Work

Unravelling dynamic trajectories of epistemic emotions in a technology‐enhanced problem‐solving task: A multimodal data approach

Year: 2026

Type: article

Abstract: BACKGROUND: Scholars have reached a consensus on the influential roles and dynamic characteristics of epistemic emotions during the problem-solving process; however, few research provided empirical evidence to reveal the temporal progression of these emotions. AIMS: This study leverages multimodal data-self-reports and physiological sensors-to examine variations in a focal epistemic emotion across learning phases and performance levels and to uncover transition patterns among different epistemic emotions. SAMPLES: A total of 105 students enrolled in a five-year undergraduate medical program were involved in the data analysis. METHODS: Participants diagnosed a virtual patient in an intelligent tutoring system designed based on the self-regulated learning (SRL) model. We recorded electrodermal activity (EDA) peaks as an indicator of physiological arousal and collected self-reports of surprise, curiosity, confusion, enjoyment and boredom across three SRL phases. Two-way mixed-design ANOVAs examined variations in the focal emotion and EDA peaks across the forethought, performance and self-reflection phases, comparing high- and low-performing groups. Temporal network analysis was used to identify transition patterns between emotions during the self-regulated problem-solving process. RESULTS: Epistemic emotions varied distinctly by performance level and SRL phase. For instance, high performers' confusion decreased over time, while low performers showed persistently high confusion. EDA peaks were more frequent during forethought and self-reflection phases for both groups. More importantly, temporal network analysis revealed several key emotional transitions, including surprise/curiosity → confusion and enjoyment → surprise/boredom. CONCLUSIONS: Overall, this study offers empirical insights into the temporal trajectories of epistemic emotions, informing targeted emotional scaffolding across learning phases.

Source: British Journal of Educational Psychology

DOI: 10.1111/bjep.70073

Authors: Tingting Wang, Jianhua Zhang, Shasha Li, Jie Gao, Lingyun Huang, Susanne P. Lajoie

Institutions: Renmin University of China, Chinese University of Hong Kong, McGill University, Education University of Hong Kong

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $3,760