Clustering-Based Academic Performance Assessment Using Educational Data

Educational institutions generate large amounts of student data that can be analyzed to improve academic outcomes. This study applies clustering techniques to identify performance patterns among students based on factors such as attendance, assignment completion, assessment scores, and learning engagement. The K-Means clustering algorithm is used to group students with similar academic characteristics into distinct clusters. The analysis helps classify students into categories such as high-performing, average-performing, and low-performing groups. The discovered clusters provide valuable insights for educators to design targeted interventions, enhance student support, and improve overall academic performance. The results demonstrate that clustering is an effective educational data mining technique for identifying student learning patterns and supporting data-driven decision-making in higher education.