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Early prediction of undergraduate Student's academic performance in completely online learning: A five-year study

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dc.contributor.authorBravo-Agapito, Javier
dc.contributor.authorRomero Martínez, Sonia Janeth
dc.contributor.authorPamplona, Sonia
dc.contributor.orcidhttps://orcid.org/0000-0002-3516-7367 View this author’s ORCID profile
dc.date.accessioned2024-09-13T11:39:27Z
dc.date.available2024-09-13T11:39:27Z
dc.date.issued2021
dc.descriptionThe registered version of this article, first published in “Computers in Human Behavior, 2021, vol. 115", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.chb.2020.106595 La versión registrada de este artículo, publicado por primera vez en “Computers in Human Behavior, 2021, vol. 115", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.chb.2020.106595
dc.description.abstractThis decade, e-learning systems provide more interactivity to instructors and students than traditional systemsand make possible a completely online (CO) education. However, instructors could not warn if a CO student is engaged or not in the course, and they could not predict his or her academic performance in courses. This work provides a collection of models (exploratory factor analysis, multiple linear regressions, cluster analysis, and correlation) to early predict the academic performance of students. These models are constructed using Moodle interaction data, characteristics, and grades of 802 undergraduate students from a CO university. The models result indicated that the major contribution to the prediction of the academic student performance is made by four factors: Access, Questionnaire, Task, and Age. Access factor is composed by variables related to accesses of students in Moodle, including visits to forums and glossaries. Questionnaire factor summarizes variables related to visits and attempts in questionnaires. Task factor is composed of variables related to consulted and submitted tasks. The Age factor contains the student age. Also, it is remarkable that Age was identified as a negative predictor of the performance of students, indicating that the student performance is inversely proportional to age. In addition, cluster analysis found five groups and sustained that number of interactions with Moodle are closely related to performance of students.en
dc.description.versionversión publicada
dc.identifier.citationBravo-Agapito, J. Romero-Martínez, S. Pamplona, S. (2021). Early prediction of undergraduate Student’s academic performance in completely online learning: A five-year study. Computers in Human Behavior, 115. DOI: 10.1016/j.chb.2020.106595
dc.identifier.doihttps://doi.org/10.1016/j.chb.2020.106595
dc.identifier.issn0747-5632 | eISSN 1873-7692
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23736
dc.journal.titleComputers in Human Behavior
dc.journal.volume115
dc.language.isoen
dc.publisherELSEVIER
dc.relation.centerFacultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject61 Psicología
dc.subject.keywordsanalyticsen
dc.subject.keywordslearning management systemsen
dc.subject.keywordsonline learningen
dc.subject.keywordsmodelingen
dc.subject.keywordspredictionen
dc.titleEarly prediction of undergraduate Student's academic performance in completely online learning: A five-year studyes
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dc.typejournal articleen
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relation.isAuthorOfPublication.latestForDiscoverya003cb5c-e676-41c3-9586-b3a19ea82b0b
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