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.

社交网络分析（SNA）与计算机支持的协作学习（CSCL）共享相同的“关系问题”哲学，已成为CSCL研究中的一种通用方法。在这项研究中，我从关系，网络模式和集成方法中使用了SNA方法了解CSCL中关系属性的观点。我通过在线课程设计，执行和评估同一数据集上的三个SNA分析，以了解CSCL实体，关系和流程。本课程中的在线协作讨论着重于学生的知识探究，构建和通过同伴交互来构建。结果表明，与传统的SNA方法相比，这三种SNA方法可以揭示协作学习过程的更详细，更丰富的图片，尤其是在交互，多模式和时间方面。此外，这些SNA方法可用于理解相似的CSCL设置。根据结果​​，本研究提出了在CSCL领域进一步应用和发展SNA的方法论意义。