Enhancing Mathematical Reasoning in the Classroom: The Effects of Cooperative Learning and Metacognitive Training
Abstract
The purpose of this study was to investigate the effects of four instructional methods on students’ mathematical reasoning and metacognitive knowledge. The participants were 384 eighth-grade students. The instructional methods were cooperative learning combined with metacognitive training (COOP+META), individualized learning combined with metacognitive training (IND+META), cooperative learning without metacognitive training (COOP), and individualized learning without metacognitive training (IND). Results showed that the COOP+META group significantly outperformed the IND +META group, which in turn significantly outperformed the COOP and IND groups on graph interpretation and various aspects of mathematical explanations. Furthermore, the metacognitive groups (COOP+META and IND +META) outperformed their counterparts (COOP and IND) on graph construction (transfer tasks) and metacognitive knowledge. This article presents theoretical and practical implications of the findings.
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Monitoring and control are interrelated.
“Monitoring and control are interrelated [93,95].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [95]
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Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990) · Review of Educational Research · doi:10.3102/00346543060001001 · Cited by 1,012 · see citing works
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Metacognition and learning: conceptual and methodological considerations
Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006) · Metacognition and Learning · doi:10.1007/s11409-006-6893-0 · Cited by 2,023 · see citing works
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Effectiveness of learning strategy instruction on academic performance: A meta-analysis
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The Metacognitive Demands and Opportunities of Generative AI
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642902 · Cited by 280 · see citing works
“That is, users must have self-awareness of the applicability and potential impact of using GenAI for their workflow; well-adjusted confidence in the ability to complete a task manually versus with GenAI; and metacognitive flexibility in adapting workflows to GenAI (Figure 2d-e).”
MethodA meta-analysis and systematic review of reactivity to judgements of learning
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- Authors
- Kramarski, B., & Mevarech, Z. R.
- Affiliations
- Bar-Ilan University (IL)
- Venue
- American Educational Research Journal
- Published
- 2003-01
- Volume
- 40
- Issue
- 1
- Pages
- 281-310
- ISSN
- 0002-8312 (ISSN-L)
- 1935-1011
- OpenAlex
- W2104369180
- Cited by
- 450
- References
- 3
- Citations by year
- 2026: 16 · 2025: 12 · 2024: 21 · 2023: 24 · 2022: 32 · 2021: 22 · 2020: 32 · 2019: 33 · 2018: 41 · 2017: 32 · 2016: 16 · 2015: 26 · 2014: 19 · 2013: 26 · 2012: 18
- FWCI
- 9.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Educational Research Journal (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Applied Mathematics › Mathematics Education and Pedagogy
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Mathematics Education and Teaching Techniques
- Keywords
- mathematical reasoning, cooperative learning, metacognitive training, personalized learning, metacognitive knowledge, graph interpretation, graph construction, mathematical explanation, eighth grade students, mathematics education
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