Prediction of students’ early dropout based on their interaction logs in online learning environment

ABSTRACT

Online learning has become more popular in higher education since it adds convenience and flexibility to students’ schedule. But, it has faced difficulties in the retention of the continuity of students and ensure continual growth in course. Dropout is a concerning factor in online course continuity. Therefore, it has sparked great interest among educators and researchers to offer many models and strategies to reduce online course students’ dropout by analyzing students’ behavior and their individual and academic information. However, online education platforms still face challenges of high dropout rate and the difficulty of accurate prediction to reduce it. The key aim of the present study is to construct a predictive model to early predict students who are at-risk of dropout. This model is useful to the course instructors to make effective and timely interventions. We have noticed that dropout prediction is basically a sequence labeling or time series prediction problem. For these reasons, we proposed two models; the Logistic Regression by adding a regularization term and the Input-Output Hidden Markov Model (IOHMM). Results showed that the proposed models achieved an accuracy of 84% compared to the baseline of Machine Learning Models for prediction of the students at-risk of dropping out.

KEYWORDS:

• Prediction
• online learning environment
• extract feature
• machine learning
• logistic regression
• input-output hidden Markov model

Acknowledgements

Many thanks to the anonymous reviewers for their valuable comments.

Disclosure statement

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

Notes of contributors

Han Cao She received a BS degree in Computer Science and MS degree in Computer Science, Computer Science Department, Northwest University, Xi’an, China in 1986 and 1989, respectively, and the PhD degree in Geographic Information Science from National Key of Engineering on Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China in 2003. She is a professor at Shaanxi Normal University, and has taken charge of several Natural Science Foundation (NSF) projects of China. Now, she is responsible for emerging engineering education research and practice research projects of the Ministry of Education of China, Science and Technology Plan of Shaanxi Province, China. Her main research interests include knowledge graph, ontology modeling, parallel computing and big data analysis, learning analysis and tourism intelligence.

Ahmed Mubarak He received his BSc in computer science from Ibb University, Yemen. He received MS degree in Computer Science and information from Menoufia University, Menoufia, Egypt, in 2016. Currently, he is a Ph.D. candidate in the Department of Computer Science at Shaanxi Normal University, Xi’an, China. He works as a teaching assistant in Education and Computer Sciences faculty, Ibb University, Ibb, Yemen. His main research interest is Education Big data analysis, data mining.

Weizhen Zhang He received a BE degree in the School of Information Science and Technology from Tibet University in 2015. And he worked as a programmer in Nantian Electronics Information Co., Ltd, Beijing, China for 2 years. Now, he is a postgraduate student in the School of Computer Science at Shaanxi Normal University at Xi’an, China. His main research interests include Knowledge Graph (KG) and Natural Language Processing (NLP).