitive interactions of plants, but none has examined those of heterogeneous distribution of two co-variable resources. In a greenhouse experiment, we grew one (without intraspecific competition) or nine isolated ramets (with competition) of a rhizomatous herb Iris japonica under a homogeneous environment and four heterogeneous environments differing in patch arrangement (reciprocal and parallel patchiness of light and soil water) and patch scale (large and small patches of light and water). Intraspecific competition significantly decreased the growth of I. japonica, but at the whole container level there were no significant interaction effects of competition by spatial heterogeneity or significant effect of heterogeneity on competitive intensity. Irrespective of competition, the growth of I. japonica in the high and the low water patches did not differ significantly in the homogeneous treatments, but it was significantly larger in the high than in the low water patches in the heterogeneous treatments with large patches. For the heterogeneous treatments with small patches, the growth of I. japonica was significantly larger in the high than in the low water patches in the presence of competition, but such an effect was not significant in the absence of competition. Furthermore, patch arrangement and patch scale significantly affected competitive intensity at the patch level. Therefore, spatial heterogeneity in light and water supply can alter intraspecific competition at the patch level and such effects depend on patch arrangement and patch scale. PMID:27375630

Effects of Spatial Patch Arrangement and Scale of Covarying Resources on Growth and Intraspecific Competition of a Clonal Plant.

PubMed

Wang, Yong-Jian; Shi, Xue-Ping; Meng, Xue-Feng; Wu, Xiao-Jing; Luo, Fang-Li; Yu, Fei-Hai

2016-01-01

Spatial heterogeneity in two co-variable resources such as light and water availability is common and can affect the growth of clonal plants. Several studies have tested effects of spatial heterogeneity in the supply of a single resource on competitive interactions of plants, but none has examined those of heterogeneous distribution of two co-variable resources. In a greenhouse experiment, we grew one (without intraspecific competition) or nine isolated ramets (with competition) of a rhizomatous herb Iris japonica under a homogeneous environment and four heterogeneous environments differing in patch arrangement (reciprocal and parallel patchiness of light and soil water) and patch scale (large and small patches of light and water). Intraspecific competition significantly decreased the growth of I. japonica, but at the whole container level there were no significant interaction effects of competition by spatial heterogeneity or significant effect of heterogeneity on competitive intensity. Irrespective of competition, the growth of I. japonica in the high and the low water patches did not differ significantly in the homogeneous treatments, but it was significantly larger in the high than in the low water patches in the heterogeneous treatments with large patches. For the heterogeneous treatments with small patches, the growth of I. japonica was significantly larger in the high than in the low water patches in the presence of competition, but such an effect was not significant in the absence of competition. Furthermore, patch arrangement and patch scale significantly affected competitive intensity at the patch level. Therefore, spatial heterogeneity in light and water supply can alter intraspecific competition at the patch level and such effects depend on patch arrangement and patch scale.

Ecological feedback in quorum-sensing microbial populations can induce heterogeneous production of autoinducers

PubMed Central

Bauer, Matthias; Knebel, Johannes; Lechner, Matthias; Pickl, Peter; Frey, Erwin

2017-01-01

Autoinducers are small signaling molecules that mediate intercellular communication in microbial populations and trigger coordinated gene expression via ‘quorum sensing’. Elucidating the mechanisms that control autoinducer production is, thus, pertinent to understanding collective microbial behavior, such as virulence and bioluminescence. Recent experiments have shown a heterogeneous promoter activity of autoinducer synthase genes, suggesting that some of the isogenic cells in a population might produce autoinducers, whereas others might not. However, the mechanism underlying this phenotypic heterogeneity in quorum-sensing microbial populations has remained elusive. In our theoretical model, cells synthesize and secrete autoinducers into the environment, up-regulate their production in this self-shaped environment, and non-producers replicate faster than producers. We show that the coupling between ecological and population dynamics through quorum sensing can induce phenotypic heterogeneity in microbial populations, suggesting an alternative mechanism to stochastic gene expression in bistable gene regulatory circuits. DOI: http://dx.doi.org/10.7554/eLife.25773.001 PMID:28741470

Medical Student Perceptions of the Learning Environment in Medical School Change as Students Transition to Clinical Training in Undergraduate Medical School.

PubMed

Dunham, Lisette; Dekhtyar, Michael; Gruener, Gregory; CichoskiKelly, Eileen; Deitz, Jennifer; Elliott, Donna; Stuber, Margaret L; Skochelak, Susan E

2017-01-01

Phenomenon: The learning environment is the physical, social, and psychological context in which a student learns. A supportive learning environment contributes to student well-being and enhances student empathy, professionalism, and academic success, whereas an unsupportive learning environment may lead to burnout, exhaustion, and cynicism. Student perceptions of the medical school learning environment may change over time and be associated with students' year of training and may differ significantly depending on the student's gender or race/ethnicity. Understanding the changes in perceptions of the learning environment related to student characteristics and year of training could inform interventions that facilitate positive experiences in undergraduate medical education. The Medical School Learning Environment Survey (MSLES) was administered to 4,262 students who matriculated at one of 23 U.S. and Canadian medical schools in 2010 and 2011. Students completed the survey at the end of each year of medical school as part of a battery of surveys in the Learning Environment Study. A mixed-effects longitudinal model, t tests, Cohen's d effect size, and analysis of variance assessed the relationship between MSLES score, year of training, and demographic variables. After controlling for gender, race/ethnicity, and school, students reported worsening perceptions toward the medical school learning environment, with the worst perceptions in the 3rd year of medical school as students begin their clinical experiences, and some recovery in the 4th year after Match Day. The drop in MSLES scores associated with the transition to the clinical learning environment (-0.26 point drop in addition to yearly change, effect size = 0.52, p < .0001) is more than 3 times greater than the drop between the 1st and 2nd year (0.07 points, effect size = 0.14, p < .0001). The largest declines were from items related to work-life balance and informal student relationships. There was some, but not complete, recovery in perceptions of the medical school learning environment in the 4th year. Insights: Perceptions of the medical school learning environment worsen as students continue through medical school, with a stronger decline in perception scores as students' transition to the clinical learning environment. Students reported the greatest drop in finding time for outside activities and students helping one another in the 3rd year. Perceptions differed based on gender and race/ethnicity. Future studies should investigate the specific features of medical schools that contribute most significantly to student perceptions of the medical school learning environment, both positive and negative, to pinpoint potential interventions and improvements.

Combined effects of waggle dance communication and landscape heterogeneity on nectar and pollen uptake in honey bee colonies

PubMed Central

Steffan-Dewenter, Ingolf; Härtel, Stephan

2017-01-01

The instructive component of waggle dance communication has been shown to increase resource uptake of Apis mellifera colonies in highly heterogeneous resource environments, but an assessment of its relevance in temperate landscapes with different levels of resource heterogeneity is currently lacking. We hypothesized that the advertisement of resource locations via dance communication would be most relevant in highly heterogeneous landscapes with large spatial variation of floral resources. To test our hypothesis, we placed 24 Apis mellifera colonies with either disrupted or unimpaired instructive component of dance communication in eight Central European agricultural landscapes that differed in heterogeneity and resource availability. We monitored colony weight change and pollen harvest as measure of foraging success. Dance disruption did not significantly alter colony weight change, but decreased pollen harvest compared to the communicating colonies by 40%. There was no general effect of resource availability on nectar or pollen foraging success, but the effect of landscape heterogeneity on nectar uptake was stronger when resource availability was high. In contrast to our hypothesis, the effects of disrupted bee communication on nectar and pollen foraging success were not stronger in landscapes with heterogeneous compared to homogenous resource environments. Our results indicate that in temperate regions intra-colonial communication of resource locations benefits pollen foraging more than nectar foraging, irrespective of landscape heterogeneity. We conclude that the so far largely unexplored role of dance communication in pollen foraging requires further consideration as pollen is a crucial resource for colony development and health. PMID:28603677

Deep learning for healthcare: review, opportunities and challenges.

PubMed

Miotto, Riccardo; Wang, Fei; Wang, Shuang; Jiang, Xiaoqian; Dudley, Joel T

2017-05-06

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

When drug discovery meets web search: Learning to Rank for ligand-based virtual screening.

PubMed

Zhang, Wei; Ji, Lijuan; Chen, Yanan; Tang, Kailin; Wang, Haiping; Zhu, Ruixin; Jia, Wei; Cao, Zhiwei; Liu, Qi

2015-01-01

The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms. A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. Graphical AbstractThe analogy between web search and ligand-based drug discovery.

The Use of Deep and Surface Learning Strategies among Students Learning English as a Foreign Language in an Internet Environment

ERIC Educational Resources Information Center

Aharony, Noa

2006-01-01

Background: The learning context is learning English in an Internet environment. The examination of this learning process was based on the Biggs and Moore's teaching-learning model (Biggs & Moore, 1993). Aim: The research aims to explore the use of the deep and surface strategies in an Internet environment among EFL students who come from…

Virtual Workshop Environment (VWE): A Taxonomy and Service Oriented Architecture (SOA) Framework for Modularized Virtual Learning Environments (VLE)--Applying the Learning Object Concept to the VLE

ERIC Educational Resources Information Center

Paulsson, Fredrik; Naeve, Ambjorn

2006-01-01

Based on existing Learning Object taxonomies, this article suggests an alternative Learning Object taxonomy, combined with a general Service Oriented Architecture (SOA) framework, aiming to transfer the modularized concept of Learning Objects to modularized Virtual Learning Environments. The taxonomy and SOA-framework exposes a need for a clearer…

Environmental heterogeneity generates opposite gene-by-environment interactions for two fitness-related traits within a population.

PubMed

Culumber, Zachary W; Schumer, Molly; Monks, Scott; Tobler, Michael

2015-02-01

Theory predicts that environmental heterogeneity offers a potential solution to the maintenance of genetic variation within populations, but empirical evidence remains sparse. The live-bearing fish Xiphophorus variatus exhibits polymorphism at a single locus, with different alleles resulting in up to five distinct melanistic "tailspot" patterns within populations. We investigated the effects of heterogeneity in two ubiquitous environmental variables (temperature and food availability) on two fitness-related traits (upper thermal limits and body condition) in two different tailspot types (wild-type and upper cut crescent). We found gene-by-environment (G × E) interactions between tailspot type and food level affecting upper thermal limits (UTL), as well as between tailspot type and thermal environment affecting body condition. Exploring mechanistic bases underlying these G × E patterns, we found no differences between tailspot types in hsp70 gene expression despite significant overall increases in expression under both thermal and food stress. Similarly, there was no difference in routine metabolic rates between the tailspot types. The reversal of relative performance of the two tailspot types under different environmental conditions revealed a mechanism by which environmental heterogeneity can balance polymorphism within populations through selection on different fitness-related traits. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Disease Spread and Its Effect on Population Dynamics in Heterogeneous Environment

NASA Astrophysics Data System (ADS)

Upadhyay, Ranjit Kumar; Roy, Parimita

In this paper, an eco-epidemiological model in which both species diffuse along a spatial gradient has been shown to exhibit temporal chaos at a fixed point in space. The proposed model is a modification of the model recently presented by Upadhyay and Roy [2014]. The spatial interactions among the species have been represented in the form of reaction-diffusion equations. The model incorporates the intrinsic growth rate of fish population which varies linearly with the depth of water. Numerical results show that diffusion can drive otherwise stable system into aperiodic behavior with sensitivity to initial conditions. We show that spatially induced chaos plays an important role in spatial pattern formation in heterogeneous environment. Spatiotemporal distributions of species have been simulated using the diffusivity assumptions realistic for natural eco-epidemic systems. We found that in heterogeneous environment, the temporal dynamics of both the species are drastically different and show chaotic behavior. It was also found that the instability observed in the model is due to spatial heterogeneity and diffusion-driven. Cumulative death rate of predator has an appreciable effect on model dynamics as the spatial distribution of all constituent populations exhibit significant changes when this model parameter is changed and it acts as a regularizing factor.

An approach for heterogeneous and loosely coupled geospatial data distributed computing

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Fengru; Fang, Yu; Huang, Zhou; Lin, Hui

2010-07-01

Most GIS (Geographic Information System) applications tend to have heterogeneous and autonomous geospatial information resources, and the availability of these local resources is unpredictable and dynamic under a distributed computing environment. In order to make use of these local resources together to solve larger geospatial information processing problems that are related to an overall situation, in this paper, with the support of peer-to-peer computing technologies, we propose a geospatial data distributed computing mechanism that involves loosely coupled geospatial resource directories and a term named as Equivalent Distributed Program of global geospatial queries to solve geospatial distributed computing problems under heterogeneous GIS environments. First, a geospatial query process schema for distributed computing as well as a method for equivalent transformation from a global geospatial query to distributed local queries at SQL (Structured Query Language) level to solve the coordinating problem among heterogeneous resources are presented. Second, peer-to-peer technologies are used to maintain a loosely coupled network environment that consists of autonomous geospatial information resources, thus to achieve decentralized and consistent synchronization among global geospatial resource directories, and to carry out distributed transaction management of local queries. Finally, based on the developed prototype system, example applications of simple and complex geospatial data distributed queries are presented to illustrate the procedure of global geospatial information processing.

Pre-registration student nurses perception of the hospital-learning environment during clinical placements.

PubMed

Midgley, Kirsten

2006-05-01

If we subscribe to the notion that nursing is an action profession, that nurses learn by doing [Neary, M., 2000. Responsive assessment: assessing student nurses' clinical competence. Nurse Education Today 21, 3-17], then the mastery of fundamental clinical skills must be a key component of courses leading to registration. The last two decades have seen widespread changes to nurse education but the clinical field remains an invaluable resource in preparing students for the reality of their professional role supporting the integration of theory and practice and linking the 'knowing what' with the 'knowing how'. The clinical-learning environment represents an essential element of nurse education that needs to be measurable and warrants further investigation. This exploratory cohort study (n = 67) examined pre-registration student nurses' perception of the hospital-learning environment during clinical placements together with the key characteristics of the students' preferred learning environment utilising an established tool, the clinical-learning environment inventory (CLEI) tool [Chan, D., 2001a. Development of an innovative tool to assess hospital-learning environments. Nurse Education Today 21, 624-631; Chan, D., 2001b. Combining qualitative and quantitative methods in assessing hospital-learning environments. International Journal of Nursing Studies 3, 447-459]. The results demonstrated that in comparison with the actual hospital environment, students would prefer an environment with higher levels of individualisation, innovation in teaching and learning strategies, student involvement, personalisation and task orientation.

