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Título: An approach to build in situ models for the prediction of the decrease of academic engagement indicators in Massive Open Online Courses
Autor: Bote Lorenzo, Miguel L.
Gómez Sánchez, Eduardo
Año del Documento: 2018
Editorial: Graz University of Technology, Institut für Informationssysteme und Computer Medien
Descripción: Producción Científica
Documento Fuente: Journal of Universal Computer Science (accepted 2018)
Resumen: The early detection of learners who are expected to disengage with typical MOOC tasks such as watching lecture videos or submitting assignments is necessary to enable timely interventions aimed at preventing it. This can be done by predicting the decrease of academic engagement indicators that can be derived for di_erent MOOC tasks and computed for each learner. A posteriori prediction models can yield a good performance but cannot be built using the information that is available in an ongoing course at the moment the predictions are required. This paper proposes an approach to build in situ prediction models using such information. Models were derived following both approaches and employed to predict the decrease of three indicators that quantify the engagement of learners with the main tasks typically proposed in a MOOC: watching lectures, solving _nger exercises, and submitting assignments. The results show that in situ models yielded a good performance for the prediction of all engagement indicators, thus showing the feasibility of the proposed approach. This performance was very similar to that of a posteriori models, which have the clear disadvantage that they cannot be used to make predictions in an ongoing course based on its data.
Palabras Clave: MOOC
Aprendizaje automático
ISSN: 0948-695X
Revisión por Pares: SI
Patrocinador: Ministerio de Economía, Industria y Competitividad (Projects TIN2014-53199-C3-2-R (AEI, FEDER), TIN2017-85179-C3-2-R)
Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA277U14)
European Commission (Proyect 588438-EPP-1-2017-1-EL-EPPKA2-KA)
Version del Editor:
Idioma: eng
Derechos: info:eu-repo/semantics/openAccess
Aparece en las colecciones:DEP71 - Artículos de revista

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