Title: Research on students' classroom performance evaluation algorithm based on machine learning

Authors: Enwei Cao

Addresses: School of Management and Economics, Jingdezhen Ceramic Institute, Jingdezhen, Jiangxi, 333000, China

Abstract: In order to overcome the poor accuracy of traditional classroom performance evaluation algorithm, a machine learning-based classroom performance evaluation algorithm was designed. This paper makes an empirical analysis of the statistical data and constructs a statistical information analysis model for students' classroom performance evaluation. According to the mining results of students' classroom performance evaluation information, the adaptive mining and feature clustering of students' classroom performance evaluation data are carried out. This paper uses quantitative game method to evaluate students' classroom performance, constructs the explanatory variable and control variable model of students' classroom performance evaluation, and then uses machine learning method to optimise the evaluation of students' classroom performance. The simulation results show that the evaluation accuracy of the proposed method is always above 0.77, which has high reliability and adaptability, and improves the quantitative evaluation ability of students' classroom performance.

Keywords: machine learning; students' classroom performance; evaluation; test statistics; intelligent teaching.

DOI: 10.1504/IJCEELL.2022.10042099

International Journal of Continuing Engineering Education and Life-Long Learning, 2022 Vol.32 No.2, pp.227 - 239

Received: 29 Nov 2019
Accepted: 07 Jan 2020

Published online: 07 Apr 2022 *

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