Clustering Analysis of Online Teaching Cases and Evaluation of Teaching Results

被引：0

作者：

Bi S. ^{[1
]}

Liu W. ^{[1
]}

机构：

[1] Department of Foreign Languages, Tangshan Normal University, Tangshan

来源：

International Journal of Emerging Technologies in Learning | 2023年 / 18卷 / 03期

关键词：

case teaching method; clustering analysis; evaluation of teaching results; online teaching;

D O I：

10.3991/ijet.v18i03.38055

中图分类号：

学科分类号：

摘要：

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 textbased 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. © 2023, International Journal of Emerging Technologies in Learning. All Rights Reserved.

引用

页码：128 / 142

页数：14

共 20 条

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