Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses
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Self-assessment can positively influence self-regulated learning and self-efficacy because it requires learners to compare their work against explicit criteria and to generate internal judgments before receiving or acting on external feedback.
“Meta-analytic findings indicate that self-assessment can positively influence self-regulated learning and self-efficacy because it requires learners to compare their work against explicit criteria and to generate internal judgments before receiving or acting on external feedback”
From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Panadero et al., 2017
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThere is a positive correlation between self-assessment, self-regulated learning (SRL), and self-efficacy strategies.
“e findings of these studies clearly showed that there is a positive correlation between self-assessment, self-regulated learning (SRL), and self-efficacy strategies.”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [47]
Self-assessment interventions have a direct and positive relationship with students' SRL strategies.
“e results of their findings showed that self-assessment interventions have a direct and positive relationship with students' SRL strategies.”
From Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education (2022) · cites this paper as [47]
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“…evaluative judgement can also impact positively (see 2) as research shows that self-assessment practice has a positive influence in Fi g. 1 E ff ec ts of ev al ua tiv e ju dg em en t on Z im m er m an ’s m od el self-efficacy and motivational self-regulated learning aspects (Panadero et al. 2017).”
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- Authors
- Panadero, E., Jonsson, A., & Botella, J.
- Affiliations
- Universidad Autónoma de Madrid (ES)
- Corresponding author
- Panadero, Ernesto
- Venue
- Educational Research Review
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 22
- Pages
- 74-98
- ISSN
- 1747-938X (ISSN-L)
- OpenAlex
- W2745915040
- Cited by
- 712
- References
- 96
- Citations by year
- 2026: 93 · 2025: 103 · 2024: 82 · 2023: 113 · 2022: 118 · 2021: 84 · 2020: 50 · 2019: 43 · 2018: 23 · 2017: 2
- FWCI
- 114.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Educational Research Review (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion)
- Figshare (submittedVersion, cc-by)
- Hispana (submittedVersion)
- Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion, public-domain)
- Funders
- Ministerio de Economía y Competitividad via Spanish Ramón y Cajal programme (RYC-2013-13469)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
- Keywords
- self-assessment, student self-regulation, self-efficacy, self-monitoring, gender differences, learning strategies, student motivation, educational intervention
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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