Teaching Evaluation System by use of Machine Learning and Artificial Intelligence Methods


  • Jingjing Hu Zhejiang Business College




AI, machine learning, regression analysis, naive Bayes


To explore the adoption of artificial intelligence (AI) technology in the field of teacher teaching evaluation, the machine learning algorithm is proposed to construct a teaching evaluation model, which is suitable for the current educational model, and can help colleges and universities to improve the existing problems in teaching. Firstly, the existing problems in the current teaching evaluation system are put forward and a novel teaching evaluation model is designed. Then, the relevant theories and techniques required to build the model are introduced. Finally, the experiment methods and process are carried out to find out the appropriate machine learning algorithm and optimize the obtained weighted naive Bayes (WNB) algorithm, which is compared with traditional naive Bayes (NB) algorithm and back propagation (BP) algorithm. The results reveal that compared with NB algorithm, the average classification accuracy of WNB algorithm is 0.817, while that of NB algorithm is 0.751. Compared with BP algorithm, WNB algorithm has a classification accuracy of 0.800, while that of BP algorithm is 0.680. Therefore, it is proved that WNB algorithm has favorable effect in teaching evaluation model.




How to Cite

Hu, J. (2021). Teaching Evaluation System by use of Machine Learning and Artificial Intelligence Methods. International Journal of Emerging Technologies in Learning (iJET), 16(05), pp. 87–101. https://doi.org/10.3991/ijet.v16i05.20299