Examining the Effects of a Peer-Learning Research Community on the Development of Students’ Researcher Identity, Confidence, and STEM Interest and Engagement

The machine learning research community is presently working on human action/activity recognition issue in real-time videos, and facing several hundreds of confronts. In this scenario, deep convolutional neural networks have initiated their powerful role in strengthen the numerous vision-based HAR systems. In recent years there has been impressive performance and great potential for imaging tasks with introducing residual connections along with a traditional CNN model in a single architecture known as the Residual Network (ResNet). In this paper we propose to use skeletal trajectory maps for the detection of human actions. A new ResNet based algorithm named dense ResNet has been proposed to perform the classification task. The trajectories of 3D joint locations are converted into color coded RGB images. These trajectory plotted images are able to capture the spatio-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by deep learning algorithms. We then train the proposed dense ResNet to learn the features from these color coded RGB trajectory information of the human body 3D joint locations. The novelty of the proposed method is evaluated on MSR Action 3D, UTKinect-Action3D, G3D and NTU RGB-D datasets. Experimental results shows that the proposed architecture attains good recognition rates with less computation resource.

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Linking CSCL Script Design Patterns

Investigations of E-Learning Patterns ◽

10.4018/978-1-60960-144-7.ch005 ◽

2011 ◽

pp. 72-85 ◽

Cited By ~ 1

Author(s):

Eloy D. Villasclaras-Fernández ◽

Davinia Hernández-Leo ◽

Juan I. Asensio Pérez ◽

Yannis Dimitriadis ◽

Alejandra Martínez-Monés

Keyword(s):

Collaborative Learning ◽

Design Patterns ◽

Research Community ◽

Learning Patterns ◽

Cscl Scripts ◽

E Learning ◽

Learning Scenarios ◽

Potential Benefits ◽

Learning Research ◽

Complex Planning

Among the fields in which design patterns have been applied, the design of CSCL scripts has received the attention of the e-learning research community. The usage of design patterns is justified by the complexity of the task of planning collaborative learning scenarios. Making this task even more complex, planning assessment activities and/or resources is one of the aspects that need to be taken into account in the design of a CSCL script. Focusing on this issue, this chapter deals with the application of learning and assessment patterns along with the creation of such scripts. More specifically, this chapter is focused on the potential benefits of using detailed information concerning the relationships between assessment and learning patterns. Different types of links between CSCL scripting design patterns are illustrated, and finally this chapter discusses the possibilities of using them in CSCL script authoring software tools.

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A Knowledge Management Roadmap for E-Learning

Knowledge Management ◽

10.4018/978-1-59904-933-5.ch275 ◽

2011 ◽

pp. 3328-3335 ◽

Cited By ~ 1

Author(s):

Miltiadis D. Lytras ◽

Ambjörn Naeve ◽

Athanasia Pouloudi

Keyword(s):

Knowledge Management ◽

Knowledge Society ◽

Research Community ◽

Critical Research ◽

The Social ◽

New Period ◽

E Learning ◽

Research Questions ◽

Learning Research ◽

The New Period

The technological pace and the advent of the knowledge society will set in the next years the new context for e-learning evolution: The convergence of learning and daily life of citizens worldwide will be evident in new services and transparent technologies. Pervasive or ubiquitous learning will be a critical cornerstone and an ultimate achievement of the e-learning research community. Additionally, worldwide efforts will define the social responsibility character of e-learning. In this short visioning paper, we try to address two critical questions: How will knowledge management and relevant technologies affect e-learning in the forthcoming six years? and What are the critical research questions for the new period of e-learning evolution? Many of these aspects could initiate interesting PhD research.

