AI-Enhanced Predictive Modelling of Virtual Laboratory Microlearning in Online Distance Education

Abstract

This study examines the impact of an AI-enhanced, virtual-laboratory (VL)–integrated microlearning model on learners’ motivation, engagement, and academic achievement in online and distance learning (ODL). In a four-week experiment, 126 undergraduates were randomly assigned to VL-assisted microlearning, traditional microlearning, or lecture-based instruction. Data comprised pretest–posttest scores, motivation and engagement questionnaires, and interaction logs. ANCOVA/MANOVA showed that the VL-assisted group outperformed the others on cognitive and practical assessments, with large effects (Cohen’s d > 0.80) and higher normalized gains (N-gain ≈ 0.72), and reported stronger motivation and engagement across dimensions. Beyond these tests, AI-based analysis uncovered non-linear relationships. It identified key behavioral predictors—such as simulation attempts, behavioral engagement, self-efficacy, and time-on-task—that explained performance differences. Comparative AI models (Gradient Boosting, Random Forest, SVM) confirmed these results, with Gradient Boosting achieving the highest accuracy (0.91) under 10-fold cross-validation. Interaction-log features outweighed demographic variables in predictive power, revealing hidden behavioural patterns linked to learning success. These findings indicate that coupling virtual laboratories with AI-driven analytics can improve both cognitive and affective outcomes, offering a scalable, data-informed approach to enhance ODL quality.