Old models explain new observations of butterfly movement at patch edges.

PubMed

Crone, Elizabeth E; Schultz, Cheryl B

2008-07-01

Understanding movement in heterogeneous environments is central to predicting how landscape changes affect animal populations. Several recent studies point out an intriguing and distinctive looping behavior by butterflies at habitat patch edges and hypothesize that this behavior requires a new framework for analyzing animal movement. We show that this looping behavior could be caused by a longstanding movement model, biased correlated random walk, with bias toward habitat patches. The ability of this longstanding model to explain recent observations reinforces the point that butterflies respond to habitat heterogeneity and do not move randomly through heterogeneous environments. We discuss the implications of different movement models for predicting butterfly responses to landscape change, and our rationale for retaining longstanding movement models, rather than developing new modeling frameworks for looping behavior at patch edges.

Nursing students' perceptions of factors influencing their learning environment in a clinical skills laboratory: A qualitative study.

PubMed

Haraldseid, Cecilie; Friberg, Febe; Aase, Karina

2015-09-01

The mastery of clinical skills learning is required to become a trained nurse. Due to limited opportunities for clinical skills training in clinical practice, undergraduate training at clinical skills laboratories (CSLs) is an essential part of nursing education. In a sociocultural learning perspective learning is situated in an environment. Growing student cohorts, rapid introduction of technology-based teaching methods and a shift from a teaching- to a learning-centered education all influence the environment of the students. These changes also affect CSLs and therefore compel nursing faculties to adapt to the changing learning environment. This study aimed to explore students' perceptions of their learning environment in a clinical skills laboratory, and to increase the knowledge base for improving CSL learning conditions identifying the most important environmental factors according to the students. An exploratory qualitative methodology was used. Nineteen second-year students enrolled in an undergraduate nursing program in Norway participated in the study. They took the same clinical skills course. Eight were part-time students (group A) and 11 were full-time students (group B). Focus group interviews and content analysis were conducted to capture the students' perception of the CSL learning environment. The study documents students' experience of the physical (facilities, material equipment, learning tools, standard procedures), psychosocial (expectations, feedback, relations) and organizational (faculty resources, course structure) factors that affect the CSL learning environment. Creating an authentic environment, facilitating motivation, and providing resources for multiple methods and repetitions within clinical skills training are all important for improving CSL learning environments from the student perspective. Copyright © 2015 Elsevier Ltd. All rights reserved.

Learning and Consolidation of New Spoken Words in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Henderson, Lisa; Powell, Anna; Gaskell, M. Gareth; Norbury, Courtenay

2014-01-01

Autism spectrum disorder (ASD) is characterized by rich heterogeneity in vocabulary knowledge and word knowledge that is not well accounted for by current cognitive theories. This study examines whether individual differences in vocabulary knowledge in ASD might be partly explained by a difficulty with consolidating newly learned spoken words…

Design Guidelines for the Development of Digital Nutrigenomics Learning Material for Heterogeneous Target Groups

ERIC Educational Resources Information Center

Busstra, Maria C.; Hartog, Rob; Kersten, Sander; Muller, Michael

2007-01-01

Nutritional genomics, or nutrigenomics, can be considered as the combination of molecular nutrition and genomics. Students who attend courses in nutrigenomics differ with respect to their prior knowledge. This study describes digital nutrigenomics learning material suitable for students from various backgrounds and provides design guidelines for…

Computers and Cultural Diversity. Restructuring for School Success. SUNY Series, Computers in Education.

ERIC Educational Resources Information Center

DeVillar, Robert A.; Faltis, Christian J.

This book offers an alternative conceptual framework for effectively incorporating computer use within the heterogeneous classroom. The framework integrates Vygotskian social-learning theory with Allport's contact theory and the principles of cooperative learning. In Part 1 an essential element is identified for each of these areas. These are, in…

Visible Machine Learning for Biomedicine.

PubMed

Yu, Michael K; Ma, Jianzhu; Fisher, Jasmin; Kreisberg, Jason F; Raphael, Benjamin J; Ideker, Trey

2018-06-14

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology. Copyright © 2018. Published by Elsevier Inc.

Competencies in Geriatric Nursing: Empirical Evidence from a Computer-Based Large-Scale Assessment Calibration Study

ERIC Educational Resources Information Center

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

2016-01-01

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

Phenotypic heterogeneity in metabolic traits among single cells of a rare bacterial species in its natural environment quantified with a combination of flow cell sorting and NanoSIMS

PubMed Central

Zimmermann, Matthias; Escrig, Stéphane; Hübschmann, Thomas; Kirf, Mathias K.; Brand, Andreas; Inglis, R. Fredrik; Musat, Niculina; Müller, Susann; Meibom, Anders; Ackermann, Martin; Schreiber, Frank

2015-01-01

Populations of genetically identical microorganisms residing in the same environment can display marked variability in their phenotypic traits; this phenomenon is termed phenotypic heterogeneity. The relevance of such heterogeneity in natural habitats is unknown, because phenotypic characterization of a sufficient number of single cells of the same species in complex microbial communities is technically difficult. We report a procedure that allows to measure phenotypic heterogeneity in bacterial populations from natural environments, and use it to analyze N2 and CO2 fixation of single cells of the green sulfur bacterium Chlorobium phaeobacteroides from the meromictic lake Lago di Cadagno. We incubated lake water with 15N2 and 13CO2 under in situ conditions with and without NH4+. Subsequently, we used flow cell sorting with auto-fluorescence gating based on a pure culture isolate to concentrate C. phaeobacteroides from its natural abundance of 0.2% to now 26.5% of total bacteria. C. phaeobacteroides cells were identified using catalyzed-reporter deposition fluorescence in situ hybridization (CARD-FISH) targeting the 16S rRNA in the sorted population with a species-specific probe. In a last step, we used nanometer-scale secondary ion mass spectrometry to measure the incorporation 15N and 13C stable isotopes in more than 252 cells. We found that C. phaeobacteroides fixes N2 in the absence of NH4+, but not in the presence of NH4+ as has previously been suggested. N2 and CO2 fixation were heterogeneous among cells and positively correlated indicating that N2 and CO2 fixation activity interact and positively facilitate each other in individual cells. However, because CARD-FISH identification cannot detect genetic variability among cells of the same species, we cannot exclude genetic variability as a source for phenotypic heterogeneity in this natural population. Our study demonstrates the technical feasibility of measuring phenotypic heterogeneity in a rare bacterial species in its natural habitat, thus opening the door to study the occurrence and relevance of phenotypic heterogeneity in nature. PMID:25932020

High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers

PubMed Central

Linderman, Michael D.; Athalye, Vivek; Meng, Teresa H.; Asadi, Narges Bani; Bruggner, Robert; Nolan, Garry P.

2017-01-01

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20–50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations. PMID:28819655

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

PubMed

Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

2015-01-01

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

ALLIANCE: An architecture for fault tolerant, cooperative control of heterogeneous mobile robots

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

Parker, L.E.

1995-02-01

This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. The author describes a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled, largely independent tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. Since suchmore » cooperative teams often work in dynamic and unpredictable environments, the software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, the author describes in detail experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. These experiments illustrate the ability of ALLIANCE to achieve adaptive, fault-tolerant cooperative control amidst dynamic changes in the capabilities of the robot team.« less

A content analysis of kindergarten-12th grade school-based nutrition interventions: taking advantage of past learning.

PubMed

Roseman, Mary G; Riddell, Martha C; Haynes, Jessica N

2011-01-01

To review the literature, identifying proposed recommendations for school-based nutrition interventions, and evaluate kindergarten through 12th grade school-based nutrition interventions conducted from 2000-2008. Proposed recommendations from school-based intervention reviews were developed and used in conducting a content analysis of 26 interventions. Twenty-six school-based nutrition interventions in the United States first published in peer-reviewed journals from 2000-2008. VARIABLE MEASURED: Ten proposed recommendations based on prior analyses of school-based nutrition interventions: (1) behaviorally focused, (2) multicomponents, (3) healthful food/school environment, (4) family involvement, (5) self-assessments, (6) quantitative evaluation, (7) community involvement, (8) ethnic/heterogeneous groups, (9) multimedia technology, and (10) sequential and sufficient duration. Descriptive statistics. The most frequent recommendations used were: (1) behaviorally focused components (100%) and (2) quantitative evaluation of food behaviors (96%). Only 15% of the interventions included community involvement or ethnic/heterogeneous groups, whereas 31% included anthropometric measures. Five of the 10 proposed recommendations were included in over 50% of the interventions. Rising trend of overweight children warrants the need to synthesize findings from previous studies to inform research and program development and assist in identification of high-impact strategies and tactics. Copyright © 2011 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

Validation of the Clinical Learning Environment Inventory.

PubMed

Chan, Dominic S

2003-08-01

One hundred eight preregistration nursing students took part in this survey study, which assessed their perceptions of the clinical learning environment. Statistical data based on the sample confirmed the reliability and validity of the Clinical Learning Environment Inventory (CLEI), which was developed using the concept of classroom learning environment studies. The study also found that there were significant differences between students' actual and preferred perceptions of the clinical learning environments. In terms of the CLEI scales, students preferred a more positive and favorable clinical environment than they perceived as being actually present. The achievement of certain outcomes of clinical field placements might be enhanced by attempting to change the actual clinical environment in ways that make it more congruent with that preferred by the students.

Nursing students' perceptions of learning in practice environments: a review.

PubMed

Henderson, Amanda; Cooke, Marie; Creedy, Debra K; Walker, Rachel

2012-04-01

Effective clinical learning requires integration of nursing students into ward activities, staff engagement to address individual student learning needs, and innovative teaching approaches. Assessing characteristics of practice environments can provide useful insights for development. This study identified predominant features of clinical learning environments from nursing students' perspectives across studies using the same measure in different countries over the last decade. Six studies, from three different countries, using the Clinical Leaning Environment Inventory (CLEI) were reviewed. Studies explored consistent trends about learning environment. Students rated sense of task accomplishment high. Affiliation also rated highly though was influenced by models of care. Feedback measuring whether students' individual needs and views were accommodated consistently rated lower. Across different countries students report similar perceptions about learning environments. Clinical learning environments are most effective in promoting safe practice and are inclusive of student learners, but not readily open to innovation and challenges to routine practices. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

ERIC Educational Resources Information Center

Choi, Woojae; Jacobs, Ronald L.

2011-01-01

While workplace learning includes formal and informal learning, the relationship between the two has been overlooked, because they have been viewed as separate entities. This study investigated the effects of formal learning, personal learning orientation, and supportive learning environment on informal learning among 203 middle managers in Korean…

Effects of Presence, Copresence, and Flow on Learning Outcomes in 3D Learning Spaces

ERIC Educational Resources Information Center

Hassell, Martin D.; Goyal, Sandeep; Limayem, Moez; Boughzala, Imed

2012-01-01

The level of satisfaction and effectiveness of 3D virtual learning environments were examined. Additionally, 3D virtual learning environments were compared with face-to-face learning environments. Students that experienced higher levels of flow and presence also experienced more satisfaction but not necessarily more effectiveness with 3D virtual…

Student Experiences on Interaction in an Online Learning Environment as Part of a Blended Learning Implementation: What Is Essential?

ERIC Educational Resources Information Center

Salmi, Laura

2013-01-01

Interaction and community building are essential elements of a well functioning online learning environment, especially in learning environments based on investigative learning with a strong emphasis on teamwork. In this paper, practical solutions covering quality criteria for interaction in online education are presented for a simple…

Investigating Learners' Attitudes toward Virtual Reality Learning Environments: Based on a Constructivist Approach

ERIC Educational Resources Information Center

Huang, Hsiu-Mei; Rauch, Ulrich; Liaw, Shu-Sheng

2010-01-01

The use of animation and multimedia for learning is now further extended by the provision of entire Virtual Reality Learning Environments (VRLE). This highlights a shift in Web-based learning from a conventional multimedia to a more immersive, interactive, intuitive and exciting VR learning environment. VRLEs simulate the real world through the…

Factors of Learner-Instructor Interaction Which Predict Perceived Learning Outcomes in Online Learning Environment

ERIC Educational Resources Information Center

Kang, M.; Im, T.

2013-01-01

Interaction in the online learning environment has been regarded as one of the most critical elements that affect learning outcomes. This study examined what factors in learner-instructor interaction can predict the learner's outcomes in the online learning environment. Learners in K Online University participated by answering the survey, and data…

Self-Regulated Learning in Technology Enhanced Learning Environments: An Investigation with University Students

ERIC Educational Resources Information Center

Lenne, Dominique; Abel, Marie-Helene; Trigano, Philippe; Leblanc, Adeline

2008-01-01

In Technology Enhanced Learning Environments, self-regulated learning (SRL) partly relies on the features of the technological tools. The authors present two environments they designed in order to facilitate SRL: the first one (e-Dalgo) is a website dedicated to the learning of algorithms and computer programming. It is structured as a classical…

Using Student-Centred Learning Environments to Stimulate Deep Approaches to Learning: Factors Encouraging or Discouraging Their Effectiveness

ERIC Educational Resources Information Center

Baeten, Marlies; Kyndt, Eva; Struyven, Katrien; Dochy, Filip

2010-01-01

This review outlines encouraging and discouraging factors in stimulating the adoption of deep approaches to learning in student-centred learning environments. Both encouraging and discouraging factors can be situated in the context of the learning environment, in students' perceptions of that context and in characteristics of the students…

Perceived Satisfaction, Perceived Usefulness and Interactive Learning Environments as Predictors to Self-Regulation in e-Learning Environments

ERIC Educational Resources Information Center

Liaw, Shu-Sheng; Huang, Hsiu-Mei

2013-01-01

The research purpose is to investigate learner self-regulation in e-learning environments. In order to better understand learner attitudes toward e-learning, 196 university students answer a questionnaire survey after use an e-learning system few months. The statistical results showed that perceived satisfaction, perceived usefulness, and…

Learning in a Changing Environment

ERIC Educational Resources Information Center

Speekenbrink, Maarten; Shanks, David R.

