Construction of the Mathematical Model for Teaching Classroom Evaluation in Colleges and Universities Using an Optimized Apriori Algorithm

Abstract

In order to increase the effectiveness and teaching quality of numerous classroom activities in colleges and universities, this paper puts forward the effectiveness assessment technique of teaching activities in colleges, universities, and institutions of higher education based on the optimized Apriori algorithm. A mathematical model for assessing the efficacy and usefulness of classroom activities in colleges and universities is constructed. Teaching contents, teaching attitudes, teaching methods, teaching effects, test results, and students’ performance are introduced as comprehensive evaluation factors, and a decision-making model for assessing the success and efficiency of classroom activities in colleges and universities is established by adopting scientific, reasonable, and systematic teaching methods. Through the grey correlation analysis of classroom teaching quality, the delay characteristic analysis and the adaptive parameter adjustment method are adopted. This paper constructs the optimal Apriori algorithm model of college teaching classroom activities, constructs the optimal evaluation function model of college teaching classroom activities effectiveness evaluation by the method of support-confidence joint estimation, extracts the optimal quality parameter set of college teaching classroom activities by the optimization detection method and association rule mining, and realizes the effectiveness evaluation and multi-dimensional parameter estimation of college teaching classroom activities by the optimized Apriori algorithm. The test outcomes confirm that this technique is reliable in evaluating the effectiveness of classroom activities in colleges and universities, and the directional distribution of association rules of classroom quality in colleges and universities is significant, which meaningfully advances the classroom teaching levels and quantitative evaluation abilities.