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Predicting Learners' Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

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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.authorMaldonado-Mahauad, Jorge
dc.contributor.authorPerez Sanagustin, Maria Del Mar
dc.contributor.authorMoreno-Marcos, Pedro Manuel
dc.contributor.authorAlario-Hoyos, Carlos
dc.contributor.authorMuñoz Merino, Pedro José
dc.contributor.authorDelgado Kloos, Carlos
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderMinisterio de Educación, Cultura y Deporte (España)es
dc.date.accessioned2021-05-24T12:44:04Z
dc.date.available2021-05-24T12:44:04Z
dc.date.issued2018-09-03
dc.descriptionProceeding of: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018.en
dc.description.abstractIn the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners' success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners' self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most predictive: (1) the self-reported SRL strategies 'goal setting', 'strategic planning', 'elaboration' and 'help seeking'; (2) the activity sequences patterns 'only assessment', 'complete a video-lecture and try an assessment', 'explore the content' and 'try an assessment followed by a video-lecture'; and (3) learners' prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to predict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.en
dc.description.sponsorshipThis work was supported by FONDECYT (Chile) under project initiation grant No.11150231, the MOOC-Maker Project (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), the LALA Project (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and CONICYT/DOCTORADO NACIONAL 2016/21160081, the Spanish Ministry of Education, Culture and Sport, under an FPU fellowship (FPU016/00526) and the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).en
dc.description.statusPublicadoes
dc.format.extent14
dc.identifier.bibliographicCitationPammer-Schindler V., et al. (eds.).(2018) Lifelong Technology Enhanced Learning: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018. Proceedings, (Lecture Notes in Computer Science; 11082). Springer, (pp. 355-369).en
dc.identifier.doihttps://doi.org/10.1007/978-3-319-98572-5_27
dc.identifier.isbn978-3-319-98571-8
dc.identifier.publicationfirstpage355
dc.identifier.publicationlastpage369
dc.identifier.publicationtitleLifelong Technology Enhanced Learning: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018. Proceedingsen
dc.identifier.urihttps://hdl.handle.net/10016/32731
dc.identifier.uxxiCC/0000029645
dc.language.isoengen
dc.publisherSpringeres
dc.relation.eventdate3-5 September, 2018.en
dc.relation.eventnumber13
dc.relation.eventplaceLeeds, Reino Unidoen
dc.relation.eventtitleEuropean Conference on Technology Enhanced Learning, EC-TEL 2018en
dc.relation.ispartofseriesLecture Notes in Computer Scienceen
dc.relation.ispartofseries11082
dc.relation.projectIDGobierno de España. FPU016/00526es
dc.relation.projectIDGobierno de España. TIN2017-85179-C3-1-Res
dc.rights© Springer Nature Switzerland AG. 2018en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaEducaciónes
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherSelf-regulated learningen
dc.subject.otherPredictionen
dc.subject.otherMassive Open Online Coursesen
dc.subject.otherMOOCsen
dc.subject.otherAchievementen
dc.subject.otherSuccessen
dc.titlePredicting Learners' Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learningen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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