Application of improved eclat algorithm in students' evaluation of teaching

Abstract

The evaluation system of students is to find a way to solve the status way according to the exact needs of students and the teaching requirements of teachers, so as to improve the teaching level of teachers and improve the quality of school education. This paper uses the real evaluation sample and uses the data mining association rule algorithm to comprehensively analyze the massive data of the evaluation data and the basic information of the teacher. The purpose is to obtain the association rules between the teacher's comprehensive information and its evaluation results. Using the evaluation data to explore its core issues. In this paper, the Eclat algorithm of association rules improves the problem of insufficient memory and occupying a large amount of time when searching for frequent itemsets in the data. The breadth-first algorithm is added to save operation time and improve the efficiency of the algorithm. The effectiveness of the improved algorithm is verified by comparative experiments and applied to the evaluation system so as to provide suggestions for the professional development of teachers from an objective perspective, and to build a harmonious, »people-oriented» evaluation system for students.