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High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students.
Alvarado-Uribe et al. (Thu,) studied this question.