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Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems

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Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems

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dc.contributor.author Alcaraz, Raúl es_ES
dc.contributor.author Martínez-Rodrigo, Arturo es_ES
dc.contributor.author Zangróniz, Roberto es_ES
dc.contributor.author Rieta, J J es_ES
dc.date.accessioned 2022-07-26T18:07:15Z
dc.date.available 2022-07-26T18:07:15Z
dc.date.issued 2021-10-01 es_ES
dc.identifier.uri http://hdl.handle.net/10251/184795
dc.description.abstract [EN] Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules with different characteristics, those designed from a course-specific perspective have recently provided better outcomes. Hence, the main goal of this article is to design a tailored EWS for a conventional course in power electronic circuits. For that purpose, effectiveness of some common classifiers in predicting at-risk students has been analyzed. Although slight differences in their performance have only been noticed, an ensemble classifier combining outputs from several of them has provided to be the best performer. As a major contribution, a novel weighted voting combination strategy has been proposed to exploit global information about how basic prediction algorithms perform on several time points during the semester and diverse subsets of student-related features. Predictions at five critical points have been analyzed, revealing that the end of the fourth week is the optimal time to identify students at risk of failing the course. At that moment, accuracies about 85%-90% have been reached. Moreover, several scenarios with different subsets of student-related attributes have been considered in every time point. Besides common parameters from student's background and continuous assessment, novel features estimating student's performance progression on weekly assignments have been introduced. The proposal of this set of new input variables is another key contribution, because they have allowed to improve more than 5% predictions of at-risk students at every time point. es_ES
dc.description.sponsorship This work was supported in part by the Research Group in Electronic, Biomedical, and Telecommunication Engineering through the University of Castilla-La Mancha and the European Regional Development Fund under Grant 2018/11744, and in part by the XII Call for Innovation Projects and Teaching Improvment of the University of Castilla-La Mancha. es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.relation.ispartof IEEE Transactions on Learning Technologies es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Power electronics es_ES
dc.subject Data mining es_ES
dc.subject Input variables es_ES
dc.subject Alarm systems es_ES
dc.subject Task analysis es_ES
dc.subject Prediction algorithms es_ES
dc.subject Magnetic circuits es_ES
dc.subject At-risk students es_ES
dc.subject Early warning system (EWS) es_ES
dc.subject Educational data mining (EDM) es_ES
dc.subject Performance prediction es_ES
dc.subject Power electronic systems es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/TLT.2021.3118279 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/FEDER//2018%2F11744/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Alcaraz, R.; Martínez-Rodrigo, A.; Zangróniz, R.; Rieta, JJ. (2021). Early Prediction of Students at Risk of Failing a Face-to-Face Course in Power Electronic Systems. IEEE Transactions on Learning Technologies. 14(5):590-603. https://doi.org/10.1109/TLT.2021.3118279 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/TLT.2021.3118279 es_ES
dc.description.upvformatpinicio 590 es_ES
dc.description.upvformatpfin 603 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 14 es_ES
dc.description.issue 5 es_ES
dc.identifier.eissn 1939-1382 es_ES
dc.relation.pasarela S\463407 es_ES
dc.contributor.funder Universidad de Castilla-La Mancha es_ES
dc.contributor.funder European Regional Development Fund es_ES


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