Identifying Patterns in Socioeconomic Data of University Students: A Clustering and Association Rules Approach at the Federal University of Alagoas
DOI:
https://doi.org/10.5753/rbie.2026.6052Keywords:
Student Assistance, Machine Learning, Unsupervised Learning, Data Science, Clustering, Association Rules, Dropout preventionAbstract
Student assistance plays an important role in reducing dropout rates, but its effectiveness requires a deeper understanding of students’ vulnerability profiles. This study investigates the socioeconomic patterns of university students at the Federal University of Alagoas (UFAL) using unsupervised learning techniques, focusing on clustering and association rules. The analysis was based on data from applications for student aid programs, including information on income, transportation, parents' education level, racial identification, and participation in social programs. Clustering was performed using the K-Modes algorithm, suitable for predominantly categorical data, with Hamming distance as the dissimilarity measure. The choice of the number of clusters was guided by several validation metrics and visual analyses, such as t-SNE projections and dendrograms. The results revealed distinct groups, highlighting profiles related to transportation difficulties and lower household income. In the association rules analysis, per capita income, income source, and participation in social programs emerged as central factors, reinforcing their relevance in characterizing socioeconomic vulnerability. Additionally, associations were found between parents' education level and racial identification. The patterns identified can support the development of more targeted and effective student assistance policies, contributing to the promotion of academic persistence. Future work suggests expanding the analysis by incorporating academic performance data and evaluating changes in student profiles over time.
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