Research on Teaching Evaluation System Based on Machine Learning

• Published in: Mobile information systems Vol. 2022; pp. 1 - 10
• Main Authors: Yang, Aijun, Yu, Shuyan
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 12.02.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Big Data; Data mining; Decision trees; Education reform; Feature selection; Higher education; Indicators; Machine learning; Quality of education; Regression analysis; Research projects; Schools; Students; Teachers; Teaching; Teaching machines
• ISSN: 1574-017X; 1875-905X
• DOI: 10.1155/2022/9255064

Teaching evaluation, as a written measure of teachers’ teaching work achievements, can motivate teachers to teach rigorously and work hard. However, the existing teaching evaluation system in China lacks sound standards and approaches. On the one hand, scientific research projects, funding, papers, etc. have become the criteria for measuring a teacher’s performance, and this situation has contributed to the culture of quick success; on the other hand, the current teaching evaluation system is based on indicators of teaching quality achievements or subjective judgments of experts and subject groups, and these evaluation methods can only respond to some teaching quality from a one-sided perspective and cannot make a comprehensive, systematic, and scientific. The analysis and judgment of teachers’ work cannot be made comprehensively, systematically, and scientifically. A good teaching evaluation system can not only help teachers distinguish the shortcomings of the current course teaching but also motivate them to make further efforts and devote themselves to the teaching tasks. In this paper, we hope to find a reasonable way to evaluate teachers’ teaching work based on the proportion of different indicators and different influencing factors from machine learning, in view of the current unscientific evaluation methods that differentiate teachers’ performance of various work indicators. Through experiments, it can be found that using gradient descent methods, we can obtain such a scientific model that can make a positive contribution to teaching evaluation.