{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T04:42:23Z","timestamp":1788324143995,"version":"build-2803163510"},"reference-count":20,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,7,8]]},"abstract":"<jats:p>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 \u201ccatch-up\u201d, 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.<\/jats:p>","DOI":"10.3233\/jcm226047","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T13:25:49Z","timestamp":1648560349000},"page":"1277-1285","source":"Crossref","is-referenced-by-count":6,"title":["The application of improving machine learning algorithm and voice technology in the teaching evaluation of ideological and political education"],"prefix":"10.66113","volume":"22","author":[{"given":"Qimeng","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"55691","reference":[{"issue":"2-3","key":"10.3233\/JCM226047_ref1","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1017\/S103001120002460X","article-title":"Curriculum-based evaluation: Teaching and decision making (3rd), Howell Ken","volume":"23","author":"Evans","year":"2016","journal-title":"Australas J Spec Educ."},{"issue":"4","key":"10.3233\/JCM226047_ref2","first-page":"1","article-title":"Does more accurate knowledge of course grade impact teaching evaluation","volume":"12","author":"Cho","year":"2016","journal-title":"Educ Finance Policy."},{"issue":"4","key":"10.3233\/JCM226047_ref3","first-page":"1317","article-title":"Teaching evaluation practices in engineering programs: Current approaches and usefulness","volume":"33","author":"Villanueva","year":"2017","journal-title":"Int J Eng Educ."},{"issue":"6","key":"10.3233\/JCM226047_ref4","first-page":"101","article-title":"Evaluation and analysis of primary school music classroom teaching based on students\u2019 core literacy","author":"Huang","year":"2020","journal-title":"Exam Eval."},{"key":"10.3233\/JCM226047_ref5","doi-asserted-by":"crossref","unstructured":"Beresneva E, Gordenko M. 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