Asian Journal of Probability and Statistics

This study uses a factor analysis approach to examine the fundamental aspects of academic accomplishment among students at the University of Calabar's Faculty of Computing. Performance was evaluated using a dataset that included 500 students' exam results from six fundamental computing courses: System Analysis, Structured Programming, Object Oriented Programming, Numerical Analysis, Distributed Computing, and Algorithm & Complexity Analysis.

Finding latent factors that explain performance differences and provide insights beyond conventional grading schemes was the main goal. The Kaiser-Meyer-Olkin (KMO) measure produced a value of 0.85, and Bartlett's Test of Sphericity was very significant (p <.001), confirming the applicability of the dataset. Two components that accounted for 75.7% of the total variation were identified using principal component analysis.

The choice to keep two factors was supported by the scree plot, which showed a distinct "elbow" at Component 2. By grouping the courses into two significant dimensions; programming and analytical skill and systems and computational complexity, varimax rotation improved interpretability. These observations show several cognitive domains in computer education.

The results emphasize the importance of data-driven curriculum design and recommend that teaching methods be in line with the structure of student performance. Teachers can improve learning outcomes and optimize instructional routes by classifying courses based on prevailing skill sets. By showing how factor analysis can reveal underlying structures in educational results and direct more successful academic interventions, this study advances academic analytics.