CoChat
Sign Up
Cited by 160Innovative Teaching and Learning MethodsStudent Assessment and FeedbackMemory Processes and Influences

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Self-Report is Indispensable to Assess Students’ Learning

Open access
Pekrun, Reinhard·2020·Frontline Learning Research
DOI 10.14786/flr.v8i3.637
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.14786/flr.v8i3.637

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

Citations

Cites

Flagged first, then by citations

Show all 25 references

Research works printed but not linked: 2

  1. Azevedo, R., Taub, M., & Mudrick, N.V. (2018). Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies. In D. Schunk & Greene, J.A (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 254-270). Routledge.
  2. Fryer, L. & Nakao K. (2020). The future of survey self-report: An experiment contrasting Likert, VAS, slide, and swipe touch interfaces. Frontline Learning Research, 8(2), 10-25.

Cited by

Sorted by citations

Items 101–160 of 160

Related

See all

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)
DOI
10.14786/flr.v8i3.637
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
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 ›

Are you an author, or do you see a problem with this page? Request a review