Using Cluster Analysis to Classify Student Performance: A Case Study of Secondary Schools in Tartus, Syria

Abstract

This study aimed to classify student performance in secondary schools in Tartus, Syria using cluster analysis. A descriptive analytical approach was employed, and data were collected from official student records provided by the Tartus Directorate of Education. The data included weekly study hours, attendance rate, number of academic activities, average performance in previous exams, and student grades as the dependent variable. SPSS27 was utilized for data analysis, employing descriptive statistics and cluster analysis. The results demonstrated the effectiveness of cluster analysis in classifying students into three distinct groups. Furthermore, the analysis revealed that the number of academic activities and attendance rate were the most influential variables on academic performance. Based on these findings, the study recommends encouraging participation in academic activities and promoting attendance as effective strategies for improving student performance.