Determinants of Students’ Performance in Electrical and Electronics Engineering at Mbeya University of Science and Technology, Tanzania

Abstract

The Electrical and Electronics Engineering program requires a balance between theoretical knowledge and practical application, making students’ performance optimization essential in meeting industry demands. This study utilized descriptive statistics, Pearson Correlation Analysis, and Principal Component Analysis (PCA) to evaluate academic performance in the EEE program at Mbeya University of Science and Technology (MUST). By examining 16 core courses, the study identified key determinants of students’ success, course interdependencies and areas for curriculum enhancement. Descriptive statistics revealed significant variability in performance, with EE 8401 (Industrial Practical Training 3) recording the highest mean (79.98) and EE 8402 (Phase AC Synchronous Machines) the lowest (48.11), highlighting disparities in instructional effectiveness. Pearson Correlation Analysis shows strong correlations among theoretically aligned courses, moderate correlations among related subjects, and weak or negative correlations in distinct learning domains, emphasizing the need for targeted interventions and curriculum adjustments. PCA findings confirmed that three Principal Components explained 58.85% of the variance, representing theoretical foundations, applied project-based learning and specialized hands-on training. Scree plot and eigenvalue analysis validated dimensionality reduction, enhancing data interpretation. Principal Component loadings highlight academic constructs, with PC1 reflecting analytical competencies, PC2 capturing project-based courses and PC3 representing specialized training. This study recommends aligning theoretical courses with standardized assessments, integrating industry collaborations in project-based learning and refining assessment models for specialized training. Future research should explore longitudinal trends in Principal Components, external influences on high-uniqueness courses and students’ feedback integration. By implementing data-driven strategies, institutions can refine engineering curricula, bridge performance gaps and enhance student success outcomes.