Using online student interactions to predict performance in a first-year computing science course

Goundar, Sam and Deb, Arpana S. and Lal, Goel and Naseem, Mohammed (2022) Using online student interactions to predict performance in a first-year computing science course. Technology, Pedagogy and Education, TBC . pp. 1-19. ISSN 1475-939X

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Abstract

Student performance is a critical factor in determining a university’s reputation because it has a negative effect on student retention. Students who do not perform well in a course are more likely to drop out from their programmes before graduating. Many students who enrol in Computing Science programmes struggle to find success because it is considered a difficult discipline. In this study, a sample of 918 observations were selected containing demographic and academic information about students enrolled in a first-year undergraduate Computing Science course at a university. Classification algorithms such as Decision Tree, Random Forest, Naïve Bayes and Support Vector Machine were used to build predictive models to determine whether a student will pass or fail the course. The results showed the Random Forest algorithms are capable of producing better predictive performance compared with traditional Decision Tree algorithms.

Item Type:	Journal Article
Subjects:	T Technology > T Technology (General)
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Mohammed Naseem
Date Deposited:	17 Aug 2022 23:11
Last Modified:	17 Aug 2022 23:39
URI:	https://repository.usp.ac.fj/id/eprint/13561

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