RT Conference Proceedings
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T1 An early warning dropout model in higher education degree programs: A case study in Ecuador
A1 Heredia-Jimenez, Vanessa
A1 Jiménez Macías, Alberto Alejandro
A1 Ortiz Rojas, Margarita
A1 Imaz Marín, Jon
A1 Moreno-Marcos, Pedro Manuel
A1 Muñoz Merino, Pedro José
A1 Delgado Kloos, Carlos
AB 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.
PB CEUR-WS.org
SN 1613-0073
YR 2020
FD 2020-09-14
LK https://hdl.handle.net/10016/44015
UL https://hdl.handle.net/10016/44015
LA en
NO 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.
NO Work partially funded by the LALA project (grant no.586120-EPP-1-2017-1-ES EPPKA2-CBHE-JP). This project has been funded with support from the Eu ropean Commission. This work has also been partially funded by the Madrid Re gional Government through the e-Madrid-CM Project under Grant S2018/TCS 4307, a project which is co-funded by the European Structural Funds (FSE and FEDER). This work has also been partially funded by the FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación, through the Smartlet Project under Grant TIN2017-85179-C3-1-R. This publication re flects only the views of the authors, and the funders cannot be held responsible for any use which may be made of the information contained therein.
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