Rule Analysis of Teaching Evaluation System Based on Data Mining under Web Platform

Abstract

In order to understand the advantages of the teaching evaluation system to teachers and students more intuitively, according to the data analysis, the lowest total score of teachers is ˗1.04, and the five index values are all negative, so the relevant teaching contents should be changed according to the index factors of relevant teaching evaluation to make it easier for students to understand and accept. Through the analysis of the total evaluation scores of several teachers surveyed in each semester, we can see that the total evaluation scores of teachers 4 and 7 fluctuate greatly, so we should choose suitable teaching content for rectification. By classifying teachers, they are divided into three categories by means of discipline colleges, etc. It can be seen from the figure that the lowest total score of teachers in category 3 is ˗2.12, which shows that teachers in this category may have similar problems in teaching methods. By comparing the information entropy algorithm of teaching evaluation, we know that the information entropy under the AVF algorithm model takes about 1 ms to the teaching evaluation algorithm, while the greedy algorithm model takes about 20 ms to test the teaching evaluation system. We know that the teaching evaluation system based on the proposed algorithm information entropy algorithm and the teaching evaluation system under the AVF algorithm model and traditional information entropy all pass the test. However, combined with the images, the AVF algorithm model has the highest computational efficiency and the shortest time, followed by the proposed algorithm model. Compared with the traditional teaching evaluation algorithm, it is inefficient and takes a long time. When the number of requests is 1000, the average response time of the AVF algorithm method is 17 ms, and the longest time of the traditional method is 32 ms. Combined with images, when the number of requests is 1000, 1500, 200, 2500, 3000, 350, and 4000, the average response time increases with the increase of the number of requests, but the AVF algorithm method takes the shortest time and meets most teaching evaluation requirements. Combined with the success rate image, we can see that the success rate decreases slightly with the increase of request times, but the success rate of the AVF algorithm method is relatively stable, with the lowest of 99.6% when the request times are 3500. Through a large number of charts and analyses, we can see that the teaching evaluation under the AVF algorithm method has the highest efficiency and the shortest time.