Yizhuo Zhou , Jin Zhao, Jianjun Zhang*

Interactive Learning Environments, forthcoming

Recommend reason

E-learning (electronic learning) breaks the time and space restrictions existing in traditional teaching, improves the learning effect by watching videos repeatedly, quickly and selectively, and the digitalized learning resources can improve learning efficiency. However, 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. Therefore, e-learning platform must handle the problem of improving the effect and reducing the dropout rate in quickly popularization for sustainable development. This study provides valuable reference for e-learners’ dropout prediction and prevention, and helps the commercial e-learning platforms to make appropriate interventions and incentives

About the author

Yizhuo Zhou, School of Economic and Management, Tongji University.

Jin Zhao, Institute of Vocational Education, Tongji University.

Jianjun Zhang*, School of Economic and Management, Tongji University.

Keywords

E-learning; Unusual behaviors; Survival analysis; Cox model; Dropout prediction

Brief introduction

This paper established a predictive model to describe e-learners’ dropout behavior. 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.

Link

https://www.tandfonline.com/doi/full/10.1080/10494820.2020.1857788