Determinants of Students’ Performance in Electrical and Electronics Engineering at Mbeya University of Science and Technology, Tanzania
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Date
2025
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G-Card
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.
Description
This Journal Article was published by G-Card in 2025