Examining the Impact of Mathematics Ancillary Courses on Computational Programming Intelligence of Computer Science Students Using Machine Learning Techniques

Abstract

Mathematics courses make up a good percentage of the undergraduate curriculum in the Computer Science and Engineering discipline. It is however important to ascertain how the mathematics courses impact the programming skills of the students. This article examines the impact of mathematics ancillary courses on the Computational Programming Intelligence (CPI) of Computer Science and Engineering students. Using the results of Computer Science students on mathematics and programming courses for seven (7) sessions, Random forest regression and K-means clustering machine learning models were used to study the relationship between the ancillary courses and their performance in programming courses. A Pearson correlation coefficient was computed to assess the linear relationship between five (5) Mathematics ancillary courses and the programming courses. A significant positive correlation (0.29, 0.27, 0.20, 0.10, and 0.09, p = 0.02) was obtained with Linear algebra having the highest and Mathematical methods the least. Consequently, variable importance results show that linear algebra had the highest impact on CPI while Mathematical methods had the least in the following order (29.11%, 20.39%, 19.80%, 17.15%, and 13.55%). Mostly female students of age range 19–20 were found to have been positively impacted more by the mathematics courses. A curriculum guide was presented based on the findings.