Clustering Student Behavioral Patterns: A Data Mining Approach Using K-Means for Analyzing Study Hours, Attendance, and Tutoring Sessions in Educational Achievement

This study utilizes K-Means clustering to analyze student behavioral patterns based on study hours, attendance, and tutoring sessions, aiming to understand their impact on educational achievement. Educational Data Mining (EDM) methods have increasingly been applied to uncover patterns in student engagement, providing valuable insights for personalized education. By clustering students into groups such as high achievers, average performers, and those needing support, the study highlights distinct patterns of academic engagement, which can inform targeted interventions. The dataset includes 6,607 students, with clustering conducted after preprocessing steps like handling missing values and feature scaling. Using the Elbow Method, three clusters were identified as optimal, each representing unique behavioral profiles among students. The results demonstrate clear distinctions in student engagement across clusters. High achievers exhibit high study hours, regular attendance, and frequent tutoring sessions, suggesting a proactive approach to academic support. Average performers maintain moderate engagement, while students needing support show lower values across all metrics, indicating potential academic risks. The clustering was validated using metrics such as the Silhouette Score, which confirmed the clusters’ coherence and relevance. The findings carry practical implications for educators and policymakers. Identifying students at risk early enables institutions to allocate resources effectively, tailoring support to foster better educational outcomes. However, the study’s focus on three behavioral metrics is a limitation, and future research could incorporate additional variables such as motivation and parental involvement for a more comprehensive analysis. Advanced clustering methods and predictive models could further refine these insights, paving the way for more nuanced educational interventions.