Predicting academic success through students’ interaction with Version Control Systems

Abstract

Version Control Systems are commonly used by Information and communication technology professionals. These systems allow monitoring programmers activity working in a project. Thus, Version Control Systems are also used by educational institutions. The aim of this work is to evaluate if the academic success of students may be predicted by monitoring their interaction with a Version Control System. In order to do so, we have built a Machine Learning model which predicts student results in a specific practical assignment of the Operating Systems Extension subject, from the second course of the degree in Computer Science of the University of León, through their interaction with a Git repository. To build the model, several classifiers and predictors have been evaluated. In order to do so, we have developed Model Evaluator (MoEv), a tool to evaluate Machine Learning models in order to get the most suitable for a specific problem. Prior to the model development, a feature selection from input data is done. The resulting model has been trained using results from 2016–2017 course and later validated using results from 2017–2018 course. Results conclude that the model predicts students’ success with a success high percentage.

Keywords

• version control system
• machine learning
• learning analytics