Knowledge Graph-based Interactive Sharing Platform for Teaching English Majors in Colleges and Universities

Abstract

Abstract In this paper, we found that the traditional FCM clustering algorithm is over-sensitive to noise or outliers, and AP clustering is added on the basis of the FCM algorithm to improve and optimize the FCM algorithm. The improved and optimized algorithm is used to derive high-frequency words for teaching English majors, and the hot knowledge map of English teaching is studied according to the high-frequency words. Construct the knowledge map framework and utilize the knowledge map framework to build the U+ English teaching interactive sharing platform. Adopt a top-down approach to construct the knowledge graph, and after defining entity relationships and attributes, propose the learning path recommendation method of the U+ platform based on the knowledge graph. Comparative experiments are used to analyze the teaching effect of the U+ platform and students’ English learning engagement. After 8 weeks of interactive and shared learning on the U+ platform, the students in the experimental class improved their English single choice, perfect fill-in-the-blank, and reading comprehension scores by 2, 4, and 3 points, respectively, and the U+ platform had obvious help in improving the students’ English scores, and the platform was effective in teaching. The mean value of the positive dimension of learning engagement of the students in the experimental class is 4.1634, with an interval range of [4,4.3], and they are positively engaged in English learning, and the mean value of the negative dimension is 1.8341, with a range of [1.6,2.4], and there are fewer negative performances, and the U+ platform has a positive impact on the student’s English learning engagement.