Understanding Dropouts in MOOCs


  • Wenzheng Feng Tsinghua University
  • Jie Tang Tsinghua University
  • Tracy Xiao Liu Tsinghua University




Massive open online courses (MOOCs) have developed rapidly in recent years, and have attracted millions of online users. However, a central challenge is the extremely high dropout rate — recent reports show that the completion rate in MOOCs is below 5% (Onah, Sinclair, and Boyatt 2014; Kizilcec, Piech, and Schneider 2013; Seaton et al. 2014).

What are the major factors that cause the users to drop out?

What are the major motivations for the users to study in MOOCs? In this paper, employing a dataset from XuetangX1, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs. We found that the users’ learning behavior can be clustered into several distinct categories. Our statistics also reveal high correlation between dropouts of different courses and strong influence between friends’ dropout behaviors. Based on the gained insights, we propose a Context-aware Feature Interaction Network (CFIN) to model and to predict users’ dropout behavior. CFIN utilizes context-smoothing technique to smooth feature values with different context, and use attention mechanism to combine user and course information into the modeling framework. Experiments on two large datasets show that the proposed method achieves better performance than several state-of-the-art methods. The proposed method model has been deployed on a real system to help improve user retention.




How to Cite

Feng, W., Tang, J., & Liu, T. X. (2019). Understanding Dropouts in MOOCs. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 517-524. https://doi.org/10.1609/aaai.v33i01.3301517



AAAI Special Technical Track: AI for Social Impact