Predictive Modelling with the Open University Learning Analytics Dataset (OULAD): A Systematic Literature Review

Higher education has experienced an unparalleled digital transformation, driven by the widespread adoption of online learning with massive users, which has risen to an explosive growth in the generation and analysis of student-related data. Within this transformation, predictive modeling has emerged as a useful tool for predicting critical indicators in the learning process, encompassing students’ academic performance, class retention, and dropout rates. With this backdrop, this study aims to conduct a systematic review of recent publications focused on predictive modeling, with a specific emphasis on the Open University Learning Analytics Datasets (OULAD). Following the PRISMA process, we identified 17 research articles published from 2017 to 2024, concentrating on OULAD in higher education. For our analysis, we categorized the purpose of predictive modeling into three types: (a) predicting students’ performance, (b) identifying at-risk students, and (c) predicting student engagement. The central focus lies on the identification of algorithms predominantly employed in these studies, including machine learning, deep learning, and statistical models. By investigating the methodologies and algorithms employed, this review informs researchers in learning analytics and educational data mining of the potential opportunities and challenges associated with predictive modeling using OULAD in higher education.