Prediction in MOOCs: a review and future research directions

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dc.contributor.author Moreno-Marcos, Pedro Manuel
dc.contributor.author Alario-Hoyos, Carlos
dc.contributor.author Muñoz Merino, Pedro José
dc.contributor.author Delgado Kloos, Carlos
dc.date.accessioned 2020-10-07T11:22:56Z
dc.date.available 2020-10-07T11:22:56Z
dc.date.issued 2018-07-17
dc.identifier.bibliographicCitation IEEE Transactions on Learning Technologies, 2019, 12 (3), 384-401
dc.identifier.issn 1939-1382
dc.identifier.uri http://hdl.handle.net/10016/31090
dc.description.abstract This paper surveys the state of the art on prediction in MOOCs through a Systematic Literature Review (SLR). The main objectives are: (1) to identify the characteristics of the MOOCs used for prediction, (2) to describe the prediction outcomes, (3) to classify the prediction features, (4) to determine the techniques used to predict the variables, and (5) to identify the metrics used to evaluate the predictive models. Results show there is strong interest in predicting dropouts in MOOCs. A variety of predictive models are used, though regression and Support Vector Machines stand out. There is also wide variety in the choice of prediction features, but clickstream data about platform use stands out. Future research should focus on developing and applying predictive models that can be used in more heterogeneous contexts (in terms of platforms, thematic areas, and course durations), on predicting new outcomes and making connections among them (e.g., predicting learners' expectancies), on enhancing the predictive power of current models by improving algorithms or adding novel higher-order features (e.g., efficiency, constancy, etc.).
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018 IEEE
dc.subject.other Discussion Forums
dc.subject.other Distance Learning
dc.subject.other Learning Environments
dc.subject.other Machine Learning
dc.title Prediction in MOOCs: a review and future research directions
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TLT.2018.2856808
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2715
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2715
dc.relation.projectID Gobierno de España. TIN2014-53199-C3-1-R
dc.relation.projectID Gobierno de España. TIN2017-85179-C3-1-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 384
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 401
dc.identifier.publicationtitle IEEE Transactions on Learning Technologies
dc.identifier.publicationvolume 12
dc.identifier.uxxi AR/0000022299
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