Ordered Network Analysis of Epistemic Emotions during Collaborative Problem Solving

Abstract:Investigating how affective states such as confusion and frustration persist and transition during co-situated collaborative problem solving (CPS) is important for understanding the dynamics of epistemic emotions. However, the accurate identification of affective states remain challenging as there is no gold-standard truth in this space. Here, we analyze affective states collected through retrospective cued-recall during an in-person CPS task. Using ordered network analysis (ONA), we examine (1) the overall ordered structure of affective states and how this structure differs across self-caught and probe-caught reporting methods, and (2) what aspects of this ordered structure are emphasized differently in slower and faster groups. We find that ONA reveals differences in persistence and transition patterns that are not apparent from descriptive summaries alone. In particular, we observe a stable epistemic core linking curiosity, optimism, and confusion, with different reporting methods emphasizing different connections among states. An analysis between faster and slower groups show that roles of confusion and disengagement also shift significantly during collaboration, particularly in their relationship to conflict. We interpret our findings in the context of collaboration and discuss their implications in developing AI systems that support CPS.

Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2607.23317 [cs.AI]

(or arXiv:2607.23317v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.23317 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

In Artificial Intelligence in Education. AIED 2026. Lecture Notes in Computer Science(), vol 16584. Springer, Cham

https://doi.org/10.1007/978-3-032-29763-1_20 Focus to learn more DOI(s) linking to related resources

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