research-article

Analyzing collaboration and interaction in learning environments to form learner groups

Authors:

Domingo Gómez-Pérez,

Alicia Nieto-Reyes, and

Crescencio BravoAuthors Info & Claims

Computers in Human Behavior, Volume 47, Issue C

Pages 42 - 49

https://doi.org/10.1016/j.chb.2014.07.012

Published: 01 June 2015 Publication History

Abstract

We present a method to form learner groups in collaborative environments.The method analyses the collaboration among learners by means of variables.These variables are used to form homogenous and/or heterogeneous learner groups.The criteria (homogeneous/heterogeneous) and variables are defined by the teacher.The method is automatically performed by a software tool. An important number of academic tasks should be solved collaboratively by groups of learners. The Computer-Supported Collaborative Learning (CSCL) systems support this collaboration by means of shared workspaces and tools that enable communication and coordination between learners. Successful collaboration and interaction can depend on the criteria followed when forming the groups of learners. This paper proposes a method that analyses the collaboration and interaction between learners using a set of indicators or variables about how they solve academic tasks. Then, the concept of data depth is used as a measurement of the closeness of the analysis indicators' values for a learner with respect to the values that the same indicators take for the other learners. Finally, the data depth is used to form new groups of learners whose analysis indicators take similar or different values. Thus, the method enables teachers to form homogeneous and heterogeneous groups according to their preferences. This group formation process is carried out automatically by a software tool. This paper presents two case studies in which the method is applied to form groups of learners who solve academic tasks in different domains (computer programming and data mining).

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Cited By

González-De La Fuente LNieto-Reyes ATerán P(2023)Simplicial depths for fuzzy random variablesFuzzy Sets and Systems10.1016/j.fss.2023.108678471:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.fss.2023.108678
Basilotta Gmez-Pablos VMartn del Pozo MGarca-Valcrcel Muoz-Repiso A(2017)Project-based learning (PBL) through the incorporation of digital technologiesComputers in Human Behavior10.1016/j.chb.2016.11.05668:C(501-512)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.chb.2016.11.056

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cover image Computers in Human Behavior

Computers in Human Behavior Volume 47, Issue C

June 2015

182 pages

ISSN:0747-5632

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Copyright © Elsevier Ltd.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 June 2015

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Cited By

González-De La Fuente LNieto-Reyes ATerán P(2023)Simplicial depths for fuzzy random variablesFuzzy Sets and Systems10.1016/j.fss.2023.108678471:COnline publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1016/j.fss.2023.108678
Basilotta Gmez-Pablos VMartn del Pozo MGarca-Valcrcel Muoz-Repiso A(2017)Project-based learning (PBL) through the incorporation of digital technologiesComputers in Human Behavior10.1016/j.chb.2016.11.05668:C(501-512)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.chb.2016.11.056

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