2010-01-01

Multiple cue probability learning studies have typically focused on stationary environments. We present 3 experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that…

Student-Teacher Interaction in Online Learning Environments

ERIC Educational Resources Information Center

Wright, Robert D., Ed.

2015-01-01

As face-to-face interaction between student and instructor is not present in online learning environments, it is increasingly important to understand how to establish and maintain social presence in online learning. "Student-Teacher Interaction in Online Learning Environments" provides successful strategies and procedures for developing…

Preferred-Actual Learning Environment "Spaces" and Earth Science Outcomes in Taiwan

ERIC Educational Resources Information Center

Chang, Chun-Yen; Hsiao, Chien-Hua; Barufaldi, James P.

2006-01-01

This study examines the possibilities of differential impacts on students' earth science learning outcomes between different preferred-actual learning environment spaces by using a newly developed ESCLEI (Earth Science Classroom Learning Environment Instrument). The instrument emphasizes three simultaneously important classroom components:…

Causal Model Progressions as a Foundation for Intelligent Learning Environments.

DTIC Science & Technology

1987-11-01

Foundation for Intelligent Learning Environments 3Barbara Y. White and John R. Frederiksen ~DTIC Novemr1987 ELECTE November1987 JUNO 9 88 Approved I )’I...Learning Environments 12. PERSONAL AUTHOR(S? Barbara Y. White and John R. Frederiksen 13a. TYPE OF REPORT 13b TIME COVERED 14. DATE OF REPORT (Year...architecture of a new type of learning environment that incorporates features of microworlds and of intelligent tutorng systems. The environment is based on

The use of deep and surface learning strategies among students learning English as a foreign language in an Internet environment.

PubMed

Aharony, Noa

2006-12-01

The learning context is learning English in an Internet environment. The examination of this learning process was based on the Biggs and Moore's teaching-learning model (Biggs & Moore, 1993). The research aims to explore the use of the deep and surface strategies in an Internet environment among EFL students who come from different socio-economic backgrounds. The results of the research may add an additional level to the understanding of students' functioning in the Internet environment. One hundred fourty-eight Israeli junior and high school students participated in this research. The methodology was based on special computer software: Screen Cam, which recorded the students' learning process. In addition, expert judges completed a questionnaire which examined and categorized the students' learning strategies. The research findings show a clear preference of participants from all socio-economic backgrounds towards the surface learning strategy. The findings also showed that students from the medium to high socio-economic background used both learning strategies more frequently than low socio-economic students. The results reflect the habits that students acquire during their adjustment process throughout their education careers. A brief encounter with the Internet learning environment apparently cannot change norms or habits, which were acquired in the non-Internet learning environment.

Designing new collaborative learning spaces in clinical environments: experiences from a children's hospital in Australia.

PubMed

Bines, Julie E; Jamieson, Peter

2013-09-01

Hospitals are complex places that provide a rich learning environment for students, staff, patients and their families, professional groups and the community. The "new" Royal Children's Hospital opened in late 2011. Its mission is focused on improving health and well-being of children and adolescents through leadership in healthcare, research and education. Addressing the need to create "responsive learning environments" aligned with the shift to student-centred pedagogy, two distinct learning environments were developed within the new Royal Children's Hospital; (i) a dedicated education precinct providing a suite of physical environments to promote a more active, collaborative and social learning experience for education and training programs conducted on the Royal Children's Hospital campus and (ii) a suite of learning spaces embedded within clinical areas so that learning becomes an integral part of the daily activities of this busy Hospital environment. The aim of this article is to present the overarching educational principles that lead the design of these learning spaces and describe the opportunities and obstacles encountered in the development of collaborative learning spaces within a large hospital development.

Perceptions of Pre-Service Teachers on the Design of a Learning Environment Based on the Seven Principles of Good Practice

ERIC Educational Resources Information Center

Al-Furaih, Suad Abdul Aziz

2017-01-01

This study explored the perceptions of 88 pre-service teachers on the design of a learning environment using the Seven Principles of Good Practice and its effect on participants' abilities to create their Cloud Learning Environment (CLE). In designing the learning environment, a conceptual model under the name 7 Principles and Integrated Learning…

A Case Study of the Experiences of Instructors and Students in a Virtual Learning Environment (VLE) with Different Cultural Backgrounds

ERIC Educational Resources Information Center

Lim, Keol; Kim, Mi Hwa

2015-01-01

The use of virtual learning environments (VLEs) has become more common and educators recognized the potential of VLEs as educational environments. The learning community in VLEs can be a mixture of people from all over the world with different cultural backgrounds. However, despite many studies about the use of virtual environments for learning,…

Virtual Representations in 3D Learning Environments

ERIC Educational Resources Information Center

Shonfeld, Miri; Kritz, Miki

2013-01-01

This research explores the extent to which virtual worlds can serve as online collaborative learning environments for students by increasing social presence and engagement. 3D environments enable learning, which simulates face-to-face encounters while retaining the advantages of online learning. Students in Education departments created avatars…

Experiences of a student-run clinic in primary care: a mixed-method study with students, patients and supervisors

PubMed Central

Fröberg, Maria; Leanderson, Charlotte; Fläckman, Birgitta; Hedman-Lagerlöf, Erik; Björklund, Karin; Nilsson, Gunnar H.; Stenfors, Terese

2018-01-01

Objective To explore how a student-run clinic (SRC) in primary health care (PHC) was perceived by students, patients and supervisors. Design A mixed methods study. Clinical learning environment, supervision and nurse teacher evaluation scale (CLES + T) assessed student satisfaction. Client satisfaction questionnaire-8 (CSQ-8) assessed patient satisfaction. Semi-structured interviews were conducted with supervisors. Setting Gustavsberg PHC Center, Stockholm County, Sweden. Subjects Students in medicine, nursing, physiotherapy, occupational therapy and psychology and their patients filled in questionnaires. Supervisors in medicine, nursing and physiotherapy were interviewed. Main outcome measures Mean values and medians of CLES + T and CSQ-8 were calculated. Interviews were analyzed using content analysis. Results A majority of 199 out of 227 student respondents reported satisfaction with the pedagogical atmosphere and the supervisory relationship. Most of the 938 patient respondents reported satisfaction with the care given. Interviews with 35 supervisors showed that the organization of the SRC provided time and support to focus on the tutorial assignment. Also, the pedagogical role became more visible and targeted toward the student’s individual needs. However, balancing the student’s level of autonomy and the own control over care was described as a challenge. Many expressed the need for further pedagogical education. Conclusions High student and patient satisfaction reported from five disciplines indicate that a SRC in PHC can be adapted for heterogeneous student groups. Supervisors experienced that the SRC facilitated and clarified their pedagogical role. Simultaneously their need for continuous pedagogical education was highlighted. The SRC model has the potential to enhance student-centered tuition in PHC. Key Points Knowledge of student-run clinics (SRCs) as learning environments within standard primary health care (PHC) is limited. We report experiences from the perspectives of students, their patients and supervisors, representing five healthcare disciplines. Students particularly valued the pedagogical atmosphere and the supervisory relationship. Patients expressed high satisfaction with the care provided. Supervisors expressed that the structure of the SRC supported the pedagogical assignment and facilitated student-centered tuition – simultaneously the altered learning environment highlighted the need for further pedagogical education. Student-run clinics in primary health care have great potential for student-regulated learning. PMID:29368978

Optimizing T-Learning Course Scheduling Based on Genetic Algorithm in Benefit-Oriented Data Broadcast Environments

ERIC Educational Resources Information Center

Huang, Yong-Ming; Chen, Chao-Chun; Wang, Ding-Chau

2012-01-01

Ubiquitous learning receives much attention in these few years due to its wide spectrum of applications, such as the T-learning application. The learner can use mobile devices to watch the digital TV based course content, and thus, the T-learning provides the ubiquitous learning environment. However, in real-world data broadcast environments, the…

Analysis of Means for Building Context-Aware Recommendation System for Mobile Learning

ERIC Educational Resources Information Center

Shcherbachenko, Larysa; Nowakowski, Samuel

2017-01-01

One of the rapidly developing tools for online learning is learning through a mobile environment. Therefore, developing and improving mobile learning environments is an active topic now. One of the ways to adapt the learning environment to the user's needs is to use his context. Context of the user consists of the current context in online…

Design Patterns for Learning and Assessment: Facilitating the Introduction of a Complex Simulation-Based Learning Environment into a Community of Instructors

ERIC Educational Resources Information Center

Frezzo, Dennis C.; Behrens, John T.; Mislevy, Robert J.

2010-01-01

Simulation environments make it possible for science and engineering students to learn to interact with complex systems. Putting these capabilities to effective use for learning, and assessing learning, requires more than a simulation environment alone. It requires a conceptual framework for the knowledge, skills, and ways of thinking that are…

Predicting protein function and other biomedical characteristics with heterogeneous ensembles

PubMed Central

Whalen, Sean; Pandey, Om Prakash

2015-01-01

Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a powerful approach to improving prediction performance is to construct heterogeneous ensemble predictors that combine the output of diverse individual predictors that capture complementary aspects of the problems and/or datasets. In this paper, we demonstrate the potential of such heterogeneous ensembles, derived from stacking and ensemble selection methods, for addressing PFP and other similar biomedical prediction problems. Deeper analysis of these results shows that the superior predictive ability of these methods, especially stacking, can be attributed to their attention to the following aspects of the ensemble learning process: (i) better balance of diversity and performance, (ii) more effective calibration of outputs and (iii) more robust incorporation of additional base predictors. Finally, to make the effective application of heterogeneous ensembles to large complex datasets (big data) feasible, we present DataSink, a distributed ensemble learning framework, and demonstrate its sound scalability using the examined datasets. DataSink is publicly available from https://github.com/shwhalen/datasink. PMID:26342255

Sustaining Teacher Control in a Blog-Based Personal Learning Environment

ERIC Educational Resources Information Center

Tomberg, Vladimir; Laanpere, Mart; Ley, Tobias; Normak, Peeter

2013-01-01

Various tools and services based on Web 2.0 (mainly blogs, wikis, social networking tools) are increasingly used in formal education to create personal learning environments, providing self-directed learners with more freedom, choice, and control over their learning. In such distributed and personalized learning environments, the traditional role…

Supporting the Transition of Learning Disabled Students to the Postsecondary Environment

ERIC Educational Resources Information Center

Gray, Patricia Jean

2012-01-01

Students with learning disabilities present a diverse spectrum of learning needs; research suggest they may have difficulty making the transition to the postsecondary environment. Learning disabled students at the subject high school were not successfully making the transition from the secondary to the postsecondary environment. This study was…

Utilizing Virtual and Personal Learning Environments for Optimal Learning

ERIC Educational Resources Information Center

Terry, Krista, Ed.; Cheney, Amy, Ed.

2016-01-01

The integration of emerging technologies in higher education presents a new set of challenges and opportunities for educators. With a growing need for customized lesson plans in online education, educators are rethinking the design and development of their learning environments. "Utilizing Virtual and Personal Learning Environments for…

Flipped Education: Transitioning to the Homeschool Environment

ERIC Educational Resources Information Center

Alamry, Adel; karaali, Abeer

2016-01-01

This paper seeks to introduce flipped learning as a viable learning method that can be used in the homeschool environment. Flipped learning can become a valuable aspect of homeschooling when the learning environment is conducive to the application of self-taught knowledge. In fact, the sessions evidently act as clarification bridges and…

Visualising Learning Design in LAMS: A Historical View

ERIC Educational Resources Information Center

Dalziel, James

2011-01-01

The Learning Activity Management System (LAMS) provides a web-based environment for the creation, sharing, running and monitoring of Learning Designs. A central feature of LAMS is the visual authoring environment, where educators use a drag-and-drop environment to create sequences of learning activities. The visualisation is based on boxes…

Determination of Science Teachers' Opinions about Outdoor Education

ERIC Educational Resources Information Center

Kubat, Ulas

2017-01-01

The aim of this research is to discover what science teachers' opinions about outdoor education learning environments are. Outdoor education learning environments contribute to problem-solving, critical and creative thinking skills of students. For this reason, outdoor education learning environments are very important for students to learn by…

Offering a Framework for Value Co-Creation in Virtual Academic Learning Environments

ERIC Educational Resources Information Center

Ranjbarfard, Mina; Heidari Sureshjani, Mahboobeh

2018-01-01

Purpose: This research aims to convert the traditional teacher-student models, in which teachers determine the learning resources, into a flexible structure and an active learning environment so that students can participate in the educational processes and value co-creation in virtual academic learning environments (VALEs).…

Constructivist Learning Environment among Palestinian Science Students

ERIC Educational Resources Information Center

Zeidan, Afif

2015-01-01

The purpose of this study was to investigate the constructivist learning environment among Palestinian science students. The study also aimed to investigate the effects of gender and learning level of these students on their perceptions of the constructivist learning environment. Data were collected from 125 male and 101 female students from the…

Disrupting a Learning Environment for Promotion of Geometry Teaching

ERIC Educational Resources Information Center

Jojo, Zingiswa

2017-01-01

Creating a classroom learning environment that is suitably designed for promotion of learners' performance in geometry, a branch of mathematics that addresses spatial sense and geometric reasoning, is a daunting task. This article focuses on how grade 8 teachers' action learning changed the learning environment for the promotion of geometry…

Criteria, Strategies and Research Issues of Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Tsai, Chin-Chung; Yang, Stephen J. H.

2008-01-01

Recent progress in wireless and sensor technologies has lead to a new development of learning environments, called context-aware ubiquitous learning environment, which is able to sense the situation of learners and provide adaptive supports. Many researchers have been investigating the development of such new learning environments; nevertheless,…

Kitchen Science Investigators: Promoting Identity Development as Scientific Reasoners and Thinkers

ERIC Educational Resources Information Center

Clegg, Tamara Lynnette

2010-01-01

My research centers upon designing transformative learning environments and supporting technologies. Kitchen Science Investigators (KSI) is an out-of-school transformative learning environment we designed to help young people learn science through cooking. My dissertation considers the question, "How can we design a learning environment in which…

Improving Collaborative Learning by Supporting Casual Encounters in Distance Learning.

