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%.
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Acknowledgements
This work is supported by National Key Research and Development Program of China (2018YFB1004500), National Nature Science Foundation of China (61877048, 61472315), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Center for Engineering Science and Technology, Project of Chinese academy of engineering “The Online and Offline Mixed Educational Service System for ‘The Belt and Road’ Training in MOOC China.” Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-458).
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Chen, Y., Zheng, Q., Ji, S. et al. Identifying at-risk students based on the phased prediction model. Knowl Inf Syst 62, 987–1003 (2020). https://doi.org/10.1007/s10115-019-01374-x
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DOI: https://doi.org/10.1007/s10115-019-01374-x