Research on Prediction of College Students’ Performance Based on Support Vector Machine
- 10.2991/assehr.k.200727.021How to use a DOI?
- Support vector machine, college entrance examination results, professional course results, prediction, cross validation
Using college entrance examination results to make accurate predictions of university course results can help students determine their efforts and help colleges and universities take effective measures to improve teaching quality. Based on the 2016 undergraduate college entrance examination results and university course scores of a general undergraduate college in China, a support vector machine was used to establish a college course performance prediction model, and cross-validation methods were used to obtain the best parameters and a reliable and stable model. Finally, in 2017 the model was applied to the college of computer science and technology major and communication engineering major performance forecast, the prediction accuracy rate reached 73.6%. The prediction results show that the support vector machine can accurately predict college course performance based on the college entrance examination results.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Peng Wang AU - Yinshan Jia PY - 2020 DA - 2020/07/28 TI - Research on Prediction of College Students’ Performance Based on Support Vector Machine BT - Proceedings of the 2020 International Conference on Advanced Education, Management and Information Technology (AEMIT 2020) PB - Atlantis Press SP - 92 EP - 95 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200727.021 DO - 10.2991/assehr.k.200727.021 ID - Wang2020 ER -