Evidence map›Paper›PMID 41839783›Full record

ArticleThe British journal of educational psychology2026

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

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

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Article in The British journal of educational psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Tingting WangSchool of Education, Renmin University of China, Beijing, China.
Jianhua ZhangFaculty of Education, the Chinese University of Hong Kong, Hong Kong, China.
Shasha LiFaculty of Education, McGill University, Montreal, Canada.
Jie GaoFaculty of Education, McGill University, Montreal, Canada.
Lingyun HuangDepartment of Curriculum and Instruction, The Education University of Hong Kong, Hong Kong, China.
Susanne P LajoieFaculty of Education, McGill University, Montreal, Canada.

Funding

Renmin University of China
6 · The paper itself

Abstract

backgroundScholars 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.

aimsThis 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.

methodsParticipants 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.

resultsEpistemic 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.

conclusionsOverall, this study offers empirical insights into the temporal trajectories of epistemic emotions, informing targeted emotional scaffolding across learning phases.

Indexed as

Education, Medical, UndergraduateEmotionsGalvanic Skin ResponseLearningProblem SolvingAdultFemaleHumansMaleYoung Adultemotional trajectoriesepistemic emotionsmultimodal data

Identifiers

PMID41839783
PMCPMC13441002

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.