LT Faulty Bodong Chen, PhD student Xinran Zhu and PhD student Hong Shui presented a paper Socio-Semantic Network Motifs Framework for Discourse Analysis at LAK22: 12th International Learning Analytics and Knowledge Conference.
Effective collaborative discourse requires both cognitive and social engagement of students. To investigate complex socio-cognitive dynamics in collaborative discourse, this paper proposes to model collaborative discourse as a socio-semantic network (SSN) and then use network motifs – defined as recurring, significant subgraphs – to characterize the network and hence the discourse. To demonstrate the utility of our SSN motifs framework, we applied it to a sample dataset. While more work needs to be done, the SSN motifs framework shows promise as a novel, theoretically informed approach to discourse analysis.
This study is a part of the Collaborative Annotation in College Classrooms Project.
Link: Chen, B., Zhu, X., & Shui, H. (2022). Socio-Semantic Network Motifs Framework for Discourse Analysis. In LAK22: 12th International Learning Analytics and Knowledge Conference (LAK22), March 21–25, 2022. https://doi.org/10.1145/3506860.3506893