An analysis approach for blended learning based on weighted multiplex networks

被引:2
作者
Su, Zhu [1 ,3 ]
Li, Yue [1 ,3 ]
Liu, Zhi [1 ,3 ]
Sun, Jianwen [1 ,3 ]
Yang, Zongkai [1 ,2 ,3 ]
Liu, Sannyuya [1 ,2 ,3 ]
机构
[1] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
[2] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
[3] Cent China Normal Univ, Fac Artificial Intelligence Educ, Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
来源
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT | 2023年 / 71卷 / 05期
基金
中国国家自然科学基金;
关键词
Blended learning; Weighted multiplex networks; Interaction; Sentiment; Cognition; NODE CENTRALITY; SOCIAL NETWORKS; ONLINE; PERFORMANCE; SATISFACTION; COMMUNITIES; ENGAGEMENT;
D O I
10.1007/s11423-023-10266-5
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
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 frequency of interactions. Under the framework of WMNs, learners' attributions such as behavior, sentiment and cognition can be systematically analyzed. We use a case study to compare the differences in various indicators between the online and offline networks, and investigate the relationships between network structure and individual sentiment, cognition and grade, respectively. Results show that the correlations between network centrality and cognition or grade are significantly improved in the WMN, which demonstrate WMNs have natural advantages in the analysis of blended learning. This study provides methodological and practical implications for the analysis and understanding of learner multiple interactions, which might contribute to improving the dynamic regulation and accurate guidance of blended learning processes and optimizing existing teaching models.
引用
收藏
页码:1941 / 1963
页数:23
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