ERIC Educational Resources Information Center

Contreras, Juan; Llamas, Rafael; Vizcaino, Aurora; Vavela, Jesus

Casual encounters in a learning environment are very useful in reinforcing previous knowledge and acquiring new knowledge. Such encounters are very common in traditional learning environments and can be used successfully in social environments in which students can discover and construct knowledge through a process of dialogue, negotiation, or…

Homogeneous v. Heterogeneous: Is Tracking a Barrier to Equity?

ERIC Educational Resources Information Center

Polansky, Harvey B.

1995-01-01

Tracking has contributed considerably to the basic inequality of funding among American schools. To move to a heterogenous environment, districts must understand the concept of resource and program equity, commit to a planning process that allocates time and resources, provide ongoing inservice, downplay standardized test results, and phase-in…

Citizen Science as a REAL Environment for Authentic Scientific Inquiry

ERIC Educational Resources Information Center

Meyer, Nathan J.; Scott, Siri; Strauss, Andrea Lorek; Nippolt, Pamela L.; Oberhauser, Karen S.; Blair, Robert B.

2014-01-01

Citizen science projects can serve as constructivist learning environments for programming focused on science, technology, engineering, and math (STEM) for youth. Attributes of "rich environments for active learning" (REALs) provide a framework for design of Extension STEM learning environments. Guiding principles and design strategies…

Nursing Students' Qualitative Experiences in the Medical-Surgical Clinical Learning Environment: A Cross-Cultural Integrative Review.

PubMed

Hooven, Katie

2015-08-01

The nature of the clinical learning environment has a huge impact on student learning. For instance, research has supported the idea that a positive learning environment increases student learning. Therefore, the ability to gain information from the student perspective about the learning environment is essential to nursing education. This article reviews qualitative research on nursing students' experiences of the clinical learning environment. The significance of the issue, the purpose of the integrative review, the methods used in the literature search, and the results of the review are presented. Seventeen studies from 12 countries are identified for review, and six common themes are discussed. An exhaustive literature review revealed that among the 17 articles evaluated, six themes were common. The findings indicate the need to continue quality improvement to advance clinical education. Copyright 2015, SLACK Incorporated.

The Relationship among Self-Regulated Learning, Procrastination, and Learning Behaviors in Blended Learning Environment

ERIC Educational Resources Information Center

Yamada, Masanori; Goda, Yoshiko; Matsuda, Takeshi; Kato, Hiroshi; Miyagawa, Hiroyuki

2015-01-01

This research aims to investigate the relationship among the awareness of self-regulated learning (SRL), procrastination, and learning behaviors in blended learning environment. One hundred seventy nine freshmen participated in this research, conducted in the blended learning style class using learning management system. Data collection was…

Behavioral Feature Extraction to Determine Learning Styles in e-Learning Environments

ERIC Educational Resources Information Center

Fatahi, Somayeh; Moradi, Hadi; Farmad, Elaheh

2015-01-01

Learning Style (LS) is an important parameter in the learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments. Consequently, an important capability of an e-learning system could be the automatic determination of a student's learning style. In this paper, a set of…

Effects of Collaborative Learning Styles on Performance of Students in a Ubiquitous Collaborative Mobile Learning Environment

ERIC Educational Resources Information Center

Fakomogbon, Michael Ayodele; Bolaji, Hameed Olalekan

2017-01-01

Collaborative learning is an approach employed by instructors to facilitate learning and improve learner's performance. Mobile learning can accommodate a variety of learning approaches. This study, therefore, investigated the effects of collaborative learning styles on performance of students in a mobile learning environment. The specific purposes…

Equitable Learning Outcomes: Supporting Economically and Culturally Disadvantaged Students in "Formative Learning Environments"

ERIC Educational Resources Information Center

Clark, Ian

2014-01-01

The central and distinguishing thesis of social and cultural perspectives on outcome equity is that public school classrooms are culturally biased environments. Such environments disaffect children who arrive at school from the economic or cultural margin. The "formative learning environment" (FoLE) establishes and sustains legitimate…

Characteristics of an Innovative Learning Environment According to Students' Perceptions: Actual versus Preferred

ERIC Educational Resources Information Center

Magen-Nagar, Noga; Steinberger, Pnina

2017-01-01

An innovative learning environment is the current outcome of the constructivist approach, the essence of which is co-construction of knowledge in an Information and Communication Technology (ICT) environment. We examined how Israeli students perceived 10 characteristics of their classroom learning environment--student cohesiveness, teacher…

Conducting and Supporting a Goal-Based Scenario Learning Environment.

ERIC Educational Resources Information Center

Montgomery, Joel; And Others

1994-01-01

Discussion of goal-based scenario (GBS) learning environments focuses on a training module designed to prepare consultants with new skills in managing clients, designing user-friendly graphical computer interfaces, and working in a client/server computing environment. Transforming the environment from teaching focused to learning focused is…

Anatomy Education Environment Measurement Inventory: A Valid Tool to Measure the Anatomy Learning Environment

ERIC Educational Resources Information Center

Hadie, Siti Nurma Hanim; Hassan, Asma'; Ismail, Zul Izhar Mohd; Asari, Mohd Asnizam; Khan, Aaijaz Ahmed; Kasim, Fazlina; Yusof, Nurul Aiman Mohd; Manan@Sulong, Husnaida Abdul; Tg Muda, Tg Fatimah Murniwati; Arifin, Wan Nor; Yusoff, Muhamad Saiful Bahri

2017-01-01

Students' perceptions of the education environment influence their learning. Ever since the major medical curriculum reform, anatomy education has undergone several changes in terms of its curriculum, teaching modalities, learning resources, and assessment methods. By measuring students' perceptions concerning anatomy education environment,…

Apprentissage et Environment (Learning and Environment)

ERIC Educational Resources Information Center

Care, Jean-Marc

1977-01-01

Detailed typical daily program in the life of an American and a Moroccan teenager introduces the discussion of the influence of the environment on the learning process, particularly on foreign language learning. A "kit" method for responding to needs determined by the environment is described. (Text is in French.) (AMH)

The Prepared Environment and Its Relationship to Learning.

ERIC Educational Resources Information Center

Loeffler, Margaret Howard

A "prepared environment" is a planned learning facility for young children which offers a supportive and stimulating environment. Current thinking on early learning and the resultant implications for the design of a physical environment which would encourage conceptual development are examined in this book. Each section considers a particular…

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

An Empirical Verification of a-priori Learning Models on Mailing Archives in the Context of Online Learning Activities of Participants in Free\\Libre Open Source Software (FLOSS) Communities

ERIC Educational Resources Information Center

Mukala, Patrick; Cerone, Antonio; Turini, Franco

2017-01-01

Free\\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning…

Directive versus Facilitative Peer Tutoring? A View on Students' Appraisal, Reported Learning Gains and Experiences within Two Differently-Tutored Learning Environments

ERIC Educational Resources Information Center

Berghmans, Inneke; Michiels, Lotte; Salmon, Sara; Dochy, Filip; Struyven, Katrien

2014-01-01

The present study aimed to shed light on students' appraisal and reported learning gains in two differently-tutored learning environments (i.e. directively and facilitatively tutored). In order to investigate this, a quasi-experimental study was set up in the context of a clinical skills learning environment. Not only were participating…

Smart Learning Adoption in Employees and HRD Managers

ERIC Educational Resources Information Center

Lee, Junghwan; Zo, Hangjung; Lee, Hwansoo

2014-01-01

The innovation of online technologies and the rapid diffusion of smart devices are changing workplace learning environment. Smart learning, as emerging learning paradigm, enables employees' learning to take place anywhere and anytime. Workplace learning studies, however, have focused on traditional e-learning environment, and they have failed…

Perception of hospital learning environment: a survey of Hong Kong nursing students.

PubMed

Chan, Dominic S K; Ip, Wan Y

2007-10-01

The last two decades have seen widespread changes to nurse education but the clinical field remains an essential and invaluable resource in preparing students for the reality of their professional role, supporting the integration of theory and practice, and linking the 'knowing what' to do with the 'knowing how' to deliver care. The clinical learning environment represents a vital element of nurse education that needs to be measurable and warrants further investigation. This survey study examined Hong Kong nursing students' perception of the social climate of the clinical learning environment. Participants were invited to complete the two versions, the Actual and Preferred Forms, of the Clinical Learning Environment Inventory following the completion of their clinical field placement. Two hundred eighty one Actual Forms and 243 Preferred Forms returned. SPPS version 11 was employed to analyse data with descriptive and inferential statistics. It was found that there were significant differences between students' perceptions of the actual clinical learning environment and the ideal clinical learning environment they desired. The study highlights the need for a supportive clinical learning environment which is of paramount importance for students in clinical practice.

Relationship between clinical fieldwork educator performance and health professional students' perceptions of their practice education learning environments.

PubMed

Brown, Ted; Williams, Brett; Lynch, Marty

2013-12-01

The Dundee Ready Education Environment Measure, Clinical Teaching Effectiveness Instrument, and Clinical Learning Environment Inventory were completed by 548 undergraduate students (54.5% response rate) enrolled in eight health professional bachelor degree courses. Regression analysis was used to investigate the significant predictors of the Clinical Teaching Effectiveness Instrument with the Dundee Ready Education Environment Measure and Clinical Learning Environment Inventory subscales as independent variables. The results indicated that the Dundee Ready Education Environment Measure and Clinical Learning Environment Inventory Actual version subscale scores explained 44% of the total variance in the Clinical Teaching Effectiveness Instrument score. The Dundee Ready Education Environment Measure subscale Academic Self-Perception explained 1.1% of the variance in the Clinical Teaching Effectiveness Instrument score. The Clinical Learning Environment Inventory Actual subscales accounted for the following variance percentages in the Clinical Teaching Effectiveness Instrument score: personalization, 1.1%; satisfaction, 1.7%; task orientation, 5.1%; and innovation, 6.2%. Aspects of the clinical learning environment appear to be predictive of the effectiveness of the clinical teaching that students experience. Fieldwork educator performance might be a significant contributing factor toward student skill development and practitioner success. © 2013 Wiley Publishing Asia Pty Ltd.

Individual Differences in the Severely Retarded Child in Acquisition, Stimulus Generalization, and Extinction in Go-No-Go Discrimination Learning

ERIC Educational Resources Information Center

Evans, P. L. C.; Hogg, J. H.

1975-01-01

This study relates excitatory and inhibitory personality variables of a heterogeneous group of severely retarded children to performance on a discrete trial, successive go-no-go intradimensional discrimination learning problem, which was followed by stimulus generalization tests on a color hue continuum and extinction trials. (GO)

Investigating Gender and Racial/Ethnic Invariance in Use of a Course Management System in Higher Education

ERIC Educational Resources Information Center

Li, Yi; Wang, Qiu; Campbell, John

2015-01-01

This study focused on learning equity in colleges and universities where teaching and learning depends heavily on computer technologies. The study used the Structural Equation Modeling (SEM) to investigate gender and racial/ethnic heterogeneity in the use of a computer based course management system (CMS). Two latent variables (CMS usage and…

The World Bank Rural Development Field Staff Distance Learning and Training Strategy.

ERIC Educational Resources Information Center

Mortera-Gutierrez, Fernando

The Rural Development Distance Learning and Training Strategy targets locally recruited field staff of the World Bank Rural Sector. Field staff at the bank's mission offices worldwide are heterogeneous in terms of culture, ethnicity, race, gender, social class, and religion. However, they have the following in common: they follow the Bank's work…

Inclusive Education for Children with Specific Learning Difficulties: Analysis of Opportunities and Barriers in Inclusive Education in Slovenia

ERIC Educational Resources Information Center

Kavkler, Marija; Babuder, Milena Košak; Magajna, Lidija

2015-01-01

Inclusive education allows for universal inclusion, participation and achievement of all children, including children with specific learning difficulties (SpLD). Children with SpLD form a heterogeneous group with diverse cognitive deficits, special educational needs (SEN) and strengths, and have a legislated right to the continuum of both…

A Longitudinal Comparison of Systems Used to Identify Subgroups of Learning Disabled Children.

ERIC Educational Resources Information Center

Goldstein, David; Dundon, William D.

This paper addresses the problem of heterogeneity of samples of learning disabled (LD) children by comparing five different systems for identifying homogeneous subgroups in terms of their ability to predict longitudinal reading and mathematics scores. One hundred and sixty LD children served as subjects. Three of the five subgrouping systems were…

Numerical Magnitude Processing Impairments in Genetic Syndromes: A Cross-Syndrome Comparison of Turner and 22Q11.2 Deletion Syndromes

ERIC Educational Resources Information Center

Brankaer, Carmen; Ghesquière, Pol; De Wel, Anke; Swillen, Ann; De Smedt, Bert

2017-01-01

Cross-syndrome comparisons offer an important window onto understanding heterogeneity in mathematical learning disabilities or dyscalculia. The present study therefore investigated symbolic numerical magnitude processing in two genetic syndromes that are both characterized by mathematical learning disabilities: Turner syndrome and 22q11.2 deletion…

The Relation of Learners' Motivation with the Process of Collaborative Scientific Discovery Learning

ERIC Educational Resources Information Center

Saab, Nadira; van Joolingen, Wouter R.; van Hout-Wolters, B. H. A. M.

2009-01-01

In this study, we investigated the influence of individual learners' motivation on the collaborative discovery learning process. In this we distinguished the motivation of the individual learners and had eye for the composition of groups, which could be homogeneous or heterogeneous in terms of motivation. The study involved 73 dyads of 10th-grade…

Measuring and predicting reservoir heterogeneity in complex deposystems: The fluvial-deltaic Big Injun sandstone in West Virginia

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

Patchen, D.G.; Hohn, M.E.; Aminian, K.

