Authors - Mary Diana C. Yamzon, Janelli M. Mendez Abstract - This study provides a data-driven analysis of the academic performance of Bachelor of Science in Office Administration (BSOAD) students at Tagbilaran City College from Academic Year 2021 to 2024, employing data mining clustering techniques to ascertain the five most challenging subjects. The study specifically aimed to: (1) construct and preprocess a dataset of pertinent academic attributes; (2) employ K-Means, K-Medoids, and Agglomerative Hierarchical Clustering algorithms to discern groupings of subject difficulty; (3) validate clustering results utilizing the Davies-Bouldin Index (DBI); and (4) develop evidence-based recommendations for curriculum enhancement and academic assistance. The analysis involved a dataset of 26,965 valid student grade records across 68 subjects, all of which were processed using RapidMiner Studio. The research utilized the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework within the context of Educational Data Mining (EDM). The DBI for K-Means (DBI = 0.461; Excellent) and K-Medoids (DBI = 0.9145) were used to check the clusters, and the visual dendrogram was used to check the Agglomerative Hierarchical Clustering. All three algorithms consistently recognized OA113 Advanced Shorthand and OA111 Foundations of Shorthand as the two most challenging subjects in the program. The results offer statistically substantiated, evidence-based insights to facilitate curriculum evaluation, instructional enhancement, and the formulation of specialized academic intervention programs for BSOAD students.
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