Author:
Li Xianyue,Xu Wei,Xie Shuo
Publisher
Springer Nature Singapore
Reference48 articles.
1. Prenkaj, B., Velardi, P., Stilo, G., Distante, D., Faralli, S.: A survey of machine learning approaches for student dropout prediction in online courses. ACM Comput. Surv. (CSUR) 53(3), 1–34 (2020)
2. Taylor, C., Veeramachaneni, K., O’Reilly, U.-M.: Likely to stop? Predicting stopout in massive open online courses. arXiv preprint arXiv:1408.3382 (2014)
3. Amnueypornsakul, B., Bhat, S., Chinprutthiwong, P.: Predicting attrition along the way: the UIUC model. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs, pp. 55–59 (2014)
4. Al-Radaideh, Q.A., Al-Shawakfa, E.M., Al-Najjar, M.I.: Mining student data using decision trees. In: International Arab Conference on Information Technology (ACIT 2006), Yarmouk University, Jordan (2006)
5. Waheed, H., Hassan, S.U., Aljohani, N.R., Hardman, J., Alelyani, S., Nawaz, R.: Predicting academic performance of students from VLE big data using deep learning models. Comput. Hum. Behav. 104, 106189 (2020)