Applications of Machine Learning for at-Risk Student Prediction in Online Education: A 10-Year Systematic Review of Literature

Background

The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been addressed concerning the definition of at-risk students, as well as the strengths and limitations of different machine learning models to predict at-risk students.

Objectives

This systematic review aims to provide a comprehensive overview of the past 10-year research focusing on applying machine learning techniques for predicting at-risk students (i.e., failure, dropouts) in online learning environments.

Methods

Studies were extracted from the ACM Digital Library, IEEE Xplore Digital Library, Web of Science, ERIC, ProQuest, and EBSCO. A total of 161 studies published from 2014 to 2024 were included in the review.

Results and Conclusions

Findings revealed (1) four primary at-risk definitions outlined in the reviewed studies, each focusing on specific stages of student engagement and performance in a course; (2) most studies relied on student behavioural engagement and academic factors as at-risk predictors; (3) the adoption of deep learning and ensemble deep learning networks has significantly increased in the past 5 years, often outperforming classical machine learning models. While studies in which classical machine learning excelled often relied on the ensemble methodology and smaller sample sizes; (4) current machine learning practice evaluated by a list of criteria showed concerns regarding reproducibility, generalisability, and interpretability.