Analysis and prediction of academic proficiency test scores based on LSTM

Li Hongmei; Haihang Zhang

doi:10.1117/12.2638611

23 May 2022 Analysis and prediction of academic proficiency test scores based on LSTM

Li Hongmei, Haihang Zhang

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225424 (2022) https://doi.org/10.1117/12.2638611
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

This paper analyzes 48923 examinee's achievement data in a high school academic level examination in a city, and comprehensively evaluates the overall distribution of the examination from the aspects of average score and distribution of examination results. Pearson correlation coefficient is used to study the correlation between courses, cluster analysis is carried out for the courses with high correlation coefficient, and the courses with high correlation with the total score are also analyzed. Improving the score of the course can more effectively improve the total score. Finally, the LSTM method is used to predict and analyze students' performance, and compared with the results of SVM and logistic regression to verify the relationship between courses with high correlation, which provides a scientific reference for teaching management, teachers' teaching according to their aptitude and students' learning.

Citation Download Citation

Li Hongmei and Haihang Zhang "Analysis and prediction of academic proficiency test scores based on LSTM", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225424 (23 May 2022); https://doi.org/10.1117/12.2638611

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