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Generalizing predictive models of admission test success based on online interactions

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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.authorLaet, Tinne De
dc.contributor.authorMuñoz Merino, Pedro José
dc.contributor.authorSoom, Carolien Van
dc.contributor.authorBroos, Tom
dc.contributor.authorVerbert, Katrien
dc.contributor.authorDelgado Kloos, Carlos
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-05-25T08:57:09Z
dc.date.available2020-05-25T08:57:09Z
dc.date.issued2019-09-10
dc.descriptionThis article belongs to the Special Issue Sustainability of Learning Analyticsen
dc.description.abstractTo start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners' interactions and to develop predictive models to forecast success in the test. Moreover, the use of different courses allows analyzing how models can generalize across courses. This article has the following objectives: (1) to develop and analyze predictive models to forecast who will pass the admission test, (2) to discover which variables have more effect on success in different courses, (3) to analyze to what extent models can be generalized to other courses and subsequent cohorts, and (4) to discuss the conditions to achieve generalizability. The results show that the average grade in SPOC exercises using only first attempts is the best predictor and that it is possible to transfer predictive models with enough reliability when some context-related conditions are met. The best performance is achieved when transferring within the same cohort to other SPOCs in a similar context. The performance is still acceptable in a consecutive edition of a course. These findings support the sustainability of predictive models.en
dc.description.sponsorshipThis work was partially funded by the LALA project (grant no. 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP). The LALA project has been funded with support from the European Commission. In addition, this work has been partially funded 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 cofinanced by the Structural Funds (FSE and FEDER). It has also been supported by the Spanish Ministry of Science, Innovation, and Universities, under an FPU fellowship (FPU016/00526)en
dc.format.extent19
dc.identifier.bibliographicCitationMoreno-Marcos PM... [et al.]. Generalizing Predictive Models of Admission Test Success Based on Online Interactions. Sustainability. 2019; 11(18):4940.en
dc.identifier.doihttps://doi.org/10.3390/su11184940
dc.identifier.issn2071-1050
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue18
dc.identifier.publicationlastpage19
dc.identifier.publicationtitleSustainability (Switzerland)en
dc.identifier.publicationvolume11
dc.identifier.urihttps://hdl.handle.net/10016/30485
dc.identifier.uxxiAR/0000024614
dc.language.isoengen
dc.publisherMDPIen
dc.relation.projectIDGobierno de España. TIN2017-85179-C3-1-Res
dc.relation.projectIDComunidad de Madrid. S2018/TCS-4307es
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherGeneralizabilityen
dc.subject.otherIndicatorsen
dc.subject.otherLearners' Successen
dc.subject.otherLearning Analyticsen
dc.subject.otherPredictionen
dc.subject.otherSpocsen
dc.titleGeneralizing predictive models of admission test success based on online interactionsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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