Optimization of Education and Teaching Management Based on Differential Interest Apriori Association Rule Mining Algorithm

In order to study the personalized management of course evaluation and undergraduate course learning based on educational data analysis, an artificial intelligence based optimization method for educational and teaching management has been proposed. Through visiting and researching the functional departments of teaching management, a high-quality course evaluation index system covering dimensions such as teaching team, teaching content, and teaching resources was obtained. Observation points such as teacher style, teaching level, teaching and research activities, and achievements were set up; The data structure for multidimensional evaluations, including students, teachers, peer experts, and teaching supervisors, has been determined; After cleaning the survey questionnaire data, construct a course evaluation dataset; Considering misleading rule suppression, an Apriori association rule mining algorithm based on differential interest is proposed. The results show that using the author's algorithm can extract fewer rules than using traditional Apriori algorithm and probabilistic Apriori algorithm, effectively suppressing the generation of meaningless and misleading association rules, improving the efficiency of data mining and the accuracy of implicit knowledge in the data. The effectiveness of the proposed method in improving the accuracy of knowledge discovery was verified.