An analysis approach for blended learning based on weighted multiplex networks

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.