Direct instruction of metacognition benefits adolescent science learning, transfer, and motivation: An in vivo study.
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Prior studies have not tested whether an instructional intervention aimed at improving metacognitive skills results in changes to student metacognition, motivation, learning, and future learning in the classroom. We examined whether a 6-hr intervention designed to teach the declarative and procedural components of planning, monitoring, and evaluation could increase students ’ metacognition, motivation, learning, and preparation for future learning for middle school science. Forty-six eighth-grade students were randomly assigned to either a control group, which received extensive problem-solving practice, or an experimental group, which received more limited problem-solving practice along with metacognitive instruction and training. Results revealed that those who received the metacognitive instruction and training were less biased when making metacognitive judgments, p .03, d 0.65, endorsed higher levels of motivation after instruction (e.g., there was a large effect on task value, p .006, d 0.87), performed better on a conceptual physics test, p .03, d 0.64, and performed better on a novel self-guided learning activity, p .007, d 0.87. This study demonstrates that metacognitive instruction can lead to better self-regulated learning outcomes during adolescence, a period in which students’ academic achievement and motivation often decline.
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Metacognitive skills are positively related to learning performance and achievement.
“Therefore, metacognitive skills are positively related to learning performance and achievement (e.g., Veenman et al. 2005) or motivation (e.g., Zepeda et al. 2015).”
From Transfer of metacognitive skills in self-regulated learning: an experimental training study (2020) · cites this paper as Zepeda et al. 2015
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Aprendizaje basado en el modelo STEM y la clave de la metacognición
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Cognitive and Neurocognitive Development in Adolescence
Keating, D. P., Demidenko, M. I., & Kelly, D. P. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.23636-5 · Cited by 10 · see citing works
Kleka, P., Brycz, H., Fanslau, A., & Pilarska, A. (2019) · Advances in Cognitive Psychology · doi:10.5709/acp-0278-y · Cited by 12 · see citing works
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Fu, L., Wang, Y., Leung, S. O., & Hu, W. L. (2023) · Health Education Journal · doi:10.1177/00178969221145804 · Cited by 9 · see citing works
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Rajcoomar, R., Morabe, O. N., & Breed, B. (2022) · Journal of Education · doi:10.1177/00220574221104974 · Cited by 11 · see citing works
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Bablekou, Z., Chrysochoou, E., & Kazi, S. (2023) · Psychology the Journal of the Hellenic Psychological Society · doi:10.12681/psy_hps.36222 · Cited by 7 · see citing works
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STREAM DigLIM: Learning Innovation in Madrasah to Develop Students’ Literacy
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- Authors
- Zepeda, C. D., Richey, J. E., Ronevich, P., & Nokes-Malach, T. J.
- Affiliations
- University of Pittsburgh (US)
- Venue
- Journal of Educational Psychology
- Published
- 2015-11
- Volume
- 107
- Issue
- 4
- Pages
- 954-970
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2015584585
- Cited by
- 167
- References
- 63
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 17 · 2023: 24 · 2022: 20 · 2021: 7 · 2020: 22 · 2019: 19 · 2018: 12 · 2017: 5 · 2016: 7 · 2015: 2
- FWCI
- 21.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Educational Psychology (publishedVersion, other-oa)
- Funders
- National Science Foundation (SBE 0836012)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- metacognition instruction, student self-regulation, junior high school science, student motivation, transfer of learning, self-directed learning, physics learning, problem-solving practice, planning, monitoring and evaluation
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.
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