Affiliations: [a]
Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
| [b]
Jinling Institute of Technology, Nanjing, Jiangsu, China
Correspondence:
[*]
Corresponding author. Liu Lin, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China. E-mail: llliulin666@163.com.
Abstract: There is a certain subjectivity in the teaching evaluation process, which leads to a low accuracy of the intelligent scoring system. In order to promote the intelligent development of teaching evaluation, based on machine learning, this study briefly introduces the background and current status of teaching evaluation, and describes in detail the relevant algorithm principles of data analysis and modeling using data mining technology and machine learning methods. Moreover, this study describes the establishment process of the traditional classroom teaching evaluation system and uses the classification algorithm in machine learning in the construction of evaluation models to further improve the scientificity and feasibility of teaching evaluation. In addition, in this study, empirical algorithm is used as the basic algorithm to evaluate teaching quality, and the topic word distribution obtained by joint model training is used as the original knowledge. Finally, this research analyzes the performance of this research system through a control experiment. The research results show that the scores of the research model are close to the standard manual scores and can provide a theoretical reference for subsequent related research.
Keywords: Bayesian algorithm, improved algorithm, teaching evaluation, text features
