Using GitLab Interactions To Predict Student Success When Working As Part Of A Team

Ekuban, Audrey; Mikroyannidis, Alexander; Third, Allan and Domingue, John (2021). Using GitLab Interactions To Predict Student Success When Working As Part Of A Team. In: Educating Engineers for Future Industrial Revolutions. ICL 2020. Advances in Intelligent Systems and Computing, vol 1328.

DOI: https://doi.org/10.1007/978-3-030-68198-2_11

Abstract

This paper explores machine learning algorithms that can be used to predict student results in an assignment of a Software Engineering course, based on weekly cumulative average source code submissions to GitLab. GitLab is a source code version control system, commonly used in Software Engineering courses in Higher Education. The aim of this work is to create models that can be used to predict if a group of students in a team will pass or fail an assignment. In this pa-per, we present results from Decision Tree, Random Forest, Extra Trees, Ada Boost and Gradient Boosting machine learning models. These models were evaluated using cross-validation, with Ada Boost achieving the highest average score.

Viewing alternatives

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations