Bayesian network modeling-based prediction and intervention for dropout risk in continuing education learners

Adult education frequently encounters complex socioeconomic and motivational barriers; predicting continuing education dropouts is a significant challenge. This research proposes a Bayesian network modeling approach to efficiently predict the likelihood that at-risk students will drop out and to intervene before it’s too late. Financial stress, course involvement, and personal drive are influencing variables, but standard machine learning models do not capture the uncertainty of causal links or make them interpretable. To address these shortcomings, the paper presented a Bayesian Network-Based Probabilistic Inference Framework (BN-PIF) that enables real-time probabilistic reasoning by modeling the conditional relationships among pupils’ attributes. Learner data from the past is used to train the framework to identify high-risk students and predict who is likely to drop out. It powers a decision-support system that, based on each student’s risk profile, proposes individualized interventions like academic counselling or financial aid. The proposed BN-PIF is both more predictable and easier for decision-makers to understand. Instead of claiming a 15% improvement in early detection rate, the results show that BN-PIF improved performance significantly from the start, increasing the best baseline model’s early-stage prediction accuracy from 78% to 89% under stratified 10-fold cross-validation. The rise is not random or incidental but occurs across folds, as supported by the paired statistical test ( p < 0.05). Adults who use massive open online courses (MOOCs) and other forms of online continuing education, yet struggle with factors such as a lack of time, money, and motivation, are the intended participants in the proposed study. Both individual intervention and dropout prediction are informed by the framework’s use of Bayesian networks.