1993-04-01

The purpose of this research is to develop techniques to measure and predict heterogeneities in oil reservoirs that are the products of complex deposystems. The unit chosen for study is the Lower Mississippian Big Injun sandstone, a prolific oil producer (nearly 60 fields) in West Virginia. This research effort has been designed and is being implemented as an integrated effort involving stratigraphy, structural geology, petrology, seismic study, petroleum engineering, modeling and geostatistics. Sandstone bodies are being mapped within their regional depositional systems, and then sandstone bodies are being classified in a scheme of relative heterogeneity to determine heterogeneity across depositionalmore » systems. Facies changes are being mapped within given reservoirs, and the environments of deposition responsible for each facies are being interpreted to predict the inherent relative heterogeneity of each facies. Structural variations will be correlated both with production, where the availability of production data will permit, and with variations in geologic and engineering parameters that affect production. A reliable seismic model of the Big Injun reservoirs in Granny Creek field is being developed to help interpret physical heterogeneity in that field. Pore types are being described and related to permeability, fluid flow and diagenesis, and petrographic data are being integrated with facies and depositional environments to develop a technique to use diagenesis as a predictive tool in future reservoir development. Another objective in the Big Injun study is to determine the effect of heterogeneity on fluid flow and efficient hydrocarbon recovery in order to improve reservoir management. Graphical methods will be applied to Big Injun production data and new geostatistical methods will be developed to detect regional trends in heterogeneity.« less

Measuring and predicting reservoir heterogeneity in complex deposystems: The fluvial-deltaic Big Injun sandstone in West Virginia. Annual report, September 20, 1991--September 20, 1992

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

Patchen, D.G.; Hohn, M.E.; Aminian, K.

1993-04-01

The purpose of this research is to develop techniques to measure and predict heterogeneities in oil reservoirs that are the products of complex deposystems. The unit chosen for study is the Lower Mississippian Big Injun sandstone, a prolific oil producer (nearly 60 fields) in West Virginia. This research effort has been designed and is being implemented as an integrated effort involving stratigraphy, structural geology, petrology, seismic study, petroleum engineering, modeling and geostatistics. Sandstone bodies are being mapped within their regional depositional systems, and then sandstone bodies are being classified in a scheme of relative heterogeneity to determine heterogeneity across depositionalmore » systems. Facies changes are being mapped within given reservoirs, and the environments of deposition responsible for each facies are being interpreted to predict the inherent relative heterogeneity of each facies. Structural variations will be correlated both with production, where the availability of production data will permit, and with variations in geologic and engineering parameters that affect production. A reliable seismic model of the Big Injun reservoirs in Granny Creek field is being developed to help interpret physical heterogeneity in that field. Pore types are being described and related to permeability, fluid flow and diagenesis, and petrographic data are being integrated with facies and depositional environments to develop a technique to use diagenesis as a predictive tool in future reservoir development. Another objective in the Big Injun study is to determine the effect of heterogeneity on fluid flow and efficient hydrocarbon recovery in order to improve reservoir management. Graphical methods will be applied to Big Injun production data and new geostatistical methods will be developed to detect regional trends in heterogeneity.« less

Heterogeneity within populations of recombinant Chinese hamster ovary cells expressing human interferon-gamma.

PubMed

Coppen, S R; Newsam, R; Bull, A T; Baines, A J

1995-04-20

The Chinese hamster ovary (CHO) cell line has great commercial importance in the production of recombinant human proteins, especially those for therapeutic use. Much attention has been paid to CHO cell population physiology in order to define factors affecting product fidelity and yield. Such studies have revealed that recombinant proteins, including human interferon-gamma (IFN-gamma), can be heterogeneous both in glycosylation and in proteolytic processing. The type of heterogeneity observed depends on the growth physiology of the cell population, although the relationship between them is complex. In this article we report results of a cytological study of the CHO320 line which expresses recombinant human IFN-gamma. When grown in suspension culture, this cell line exhibited three types of heterogeneity: (1) heterogeneity of the production of IFN-gamma within the cell population, (2) heterogeneity of the number of nuclei and mitotic spindles in dividing cells, and (3) heterogeneity of cellular environment. The last of these arises from cell aggregates which form in suspension culture: Some cells are exposed to the culture medium; others are fully enclosed within the mass with little or no direct access to the medium. Thus, live cells producing IFN-gamma are heterogeneous in their environment, with variable access to O(2) and nutrients. Within the aggregates, it appears that live cells proliferate on a dead cell mass. The layer of live cells can be several cells deep. Specific cell-cell attachments are observed between the living cells in these aggregates. Two proteins, known to be required for the formation of certain types of intercellular junctions, spectrin and vinculin, have been localized to the regions of cell-cell contact. The aggregation of the cells appears to be an active process requiring protein synthesis. (c) 1995 John Wiley & Sons, Inc.

A validated agent-based model to study the spatial and temporal heterogeneities of malaria incidence in the rainforest environment.

PubMed

Pizzitutti, Francesco; Pan, William; Barbieri, Alisson; Miranda, J Jaime; Feingold, Beth; Guedes, Gilvan R; Alarcon-Valenzuela, Javiera; Mena, Carlos F

2015-12-22

The Amazon environment has been exposed in the last decades to radical changes that have been accompanied by a remarkable rise of both Plasmodium falciparum and Plasmodium vivax malaria. The malaria transmission process is highly influenced by factors such as spatial and temporal heterogeneities of the environment and individual-based characteristics of mosquitoes and humans populations. All these determinant factors can be simulated effectively trough agent-based models. This paper presents a validated agent-based model of local-scale malaria transmission. The model reproduces the environment of a typical riverine village in the northern Peruvian Amazon, where the malaria transmission is highly seasonal and apparently associated with flooding of large areas caused by the neighbouring river. Agents representing humans, mosquitoes and the two species of Plasmodium (P. falciparum and P. vivax) are simulated in a spatially explicit representation of the environment around the village. The model environment includes: climate, people houses positions and elevation. A representation of changes in the mosquito breeding areas extension caused by the river flooding is also included in the simulation environment. A calibration process was carried out to reproduce the variations of the malaria monthly incidence over a period of 3 years. The calibrated model is also able to reproduce the spatial heterogeneities of local scale malaria transmission. A "what if" eradication strategy scenario is proposed: if the mosquito breeding sites are eliminated through mosquito larva habitat management in a buffer area extended at least 200 m around the village, the malaria transmission is eradicated from the village. The use of agent-based models can reproduce effectively the spatiotemporal variations of the malaria transmission in a low endemicity environment dominated by river floodings like in the Amazon.

Nursing students' satisfaction of the clinical learning environment: a research study.

PubMed

Papastavrou, Evridiki; Dimitriadou, Maria; Tsangari, Haritini; Andreou, Christos

2016-01-01

The acquisition of quality clinical experience within a supportive and pedagogically adjusted clinical learning environment is a significant concern for educational institutions. The quality of clinical learning usually reflects the quality of the curriculum structure. The assessment of the clinical settings as learning environment is a significant concern within the contemporary nursing education. The nursing students' satisfaction is considered as an important factor of such assessment, contributing to any potential reforms in order to optimize the learning activities and achievements within clinical settings. The aim of the study was to investigate nursing students' satisfaction of the clinical settings as learning environments. A quantitative descriptive, correlational design was used. A sample of 463 undergraduate nursing students from the three universities in Cyprus were participated. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T). Nursing students were highly satisfied with the clinical learning environment and their satisfaction has been positively related to all clinical learning environment constructs namely the pedagogical atmosphere, the Ward Manager's leadership style, the premises of Nursing in the ward, the supervisory relationship (mentor) and the role of the Nurse Teacher (p < 0.001). Students who had a named mentor reported more satisfied with the supervisory relationship. The frequency of meetings among the students and the mentors increased the students' satisfaction with the clinical learning environment. It was also revealed that 1st year students were found to be more satisfied than the students in other years. The supervisory relationship was evaluated by the students as the most influential factor in their satisfaction with the clinical learning environment. Student's acceptance within the nursing team and a well-documented individual nursing care is also related with students' satisfaction. The pedagogical atmosphere is considered pivotal, with reference to students' learning activities and competent development within the clinical setting. Therefore, satisfaction could be used as an important contributing factor towards the development of clinical learning environments in order to satisfy the needs and expectations of students. The value of the development of an organized mentorship system is illustrated in the study.

Implementing an Active Learning Environment to Influence Students' Motivation in Biochemistry

ERIC Educational Resources Information Center

Cicuto, Camila Aparecida Tolentino; Torres, Bayardo Baptista

2016-01-01

The Biochemistry: Biomolecules Structure and Metabolism course's goal is to promote meaningful learning through an active learning environment. Thus, study periods (SP) and discussion groups (DG) are used as a substitute for lecture classes. The goal of this study was to evaluate how this learning environment influences students' motivation (n =…

Using Coherence Analysis to Characterize Self-Regulated Learning Behaviours in Open-Ended Learning Environments

ERIC Educational Resources Information Center

Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2015-01-01

Researchers have long recognized the potential benefits of open-ended computer- based learning environments (OELEs) to help students develop self-regulated learning (SRL) behaviours. However, measuring self-regulation in these environments is a difficult task. In this paper, we present our work in developing and evaluating "coherence…

Emotional Presence in Online Learning Scale: A Scale Development Study

ERIC Educational Resources Information Center

Sarsar, Firat; Kisla, Tarik

2016-01-01

Although emotions are not a new topic in learning environments, the emerging technologies have changed not only the type of learning environments but also the perspectives of emotions in learning environments. This study designed to develop a survey to assist online instructors to understand students' emotional statement in online learning…

Validation of a Spanish Version of the Distance Education Learning Environments Survey (DELES) in Spain

ERIC Educational Resources Information Center

Fernández-Pascual, Maria Dolores; Ferrer-Cascales, Rosario; Reig-Ferrer, Abilio; Albaladejo-Blázquez, Natalia; Walker, Scott L.

2015-01-01

The aim of this study was to examine the validity of the Spanish version of the Distance Education Learning Environments Survey (Sp-DELES). This instrument assesses students' perceptions of virtual learning environments using six scales: Instructor Support, Student Interaction and Collaboration, Personal Relevance, Authentic Learning, Active…

The Relationship between Motivation, Learning Strategies and Choice of Environment whether Traditional or Including an Online Component

ERIC Educational Resources Information Center

Clayton, Karen; Blumberg, Fran; Auld, Daniel P.

2010-01-01

This study examined how students' achievement goals, self-efficacy and learning strategies influenced their choice of an online, hybrid or traditional learning environment. One hundred thirty-two post-secondary students completed surveys soliciting their preferences for learning environments, reasons for their preference, their motivational…

Open Learning Environments and the Impact of a Pedagogical Agent

ERIC Educational Resources Information Center

Clarebout, Geraldine; Elen, Jan

2006-01-01

Research reveals that in highly structured learning environments pedagogical agents can act as tools to direct students' learning processes by providing content or problem solving guidance. It has not yet been addressed whether pedagogical agents have a similar impact in more open learning environments that aim at fostering students' acquisition…

Clinical Environment as a Learning Environment: Student Nurses' Perceptions Concerning Clinical Learning Experiences.

ERIC Educational Resources Information Center

Papp, Inkeri; Markkanen, Marjatta; von Bonsdorff, Mikaela

2003-01-01

Finnish student nurses (n=16) described their clinical learning experiences. Several themes were identified: feeling appreciated and supported, the quality of mentoring and patient care, and self-directedness. School and clinical staff cooperation helped create a good learning environment in which theory and practice complemented each other.…

Determinants of Computer Self-Efficacy--An Examination of Learning Motivations and Learning Environments

ERIC Educational Resources Information Center

Hsu, Wen-Kai K.; Huang, Show-Hui S.

2006-01-01

The purpose of this article is to discuss determinants of computer self-efficacy from the perspective of participant internal learning motivations and external learning environments. The former consisted of three motivations--interest, trend, and employment--while the latter comprised two environments--home and school. Through an intermediate…

Architecture and Children: Learning Environments and Design Education.

ERIC Educational Resources Information Center

Taylor, Anne, Ed.; Muhlberger, Joe, Ed.

1998-01-01

This issue addresses (1) growing international interest in learning environments and their effects on behavior, and (2) design education, an integrated model for visual-spatial lifelong learning. It focuses on this new and emerging integrated field which integrates elements in education, new learning environment design, and the use of more two-…

Learning System Design Consideration in Creating an Online Learning Environment.

ERIC Educational Resources Information Center

Schaffer, Scott

This paper describes the design of a Web-based learning environment for leadership facilitators in a United States military organization. The overall aim of this project was to design a prototype of an online learning environment that supports leadership facilitators' knowledge development in the content area of motivation. The learning…

Learning Environments as Basis for Cognitive Achievements of Students in Basic Science Classrooms in Nigeria

ERIC Educational Resources Information Center

Atomatofa, Rachel; Okoye, Nnamdi; Igwebuike, Thomas

2016-01-01

The nature of classroom learning environments created by teachers had been considered very important for learning to take place effectively. This study investigated the effect of creating constructivist and transmissive learning environments on achievements of science students of different ability levels. 243 students formed the entire study…

The Effects of Different Learning Environments on Students' Motivation for Learning and Their Achievement

ERIC Educational Resources Information Center

Baeten, Marlies; Dochy, Filip; Struyven, Katrien

2013-01-01

Background: Research in higher education on the effects of student-centred versus lecture-based learning environments generally does not take into account the psychological need support provided in these learning environments. From a self-determination theory perspective, need support is important to study because it has been associated with…

Preservice Teachers' Perception and Use of Personal Learning Environments (PLEs)

ERIC Educational Resources Information Center

Sahin, Sami; Uluyol, Çelebi

2016-01-01

Personal learning environments (PLEs) are Web 2.0 tools and services by which users' access, construct, manage, and share educational contents in order to meet their learning needs. These environments enable users to manage their learning according to their own personal preferences. They further promote socialization and collaboration with their…

Seamless Learning Environments in Higher Education with Mobile Devices and Examples

ERIC Educational Resources Information Center

Marín, Victoria I.; Jääskelä, Päivikki; Häkkinen, Päivi; Juntunen, Merja; Rasku-Puttonen, Helena; Vesisenaho, Mikko

2016-01-01

The use of seamless learning environments that have the potential to support lifelong learning anytime and anywhere has become a reality. In this sense, many educational institutions have started to consider introducing seamless learning environments into their programs. The aim of this study is to analyze how various educational university…

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…

Development and Validation of the Blended Learning Environment Instrument (BLEI) in Higher Education

ERIC Educational Resources Information Center

Aljahni, Areej; Al-Begain, Khalid; Skinner, Heather

2014-01-01

Part of ongoing research into the efficacy of blended learning in higher education within the Kingdom of Saudi Arabia (KSA). The need for, and development of, a Blended Learning Environment Instrument (BLEI) are explained. This new instrument assesses student perceptions across five core aspects of blended learning environments: Infrastructure,…

Promoting Students' Problem Solving Skills and Knowledge of STEM Concepts in a Data-Rich Learning Environment: Using Online Data as a Tool for Teaching about Renewable Energy Technologies

ERIC Educational Resources Information Center

Thurmond, Brandi

2011-01-01

This study sought to compare a data-rich learning (DRL) environment that utilized online data as a tool for teaching about renewable energy technologies (RET) to a lecture-based learning environment to determine the impact of the learning environment on students' knowledge of Science, Technology, Engineering, and Math (STEM) concepts related…

The US Air Force After Vietnam: Postwar Challenges and Potential for Responses

DTIC Science & Technology

1988-12-01

process of assembling the data and bringing them into some preliminary historical order did not automatically constitute learning . Since what one can ...the world, as well as the heterogeneity ofpower . We learned anew that even the very greatest of power centers on earth can never truly monopolize... learn is delimited in some measure by what one already thinks, the Pentagon Papers were in themselves merely a data base; and the judgments and

Performance Comparison of a Matrix Solver on a Heterogeneous Network Using Two Implementations of MPI: MPICH and LAM

NASA Technical Reports Server (NTRS)

Phillips, Jennifer K.

