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- Title
Identifying at-risk students based on the phased prediction model.
- Authors
Chen, Yan; Zheng, Qinghua; Ji, Shuguang; Tian, Feng; Zhu, Haiping; Liu, Min
- Abstract
Identifying at-risk students is one of the most important issues in online education. During different stages of a semester, students display various online learning behaviors. Therefore, we propose a phased prediction model to predict at-risk students at different stages of a semester. We analyze students' individual characteristics and online learning behaviors, extract features that are closely related to their learning performance, and propose combined feature sets based on a time window constraint strategy and a learning time threshold constraint strategy. The results of our experiments show that the precision of the proposed model in different phases is from 90.4 to 93.6%.
- Subjects
AT-risk students; PREDICTION models; ONLINE education; LEARNING strategies; DATA mining
- Publication
Knowledge & Information Systems, 2020, Vol 62, Issue 3, p987
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-019-01374-x