Asian Journal of Probability and Statistics

This study investigated the influence of GCE Advanced Level (A/L) study streams on undergraduate performance in core Information Technology (IT) courses at a state university in Sri Lanka. Secondary data from five academic cohorts (2018/19–2022/23) were analyzed across four courses: Foundation of Mathematics, Fundamentals of Programming, Data Structures, and Design and Analysis of Algorithms. One-way ANOVA and Welch ANOVA examined differences in performance across Arts, Biological Science, Commerce, Physical Science, and Technology streams, while multiple regression analyses assessed the predictive effect of first year courses: Foundation of Mathematics, and Fundamentals of Programming on second year courses: Data Structures, and Design and Analysis of Algorithms. Results indicated that A/L stream had a statistically significant but small effect on first-year courses, with Physical Science students outperforming Technology students in Foundation of Mathematics, and Commerce and Physical Science students outperforming Arts and Biological Science students in Fundamentals of Programming. No significant differences were observed in second-year courses. Regression analyses demonstrated that first-year performance significantly predicted second-year outcomes, with Foundation of Mathematics showing the strongest effect. These findings highlight the importance of targeted support and strengthened foundational instruction, particularly for students from non-STEM backgrounds, to enhance analytical and computational competencies. The study offers evidence-based insights for curriculum design, admission policy refinement, and academic interventions to promote equitable achievement in IT degree programmes.