基于评教文本挖掘的高校教学质量评价指标构建与应用OA北大核心CHSSCDCSSCI

Student evaluation of teaching is not only an important activity to ensure teaching quality,but also the main means for universities to evaluate teaching quality.In order to improve the effectiveness of teaching evaluation and provide more reference teaching results for instructors and educational administration departments,the research approach based on big data teaching text mining should be put on the research a-genda,which is different from quantitative research mainly based on structured student rating data and quali-tative research based on unstructured teaching text data.Based on the subjective and objective evaluation da-ta of students,the Analytic Hierarchy Process(AHP)was used to construct a university teaching quality e-valuation index system based on evaluation text mining.The scientific validity of the index system was em-pirically tested and applied through linear regression analysis.Research has found that a massive amount of student evaluation data has both academic research value and practical guidance significance.By mining and analyzing student evaluation texts,the key information in evaluation data can be effectively obtained,such as the instructors'teaching attitude,content,ability,methods,and effectiveness.A university teaching quality evaluation index system with 5 dimensions and 23 levels constructed based on the AHP can comprehensively and accurately use student evaluation data to evaluate and elaborate the quality of undergraduate teaching.The results of multiple linear regression analysis show that the indicator system can not only understand the dimensions that students focus on,predict student evaluation scores,and reveal the relationship between in-structors teaching quality and various evaluation dimensions,but can also be applied to the evaluation of teaching quality for various types of instructors,providing refined teaching diagnosis for instructors and data consulting and service support for scientific teaching management decisions in schools.The practical applica-tion evaluation results of the evaluation index system show that the problems existing in different categories of courses are significantly different.Therefore,on the one hand,it is necessary to improve the efficiency of utilizing student evaluation data through various emerging technologies such as big data and artificial intelli-gence,deeply explore the data value and academic research value,behind the massive amount of student e-valuation data,and strengthen the collaborative application of evaluation results from different perspectives such as peer evaluation,supervisory evaluation,and AI evaluation,and carry out student-centered and out-put oriented teaching evaluation applications;on the other hand,targeted teaching improvements are needed to address the teaching weaknesses of instructors in different categories of courses and age groups,in order to better serve the continuous improvement of teaching quality in universities and the comprehensive develop-ment of students.