Clustering-Based Identification of Student Support Needs in Higher Education Transition
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Abstract
The transition from secondary to higher education represents a critical phase influenced by both academic readiness and socio-economic conditions. This study proposes a clustering-based approach to identify student support needs during this transition by analyzing multidimensional student profiles. Using secondary data from 1,226 senior high school students, three unsupervised clustering algorithms—K-Means, DBSCAN, and BIRCH—were applied to academic performance and socio-economic variables. Cluster quality was assessed using internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index. The results indicate that clustering-based methods provide richer insights than traditional rule-based approaches by capturing heterogeneous student profiles and revealing atypical cases. Among the evaluated algorithms, BIRCH demonstrated the most balanced performance in terms of cluster compactness and separation, while K-Means offered stable and interpretable results, and DBSCAN was effective in identifying outliers. Interpreted within the college readiness framework, the identified clusters highlight differentiated student support needs, enabling more targeted and equitable intervention strategies. These findings underscore the potential of educational data mining to support data-driven decision-making in facilitating students’ transition to higher education.
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