From Obstacle to Catalyst: A Narrative Review of Learning from Errors in Science Education
Open accessAbstract
Learning from errors (LFE) constitutes a complex, multilevel process whose pedagogical value depends on how errors are elicited, interpreted, and corrected. This narrative review critically synthesizes research on LFE and its implications for science instruction in school and higher education. The reporting of the purposive, non-exhaustive search was strengthened with reference to the Scale for the Assessment of Narrative Review Articles (SANRA) criteria. The synthesis traces the historical transition from error avoidance to conditional error-as-resource perspectives and integrates five complementary lenses: error type, negative knowledge, cognitive load and prior knowledge, affective and self-regulatory processes, and the Generating Errors/Detecting and Correcting Errors model. It then evaluates instructional approaches including productive failure, erroneous examples, peer instruction, formative feedback, conceptual-change strategies, Predict–Observe–Explain, refutation texts, and gamification. Evidence is strongest when errors are embedded in structured cycles of manageable challenge, explanation, feedback, and revision; however, effects are heterogeneous and moderated by learner expertise, task demands, feedback design, classroom error climate, and safety constraints. Domain-specific evidence is most developed in physics, chemistry, biology, and health-professions simulation, whereas Earth and climate sciences and teacher professional development remain comparatively underexamined. The review concludes with an integrative framework and actionable design principles for science teacher education, while identifying methodological, cultural, and empirical gaps for future research.
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Métioui, A. (2026) · Encyclopedia · doi:10.3390/encyclopedia6020033 · Cited by 1 · see citing works
Calafell, G., Esparza, M., & Valverde, G. J. (2026) · International Journal of Sustainability in Higher Education · doi:10.1108/ijshe-08-2025-1027 · Cited by 1 · see citing works
Self-citationReaño, C. (2021) · EDULEARN Proceedings · doi:10.21125/edulearn.2021.0901
Flagged first, then by citations
Research works printed but not linked: 14
- Athuman, 2017, Comparing the effectiveness of an inquiry-based approach to that of conventional style of teaching in the development of students’ science process skills, Int. J. Environ. Sci. Educ., 12, 1797
- Lohre, 2022, Errors in implant orientation in novice versus experienced surgeons: The positive impact of hands-on learning, Orthop. Proc., 104, 30
- Vanderheiden, E., and Mayer, C.-H. (2020). Failures, errors, and mistakes: A systematic review of the literature. Mistakes, Errors and Failures Across Cultures: Navigating Potentials, Springer. · DOI
- Clark, 2007, An overview of conceptual change theories, Eurasia J. Math. Sci. Technol. Educ., 3, 351
- Baki, 2009, The anatomy of mistakes: Categorizing students’ mistakes in mathematics within learning theories, Turk. J. Comput. Math. Educ., 1, 13
- Parry, 2017, Perspectives on failure in the classroom by elementary teachers new to teaching engineering, J. Pre-Coll. Eng. Educ. Res., 7, 47
- Kay, 2018, “I’m going to fail”: How youth interpret failure across contextual boundaries, Proceedings of the 13th International Conference of the Learning Sciences, Volume 2, 981
- Mandl, H., and Levin, J.R. (1989). Trends in the delayed use of information from an illustrated text. Knowledge Acquisition from Text and Pictures, Elsevier.
- Steuer, 2015, A constructive error climate as an element of effective learning environments, Psychol. Test Assess. Model., 57, 262
- Yilmaz, 2020, Misrepresentation of science concepts in Turkish picture books, Issues Educ. Res., 30, 1183
- Acar, 2010, Internet as a source of misconception, Turk. Online J. Educ. Technol., 9, 94
- Jiménez-Valverde, G. (2021). Instrucción entre iguales con viñetas conceptuales: Una propuesta en el grado de maestro de educación primaria en un contexto indagativo. Innovación en la Docencia e Investigación de las Ciencias Sociales y de la Educación, Dykinson. Available online: https://hdl.handle.net/2445/215654.
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- Authors
- Fabre-Mitjans, N., Jiménez-Valverde, G., Viciana, S., & Crespo i Torres, F.
- Affiliations
- Universitat de Barcelona (ES)
- EduCiTS Innovation and EMA Research Groups, Faculty of Education, Universitat de Barcelona, 08035 Barcelona, Spain
- Corresponding author
- Fabre-Mitjans, Noëlle
- Venue
- Encyclopedia
- Publisher
- MDPI AG
- Published
- 2026-09-02
- Volume
- 6
- Issue
- 9
- Pages
- 192
- ISSN
- 2673-8392 (ISSN-L)
- OpenAlex
- W7205000243
- Cited by
- 0
- References
- 114
- Type
- reference entry
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Encyclopedia (publishedVersion, cc-by)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Universitat de Barcelona through the Office of the Vice-Rector for Teaching Policy and the RIMDA program (2025PID-UB/021)
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- learning from mistakes, error correction, error prevention, productive failure, erroneous examples, peer instruction, formative feedback, conceptual change
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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