Identifying Adolescent Behavioral Profiles Through K-Means Clustering Based on Smartphone Usage, Mental Health, and Academic Performance

Abstract

The pervasive integration of digital devices into students’ daily lives has profoundly shaped their learning habits and psychological well-being. As technology becomes increasingly embedded in academic and personal routines, understanding the relationship between digital engagement, mental health, and academic outcomes is vital for developing effective student-support and intervention frameworks in higher education. This study seeks to uncover behavioral patterns among college students by examining the interconnections between smartphone usage, mental health indicators, and academic performance through a data-driven machine learning approach. Utilizing the K-Means clustering algorithm, students were categorized into distinct behavioral profiles derived from eight core features: daily screen time, sleep duration, grade performance, exercise frequency, anxiety level, depression level, self-confidence, and screen exposure before sleep. A dataset comprising 3,000 entries was preprocessed through normalization and analyzed within the Knowledge Discovery in Databases (KDD) framework to ensure structured and reliable data processing. The Elbow Method identified four optimal clusters, each reflecting unique behavioral characteristics. Cluster 1 represented well-balanced students with stable academic and emotional states; Cluster 2 included high-achieving yet anxious individuals; Cluster 3 captured those exhibiting excessive digital engagement and psychological distress; and Cluster 4 comprised moderately engaged students with lower self-confidence. Visual representations, including bar and radar charts, were generated to illustrate inter-cluster variations and enhance interpretability of behavioral distinctions. The findings reveal that digital usage patterns are closely linked to mental health and academic performance, suggesting that excessive or unregulated device use can heighten emotional strain and academic inconsistency. These insights highlight the necessity of personalized mental health initiatives and targeted digital literacy programs grounded in behavioral segmentation. Overall, the study demonstrates the applicability of unsupervised machine learning for behavioral profiling and provides evidence-based recommendations for educators, mental health practitioners, and policymakers seeking to foster balanced and healthy digital habits among students.