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One of the persistent problems faced by the university education system is the dropout rate. The main aim of this research was to identify the profile characteristics of those students who drop out of their studies, seeking in-depth knowledge of the reality behind the issue. The responses to a questionnaire of 149,837 students from three Spanish un...
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There is almost universal and long-standing concern regarding the high dropout rates among university students. Determining the causes in order to reduce the risk of dropout has been a recurrent research topic. Interactive-causal models, based on structural equations (SEM), have recently been joined by other procedures based on data mining or academic analytics. The aim of this work was to analyse the convergence between a predictive model on academic dropout based on big data and the results of a structural equation model (PLS-SEM) defined on the basis of the student’s personal variables (engagement and satisfaction) that previous research has shown to be highly relevant. The results confirm the relationships between the main variables and dropout probability, mediated by academic performance. However, the limited agreement between the prediction methods highlights the importance of carefully selecting variables and weighting predictive analyses. This is crucial to avoid overestimating dropout likelihood or adopting overly deterministic approaches that overlook the relational and interactive aspects of the issue.