1995-01-01

Two of the current and most popular implementations of the Message-Passing Standard, Message Passing Interface (MPI), were contrasted: MPICH by Argonne National Laboratory, and LAM by the Ohio Supercomputer Center at Ohio State University. A parallel skyline matrix solver was adapted to be run in a heterogeneous environment using MPI. The Message-Passing Interface Forum was held in May 1994 which lead to a specification of library functions that implement the message-passing model of parallel communication. LAM, which creates it's own environment, is more robust in a highly heterogeneous network. MPICH uses the environment native to the machine architecture. While neither of these free-ware implementations provides the performance of native message-passing or vendor's implementations, MPICH begins to approach that performance on the SP-2. The machines used in this study were: IBM RS6000, 3 Sun4, SGI, and the IBM SP-2. Each machine is unique and a few machines required specific modifications during the installation. When installed correctly, both implementations worked well with only minor problems.

A stochastic vision-based model inspired by zebrafish collective behaviour in heterogeneous environments

PubMed Central

Collignon, Bertrand; Séguret, Axel; Halloy, José

2016-01-01

Collective motion is one of the most ubiquitous behaviours displayed by social organisms and has led to the development of numerous models. Recent advances in the understanding of sensory system and information processing by animals impels one to revise classical assumptions made in decisional algorithms. In this context, we present a model describing the three-dimensional visual sensory system of fish that adjust their trajectory according to their perception field. Furthermore, we introduce a stochastic process based on a probability distribution function to move in targeted directions rather than on a summation of influential vectors as is classically assumed by most models. In parallel, we present experimental results of zebrafish (alone or in group of 10) swimming in both homogeneous and heterogeneous environments. We use these experimental data to set the parameter values of our model and show that this perception-based approach can simulate the collective motion of species showing cohesive behaviour in heterogeneous environments. Finally, we discuss the advances of this multilayer model and its possible outcomes in biological, physical and robotic sciences. PMID:26909173

Methodologies and systems for heterogeneous concurrent computing

NASA Technical Reports Server (NTRS)

Sunderam, V. S.

1994-01-01

Heterogeneous concurrent computing is gaining increasing acceptance as an alternative or complementary paradigm to multiprocessor-based parallel processing as well as to conventional supercomputing. While algorithmic and programming aspects of heterogeneous concurrent computing are similar to their parallel processing counterparts, system issues, partitioning and scheduling, and performance aspects are significantly different. In this paper, we discuss critical design and implementation issues in heterogeneous concurrent computing, and describe techniques for enhancing its effectiveness. In particular, we highlight the system level infrastructures that are required, aspects of parallel algorithm development that most affect performance, system capabilities and limitations, and tools and methodologies for effective computing in heterogeneous networked environments. We also present recent developments and experiences in the context of the PVM system and comment on ongoing and future work.

Dissociable contributions of the orbitofrontal and infralimbic cortex to pavlovian autoshaping and discrimination reversal learning: further evidence for the functional heterogeneity of the rodent frontal cortex.

PubMed

Chudasama, Y; Robbins, Trevor W

2003-09-24

To examine possible heterogeneity of function within the ventral regions of the rodent frontal cortex, the present study compared the effects of excitotoxic lesions of the orbitofrontal cortex (OFC) and the infralimbic cortex (ILC) on pavlovian autoshaping and discrimination reversal learning. During the pavlovian autoshaping task, in which rats learn to approach a stimulus predictive of reward [conditional stimulus (CS+)], only the OFC group failed to acquire discriminated approach but was unimpaired when preoperatively trained. In the visual discrimination learning and reversal task, rats were initially required to discriminate a stimulus positively associated with reward. There was no effect of either OFC or ILC lesions on discrimination learning. When the stimulus-reward contingencies were reversed, both groups of animals committed more errors, but only the OFC-lesioned animals were unable to suppress the previously rewarded stimulus-reward association, committing more "stimulus perseverative" errors. In contrast, the ILC group showed a pattern of errors that was more attributable to "learning" than perseveration. These findings suggest two types of dissociation between the effects of OFC and ILC lesions: (1) OFC lesions impaired the learning processes implicated in pavlovian autoshaping but not instrumental simultaneous discrimination learning, whereas ILC lesions were unimpaired at autoshaping and their reversal learning deficit did not reflect perseveration, and (2) OFC lesions induced perseverative responding in reversal learning but did not disinhibit responses to pavlovian CS-. In contrast, the ILC lesion had no effect on response inhibitory control in either of these settings. The findings are discussed in the context of dissociable executive functions in ventral sectors of the rat prefrontal cortex.

Investigating Factors That Influence Students' Management of Study Environment in Online Collaborative Groupwork

ERIC Educational Resources Information Center

Du, Jianxia; Xu, Jianzhong; Fan, Xitao

2015-01-01

The present study examines empirical models of students' management of the learning environment in the context of online collaborative groupwork. Such environment management is an important component of students' overall self-regulated learning strategy for effective learning. Student- and group-level predictors for study environment management in…

Changing the Metacognitive Orientation of a Classroom Environment to Enhance Students' Metacognition Regarding Chemistry Learning

ERIC Educational Resources Information Center

Thomas, Gregory P.; Anderson, David

2014-01-01

Concerns persist regarding science classroom learning environments and the lack of development of students' metacognition and reasoning processes within such environments. Means of shaping learning environments so that students are encouraged to develop their metacognition are required in order to enhance students' reasoning and…

Creating Multisensory Environments: Practical Ideas for Teaching and Learning. David Fulton/Nasen

ERIC Educational Resources Information Center

Davies, Christopher

2011-01-01

Multi-sensory environments in the classroom provide a wealth of stimulating learning experiences for all young children whose senses are still under development. "Creating Multisensory Environments: Practical Ideas for Teaching and Learning" is a highly practical guide to low-cost cost, easy to assemble multi-sensory environments. With a…

Creating Electronic Learning Environments: Games, Flow, and the User Interface.

ERIC Educational Resources Information Center

Jones, Marshall G.

A difficult task in creating rich, exploratory interactive learning environments is building an environment that is truly engaging. Engagement can be defined as the nexus of intrinsic knowledge and/or interest and external stimuli that promote the initial interest in, and continued use of a computer-based learning environment. Complete and total…

What Teachers Need to Know about Augmented Reality Enhanced Learning Environments

ERIC Educational Resources Information Center

Wasko, Christopher

2013-01-01

Augmented reality (AR) enhanced learning environments have been designed to teach a variety of subjects by having learners act like professionals in the field as opposed to students in a classroom. The environments, grounded in constructivist and situated learning theories, place students in a meaningful, non-classroom environment and force them…

Benefits of Informal Learning Environments: A Focused Examination of STEM-Based Program Environments

ERIC Educational Resources Information Center

Denson, Cameron D.; Austin Stallworth, Chandra; Hailey, Christine; Householder, Daniel L.

2015-01-01

This paper examines STEM-based informal learning environments for underrepresented students and reports on the aspects of these programs that are beneficial to students. This qualitative study provides a nuanced look into informal learning environments and determines what is unique about these experiences and makes them beneficial for students. We…

Seeking Construct Validity Grounded in Constructivist Epistemology: Development of the Survey of Contemporary Learning Environments

ERIC Educational Resources Information Center

Schuh, Kathy L.; Kuo, Yi-Lung

2015-01-01

This study focused on the development of a new classroom environment instrument for late-elementary students. The development of the survey of contemporary learning environments (SoCLE) followed a content analysis of three similar instruments on constructivist learning environments and the literature on characteristics of contemporary learning…

A Framework and a Methodology for Developing Authentic Constructivist e-Learning Environments

ERIC Educational Resources Information Center

Zualkernan, Imran A.

2006-01-01

Semantically rich domains require operative knowledge to solve complex problems in real-world settings. These domains provide an ideal environment for developing authentic constructivist e-learning environments. In this paper we present a framework and a methodology for developing authentic learning environments for such domains. The framework is…

Students' Personal Networks in Virtual and Personal Learning Environments: A Case Study in Higher Education Using Learning Analytics Approach

ERIC Educational Resources Information Center

Casquero, Oskar; Ovelar, Ramón; Romo, Jesús; Benito, Manuel; Alberdi, Mikel

2016-01-01

The main objective of this paper is to analyse the effect of the affordances of a virtual learning environment and a personal learning environment (PLE) in the configuration of the students' personal networks in a higher education context. The results are discussed in light of the adaptation of the students to the learning network made up by two…

Living and learning in a rural environment: a nursing student perspective.

PubMed

Pront, Leeanne; Kelton, Moira; Munt, Rebecca; Hutton, Alison

2013-03-01

This study investigates the influences on nursing student learning who live and learn in the same rural environment. A declining health workforce has been identified both globally and in Australia, the effects of which have become significantly apparent in the rural nursing sector. In support of rural educational programs the literature portrays rural clinical practice experiences as significant to student learning. However, there is little available research on what influences learning for the nursing student who studies in their own rural community. The aim of this study was to understand what influences student learning in the rural clinical environment. Through a multiple case study design five nursing students and two clinical preceptors from a rural clinical venue were interviewed. The interviews were transcribed and thematically analysed to identify factors that influenced student learning outcomes. The most significant influence on nursing student learning in the rural clinical environment was found to include the environment itself, the complex relationships unique to living and studying in a rural community along with the capacity to link theory to practice. The rural environment influences those in it, the demands placed on them, the relationships they form, the ability to promote learning and the time to teach and learn. Copyright © 2012. Published by Elsevier Ltd.

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale.

PubMed

Tackett, Sean; Bakar, Hamidah Abu; Shilkofski, Nicole A; Coady, Niamh; Rampal, Krishna; Wright, Scott

2015-01-01

While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES) for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM), the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. The overall response rate was 369/429 (86%). After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%), with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%). The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92) and the seven domains (α, 0.56-0.85). The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention.

Profiling medical school learning environments in Malaysia: a validation study of the Johns Hopkins Learning Environment Scale

PubMed Central

Tackett, Sean; Bakar, Hamidah Abu; Shilkofski, Nicole A.; Coady, Niamh; Rampal, Krishna; Wright, Scott

2015-01-01

Purpose: While a strong learning environment is critical to medical student education, the assessment of medical school learning environments has confounded researchers. Our goal was to assess the validity and utility of the Johns Hopkins Learning Environment Scale (JHLES) for preclinical students at three Malaysian medical schools with distinct educational and institutional models. Two schools were new international partnerships, and the third was school leaver program established without international partnership. Methods: First- and second-year students responded anonymously to surveys at the end of the academic year. The surveys included the JHLES, a 28-item survey using five-point Likert scale response options, the Dundee Ready Educational Environment Measure (DREEM), the most widely used method to assess learning environments internationally, a personal growth scale, and single-item global learning environment assessment variables. Results: The overall response rate was 369/429 (86%). After adjusting for the medical school year, gender, and ethnicity of the respondents, the JHLES detected differences across institutions in four out of seven domains (57%), with each school having a unique domain profile. The DREEM detected differences in one out of five categories (20%). The JHLES was more strongly correlated than the DREEM to two thirds of the single-item variables and the personal growth scale. The JHLES showed high internal reliability for the total score (α=0.92) and the seven domains (α, 0.56-0.85). Conclusion: The JHLES detected variation between learning environment domains across three educational settings, thereby creating unique learning environment profiles. Interpretation of these profiles may allow schools to understand how they are currently supporting trainees and identify areas needing attention. PMID:26165949

The Impact of Congruency Between Preferred and Actual Learning Environments on Tenth Graders' Science Literacy in Taiwan

NASA Astrophysics Data System (ADS)

Chang, Chun-Yen; Yeh, Ting-Kuang; Lin, Chun-Yen; Chang, Yueh-Hsia; Chen, Chia-Li D.

2010-08-01

This study explored the effects of congruency between preferred and actual learning environment (PLE & ALE) perceptions on students' science literacy in terms of science concepts, attitudes toward science, and the understanding of the nature of science in an innovative curriculum of High Scope Project, namely Sci-Tech Mind and Humane Heart (STMHH). A pre-/post-treatment experiment was conducted with 34 Taiwanese tenth graders involved in this study. Participating students' preferred learning environment perception and pre-instruction scientific literacy were evaluated before the STMHH curriculum. Their perceptions toward the actual STMHH learning environment and post-instruction scientific literacy were also examined after the STMHH. Students were categorized into two groups; "preferred alignment with actual learning environment" (PAA) and "preferred discordant with actual learning environment" (PDA), according to their PLEI and ALEI scores. The results of this study revealed that most of the students in this study preferred learning in a classroom environment where student-centered and teacher-centered learning environments coexisted. Furthermore, the ANCOVA analysis showed marginally statistically significant difference between groups in terms of students' post-test scores on scientific literacy with the students' pre-test scores as the covariate. As a pilot study with a small sample size aiming to probe the research direction of this problem, the result of marginally statistically significant and approaching large sized effect magnitude is likely to implicate that the congruency between preferred and actual learning environments on students' scientific literacy is noteworthy. Future study of this nature appears to merit further replications and investigations.

