Clustering Analysis of Online Teaching Cases and Evaluation of Teaching Results

Abstract

To facilitate front-line teachers to provide more efficient cases for classroom teaching activities in limited teaching time, it is necessary to conduct clustering analysis of online teaching cases. The existing clustering study on text-based teaching resources focuses on the representation and fusion of different views of texts and the discovery of consistent clustering allocation, and ignores the high-dimensional sparsity of text data. To this end, this paper studies clustering analysis of online teaching cases and evaluation of teaching results. This paper gives the design flow of online teaching with case teaching as the core and proposes a text multi-view clustering algorithm based on case subject alignment, and presents a clustering model structure based on subject alignment. Based on the grey system theory, this paper compares online classroom teaching results before and after teachers use clustering algorithm to process teaching cases. Finally, the experimental setup is given, and the effectiveness of the proposed algorithm is verified by experiment.