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Auroral Classification Ergonomics and the Implications for Machine Learning

10.5194/gi-2019-41 ◽

2020 ◽

Author(s):

Derek McKay ◽

Andreas Kvammen

Keyword(s):

Machine Learning ◽

Large Data ◽

Research Community ◽

Training Data ◽

Machine Learning Techniques ◽

Data Set ◽

Potential Source ◽

Learning Techniques ◽

Training Samples ◽

Learning Research

Abstract. The machine learning research community has focused greatly on bias in algorithms and have identified different manifestations of it. Bias in the training samples is recognised as a potential source of prejudice in machine learning. It can be introduced by human experts who define the training sets. As machine learning techniques are being applied to auroral classification, it is important to identify and address potential sources of expert-injected bias. In an ongoing study, 13 947 auroral images were manually classified with significant differences between classifications. This large data set allowed identification of some of these biases, especially those originating as a result of the ergonomics of the classification process. These findings are presented in this paper, to serve as a checklist for improving training data integrity, not just for expert classifications, but also for crowd-sourced, citizen science projects. As the application of machine learning techniques to auroral research is relatively new, it is important that biases are identified and addressed before they become endemic in the corpus of training data.

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Auroral classification ergonomics and the implications for machine learning

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-9-267-2020 ◽

2020 ◽

Vol 9 (2) ◽

pp. 267-273 ◽

Cited By ~ 1

Author(s):

Derek McKay ◽

Andreas Kvammen

Keyword(s):

Machine Learning ◽

Research Community ◽

Training Data ◽

Machine Learning Techniques ◽

Large Dataset ◽

Science Projects ◽

Potential Source ◽

Learning Techniques ◽

Training Samples ◽

Learning Research

Abstract. The machine-learning research community has focused greatly on bias in algorithms and have identified different manifestations of it. Bias in training samples is recognised as a potential source of prejudice in machine learning. It can be introduced by the human experts who define the training sets. As machine-learning techniques are being applied to auroral classification, it is important to identify and address potential sources of expert-injected bias. In an ongoing study, 13 947 auroral images were manually classified with significant differences between classifications. This large dataset allowed for the identification of some of these biases, especially those originating as a result of the ergonomics of the classification process. These findings are presented in this paper to serve as a checklist for improving training data integrity, not just for expert classifications, but also for crowd-sourced, citizen science projects. As the application of machine-learning techniques to auroral research is relatively new, it is important that biases are identified and addressed before they become endemic in the corpus of training data.

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Learning Maturity

Creating Knowledge Based Organizations ◽

10.4018/978-1-59140-162-9.ch012 ◽

2011 ◽

pp. 248-277 ◽

Cited By ~ 1

Author(s):

Gary F. Templeton

Keyword(s):

Organizational Learning ◽

Research Community ◽

The Body ◽

Theoretical Development ◽

Learning Paradigm ◽

Learning History ◽

Wide Range ◽

Intelligent Behavior ◽

History Of ◽

Learning Research

An explosion of research on the organizational learning paradigm has caused a great need for continued theoretical development to enable a more complete understanding of how to manage the concept for strategic advantage. At the same time, learning theory has not adequately addressed the technology variable in its framework, models, or propositions. The body of theory derived here centers around “learning maturity,” the capacity of an actor to effectively exhibit intelligent behavior in a wide range of situated actions. The theory is significant because it uniquely includes technology as a meaningful element in learning and intelligence. The research methodology uses over a century of published literature to serve as a “learning history” of an observed organization: the learning research community. The theory extends decades of cumulative research by focusing on the capabilities of actors to succeed in their interactions (use and development) with technology.

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A Meta-Review of Learning Research in Immersive Virtual Reality

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181321651326 ◽

2021 ◽

Vol 65 (1) ◽

pp. 894-898

Author(s):

Gregory McGowin ◽

Stephen M. Fiore ◽

Kevin Oden

Keyword(s):

Virtual Reality ◽

Cost Effective ◽

Research Community ◽

Immersive Virtual Reality ◽

Meta Review ◽

Effective Technology ◽

Training Research ◽

Learning Research ◽

Training Community ◽

And Training

Research and development in virtual reality (VR) continues to influence all sectors of society. This has been particularly the case in the application of VR for learning and training. Due to the affordability of VR, it increasingly is providing a safe and cost-effective technology for studying learning and training. In this paper, we summarize findings from recent compilations of research in virtual reality that examined VR and learning. From this, we identify a set of recommendations distilled from these reviews in order to help the training research community structure their research based upon the extent empirical base. Our goal is to help the training community more effectively explore VR as a technology for learning.