Shaping and Being Shaped by Environments for Learning Science: Continuities with the Space and Democratic Vision of a Century Ago

ERIC Educational Resources Information Center

Cavicchi, Elizabeth

2017-01-01

Environments of learning often remain unnoticed and unacknowledged. This study follows a student and myself as we became aware of our local environment at MIT and welcomed that environment as a vibrant contributor to our learning. We met this environment in part through its educational heritage in two centennial anniversaries: John Dewey's 1916…

A Blended Mobile Learning Environment for Museum Learning

ERIC Educational Resources Information Center

Hou, Huei-Tse; Wu, Sheng-Yi; Lin, Peng-Chun; Sung, Yao-Ting; Lin, Jhe-Wei; Chang, Kuo-En

2014-01-01

The use of mobile devices for informal learning has gained attention over recent years. Museum learning is also regarded as an important research topic in the field of informal learning. This study explored a blended mobile museum learning environment (BMMLE). Moreover, this study applied three blended museum learning modes: (a) the traditional…

Investigation of the Relationship between Learning Process and Learning Outcomes in E-Learning Environments

ERIC Educational Resources Information Center

Yurdugül, Halil; Menzi Çetin, Nihal

2015-01-01

Problem Statement: Learners can access and participate in online learning environments regardless of time and geographical barriers. This brings up the umbrella concept of learner autonomy that contains self-directed learning, self-regulated learning and the studying process. Motivation and learning strategies are also part of this umbrella…

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…

Heterogeneity in a room-temperature ionic liquid: persistent local environments and the red-edge effect.

PubMed

Hu, Zhonghan; Margulis, Claudio J

2006-01-24

In this work, we investigate the slow dynamics of 1-butyl-3-methylimidazolium hexafluorophosphate, a very popular room-temperature ionic solvent. Our study predicts the existence of heterogeneity in the liquid and shows that this heterogeneity is the underlying microscopic cause for the recently reported "red-edge effect" (REE) observed in the study of fluorescence of the organic probe 2-amino-7-nitrofluorene. This theoretical work explains in microscopic terms the relation between REE and dynamic heterogeneity in a room-temperature ionic liquid (IL). The REE is typical of micellar or colloidal systems, which are characterized by microscopic environments that are structurally very different. In contrast, in the case of this room-temperature IL, the REE occurs because of the long period during which molecules are trapped in quasistatic local solvent cages. This trapping time, which is longer than the lifetime of the excited-state probe, together with the inability of the surroundings to adiabatically relax, induces a set of site-specific spectroscopic responses. Subensembles of fluorescent molecules associated with particular local environments absorb and emit at different frequencies. We describe in detail the absorption wavelength-dependent emission spectra of 2-amino-7-nitrofluorene and show that this dependence on lambda(ex) is characteristic of the IL and, as is to be expected, is absent in the case of a normal solvent such as methanol.

Search efficiency of biased migration towards stationary or moving targets in heterogeneously structured environments

NASA Astrophysics Data System (ADS)

Azimzade, Youness; Mashaghi, Alireza

2017-12-01

Efficient search acts as a strong selective force in biological systems ranging from cellular populations to predator-prey systems. The search processes commonly involve finding a stationary or mobile target within a heterogeneously structured environment where obstacles limit migration. An open generic question is whether random or directionally biased motions or a combination of both provide an optimal search efficiency and how that depends on the motility and density of targets and obstacles. To address this question, we develop a simple model that involves a random walker searching for its targets in a heterogeneous medium of bond percolation square lattice and used mean first passage time (〈T 〉 ) as an indication of average search time. Our analysis reveals a dual effect of directional bias on the minimum value of 〈T 〉 . For a homogeneous medium, directionality always decreases 〈T 〉 and a pure directional migration (a ballistic motion) serves as the optimized strategy, while for a heterogeneous environment, we find that the optimized strategy involves a combination of directed and random migrations. The relative contribution of these modes is determined by the density of obstacles and motility of targets. Existence of randomness and motility of targets add to the efficiency of search. Our study reveals generic and simple rules that govern search efficiency. Our findings might find application in a number of areas including immunology, cell biology, ecology, and robotics.

The impact of nursing students' chemistry learning performance assessment in Taiwan: competitive versus non-competitive student team achievement division approaches

NASA Astrophysics Data System (ADS)

Wang, Kai-Ping

2012-07-01

Purpose: The purpose of this study was to determine the effectiveness of competitive Student Team Achievement Division (STAD), non-competitive STAD, and traditional learning on chemistry learning and learning perceptions. Sample, design and methods: By adopting the STAD approach, this study examined 144 nursing students at a five-year junior college in northern Taiwan during the first semester (totaling 18 weeks) of the 2008 academic year. Results: The findings reveal that both a heterogeneous group with external pressure (involving competitive STAD) and a friendship group with affective pressure (involving traditional learning) enhance group cohesion and assist students' meaningful learning; the heterogeneous group without extra pressure (involving non-competitive STAD), by contrast, fails because of apathy and lassitude. Moreover, learning effectiveness will obviously predominate until the learning strategy continues for a long period or at least one semester. Conclusions: This study revealed that the learning performance level of the competitive STAD group is significantly different from that of the non-competitive STAD group; and the learning performance level of the traditional group is significantly different from that of the non-competitive STAD group. Both the competitive STAD group and traditional group of medium ability students are significantly different from the non-competitive STAD group. Low-ability students from the competitive STAD group are significantly different from those of the non-competitive STAD, though no significant differences were found in learning perception. However, both a lack of friendship and a lack of ability in using algorithms may affect students' chemistry learning. Furthermore, gender imbalance, educational culture, and group emotions are factors that may influence student learning performance. Further study should focus on the use of grouping, improve responsibility in group discussion, and investigate group interaction patterns to determine the factors that influence learning performance of students working in groups.

Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions.

PubMed

Yan, Yuguang; Wu, Qingyao; Tan, Mingkui; Ng, Michael K; Min, Huaqing; Tsang, Ivor W

2017-10-10

In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.

Primary socialization theory. The influence of the community on drug use and deviance. III.

PubMed

Oetting, E R; Donnermeyer, J F; Deffenbacher, J L

1998-06-01

Primary socialization theory states that drug use and deviance are social behaviors learned predominantly through three sources, the family, the school, and peer clusters. This paper shows that the theory provides a parsimonious explanation of how characteristics of both the local community and the larger extended community influence drug use and deviance. These characteristics affect deviance because they either strengthen or weaken bonding with the three primary socialization sources, or affect the norms that are transmitted through the primary socialization process. The paper considers the following social structure characteristics of the local neighborhood or community: physical characteristics, rurality, ethnicity, heterogeneity, occupational type, mobility, poverty, neighborhood deviance, and age distribution. It also examines how other secondary socialization sources, the extended family, associational groups, religion, the peer environment, and the media influence the primary socialization process and, in turn, drug use and deviance.

The ergonomics of learning: educational design and learning performance.

PubMed

Smith, T J

2007-10-01

The application of ergonomics/human factors (E/HF) principles and practices, and the implementation of ergonomics programmes, have achieved proven success in improving performance, productivity, competitiveness, and safety and health in most occupational sectors. However, the benefits that the application of E/HF science might bring to promoting student learning have yet to be widely recognized. This paper deals with the fundamental purpose of education - student learning - and with the question of how the ergonomic design of the learning environment influences learning performance. The underlying premise, embodied in the quote below, is that student learning performance to a substantial degree is context specific - influenced and specialized in relation to specific design factors in the learning environment. The basic scientific question confronting learning ergonomics is which design characteristics in the learning environment have the greatest influence on variability in learning performance. Practically, the basic challenge is to apply this scientific understanding to ergonomic interventions directed at design improvements of learning environments to benefit learning. This paper expands upon these themes by addressing the origins and scope of learning ergonomics, differing perspectives on the nature of learning, evidence for context specificity in learning and conclusions and research implications regarding an ergonomics perspective on learning.

Different Faces in Our Classrooms: Teachers' Cultural Perspectives of Heterogeneous School Environments

ERIC Educational Resources Information Center

Hendricks, Paige

2016-01-01

The foundation of the United States' educational system is that all students will be educated equally by offering access to knowledge, opportunities, and services resulting in the creation of positive societal contributors. However, this task is complex and challenging. Heterogeneous student populations due to increased culturally diversity, do…

Principles of E-network modelling of heterogeneous systems

NASA Astrophysics Data System (ADS)

Tarakanov, D.; Tsapko, I.; Tsapko, S.; Buldygin, R.

2016-04-01

The present article is concerned with the analytical and simulation modelling of heterogeneous technical systems using E-network mathematical apparatus (the expansion of Petri nets). The distinguishing feature of the given system is the presence of the module6 which identifies the parameters of the controlled object as well as the external environment.

The Asset-Based Context Matrix: A Tool for Assessing Children's Learning Opportunities and Participation in Natural Environments

ERIC Educational Resources Information Center

Wilson, Linda L.; Mott, Donald W.; Batman, Deb

2004-01-01

This article provides a description of the "Asset-Based Context Matrix" (ABC Matrix). The ABC Matrix is an assessment tool for designing interventions for children in natural learning environments. The tool is based on research evidence indicating that children's learning is enhanced in contextually meaningful learning environments. The ABC Matrix…

Sociocultural Perspective of Science in Online Learning Environments. Communities of Practice in Online Learning Environments

ERIC Educational Resources Information Center

Erdogan, Niyazi

2016-01-01

Present study reviews empirical research studies related to learning science in online learning environments as a community. Studies published between 1995 and 2015 were searched by using ERIC and EBSCOhost databases. As a result, fifteen studies were selected for review. Identified studies were analyzed with a qualitative content analysis method…

Differentiated Learning Environment--A Classroom for Quadratic Equation, Function and Graphs

ERIC Educational Resources Information Center

Dinç, Emre

2017-01-01

This paper will cover the design of a learning environment as a classroom regarding the Quadratic Equations, Functions and Graphs. The goal of the learning environment offered in the paper is to design a classroom where students will enjoy the process, use their skills they already have during the learning process, control and plan their learning…

Adult Learners' Learning Environment Perceptions and Satisfaction in Formal Education--Case Study of Four East-European Countries

ERIC Educational Resources Information Center

Radovan, Marko; Makovec, Danijela

2015-01-01

The purpose of this paper is to explore attitudes towards learning and perceptions of the learning environment. Our theoretical examination is based on the social-cognitive theory of motivation and research that emphasizes the connections between an individual's perceptions of the learning environment and his/her motivation, interest, attitudes…

The Impact of Multitasking Learning Environments in the Middle Grades

ERIC Educational Resources Information Center

Drinkwine, Timothy

2013-01-01

This research study considers the status of middle school students in the 21st century in terms of their tendency to multitask in their daily lives and the overall influence this multitasking has on teaching and learning environments. Student engagement in the learning environment and students' various learning styles are discussed as primary…

A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

2010-01-01

A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

Learning How to Design a Technology Supported Inquiry-Based Learning Environment

ERIC Educational Resources Information Center

Hakverdi-Can, Meral; Sonmez, Duygu

2012-01-01

This paper describes a study focusing on pre-service teachers' experience of learning how to design a technology supported inquiry-based learning environment using the Internet. As part of their elective course, pre-service science teachers were asked to develop a WebQuest environment targeting middle school students. A WebQuest is an…

Campus Life for International Students: Exploring Students' Perceptions of Quality Learning Environment at a Private University in Malaysia

ERIC Educational Resources Information Center

Seng, Ernest Lim Kok; Khoo-Lattimore, Catheryn

2012-01-01

The number of international students enrolling at higher learning institutions in Malaysia is increasing each year. However, the quality of learning environment is not always easy to measure, particularly for private universities which are not financially aided by the government, where the learning environment is characterized by their physical…

Using Design-Based Research in Informal Environments

ERIC Educational Resources Information Center

Reisman, Molly

2008-01-01

Design-Based Research (DBR) has been a tool of the learning sciences since the early 1990s, used as a way to improve and study learning environments. Using an iterative process of design with the goal of reining theories of learning, researchers and educators now use DBR seek to identify "how" to make a learning environment work. They then draw…

Facilitating Application of Language Skills in Authentic Environments with a Mobile Learning System

ERIC Educational Resources Information Center

Shadiev, R.; Hwang, W.-Y.; Huang, Y.-M.; Liu, T.-Y.

2018-01-01

We uncovered two critical issues in earlier studies: (a) some studies have shown that mobile learning technology is not beneficial for all students due to complexity of learning environments and student prior knowledge, skills, and experience and (b) familiarity of students with the authentic environments in which they learn using mobile…

Farm Education and the Value of Learning in an Authentic Learning Environment

ERIC Educational Resources Information Center

Smeds, Pia; Jeronen, Eila; Kurppa, Sirpa

2015-01-01

Farm education is a newly emerging field of research that utilises authentic learning environments, environments that combine a subject of academic study with its real-world surroundings, actors, and activities--in this case, the practical context of a farm. The aim of the study was to investigate the effects of various learning environments…

Can More Become Less? Effects of an Intensive Assessment Environment on Students' Learning Performance

ERIC Educational Resources Information Center

Khawaja, M. Asif; Prusty, Gangadhara B.; Ford, Robin A. J.; Marcus, Nadine; Russell, Carol

2013-01-01

Online interactive systems offer the beguiling prospect of an improved environment for learning at minimum extra cost. We have developed online interactive tutorials that adapt the learning environment to the current learning status of each individual student. These Adaptive Tutorials (ATs) modify the tasks given to each student according to their…

The Effects of Cross-Modality and Level of Self-Regulated Learning on Knowledge Acquisition with Smartpads

ERIC Educational Resources Information Center

Lee, Hye Yeon; Lee, Hyeon Woo

2018-01-01

Recently, there has been a transition from traditional paper or computer-based learning environments to smartpad-based learning environments, which are based on touch and involve various cognitive strategies such as touch operation and note taking. Accordingly, the use of smartpads can provide an effective learning environment through…

Educators' Preparation to Teach, Perceived Teaching Presence, and Perceived Teaching Presence Behaviors in Blended and Online Learning Environments

ERIC Educational Resources Information Center

Gurley, Lisa E.

