A Predictive Model for the Early Identification of Student Dropout Using Data Classification, Clustering, and Association Methods

Technology development has led to increased data generated in education, sparking interest in information extraction to support educational management through automated data analysis. Using such data to create models identifying students likely to drop out has drawn research interest. A crucial factor in reducing dropout rates is the systematic and early identification of the level of student engagement, especially by detecting the students’ behavior profile in the virtual environment, such as grades in assessments. There are predictive models based on data mining processes that identify students prone to dropping out. Unfortunately, the predictive models do not characterize the profiles of these students or the specific trends associated with these profiles. This article aims to fill a gap by presenting a study that identifies and tracks the profiles of undergraduate students likely to drop out, starting with an analysis of academic performance. We propose a predictive model beyond classification by combining data mining techniques such as decision trees, clustering, and frequent pattern analysis. Decision trees, a data mining technique that uses a tree-like graph to represent decisions and their possible consequences, identify students at risk of failure from the entire dataset. Clustering analysis, a data mining technique that groups similar data points together, groups students based on similar characteristics (e.g., students who scored between 0 and 30 points on a specific activity). Frequent pattern analysis, a data mining technique that identifies patterns that occur frequently in a dataset, uncovers the underlying factors contributing to low performance (e.g., identify which activities had the most significant influence on a specific group’s low performance). This integrated approach predicts dropout risk with 93.9% precision and provides a deeper understanding of student profiles and the trends associated with academic failure. The model’s practical application is demonstrated through a study.