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

Ekuban, Audrey; Mikroyannidis, Alexander; Third, Allan and Domingue, John (2020). Using GitLab Interactions To Predict Student Success When Working As Part Of A Team. In: ICL2020, 23 Sep - 25 Sep 2020.

URL: http://icl-conference.org/current/cfp_ICL.php

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.

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