2018-01-01

Teaching in blended and online learning environments requires different pedagogical approaches than teaching in face-to-face learning environments. How educators are prepared to teach potentially impacts the quality of instruction provided in blended and online learning courses. Teaching presence is essential to achieving student learning…

Level of Intrinsic Motivation of Distance Education Students in e-Learning Environments

ERIC Educational Resources Information Center

Firat, Mehmet; Kilinç, Hakan; Yüzer, Tevfik Volkan

2018-01-01

According to researches, motivation that initiates and sustains behaviour is one of the most significant components of learning in any environment. Accordingly, level of intrinsic motivation triggers and sustains the interest of the open and distance education students when it comes to learning on their own in e-learning environments. Despite a…

Learning in 3D Virtual Environments: Collaboration and Knowledge Spirals

ERIC Educational Resources Information Center

Burton, Brian G.; Martin, Barbara N.

2010-01-01

The purpose of this case study was to determine if learning occurred within a 3D virtual learning environment by determining if elements of collaboration and Nonaka and Takeuchi's (1995) knowledge spiral were present. A key portion of this research was the creation of a Virtual Learning Environment. This 3D VLE utilized the Torque Game Engine…

Learner Self-Regulation and Web 2.0 Tools Management in Personal Learning Environment

ERIC Educational Resources Information Center

Yen, Cherng-Jyh; Tu, Chih-Hsiung; Sujo-Montes, Laura E.; Armfield, Shadow W. J.; Chan, Junn-Yih

2013-01-01

Web 2.0 technology integration requires a higher level of self-regulated learning skills to create a Personal Learning Environment (PLE). This study examined each of the four aspects of learner self-regulation in online learning (i.e., environment structuring, goal setting, time management, & task strategies) as the predictor for level of…

College Science Students' Perception Gaps in Preferred-Actual Learning Environment in a Reformed Introductory Earth Science Course in Taiwan

ERIC Educational Resources Information Center

Chang, Chun-Yeh; Chang, Yueh-Hsia

2010-01-01

This study used an instrument to examine undergraduate students' preferred and actual learning environment perceptions in an introductory earth science course. The results show that science students expect to learn in a learning environment combining teacher-centred and student-centred approaches. However, an expectation incongruence was found in…

Using an architectural approach to integrate heterogeneous, distributed software components

NASA Technical Reports Server (NTRS)

Callahan, John R.; Purtilo, James M.

1995-01-01

Many computer programs cannot be easily integrated because their components are distributed and heterogeneous, i.e., they are implemented in diverse programming languages, use different data representation formats, or their runtime environments are incompatible. In many cases, programs are integrated by modifying their components or interposing mechanisms that handle communication and conversion tasks. For example, remote procedure call (RPC) helps integrate heterogeneous, distributed programs. When configuring such programs, however, mechanisms like RPC must be used explicitly by software developers in order to integrate collections of diverse components. Each collection may require a unique integration solution. This paper describes improvements to the concepts of software packaging and some of our experiences in constructing complex software systems from a wide variety of components in different execution environments. Software packaging is a process that automatically determines how to integrate a diverse collection of computer programs based on the types of components involved and the capabilities of available translators and adapters in an environment. Software packaging provides a context that relates such mechanisms to software integration processes and reduces the cost of configuring applications whose components are distributed or implemented in different programming languages. Our software packaging tool subsumes traditional integration tools like UNIX make by providing a rule-based approach to software integration that is independent of execution environments.

A distributed scheduling algorithm for heterogeneous real-time systems

NASA Technical Reports Server (NTRS)

Zeineldine, Osman; El-Toweissy, Mohamed; Mukkamala, Ravi

1991-01-01

Much of the previous work on load balancing and scheduling in distributed environments was concerned with homogeneous systems and homogeneous loads. Several of the results indicated that random policies are as effective as other more complex load allocation policies. The effects of heterogeneity on scheduling algorithms for hard real time systems is examined. A distributed scheduler specifically to handle heterogeneities in both nodes and node traffic is proposed. The performance of the algorithm is measured in terms of the percentage of jobs discarded. While a random task allocation is very sensitive to heterogeneities, the algorithm is shown to be robust to such non-uniformities in system components and load.

Features of an effective operative dentistry learning environment: students' perceptions and relationship with performance.

PubMed

Suksudaj, N; Lekkas, D; Kaidonis, J; Townsend, G C; Winning, T A

2015-02-01

Students' perceptions of their learning environment influence the quality of outcomes they achieve. Learning dental operative techniques in a simulated clinic environment is characterised by reciprocal interactions between skills training, staff- and student-related factors. However, few studies have examined how students perceive their operative learning environments and whether there is a relationship between their perceptions and subsequent performance. Therefore, this study aimed to clarify which learning activities and interactions students perceived as supporting their operative skills learning and to examine relationships with their outcomes. Longitudinal data about examples of operative laboratory sessions that were perceived as effective or ineffective for learning were collected twice a semester, using written critical incidents and interviews. Emergent themes from these data were identified using thematic analysis. Associations between perceptions of learning effectiveness and performance were analysed using chi-square tests. Students indicated that an effective learning environment involved interactions with tutors and peers. This included tutors arranging group discussions to clarify processes and outcomes, providing demonstrations and constructive feedback. Feedback focused on mistakes, and not improvement, was reported as being ineffective for learning. However, there was no significant association between students' perceptions of the effectiveness of their learning experiences and subsequent performance. It was clear that learning in an operative technique setting involved various factors related not only to social interactions and observational aspects of learning but also to cognitive, motivational and affective processes. Consistent with studies that have demonstrated complex interactions between students, their learning environment and outcomes, other factors need investigation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Unexpected Nongenetic Individual Heterogeneity and Trait Covariance in Daphnia and Its Consequences for Ecological and Evolutionary Dynamics.

PubMed

Cressler, Clayton E; Bengtson, Stefan; Nelson, William A

2017-07-01

Individual differences in genetics, age, or environment can cause tremendous differences in individual life-history traits. This individual heterogeneity generates demographic heterogeneity at the population level, which is predicted to have a strong impact on both ecological and evolutionary dynamics. However, we know surprisingly little about the sources of individual heterogeneity for particular taxa or how different sources scale up to impact ecological and evolutionary dynamics. Here we experimentally study the individual heterogeneity that emerges from both genetic and nongenetic sources in a species of freshwater zooplankton across a large gradient of food quality. Despite the tight control of environment, we still find that the variation from nongenetic sources is greater than that from genetic sources over a wide range of food quality and that this variation has strong positive covariance between growth and reproduction. We evaluate the general consequences of genetic and nongenetic covariance for ecological and evolutionary dynamics theoretically and find that increasing nongenetic variation slows evolution independent of the correlation in heritable life-history traits but that the impact on ecological dynamics depends on both nongenetic and genetic covariance. Our results demonstrate that variation in the relative magnitude of nongenetic versus genetic sources of variation impacts the predicted ecological and evolutionary dynamics.

Personalized Virtual Learning Environment from the Detection of Learning Styles

ERIC Educational Resources Information Center

Martínez Cartas, M. L.; Cruz Pérez, N.; Deliche Quesada, D.; Mateo Quero, S.

2013-01-01

Through the previous detection of existing learning styles in a classroom, a Virtual Learning Environment (VLE) has been designed for students of several Engineering degrees, using the Learning Management System (LMS) utilized in the University of Jaen, ILIAS. Learning styles of three different Knowledge Areas; Chemical Engineering, Materials…

Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment

ERIC Educational Resources Information Center

Premlatha, K. R.; Dharani, B.; Geetha, T. V.

2016-01-01

E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…

Understanding the science-learning environment: A genetically sensitive approach.

PubMed

Haworth, Claire M A; Davis, Oliver S P; Hanscombe, Ken B; Kovas, Yulia; Dale, Philip S; Plomin, Robert

2013-02-01

Previous studies have shown that environmental influences on school science performance increase in importance from primary to secondary school. Here we assess for the first time the relationship between the science-learning environment and science performance using a genetically sensitive approach to investigate the aetiology of this link. 3000 pairs of 14-year-old twins from the UK Twins Early Development Study reported on their experiences of the science-learning environment and were assessed for their performance in science using a web-based test of scientific enquiry. Multivariate twin analyses were used to investigate the genetic and environmental links between environment and outcome. The most surprising result was that the science-learning environment was almost as heritable (43%) as performance on the science test (50%), and showed negligible shared environmental influence (3%). Genetic links explained most (56%) of the association between learning environment and science outcome, indicating gene-environment correlation.

Discrete Regularization for Calibration of Geologic Facies Against Dynamic Flow Data

NASA Astrophysics Data System (ADS)

Khaninezhad, Mohammad-Reza; Golmohammadi, Azarang; Jafarpour, Behnam

2018-04-01

Subsurface flow model calibration involves many more unknowns than measurements, leading to ill-posed problems with nonunique solutions. To alleviate nonuniqueness, the problem is regularized by constraining the solution space using prior knowledge. In certain sedimentary environments, such as fluvial systems, the contrast in hydraulic properties of different facies types tends to dominate the flow and transport behavior, making the effect of within facies heterogeneity less significant. Hence, flow model calibration in those formations reduces to delineating the spatial structure and connectivity of different lithofacies types and their boundaries. A major difficulty in calibrating such models is honoring the discrete, or piecewise constant, nature of facies distribution. The problem becomes more challenging when complex spatial connectivity patterns with higher-order statistics are involved. This paper introduces a novel formulation for calibration of complex geologic facies by imposing appropriate constraints to recover plausible solutions that honor the spatial connectivity and discreteness of facies models. To incorporate prior connectivity patterns, plausible geologic features are learned from available training models. This is achieved by learning spatial patterns from training data, e.g., k-SVD sparse learning or the traditional Principal Component Analysis. Discrete regularization is introduced as a penalty functions to impose solution discreteness while minimizing the mismatch between observed and predicted data. An efficient gradient-based alternating directions algorithm is combined with variable splitting to minimize the resulting regularized nonlinear least squares objective function. Numerical results show that imposing learned facies connectivity and discreteness as regularization functions leads to geologically consistent solutions that improve facies calibration quality.

Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.

PubMed

Walter, Carina; Rosenstiel, Wolfgang; Bogdan, Martin; Gerjets, Peter; Spüler, Martin

2017-01-01

In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.

[Social learning as an uncertainty-reduction strategy: an adaptationist approach].

PubMed

Nakanishi, Daisuke; Kameda, Tatsuya; Shinada, Mizuho

2003-04-01

Social learning is an effective mechanism to reduce uncertainty about environmental knowledge, helping individuals adopt an adaptive behavior in the environment at small cost. Although this is evident for learning about temporally stable targets (e.g., acquiring avoidance of toxic foods culturally), the functional value of social learning in a temporally unstable environment is less clear; knowledge acquired by social learning may be outdated. This paper addressed adaptive values of social learning in a non-stationary environment empirically. When individual learning about the non-stationary environment is costly, a hawk-dove-game-like equilibrium is expected to emerge in the population, where members who engage in costly individual learning and members who skip the information search and free-ride on other members' search efforts coexist at a stable ratio. Such a "producer-scrounger" structure should qualify effectiveness of social/cultural learning severely, especially "conformity bias" when using social information (Boyd & Richerson, 1985). We tested these predictions by an experiment implementing a non-stationary uncertain environment in a laboratory. The results supported our thesis. Implications of these findings and some future directions were discussed.

Trust Model to Enhance Security and Interoperability of Cloud Environment

NASA Astrophysics Data System (ADS)

Li, Wenjuan; Ping, Lingdi

Trust is one of the most important means to improve security and enable interoperability of current heterogeneous independent cloud platforms. This paper first analyzed several trust models used in large and distributed environment and then introduced a novel cloud trust model to solve security issues in cross-clouds environment in which cloud customer can choose different providers' services and resources in heterogeneous domains can cooperate. The model is domain-based. It divides one cloud provider's resource nodes into the same domain and sets trust agent. It distinguishes two different roles cloud customer and cloud server and designs different strategies for them. In our model, trust recommendation is treated as one type of cloud services just like computation or storage. The model achieves both identity authentication and behavior authentication. The results of emulation experiments show that the proposed model can efficiently and safely construct trust relationship in cross-clouds environment.

Delayed Majority Game with Heterogeneous Learning Speeds for Financial Markets

NASA Astrophysics Data System (ADS)

Yoshimura, Yushi; Yamada, Kenta

There are two famous statistical laws, so-called stylized facts, in financial markets. One is fat tail where the tail of price returns obeys a power law. The other is volatility clustering in which the autocorrelation function of absolute price returns decays with a power law. In order to understand relationships between the stylized facts and dealers' behaviors, we constructed a new agent-based model based on the grand canonical minority game (GCMG) and the Giardina-Bouchaud (GB) model. The recovery of stylized facts by GCMG and GB lacks of robustness. Therefore, based on the GCMG and GB model, we develop a new model that can reproduce stylized facts robustly. Furthermore, we find that heterogeneity of learning speeds of agents is important to reproduce the stylized facts.

Engaging Learners through Interactive Media: Findings and Implications from a Technology Enhanced Problem-Based Learning Environment

ERIC Educational Resources Information Center

Horton, Lucas; Liu, Min; Olmanson, Justin; Toprac, Paul

2011-01-01

In this paper we explore students' engagement in a new media enhanced problem-based learning (PBL) environment and investigate the characteristics of these environments that facilitate learning. We investigated both student experiences using a new media enhanced PBL environment and the specific elements students found most supportive of their…

A Computer Environment for Beginners' Learning of Sorting Algorithms: Design and Pilot Evaluation

ERIC Educational Resources Information Center

Kordaki, M.; Miatidis, M.; Kapsampelis, G.

2008-01-01

This paper presents the design, features and pilot evaluation study of a web-based environment--the SORTING environment--for the learning of sorting algorithms by secondary level education students. The design of this environment is based on modeling methodology, taking into account modern constructivist and social theories of learning while at…