The application of improving machine learning algorithm and voice technology in the teaching evaluation of ideological and political education

Article type: Research Article

Authors: Sun, Qimeng

Affiliations: Science and Technology College, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi 330004, China | E-mail: sunqimeng_vip@163.com

Correspondence: [*] Corresponding author: Science and Technology College, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi 330004, China. E-mail: sunqimeng_vip@163.com.

Abstract: After entering the new millennium, the computing capacity of information terminal has shown a rapid development. This progress has caused cross generational changes in various fields, especially in the field of communication technology, which directly spawns a new field. Compared with the development speed of information terminal, the development of communication technology is always in the position of “catch-up”, and the main work performer is the traditional data form. This backward leads to the primitive evaluation of Ideological and political education. This paper mainly studies the application of improved machine learning algorithm and voice technology in the teaching evaluation of Ideological and political education. The weighted naive Bayesian algorithm is applied to the teaching evaluation of Ideological and political education creatively. By inference of hypothesis model, the intervention curve of various conditions on the evaluation results is verified. The influence of class attribute probability on condition assignment is obtained, and it is used as a calculation tool for our evaluation of Ideological and political education teaching. The experimental results show that the improved weighted naive Bayesian algorithm can better integrate the speech technology and improve the evaluation accuracy.

Keywords: Teaching evaluation, machine learning algorithm, data mining, weighted naive Bayesian

DOI: 10.3233/JCM226047

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 22, no. 4, pp. 1277-1285, 2022