Journal Article

Predicting Student Performance in Introductory Programming Courses

Borges, Ana Rosa, Gomes, Anabela, Pires, João P. J., Brito Correia, Fernanda, Bernardino, Jorge

2024

Overview

The importance of accurately predicting student performance in education, especially in the challenging curricular unit of Introductory Programming, cannot be overstated. As institutions struggle with high failure rates and look for solutions to improve the learning experience, the need for effective prediction methods becomes critical. This study aims to conduct a systematic review of the literature on methods for predicting student performance in higher education, specifically in Introductory Programming, focusing on machine learning algorithms. Through this study, we not only present different applicable algorithms but also evaluate their performance, using identified metrics and considering the applicability in the educational context, specifically in higher education and in Introductory Programming. The results obtained through this study allowed us to identify trends in the literature, such as which machine learning algorithms were most applied in the context of predicting students’ performance in Introductory Programming in higher education, as well as which evaluation metrics and datasets are usually used.

• Publisher: MDPI AG
• Subject: Academic achievement / Accuracy / Algorithms / Computer programming / Context / Data mining / Datasets / Education / Engineers / Failure rates / Higher education / introductory programming / Literature reviews / Machine learning / Methods / Performance evaluation / Performance prediction / Problem solving / Programming / Skills / Statistical methods / Students / student’s performance prediction / Study and teaching / Success / Systematic review / Teaching / Teaching methods / Trends