To what extent has machine learning achieved in predicting online at-risk students? Evidence from quantitative meta-analysis

Abstract

Machine learning and data mining techniques hold promise in predicting at-risk students in online learning. This meta-analysis aimed to provide quantitative evidence to validate whether and to what extent machine learning techniques have been achieved in identifying online at-risk students. Meta-regressions examined the impacts of predictor data types, classical or deep learning approaches, and prediction stages on performance. A random-effects meta-analysis of 47 studies with 309 models showed good classification accuracy, higher in summative than early predictions. Deep learning models and diverse predictors can significantly enhance model performance. No publication bias was detected. Implications and recommendations for practice are discussed.