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Comulang: towards a collaborative e-learning system that supports student group modeling

This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called “Comulang” while as a test bed for its effectiveness a multiple...

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Detalles Bibliográficos
Autores principales: Troussas, Christos, Virvou, Maria, Alepis, Efthimios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3755789/
https://www.ncbi.nlm.nih.gov/pubmed/24010044
http://dx.doi.org/10.1186/2193-1801-2-387
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author Troussas, Christos
Virvou, Maria
Alepis, Efthimios
author_facet Troussas, Christos
Virvou, Maria
Alepis, Efthimios
author_sort Troussas, Christos
collection PubMed
description This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called “Comulang” while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system’s basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.
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spelling pubmed-37557892013-09-04 Comulang: towards a collaborative e-learning system that supports student group modeling Troussas, Christos Virvou, Maria Alepis, Efthimios Springerplus Research This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called “Comulang” while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system’s basic aims is to provide efficient student groups whose limitations and capabilities are well balanced. Springer International Publishing 2013-08-15 /pmc/articles/PMC3755789/ /pubmed/24010044 http://dx.doi.org/10.1186/2193-1801-2-387 Text en © Troussas et al.; licensee Springer. 2013 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Troussas, Christos
Virvou, Maria
Alepis, Efthimios
Comulang: towards a collaborative e-learning system that supports student group modeling
title Comulang: towards a collaborative e-learning system that supports student group modeling
title_full Comulang: towards a collaborative e-learning system that supports student group modeling
title_fullStr Comulang: towards a collaborative e-learning system that supports student group modeling
title_full_unstemmed Comulang: towards a collaborative e-learning system that supports student group modeling
title_short Comulang: towards a collaborative e-learning system that supports student group modeling
title_sort comulang: towards a collaborative e-learning system that supports student group modeling
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3755789/
https://www.ncbi.nlm.nih.gov/pubmed/24010044
http://dx.doi.org/10.1186/2193-1801-2-387
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