Fecha
2013
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Derechos de acceso
info:eu-repo/semantics/closedAccess
Título de la revista
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Editorial
World Scientific Publishing
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0 citas en 
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Resumen
Collaborative 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.
Descripción
The 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
La 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
La 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
Categorías UNESCO
Palabras clave
data mining, computer supported collaboration, machine learning, e-learning
Citación
Anaya, 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
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial