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Self-Report is Indispensable to Assess Students’ Learning
Open accessAbstract
Self-report is required to assess mental states in nuanced ways. By implication, self-report is indispensable to capture the psychological processes driving human learning, such as learners’ emotions, motivation, strategy use, and metacognition. As shown in the contributions to this special issue, self-report related to learning shows convergent and predictive validity, and there are ways to further strengthen its power. However, self-report is limited to assess conscious contents, lacks temporal resolution, and is subject to response sets and memory biases. As such, it needs to be complemented by alternative measures. Future research on self-report should consider not only closed-response quantitative measures but also alternative self-report methodologies, make use of within-person analysis, and investigate the impact of respondents’ emotions on processes and outcomes of self-report assessments.
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Metadata
- Authors
- Pekrun, R.
- Affiliations
- University of Essex (GB)
- Corresponding author
- Pekrun, Reinhard
- ORCID
- Pekrun, Reinhard
- Venue
- Frontline Learning Research
- Publisher
- EARLI
- Published
- 2020-03-30
- Volume
- 8
- Issue
- 3
- Pages
- 185-193
- ISSN
- 2295-3159 (ISSN-L)
- OpenAlex
- W3013946867
- Cited by
- 160
- References
- 25
- Citations by year
- 2026: 34 · 2025: 30 · 2024: 26 · 2023: 30 · 2022: 23 · 2021: 9 · 2020: 8
- FWCI
- 26.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Frontline Learning Research (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Student Assessment and Feedback
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- self-report, student learning assessment, metacognition, learning emotions, student motivation, learning strategies, response sets, memory bias, within-person analysis, convergent validity, predictive validity
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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