Abstract
The learning early-warning is an effective way to optimize the teaching effect and teach students in accordance of their aptitude. At present, the learning early-warning faces low accuracy, high value of MSE and MAE. We propose a novel learning early-warning model: LEWM-RFA. The model divides students’ learning behaviors data into three dimensions: knowledge, behavior and attitude. Then the model uses random forest algorithm to extract features that can affect students’ grades, and then predicts students’ final exam scores. Students are divided into three warning levels according to their grades. Compared with the model based on the linear regression algorithm, the LEWM-RFA’s MSE decreases by 27.498% and the LEWM-RFA’s MAE decreases by 26.960%.
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Acknowledgements
This paper was supported by National Key R&D Program of China (Grant No. 2017YFB1402400), National Natural Science Foundation of China (Grant No. 61402020), and CERNET Innovation Project (Grant No. NGII20170501).
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Cheng, X. et al. (2018). A Novel Learning Early-Warning Model Based on Random Forest Algorithm. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_32
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DOI: https://doi.org/10.1007/978-3-319-91464-0_32
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