Early Predictor for Student Success Based on Behavioural and Demographical Indicators

Abstract

As the largest distance learning university in the UK, the Open University has more than 250,000 students enrolled, making it also the largest academic institute in the UK. However, many students end up failing or withdrawing from online courses, which makes it extremely crucial to identify those “at risk” students and inject necessary interventions to prevent them from dropping out. This study thus aims at exploring an efficient predictive model, using both behavioural and demographical data extracted from the anonymised Open University Learning Analytics Dataset (OULAD). The predictive model was implemented through machine learning methods that included BART. The analytics indicates that the proposed model could predict the final result of the course at a finer granularity, i.e., classifying the students into Withdrawn, Fail, Pass, and Distinction, rather than only Completers and Non-completers (two categories) as proposed in existing studies. Our model’s prediction accuracy was at 80% or above for predicting which students would withdraw, fail and get a distinction. This information could be used to provide more accurate personalised interventions. Importantly, unlike existing similar studies, our model predicts the final result at the very beginning of a course, i.e., using the first assignment mark, among others, which could help reduce the dropout rate before it was too late.