An analysis approach for blended learning based on weighted multiplex networks

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成果类型：

期刊论文

作者：

Su, Zhu;Li, Yue;Liu, Zhi;Sun, Jianwen;Yang, Zongkai;...

通讯作者：

Liu, S

作者机构：

[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Yang, Zongkai; Liu, Sannyuya; Liu, S] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Yang, Zongkai; Liu, Sannyuya; Liu, S; Liu, Zhi; Su, Zhu; Li, Yue; Sun, Jianwen] Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

通讯机构：

[Liu, S ] C

Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

语种：

英文

关键词：

Blended learning;Weighted multiplex networks;Interaction;Sentiment;Cognition

期刊：

ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT

ISSN：

1042-1629

年：

2023

卷：

期：

页码：

1941-1963

DOI：

10.1007/s11423-023-10266-5

基金类别：

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62007012, 62293552, 61937001, 61977030, 62077017), and the Fundamental Research Funds for the Central Universities (No. CCNU22LJ005).

机构署名：

本校为第一且通讯机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Blended learning, as an efficient teaching mode that combines the advantages of both online and offline learning, has been widely applied in universities. Nevertheless, the different learning patterns induce difficulty in evaluating the learning quality. In this paper, an approach of integrating online and offline interactions is proposed by constructing a weighted multiplex network (WMN), in which online communication behavior and offline peer relations are represented as edges in respective network layers, and edge weight depends on the frequ...

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An analysis approach for blended learning based on weighted multiplex networks

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