An early warning dropout model in higher education degree programs: A case study in Ecuador
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2020-09-14
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CEUR-WS.org
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Abstract
Worldwide, a significant concern of universities is to reduceacademic dropout rate. Several initiatives have been made to avoid thisproblem; however, it is essential to recognize at-risk students as soon aspossible. In this paper, we propose a new predictive model that can iden-tify the earliest moment of dropping out of a student of any semester inany undergraduate course. Unlike most available models, our solution isbased on academic information alone, and our evidence suggests that byignoring socio-demographics or pre-college entry information, we obtainmore reliable predictions, even when a student has only one academicsemester finished. Therefore, our prediction can be used as part of anacademic counseling tool providing the performance factors that couldinfluence a student to leave the institution. With this, the counselorscan identify those students and take better decisions to guide them andfinally, minimize the dropout in the institution. As a case study, we usedthe students¿ data of all undergraduate programs from 2000 until 2019from a public high education university in Ecuador.
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Proceeding of: LAUR 2020 Workshop on Adoption, Adaptation and Pilots of Learning Analytics in Under-represented Regions co-located with the 15th European Conference on Technology Enhanced Learning 2020 (ECTEL 2020), Online, September, 14 & 15, 2020.
Keywords
Data mining, Dropout prediction, Early detection, Algorithm, Learning analytics, Higher education
Bibliographic citation
Muñoz-Merino, Pedro J. ... et al. (editors). Proceedings of the Workshop on Adoption, Adaptation and Pilots of Learning Analytics in Under-represented Regions co-located with the 15th European Conference on Technology Enhanced Learning 2020 (ECTEL 2020), Online, September, 14 & 15, 2020. Pp.: 58-67(CEUR Workshop Proceedings; 2704). CEUR-WS.org, 2020.