Ordered Network Analysis of Epistemic Emotions during Collaborative Problem Solving

2026-07-25

AI Summary

This study addresses the challenge of capturing dynamic patterns of cognitive-affective states—such as confusion and frustration—in collaborative problem solving, which has been hindered by the absence of a gold standard for affect annotation. Innovatively integrating retrospective stimulated recall with Ordinal Network Analysis (ONA), this work reveals, for the first time, the global ordinal structure of emotional states along with their persistence and transition dynamics. Findings indicate that curiosity, optimism, and confusion form a stable cognitive-affective core; distinct emotion-reporting methods highlight divergent connectivity patterns; and significant differences emerge between fast and slow collaborative groups in the role of the “confusion–disengagement–conflict” pathway. Moving beyond conventional descriptive statistics, this research offers both empirical grounding and methodological innovation for designing AI systems that support collaborative learning.

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.

Problem

• epistemic emotions
• collaborative problem solving
• affective states
• emotion dynamics
• confusion

Innovation

• ordered network analysis
• epistemic emotions
• collaborative problem solving
• affective dynamics
• cued-recall methodology