On Predicting Exam Performance Using Version Control Systems’ Features

Abstract

The advent of Version Control Systems (VCS) in computer science education has significantly improved the learning experience. The Learning Analytics community has started to analyze the interactions between students and VCSs to evaluate the behavioral and cognitive aspects of the learning process. Within the aforesaid scope, a promising research direction is the use of Artificial Intelligence (AI) to predict students’ exam outcomes early based on VCS usage data. Previous AI-based solutions have two main drawbacks: (i) They rely on static models, which disregard temporal changes in the student–VCS interactions. (ii) AI reasoning is not transparent to end-users. This paper proposes a time-dependent approach to early predict student performance from VCS data. It applies and compares different classification models trained at various course stages. To gain insights into exam performance predictions it combines classification with explainable AI techniques. It visualizes the explanations of the time-varying performance predictors. The results of a real case study show that the effect of VCS-based features on the exam success rate is relevant much earlier than the end of the course, whereas the timely submission of the first lab assignment is a reliable predictor of the exam grade.

Keywords

• version control systems
• machine learning
• explainable AI
• learning analytics