Abstract
Nowadays, how to use education industries’ data is a hot topic. Taking the scores of 70 courses of 216 students in the College of Mathematics and Data Science as the sample, the Principal Component Analysis-Support Vector Machine (PCA-SVM) model for early warning system is established by selecting the first-year course scores and graduate grades. The PCA-SVM is better than Support Vector Machine (SVM) in accuracy, probability of false alarm and F1-score. The PCA-SVM model for early warning system can provide students with more accurate academic guidance, and can provide scientific basis for teaching reform and management decision making.
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Acknowledgements
The first author was supported by the Science and Technology Project of Education Bureau of Fujian, China, under Grant JAT170447, and the second author was supported by the Science and Technology Project of Education Bureau of Fujian, China, under Grant JT180391.
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Chen, X., Jin, X., Lin, G. (2020). Research on College Students’ Academic Early Warning System Based on PCA-SVM. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_40
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DOI: https://doi.org/10.1007/978-3-030-32591-6_40
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