dc.contributor.authorBravo-Agapito, Javier
dc.contributor.authorRomero Martínez, Sonia Janeth
dc.contributor.authorPamplona, Sonia
dc.date.accessioned2021-11-10T16:02:54Z
dc.date.available2021-11-10T16:02:54Z
dc.date.issued2021-02
dc.identifier.issn0747-5632
dc.identifier.urihttp://hdl.handle.net/20.500.12226/969
dc.description.abstractThis decade, e-learning systems provide more interactivity to instructors and students than traditional systems and 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.es
dc.language.isoenes
dc.titleEarly prediction of undergraduate Student's academic performance in completely online learning: A five-year studyes
dc.typearticlees
dc.description.course2020-21es
dc.issue.number106595es
dc.journal.titleComputers in Human Behaviores
dc.page.initial1es
dc.page.final11es
dc.publisher.departmentDepartamento de Ingeniería Informáticaes
dc.publisher.facultyEscuela de Ciencias Técnicas e Ingenieríaes
dc.publisher.group(GI-20/4) Grupo de Investigación de Educación y Tecnologíaes
dc.rights.accessRightsopenAccesses
dc.subject.keywordAnalyticses
dc.subject.keywordLearning Management Systemses
dc.subject.keywordOnline learninges
dc.subject.keywordModelinges
dc.subject.keywordPredictiones
dc.volume.number115es


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