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Memória, tecnologia e aprendizagem: investigações em uma intervenção pedagógica

#Tear: Revista de Educação, Ciência e Tecnologia ◽

10.35819/tear.v7.n1.a2702 ◽

2018 ◽

Vol 7 (1) ◽

Author(s):

Aline Marcelino dos Santos Silva ◽

Fermín Alfredo Tang Montané

Keyword(s):

Information Processing ◽

Meaningful Learning ◽

Learning Objects ◽

The Other ◽

Pedagogical Intervention ◽

Significant Evidence ◽

Technology And Learning ◽

Learning Research ◽

Experimental Group ◽

Development Of Students

Resumo: O presente trabalho buscou investigar a contribuição do uso de Objetos de Aprendizagem para o desenvolvimento cognitivo de alunos, sob a questão do processamento da informação na memória e da aprendizagem significativa. A pesquisa foi realizada a partir de uma intervenção pedagógica em duas turmas de Ensino Fundamental, uma controle, e a outra, experimental. As teorias do processamento da informação, da carga cognitiva e da aprendizagem significativa serviram como base para a análise das respostas obtidas nos questionários, aplicados durante a intervenção. A partir da pesquisa, foi possível perceber que a turma experimental demonstrou maior avanço em relação à apendizagem do conteúdo. Foram observados maiores indícios de ocorrência da aprendizagem significativa e da recuperação de informações na memória pela turma experimental. Neste sentido, identificou-se a contribuição dos Objetos de Aprendizagem para a ocorrência da aprendizagem significativa e do processo de memorização.Palavras-chave: Memória. Aprendizagem significativa. Intervenção pedagógica. Objetos de Aprendizagem.MEMORY, TECHNOLOGY AND LEARNING: RESEARCH IN A PEDAGOGICAL INTERVENTIONAbstract: The present work sought to investigate the contribution of the use of Learning Objects to the cognitive development of students, under the issue of information processing in memory and meaningful learning. The research was carried out from a pedagogical intervention in two classes of Elementary School, one control, and the other, experimental. Theories of information processing, cognitive load, and meaningful learning served as the basis for the analysis of the answers obtained in the questionnaires, applied during the intervention. From the research, it was possible to perceive that the experimental group showed greater progress in relation to the learning of the content. Significant evidence of significant learning and retrieval of information in memory was observed by the experimental group. In this sense, the contribution of Learning Objects to the occurrence of meaningful learning and the memorization process was identified.Keywords: Memory. Meaningful learning. Pedagogical intervention. Learning Objects.

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High-voltage Electron Microscopy: Where it is and where it may be going

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100136775 ◽

1995 ◽

Vol 53 ◽

pp. 80-81

Author(s):

Charles W. Allen

Keyword(s):

Electron Microscopy ◽

High Voltage ◽

Construction Cost ◽

Research Community ◽

High Voltage Electron Microscopy ◽

Voltage Electron ◽

High Voltage Electron ◽

Materials Research

High voltage TEMs were introduced commercially thirty years ago, with the installations of 500 kV Hitachi instruments at the Universities of Nogoya and Tokyo. Since that time a total of 51 commercial instruments, having maximum accelerating potentials of 0.5-3.5 MV, have been delivered. Prices have gone from about a dollar per volt for the early instruments to roughly twenty dollars per volt today, which is not so unreasonable considerinp inflation and vastly improved electronics and other improvements. The most expensive HVEM (the 3.5 MV instrument at Osaka University) cost about 5 percent of the construction cost of the USA's latest synchrotron.Table 1 briefly traces the development of HVEM in this country for the materials sciences. There are now only three available instruments at two sites: the 1.2 MeV HVEM at Argonne National Lab, and 1.0 and 1.5 MeV instruments at Lawrence Berkeley National Lab. Fortunately, both sites are user facilities funded by DOE for the materials research community.

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