Prediction of Certification in MOOCs: A Systematic Literature Review

Massive Open Online Courses (MOOCs) have been proliferating, offering free or low-cost content for learners. Nevertheless, the certification rate of both free and paid courses has been low (between 4.5% - 13% and 1% - 3%, respectively). Thus, this study aims to survey MOOCs certification predictive models, synthesise results for a comprehensive and deep understanding of this field and explore how these models contributed to addressing the very low certification level. We adopted the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) for transparently conducting the present review and reporting the results from the works reviewed. Additionally, this SLR highlights several trends and limitations within the present predictive models, including some methodological concerns: the extent to which the present models are generalisable, the excessive filtration of the experimental population, the incompatibility of some experiments with real-time scenarios (nonrealistic modelling), and the shallow reporting of model performances. We have also discussed the replicability of the present models and ongoing efforts towards building a state-of-the-art predictive model. Finally, we highlight future research opportunities in the field of MOOC certification prediction that either deal with the limitations of the present models or address unanswered questions.