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Measuring emotions during epistemic activities: the Epistemically-Related Emotion Scales
Abstract
Measurement instruments assessing multiple emotions during epistemic activities are largely lacking. We describe the construction and validation of the Epistemically-Related Emotion Scales, which measure surprise, curiosity, enjoyment, confusion, anxiety, frustration, and boredom occurring during epistemic cognitive activities. The instrument was tested in a multinational study of emotions during learning from conflicting texts (N = 438 university students from the United States, Canada, and Germany). The findings document the reliability, internal validity, and external validity of the instrument. A seven-factor model best fit the data, suggesting that epistemically-related emotions should be conceptualised in terms of discrete emotion categories, and the scales showed metric invariance across the North American and German samples. Furthermore, emotion scores changed over time as a function of conflicting task information and related significantly to perceived task value and use of cognitive and metacognitive learning strategies.
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“predicting has repeatedly been shown to reliably induce both curiosity and surprise, which can be classified as epistemic emotions (Pekrun et al., 2017).”
From Predicting as a learning strategy (2021) · cites this paper as Pekrun et al., 2017
In the citing paper
“Control-value theory (Pekrun, 2006;Pekrun et al., 2017) describes how such emotions codetermine what are termed achievement behaviors-behaviors that are conducive to the achievement of academic goals.”
From Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda (2022) · cites this paper as Pekrun et al., 2017
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“…for the arousal of epistemic emotions is discrepant information and appraisals of cognitive incongruity that can trigger surprise and curiosity (Kang et al., 2009), confusion, frustration, and boredom when the incongruity cannot be resolved (D’Mello & Graesser, 2012; D’Mello et al., 2014),…”
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Metadata
- Authors
- Pekrun, R., Vogl, E., Muis, K. R., & Sinatra, G. M.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- McGill University (CA)
- University of Southern California (US)
- Corresponding author
- Pekrun, Reinhard
- Venue
- Cognition and Emotion
- Publisher
- Informa UK Limited
- Published
- 2016-07-22
- Volume
- 31
- Issue
- 6
- Pages
- 1268-1276
- ISSN
- 0269-9931 (ISSN-L)
- 1464-0600
- OpenAlex
- W2484079420
- Cited by
- 373
- References
- 26
- Citations by year
- 2026: 51 · 2025: 68 · 2024: 51 · 2023: 47 · 2022: 45 · 2021: 39 · 2020: 23 · 2019: 27 · 2018: 16 · 2017: 5 · 2016: 1
- FWCI
- 23.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cognition & Emotion (publishedVersion)
- PubMed (publishedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- Funders
- University of Munich (VII.1-H172.10)
- Social Sciences and Humanities Research Council of Canada (SSHRC 410-2011-0182)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychological and Educational Research Studies
- Social Sciences › Psychology › Developmental and Educational Psychology › Education, Achievement, and Giftedness
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- emotion measurement, surprise, curiosity, enjoyment, confusion, anxiety, frustration, boredom, metacognitive strategies, cognitive strategies, perceived task value, metric invariance, scale validation
- MeSH terms
- Adult, Cognition, Emotions, Female, Humans, Male, Models, Psychological, Psychometrics, Surveys and Questionnaires, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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