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Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorMoreno-Marcos, Pedro Manuel
dc.contributor.authorMuñoz Merino, Pedro José
dc.contributor.authorMaldonado-Mahauad, Jorge
dc.contributor.authorPérez Sanagustín, Mar
dc.contributor.authorAlario-Hoyos, Carlos
dc.contributor.authorDelgado Kloos, Carlos
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2020-10-07T08:58:52Z
dc.date.available2023-02-01T00:00:06Z
dc.date.issued2020-02-01
dc.description.abstractMOOCs (Massive Open Online Courses) have usually high dropout rates. Many articles have proposed predictive models in order to early detect learners at risk to alleviate this issue. Nevertheless, existing models do not consider complex high-level variables, such as self-regulated learning (SRL) strategies, which can have an important effect on learners' success. In addition, predictions are often carried out in instructor-paced MOOCs, where contents are released gradually, but not in self-paced MOOCs, where all materials are available from the beginning and users can enroll at any time. For self-paced MOOCs, existing predictive models are limited in the way they deal with the flexibility offered by the course start date, which is learner dependent. Therefore, they need to be adapted so as to predict with little information short after each learner starts engaging with the MOOC. To solve these issues, this paper contributes with the study of how SRL strategies could be included in predictive models for self-paced MOOCs. Particularly, self-reported and event-based SRL strategies are evaluated and compared to measure their effect for dropout prediction. Also, the paper contributes with a new methodology to analyze self-paced MOOCs when carrying out a temporal analysis to discover how early prediction models can serve to detect learners at risk. Results of this article show that event-based SRL strategies show a very high predictive power, although variables related to learners' interactions with exercises are still the best predictors. That is, event-based SRL strategies can be useful to predict if e.g., variables related to learners' interactions with exercises are not available (...)es
dc.description.sponsorshipThis work has been co-funded by the Erasmus+ Programme of the European Union, through the project LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación/project Smartlet (TIN2017-85179-C3-1-R), and by the Madrid Regional Government, through the project e-Madrid-CM (S2018/TCS-4307). The latter is also co-financed by the European Structural Funds (FSE and FEDER). It has also been supported by Ministerio de Ciencia, Innovación y Universidades, under an FPU fellowship (FPU016/00526), by the CONICYT/DOCTORADO NACIONAL 2016 (grant No. 21160081), and by Dirección de Investigación de la Universidad de Cuenca (DIUC), Cuenca-Ecuador, under Analítica del aprendizaje para el estudio de estrategias de aprendizaje autorregulado en un contexto de aprendizaje híbrido (DIUC_XVIII_2019_54).en
dc.identifier.bibliographicCitationComputers & Education (2020), 145, 103728
dc.identifier.doihttps://doi.org/10.1016/j.compedu.2019.103728
dc.identifier.issn0360-1315
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage15
dc.identifier.publicationtitleComputers & Education
dc.identifier.publicationvolume145
dc.identifier.urihttps://hdl.handle.net/10016/31087
dc.identifier.uxxiAR/0000025579
dc.language.isoeng
dc.publisherElsevier
dc.relation.projectIDGobierno de España. TIN2017-85179-C3-1-R
dc.relation.projectIDComunidad de Madrid. S2018/TCS-4307
dc.rights© 2019 Elsevier Ltd. All rights reserved.
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherData sience applications in educationen
dc.subject.otherDistance education and online learningen
dc.subject.otherLifelong learningen
dc.subject.otherPost-secondary educationen
dc.titleTemporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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