Prediction of learners’ dropout in E-learning based on the unusual behaviors

ABSTRACT

On e-learning platforms, most e-learners didn’t complete the course successfully. It means that reducing dropout is a critical problem for the sustainability of e-learning. This paper aims to establish a predictive model to describe e-learners’ dropout behavior, which can help the commercial e-learning platforms to make appropriate interventions and incentives. First of all, we defined the features of unusual learning behaviors in commercial e-learning platform, and used the Cox proportional hazard model of survival analysis to select variables that can reasonably predict dropout possibilities. Results show that there are six variables which have significant influence on dropout behavior: dropout history, number of watched videos, number of progress bar operation, number of test questions operation, number of weeks that the login frequency is higher than average, and payment status. We also proposed cumulative gain, predicted retention number and predicted dropout learner number in next period, to evaluate the application ability of the predictive model. Finally, we performed an empirical analysis and verified the predictive effectiveness. The further application of the predictive model also shows that it can help the managers of e-learning platforms to adjust their strategy to improve the retention rate of potential lost learners.

KEYWORDS:

• E-learning
• unusual behaviors
• survival analysis
• Cox model
• dropout prediction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research supported in part by National Social Science Fund of China [grant number 20BSH058] and Humanities and Social Sciences Project of the Ministry of Education of China [grant number 18YJC880137].

Notes on contributors

Yizhuo Zhou

Yizhuo Zhou received her BS degree in E-Commerce from Dalian University of Technology, China, and Master degree in Management Science and Engineering from Tongji University, China. Currently, she is a Ph.D. candidate in School of Economics & Management at Tongji University, Shanghai, China. Her main research interests include Learning Behavior Analysis, Survival Analysis, and Operational Research.

Jin Zhao

Jin Zhao received a BS degree in Business English from Nanchang University of Aeronautics, China, in 2000, the Master degree in Applied Linguistics from East China University of Science and Technology, China, in 2006, and the PhD degree in Business Administration from Tongji University, Shanghai, China, in 2011. She also has a postdoctorate experience of transportation engineering. She is now an associate professor at Tongji University, China. Her research interests are in the areas of Learning Analytics, Online Education. Her research projects are supported by National Office for Philosophy and Social Sciences of China, the Ministry of Education of China, Shanghai Science and Technology Commission.

Jianjun Zhang

Jianjun Zhang received a BS degree in Computational Mathematics from Jilin University, China, in 1999, and the Master and PhD degree in Management Science and Engineering, Tongji University, Shanghai, China in 2005 and 2008, respectively. He is a professor and doctoral supervisor at Tongji University and Nanchang University of Aeronautics. His research projects are supported by National Natural Science Foundation of China, the Ministry of Education of China, and Shanghai Science and Technology Commission. His research interests include Learning Management, Knowledge Graph, Optimization Modeling and Big Data Analysis.