Digital Management of Teaching Cases in Colleges and Universities Based on Cluster Analysis

Abstract

The necessity of digital management of teaching cases is mainly reflected in the aspects of improving teaching quality, facilitating retrieval and storage, achieving cross-platform sharing, conducting real-time updating, saving resources, and carrying out data analysis and evaluation. However, there are still some defects in the existing management models or methods, which lead to difficulties in data storage and retrieval, and affect the utilization efficiency of teaching resources. To this end, this article takes the Chinese language and literature major as an example, and studies the digital management of teaching cases in colleges and universities based on cluster analysis. First of all, it quantifies the quality of digital management of teaching cases, enables resource demanders to accurately select digital resources of teaching cases that meet their teaching or learning needs through the evaluation index of service quality when faced with diversified digital resources of teaching cases in colleges and universities. The clustering algorithm is used to mine potential topics and patterns in teaching cases, which improves the classification efficiency of teaching cases and enables educators to have a deeper understanding of teaching content and educational needs. It uses “absolute index”, “incremental index” and “fluctuation index” to construct the similarity measurement distance function of the basic attributes of teaching cases in colleges and universities and uses the Ward method based on variance analysis to classify the characteristics of teaching cases in colleges and universities. Experimental results verify the effectiveness of the proposed method.