A data-driven institutional study of academic performance: exploring course, structure, and environment in higher education

Abstract

Student performance reflects the intersection of academic, structural, and environmental factors rather than individual effort alone. This study conducts a largescale, data-driven analysis of institutional records from BRAC University to examine how these factors shape outcomes across time, delivery modes, and disciplines. Nine hypotheses were tested, encompassing the effects of online, hybrid, and inperson learning environments; the COVID-19 pandemic; course-level contributions to CGPA; prerequisite–core alignment; class size; Residential Semester (RS) contexts; high-failure course patterns; and inter-departmental performance differences. Using Pearson correlation, regression, ANOVA, Kruskal–Wallis, Welch’s t-tests, and mixed-effects models, the study identifies clear structural trends: RS participation strongly correlates with higher and more consistent GPA; class size shows weak, context-dependent effects; and persistent high-failure rates cluster in STEM gateway courses. Departments differ systematically—humanities and law programs maintain higher GPAs, while technical disciplines show greater variance due to assessment rigor and prerequisite dependency. These findings reveal that academic outcomes are institutionally patterned, not random. They underscore the need for data-informed curriculum design, departmental benchmarking, and early-risk intervention frameworks to promote equity, quality, and resilience in higher education.