Using online student interactions to predict performance in a first-year computing science course. Issue 4 (8th August 2022)

• Record Type: Journal Article
• Authors: Goundar, Sam; Deb, Arpana; Lal, Goel; Naseem, Mohammed
• Is Part Of: Technology, pedagogy and education. Volume 31:Issue 4(2022)
• Page Start: 451
• Page End: 469
• Publication Date: 2022-08-08
• Subjects: Predictive analytics -- data mining -- classification algorithms -- random forest algorithm -- decision trees; Educational technology -- Great Britain -- Periodicals; Teachers -- Training of -- Great Britain -- Computer-assisted instruction -- Periodicals; Teachers -- Training of -- Great Britain -- Periodicals; 371.33
• DOI: 10.1080/1475939X.2021.2021977
• Languages: English
• ISSNs: 1475-939X

Abstract

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.