Dropout Predictions of Ideological and Political MOOC Learners Based on Big Data

Authors

Yan Zhang, School of Marxism, China University of Geosciences, Wuhan,430074, China
Qian Zhang, Institute of Higher Education, China University of Geosciences, Wuhan,430074, China
Xu Liu, School of Marxism, China University of Geosciences, Wuhan,430074, China;Institute of Higher Education, China University of Geosciences, Wuhan,430074, ChinaFollow

Abstract

The massive open online course (MOOC) has expanded rapidly, providing users with a low-cost, high-quality learning experience. High dropout rate is a serious obstacle that restricts the development of ideological and political MOOC. One of the ways to solve this obstacle is to use the rich data resources in MOOC to explore the relevant factors of dropout. Reduce dropout rates by building drop-out prediction models and establishing early-warning mechanisms. However, the ideological MOOC data is huge and complex, which is prone to problems such as loss of data value, mismatch between data and models, and poor research reproducibility. This paper uses a more mature logistic regression method of machine learning to transfer it to the field of education, providing a new path for data-driven MOOC dropout prediction research.

Recommended Citation

Zhang, Yan; Zhang, Qian; and Liu, Xu, "Dropout Predictions of Ideological and Political MOOC Learners Based on Big Data" (2020). WHICEB 2020 Proceedings. 30.
https://aisel.aisnet.org/whiceb2020/30