Generalizing predictive models of admission test success based on online interactions

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dc.contributor.author Moreno-Marcos, Pedro Manuel
dc.contributor.author Laet, Tinne De
dc.contributor.author Muñoz Merino, Pedro José
dc.contributor.author Soom, Carolien Van
dc.contributor.author Broos, Tom
dc.contributor.author Verbert, Katrien
dc.contributor.author Delgado Kloos, Carlos
dc.date.accessioned 2020-05-25T08:57:09Z
dc.date.available 2020-05-25T08:57:09Z
dc.date.issued 2019-09-10
dc.identifier.bibliographicCitation Moreno-Marcos PM... [et al.]. Generalizing Predictive Models of Admission Test Success Based on Online Interactions. Sustainability. 2019; 11(18):4940.
dc.identifier.issn 2071-1050
dc.identifier.uri http://hdl.handle.net/10016/30485
dc.description This article belongs to the Special Issue Sustainability of Learning Analytics
dc.description.abstract To 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.
dc.description.sponsorship This 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)
dc.format.extent 19
dc.language.iso eng
dc.publisher MDPI
dc.rights © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Generalizability
dc.subject.other Indicators
dc.subject.other Learners' Success
dc.subject.other Learning Analytics
dc.subject.other Prediction
dc.subject.other Spocs
dc.title Generalizing predictive models of admission test success based on online interactions
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.3390/su11184940
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2017-85179-C3-1-R
dc.relation.projectID Comunidad de Madrid. S2018/TCS-4307
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 18
dc.identifier.publicationlastpage 19
dc.identifier.publicationtitle Sustainability (Switzerland)
dc.identifier.publicationvolume 11
dc.identifier.uxxi AR/0000024614
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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