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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/31416

    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 LuisAutoridad UVA Orcid
    Gómez Sánchez, EduardoAutoridad UVA Orcid
    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)
    Résumé
    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
    http://www.jucs.org/
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/31416
    Derechos
    openAccess
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    • DEP71 - Artículos de revista [358]
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    2018_Bote-Lorenzo_etal_JUCS_preprint.pdf
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