LAUSR

Sharing the same philosophy of “relations matter” with computer-supported collaborative learning (CSCL), social network analysis (SNA) has become a common methodology in the CSCL research. In this research, I use SNA methods from relational ties, network modes, and integrated methods perspectives to understand attributes of relations in CSCL. I design, conduct, and evaluate three SNA analytics on the same dataset from an online course to understand CSCL entities, relations, and processes. This online collaborative discussion in this course stresses students’ knowledge inquiry, construction, and building through peer interactions. Results show that compared to traditional SNA methods, these three SNA approaches can reveal more detailed, richer picture of the collaborative learning processes, particularly, the interactional, multi-modal, and temporal aspects. Moreover, these SNA approaches are generalizable for understanding similar CSCL settings. Based on the results, this research proposes methodological implications to further apply and develop SNA in the CSCL field.