Research on students' classroom performance evaluation algorithm based on machine learning
by Enwei Cao
International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), Vol. 32, No. 2, 2022

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.

Online publication date: Thu, 07-Apr-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email