Understanding student interaction and cognitive engagement in online discussions using social network and discourse analyses

Priya Sharma, Mahir Akgun, Qiyuan Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Networked and digital technologies are increasingly being used for learning in formal and informal contexts, and participant engagement occurs primarily via online discussions. In this paper, we describe an ongoing research project that focuses on examining and understanding student engagement in collaborative online discussions within a formal educational context. We use multiple methodologies including social network analysis and discourse analysis to answer our main research questions, which relate to examining students’ behavioral and cognitive engagement in online discussion at the individual and group level. To support our discourse analyses, we also designed and implemented a machine learning model that uses the Interactive–Constructive–Active–Passive framework (Chi, 2009) to rapidly categorize student discourse. Data analyses indicate that social network analyses can provide immediate visual clues about individual and group behavioral engagement, and degree centrality data can clearly highlight the direction and valence of engagement. Similarly, discourse analyses can provide useful categorization of student discourse at the individual and group level. We conclude by illustrating how both types of analyses together allow for a nuanced insight into the types of roles that individual students may assume, as well as the ways in which individual and collaborative activity might unfold. Our methodology and results support the need to assume a broader perspective on learning by focusing on process and the integration of multiple methods.

Original languageEnglish (US)
JournalEducational Technology Research and Development
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Education

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