Assessing Key Performance Factors in Final-Year Civil Engineering Students at Mbeya University of Science and Technology, Tanzania by Using Principal Component Analysis
This study applies Principal Component Analysis (PCA) to evaluate and understand academic performance among final-year Civil Engineering students at Mbeya University of Science and Technology (MUST) in the academic year 2023/2024. Responding to Tanzania’s growing demand for skilled engineers, this research provides data-driven insights into student achievement patterns, revealing the core factors impacting performance. PCA was utilised to reduce dimensionality, transforming course grades into uncorrelated components that capture underlying performance structures. The results identify three primary components: Core Academic Knowledge, explaining 41.38% of the variance; Specialized Applied Skills, contributing 10.07%; and Advanced Independent Skills, accounting for 6.71%. Together, these components explain 58.16% of total performance variance, indicating a robust framework for understanding student success drivers. Additional analysis, including correlation matrices and descriptive statistics, highlights patterns across courses, revealing strong relationships within core competencies and independent distinctions in advanced courses. Findings suggest curriculum enhancements and targeted interventions that could better prepare students for industry needs, focusing on core academic support, practical skills enhancement, and resources for advanced technical areas.
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