Predicting Student Dropout and Academic Success

Valentim Realinho (), Jorge Machado, Luís Baptista and Mónica V. Martins
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Valentim Realinho: VALORIZA—Research Center for Endogenous Resource Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
Jorge Machado: Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
Luís Baptista: Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
Mónica V. Martins: Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal

Data, 2022, vol. 7, issue 11, 1-17

Abstract: Higher education institutions record a significant amount of data about their students, representing a considerable potential to generate information, knowledge, and monitoring. Both school dropout and educational failure in higher education are an obstacle to economic growth, employment, competitiveness, and productivity, directly impacting the lives of students and their families, higher education institutions, and society as a whole. The dataset described here results from the aggregation of information from different disjointed data sources and includes demographic, socioeconomic, macroeconomic, and academic data on enrollment and academic performance at the end of the first and second semesters. The dataset is used to build machine learning models for predicting academic performance and dropout, which is part of a Learning Analytic tool developed at the Polytechnic Institute of Portalegre that provides information to the tutoring team with an estimate of the risk of dropout and failure. The dataset is useful for researchers who want to conduct comparative studies on student academic performance and also for training in the machine learning area.

Keywords: academic performance; machine learning in education; imbalanced classes; multi-class classification; educational data mining; learning management system; prediction (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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