Early Warning System to Identify Potential Dropout Factors of Undergraduate Students during the Pandemic in Paraíba (Brazil)

Abstract

This article is the result of reflections (which became more intense during the pandemic) aimed at identifying quantitative elements for developing a sure and fast method for evaluating the academic situation of undergraduate students, especially in relation to the risk that students may abandon their courses, and establishing academic management actions and interventions to prevent dropout from occurring. In this article we identify potential academic indicators, sociodemographic variables and teaching-environment factors to conduct a study focused on constructing a mathematical model. To achieve this objective, we used Cluster Analysis. Also, as a theoretical and methodological parameter, we used the Early Warning System developed at the University of Valencia (Spain) by Villar-Aguilés et al. (2017) with responses to two questionnaires (C1 and C2) as well as responses to the Teaching Environment Scale (questionnaire C3) validated by Castelo-Branco (2020). This model, which is based on our analysis of the variables’ ability to predict potential student dropout in a given year, enabled us to adapt the Early Warning System to the situation prevailing at the Federal University of Paraíba in Brazil. Since the items in questionnaires C1 and C3 helped to construct the analysis model, and this model provided good estimates, our data demonstrate the importance of Cluster Analysis for detecting signs of potential student dropout. They also reveal several characteristics that expose students to the risk of potential dropout and could help them take more informed decisions. The tendency for students to drop out during the second semester is also confirmed.