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A domain-independent, transferable and timely analysis approach to assess student collaboration

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dc.contributor.authorRodríguez Anaya, Antonio
dc.contributor.authorGonzález Boticario, Jesús
dc.date.accessioned2025-09-12T12:21:01Z
dc.date.available2025-09-12T12:21:01Z
dc.date.issued2013
dc.descriptionThe registered version of this article, first published in "International Journal on Artificial Intelligence Tools, 22(04), 1350020", is available online at the publisher's website: https://doi.org/10.1142/S0218213013500206
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en "International Journal on Artificial Intelligence Tools, 22(04), 1350020", está disponible en línea en el sitio web del editor: https://doi.org/10.1142/S0218213013500206
dc.description.abstractCollaborative learning environments require intensive, regular and frequent analysis of the increasing amount of interaction data generated by students to assess that collaborative learning takes place. To support timely assessments that may benefit students and teachers the method of analysis must provide meaningful evaluations while the interactions take place. This research proposes machine learning-based techniques to infer the relationship between student collaboration and some quantitative domain-independent statistical indicators derived from large-scale evaluation analysis of student interactions. This paper (i) compares a set of metrics to identify the most suitable to assess student collaboration, (ii) reports on student evaluations of the metacognitive tools that display collaboration assessments from a new collaborative learning experience and (iii) extends previous findings to clarify modeling and usage issues. The advantages of the approach are: (1) it is based on domain-independent and generally observable features, (2) it provides regular and frequent data mining analysis with minimal teacher or student intervention, thereby supporting metacognition for the learners and corrective actions for the teachers, and (3) it can be easily transferred to other e-learning environments and include transferability features that are intended to facilitate its usage in other collaborative and social learning tools.en
dc.description.versionversión publicada
dc.identifier.citationAnaya, A. R. and Boticario, J. G. (2013). A domain-independent, transferable and timely analysis approach to assess student collaboration. International Journal on Artificial Intelligence Tools, 22(04), 1350020. https://doi.org/10.1142/s0218213013500206
dc.identifier.doihttps://doi.org/10.1142/S0218213013500206
dc.identifier.issn0218-2130
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30049
dc.journal.issue4
dc.journal.titleInternational Journal on Artificial Intelligence Tools
dc.journal.volume22
dc.language.isoen
dc.publisherWorld Scientific Publishing
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.es
dc.subject3304 Tecnología de los ordenadores
dc.subject.keywordsdata miningen
dc.subject.keywordscomputer supported collaborationen
dc.subject.keywordsmachine learningen
dc.subject.keywordse-learningen
dc.titleA domain-independent, transferable and timely analysis approach to assess student collaborationen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication98e16ab9-3684-456b-a44c-cd5f9c8fd5bb
relation.isAuthorOfPublicatione067a1f1-6036-4974-a582-85b556587d18
relation.isAuthorOfPublication.latestForDiscovery98e16ab9-3684-456b-a44c-cd5f9c8fd5bb
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