Analysis and Prediction of Computer Science Students' Performance Based on Core Foundational Courses using Ensemble Machine Learning Methods

As the surge toward a technology-driven and dependent society increases, the demand for technology oriented workforce particularly computer science continues to rise. However, the concern that continues to persist in a number of higher institutions of learning is the low graduation rates and poor performance in computer science programs. This study, carried out in a Kenyan public university attempted to extend the understanding on the performance of computer science students The study aimed at examining whether performance in early foundational core courses had any influence on the final graduation score (through final accumulative score) among computer science students using ensemble machine learning methods. The study specifically employed Random Forest and XGBoost ensemble methods. The results showed the Random forest regression model having a prediction power of 83.20% while XGBoost had a slightly improved prediction power of 84.6%. The results also revealed better model performance when using only years 1 and 2 core courses as compared to using years 1 to 3 core courses. This is an indicator that foundational core courses are good predictors of student academic performance in computer science programs. This work in progress is part of a preliminary study aimed at addressing the challenge of low graduation rates and poor performance in computer science programs.