Prediction of Students’ Sports Performance Based on Genetic Neural Network

DOI

10.2991/978-94-6463-172-2_221How to use a DOI?

Keywords

genetic neural network; BP neural network; Performance prediction; Correlation analysis; genetic algorithm

Abstract

Achievement prediction is an important content of educational data mining, and accurate achievement prediction is of great benefit to teaching management. In this paper, we propose a performance prediction method based on genetic neural network. This method first uses correlation analysis to screen the scores of other courses with high correlation with the target courses as the input variables of the neural network model, and then optimizes the model by back propagation (BP), in which the super parameters of the neural network are optimized by genetic algorithm. We apply the model method to the prediction of college students’ sports performance. The results show that compared with BP neural network model, the root mean square error of prediction is reduced from 8.8 to 3.0.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaoyan Hu
PY  - 2023
DA  - 2023/06/30
TI  - Prediction of Students’ Sports Performance Based on Genetic Neural Network
BT  - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023)
PB  - Atlantis Press
SP  - 1990
EP  - 1994
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-172-2_221
DO  - 10.2991/978-94-6463-172-2_221
ID  - Hu2023
ER  -