Taking action to reduce dropout in MOOCs: Tested interventions

Massive Open Online Courses (MOOCs) have democratized access to higher education, but low entry and exit barriers make dropout rates in these courses much higher than in traditional in-person courses. Previous research has explored the main factors driving student dropout and proposed predictive models to identify students at risk. However, it is still unclear what type of interventions should be implemented to effectively reduce the dropout rate in MOOCs. We use findings from the literature to propose a theoretical framework to guide the design of in-terventions to reduce dropout in MOOCs. We design four interventions and implement them using A/B testing and natural experiment approaches in courses of the MITx MicroMasters (R) Program in Supply Chain Management (a MOOC-based program). Our findings reveal that ad hoc in-terventions communicated via email are not effective in reducing dropout, but interventions that modify course contents to make them more approachable for students show a positive impact on dropout reduction. By proposing a theoretical framework and uncovering the design features that can help to create effective interventions for dropout reduction, we provide a foundation for further research in the area of dropout interventions. This paper offers guidance to MOOC de-signers and instructors on how to improve student engagement and increase completion rates.

Automated summary

Massive Open Online Courses (MOOCs) have higher dropout rates than traditional in-person courses. There is still uncertainty about how to effectively reduce the dropout rate in MOOCs. This study proposes a theoretical framework and implements four interventions to reduce dropout rates in a MOOC. The findings indicate that ad hoc interventions communicated via email are ineffective, while interventions that modify course contents to engage students have a positive impact on dropout reduction.