Abstract
Student engagement is an essential device for deepening learning, achieving learning outcomes, developing competencies, and improving academic performance in education settings. It is widely receiving increased attention among various scholars and higher education leaders. However, there are increasing concerns about the academic performance of students in higher education settings. The application of statistical data analytics for mining student engagement datasets is a candidate strategy for discovering essential indicators associated with academic performance. However, widely used data analytic methods like principal component analysis are ineffective when most of the indicators captured are categorical, making them inappropriate for establishing the weighty academic performance indicators. This study’s objective was to investigate the application of multiple correspondence analysis to establish weighty student engagement indicators of academic performance. This study’s findings have indicated that higher-order learning and student-staff interaction are weighty indicators that relate student engagement to academic performance.