Enhancing University Teacher Evaluation: A Data-Driven Approach Using Machine Learning

To enhance the fairness and rationality of teacher evaluations, this paper introduces a comprehensive evaluation index system aimed at assessing educators across multiple dimensions, focusing on three key areas: teachers’ personal attributes, teaching and nurturing capabilities, and research and innovation. Building upon this framework, we introduce a teacher talent evaluation index that quantifies overall teacher quality by considering the relative importance of each evaluation criterion and converting these into standardized values for comparison and ranking. To achieve our objective, we employ a machine learning model to analyze and model the indicator data; through training, the model calculates an evaluation index for each educator, facilitating an assessment of their comprehensive quality. Empirical studies demonstrate that this approach achieves high accuracy and efficiency, with our machine learning model demonstrating an 87% accuracy rate in assessing teachers’ comprehensive qualities, while improving evaluation efficiency by a factor of 360. To encourage academic exchange and research collaboration, we published the datasets and code used in this study on the Gitee platform, accessible at https://gitee.com/limax2008/teacherevaluate.