Analyzing collaboration and interaction in learning environments to form learner groups.
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 collab...
| Publicado en: | Computers in Human Behavior Vol. 47; pp. 42 - 50 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Elsevier B.V.
Jun2015
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=101498558&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 101498558 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 07475632 JC4 jtl: Computers in Human Behavior issn: 07475632 maglogo: N pubinfo: dt: Jun2015 vid: 47 pid: 2410 pub: Elsevier B.V. artinfo: ui: 101498558 10.1016/j.chb.2014.07.012 ppf: 42 ppct: 8 formats: tig: atl: Analyzing collaboration and interaction in learning environments to form learner groups. aug: au: Duque, Rafael Gómez-Pérez, Domingo Nieto-Reyes, Alicia Bravo, Crescencio affil: Department of Mathematics, Statistics and Computer Science, University of Cantabria, Avenida de Los Castros S/N, 39005 Santander, Spain Department of Information Systems and Technologies, University of Castilla-La Mancha, Paseo de la Universidad 4, 13071 Ciudad Real, Spain su: Students Teams in the workplace Task performance Group medical practice Learning strategies sug: subj: Students Teams in the workplace Task performance All Other Outpatient Care Centers Offices of Physicians (except Mental Health Specialists) Offices of physicians Group medical practice Learning strategies keyword: Analysis indicators Computer-Supported Collaborative Learning Data depth Group formation Analysis indicators Computer-Supported Collaborative Learning Data depth Group formation ab: 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). pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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