Predicting the likelihood of dropping out from MOOC-based program
This research aims to identify the likelihood of dropping out from a MOOC-based program. Program dropout happens both, within and between courses. We identify the key dropout factors and develop a machine learning model to predict future student dropout.
Contribution:
Results from our prediction models reveal that learners are more likely to leave a MOOC-based program after the first few courses (1-3). Grades, level of education and gender are highly correlated with learner dropout. We suggested some potential initial interventions which could be highly valuable as a starting point.
Research Publications
- Borrella, I., Caballero, S., Ponce, E. Taking action to reduce dropout in MOOCs: tested interventions. Computers & Education. Accepted, Dec 2021.
- Predicting the likelihood of dropping out from the SCM MicroMasters program. Master Thesis. System Design and Management Program. June 2019.
Research Team
![Dr. Chris Caplice](/wp-content/uploads/2020/04/Caplice_Chris_Square_Web-copy.jpg)
Dr. Chris Caplice
Senior Research Scientist
![Dr. Eva Ponce](/wp-content/uploads/2020/04/Ponce_Eva_MIT_CTL_Square-copy.jpeg)
Dr. Eva Ponce
Director
![Andrea Moreno](/wp-content/uploads/2020/04/Andrea.jpeg)
Andrea Moreno
Research Assistant