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 (Auer, M.E. and Rüütmann, T. eds.), 1328 pp. 127–138.

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 paper, 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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About

• Item ORO ID
• 71707
• Item Type
• Conference or Workshop Item
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Mainstreaming Learning Analytics	594575	Institute Of Coding

• Keywords
• Learning Analytics; educational data mining; machine learning
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2021 The Author(s)
• Depositing User
• Audrey Ekuban