EXPLORING ENGINEERING STUDENT SUCCESS: A CLUSTER ANALYSIS OF ACADEMIC PERFORMANCE AND DEMOGRAPHIC FACTORS

To account for the temporal influences on student accomplishment, the grade point average (GPA) for the semester is used as a measure of student success. The results illustrate how well students do in relation to their demographics and usage of university resources, such financial aid. College campuses should not only expand their present resources but also boost public awareness of them and improve their accessibility. Retention and graduation issues are prevalent in higher education settings. Understanding the reasons why children struggle academically is one strategy to make this better. To comprehend groupings of students based on academic achievement and demographic data, a cluster analysis was carried out. These variables include things like housing situation, first-generation status, financial condition, and enrollment status. For the system's analysis of engineering student performance, clustering and machine learning algorithms were built. Here, a dataset of student academic achievement is used as input. The demographic data and academic achievement must then be clustered.