Abstract
This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students’ diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students’ productive discussions and academic success.
Highlights
Knowledge and behavior flow within social interactions which results in the adoption of innovations, endorsement of opinions, and spread of ideas, just to mention a few examples (Guilbeault et al, 2018) (Anderson et al, 2001; Fields & Kafai, 2009)
We argue that an approach to characterizing knowledge diffusion that takes advantage of well-established graph-based centrality measures is able to capture knowledge co-construction, uptake, and argumentation in networked computer-supported collaborative learning (CSCL) settings
This study builds on this immense body of research and offers a method based on graph-based diffusion centralities to help identify productive discussions as well as student roles in the discourse
Summary
Knowledge and behavior flow within social interactions which results in the adoption of innovations, endorsement of opinions, and spread of ideas, just to mention a few examples (Guilbeault et al, 2018) (Anderson et al, 2001; Fields & Kafai, 2009). These phenomena spread within the fabric of social networks through the process of diffusion (Anderson et al, 2001; Singh, 2018). We review the previous literature on traditional SNA metrics, their use in CSCL, and how this study is meant to fill some of the existing gaps
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