Classification of Online Course Teaching Cases Based on an Improved Clustering Method

Abstract

The classification and sharing of online course teaching cases based on students’ individual learning needs can help teachers save time and cost in preparation of lessons and enrich their teaching content, and what is more, it is in line with the digitalization trend of the modern data-driven education. Currently, there has been few research on the combination of new information technology with teaching case classification, and the research is not adequate on the existing algorithms for classification of online course teaching cases. Therefore, this paper takes the teaching cases of online open courses of finance as the object, and studies the classification method for online course teaching cases based on an improved clustering method. First, the text clustering problem of finance teaching cases was described, and the conversion process of the abstracts of the case text and the evaluation method for the importance of words to teaching cases were given. Then, the key sentences were extracted from the text of the finance teaching cases, and the related algorithm was introduced. After that, the clustering flow chart for long texts of finance teaching cases was shown, and the principle of the combined clustering algorithm for finance teaching cases with uneven distribution of long texts and short texts was described in detail. The experimental results verified the effectiveness of the proposed algorithm.