Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC Using Deep Learning: A Systematic Literature Review

The increasing reliance on Massive Open Online Courses (MOOCs) has transformed the landscape of education, particularly during the COVID-19 pandemic, where e-learning became essential. However, the effectiveness of MOOCs in enhancing student academic performance and engagement remains a key challenge, compounded by high dropout rates and low retention. This study presents a systematic literature review (SLR) conducted over a five-year period (2019–2024) to identify factors affecting student academic performance and engagement prediction in MOOCs, utilizing Deep Learning (DL) methods. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematically analyzing articles from five major academic databases: ScienceDirect, SpringerLink, Scopus, Taylor & Francis, and Wiley Online. A total of 70 articles were selected for in-depth analysis, focusing on key predictors of student performance and engagement, including demographic data, behavioral patterns, learning activities, and clickstream data. The review highlights the capabilities of DL techniques in predicting student outcomes, such as retention, dropout, and engagement, offering valuable insights for educators and policymakers aiming to improve MOOC-based learning environments. By conducting SLR using PRISMA model, we identified research findings and gaps by proposing a conceptual framework for developing future personalized and adaptive e-learning environment for the inclusive MOOC based hard of hearing and low vision learners. This paper concludes by discussing implications for future personalized and adaptive e-learning environments and the necessity of comprehensive teacher training programs to navigate these evolving educational technologies.