Enhancing global student success through data-driven session design in online education - Saeed, Muhammad Atif; Syed Muhammad Naeem; Imran, Muhammad; Ahmed, Saim; Almusharraf, Norah; Azar, Ahmad Taher (Journal of International Students, 2026)
Effective online learning sessions require designing sessions that address learners’ engagement and achievement across diverse groups. The duration and frequency of the session affect user satisfaction and quiz results, but it is difficult to optimize both simultaneously. This paper presents a combined optimization model that uses stepwise regression, NSGA-II, and gray relational analysis (GRA) to optimize the design of a session, leveraging a publicly available e-learning dataset (more than 2,500 anonymized records). Directional relationships between session parameters and outcomes were quantified using regression models, and Pareto-optimal solutions were identified using NSGA-II, which were further assessed under three teaching-priority scenarios using GRA. The results show that a 60-minute weekly session is the optimal balance between the more satisfaction-focused designs, and that allotting 113 minutes across eight sessions is optimal for quiz performance. The explanations of the regression models (R2 = 0.20 for satisfaction and R2 = 0.11 for quiz scores) are modest, suggesting that the results should be viewed as guidance for decision-making rather than prescriptions. Despite these shortcomings, the framework emphasizes trade-offs between the timing and frequency of online learning and offers a data-driven, systematic approach to optimizing online learning. This research contributes to evidence-based instructional design and provides practitioners with actionable insights to enhance international online learning.