Teaching Quality Evaluation and Improvement Based on Big Data Analysis

Abstract

To address the limitations of Problem-Based Learning (PBL) and to foster student initiative while enhancing teaching quality, the author suggests a novel approach: leveraging big data analysis for teaching quality evaluation and improvement. This method involves conducting diverse and dynamic evaluations, randomly and repeatedly, involving students, teachers, and supervisors. By applying an enhanced Dempster evidence synthesis formula and weights derived from the Analytic Hierarchy Process, the system dynamically calculates each teacher's rating in their respective courses, allowing for continuous improvement. Additionally, personalized feature indicators and teaching quality evaluation metrics are developed to provide a comprehensive assessment. The results indicate that in the coarse evidence set algorithm, is obtained through experience. If is used as the weight alone, the subjectivity is too heavy, and is added for fusion operation, as well as the intervention of experience factor, a balance point between subjectivity and objectivity is found. The final score of 4.2878 was obtained by combining the weights between subjects obtained through Analytic Hierarchy Process, which is consistent with the survey and the public's opinion. This method avoids the deficiency of traditional evidence theory that treats all evidence equally, enhances the ability of information fusion, and obtains more realistic conclusions. Further validated the feasibility and usability of the personalized teaching quality evaluation and improvement model for software engineering.