Estratégias metacognitivas em modelação: comparação de resultados alcançados através de diferentes métodos

Autores

DOI:

https://doi.org/10.48489/quadrante.23653

Palavras-chave:

modelação, estratégias metacognitivas, perspetivas dos alunos, medição da utilização de estratégias, autoavaliação, planificação

Resumo

A metacognição parece ter uma grande influência nos processos de modelação e no desenvolvimento de competências de modelação. Em contraste com a importância que lhe é atribuída, um número relativamente pequeno de estudos tem sido conduzido até agora, uma vez que a metacognição é um conceito bastante complexo e a medição da utilização da metacognição pelos estudantes é bastante desafiante. Como parte do nosso projeto, a utilização de estratégias metacognitivas em tarefas de modelação, bem como as atitudes dos estudantes em relação à sua utilização, foram medidas e avaliadas, utilizando questionários de autoavaliação e entrevistas baseadas na retrospetiva estimulada. Neste artigo, o resultado dos diferentes métodos será comparado e complementado pela análise do processo de trabalho gravado em vídeo, como uma terceira fonte de dados. Para este efeito, as autoavaliações relativas à utilização de estratégias metacognitivas, bem como as declarações feitas nas entrevistas por dois estudantes são consideradas como exemplos. Serão tiradas conclusões sobre os diferentes métodos e resultados.

Referências

Artzt, A. F., & Armour-Thomas, E. (1992). Development of a cognitive-metacognitive framework for protocol analysis of mathematical problem solving in small groups. Cognition and Instruction, 9(2), 137–175.

Blum, W. (2015). Quality teaching of mathematical modelling: What do we know, what can we do? In S. J. Cho (Ed.), The Proceedings of the 12th International Congress on Mathematical Education (pp. 73–96). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-12688-3_9

Buchholtz, N. (2019). Planning and conducting mixed methods studies in mathematics educational research. In G. Kaiser & N. Presmeg (Eds.), Compendium for early career researchers in mathematics education (pp. 131–152). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-15636-7_6

Chalmers, C. (2009). Group metacognition during mathematical problem solving. In R. K. Hunter, B. A. Bicknell, & T. A. Burgess (Eds.), Crossing divides: MERGA 32 Conference Proceedings, (pp. 105–111). Palmerston North, New Zealand: MERGA.

Cooke, N. J., Salas, E., Kiekel, P. A., & Bell, B. (2004). Advances in measuring team cognition. In E. Salas & S. M. Fiore (Eds.), Team cognition: Understanding the factors that drive process and performance (pp. 83–106). Washington, DC: American Psychological Assoc. https://doi.org/10.1037/10690-005

Efklides, A. (2008). Metacognition. European Psychologist, 13(4), 277–287. https://doi.org/10.1027/1016-9040.13.4.277

Gass, S. M., & Mackey, A. (2000). Stimulated recall methodology in second language research. Mahwah NJ: Erlbaum.

Goos, M. (2002). Understanding metacognitive failure. Journal of Mathematical Behavior, 21(3), 283–302. https://doi.org/10.1016/S0732-3123(02)00130-X

Goos, M., Galbraith, P., & Renshaw, P. (2002). Socially mediated metacognition: Creating collaborative zones of proximal development in small group problem solving. Educational Studies in Mathematics, 49, 193–223. https://doi.org/10.1023/A:1016209010120

Hankeln, C., Adamek, C., & Greefrath, G. (2019). Assessing sub-competencies of mathematical modelling – Development of a new test instrument. In G. A. Stillman & J. P. Brown (Eds.), Lines of inquiry in mathematical modelling research in education (pp. 143–160). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-14931-4_8

Kaiser, G. (2007). Modelling and modelling competencies in school. In C. Haines, P. L. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modelling: Education, engineering and economics (pp. 110–119). Chichester: Horwood Publishing. https://doi.org/10.1533/9780857099419.3.110

Kaiser, G., & Brand, S. (2015). Modelling competencies: Past development and further perspectives. In G. A. Stillman, W. Blum, & M. Biembengut (Eds.), Mathematical modelling in education research and practice (pp. 129–149). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-18272-8_10

Krüger, A. (2021). Metakognition beim mathematischen modellieren. Strategieeinsatz aus schülerperspektive. Wiesbaden: Springer. https://doi.org/10.1007/978-3-658-33622-6

Kuckartz, U. (2019). Qualitative text analysis: A systematic approach. In G. Kaiser & N. Presmeg (Eds.), Compendium for early career researchers in mathematics education (pp. 181–197). Cham, Switzerland. https://doi.org/10.1007/978-3-030-15636-7_8

Leiss, D., Möller, V., & Schukajlow, S. (2006). Bier für den regenwald. Diagnostizieren und fördern mit modellierungsaufgaben. Friedrich Jahresheft, 24, 89–91.

Maaß, K. (2006). What are modelling competencies? ZDM - Mathematics Education, 38(2), 113–142. https://doi.org/10.1007/BF02655885

Ng, K. E. D. (2010). Partial metacognitive blindness in collaborative problem solving. In L. Sparrow, B. Kissane, & C. Hurst (Eds.), Shaping the future of mathematics education (pp. 446–453). St Lucia, Qld.: MERGA.

Ohtani, K., & Hisasaka, T. (2018). Beyond intelligence: A meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition and Learning, 13(2), 179–212. https://doi.org/10.1007/s11409-018-9183-8

Rogat, T. K., & Adams-Wiggins, K. R. (2014). Other-regulation in collaborative groups: Implications for regulation quality. Instructional Science, 42(6), 879–904. https://doi.org/10.1007/s11251-014-9322-9

Schellings, G. L.M. (2011). Applying learning strategy questionnaires: Problems and possibilities. Metacognition and Learning, 6(2), 91–109. https://doi.org/10.1007/s11409-011-9069-5

Schellings, G., van Hout-Wolters, B., Veenman, M., & Meijer, J. (2013). Assessing metacognitive activities: The in-depth comparison of a task-specific questionnaire with think-aloud protocols. European Journal of Psychology of Education, 28(3), 963–990. https://doi.org/10.1007/s10212-012-0149-y

Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense-making in mathematics. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 334–370). New York, NY: MacMillan.

Schraw, G. (2009). Measuring metacognitive judgments. In A. C. Graesser, D. J. Hacker, & J. Dunlosky (Eds.), Handbook of metacognition in education (pp. 415–429). New York, NY: Routledge.

Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7(4), 351–371. https://doi.org/10.1007/BF02212307

Schukajlow, S., & Krug, A. (2013). Planning, monitoring and multiple solutions while solving modelling problems. In A. M. Lindmeier & A. Heinze (Eds.), Mathematics learning across the life span: Proceedings of the 37th Annual Meeting of the International Group for the Psychology of Mathematics Education (Vol. 4, pp. 177–184). Kiel, Germany: PME.

Schukajlow, S., & Leiß, D. (2011). Selbstberichtete strategienutzung und mathematische modellierungskompetenz. Journal für Mathematikdidaktik 32, 53–77. https://doi.org/10.1007/s13138-010-0023-x

Stillman, G. A. (2004). Strategies employed by upper secondary students for overcoming or exploiting conditions affecting accessibility of applications tasks. Mathematics Education Research Journal, 16(1), 41–71. https://doi.org/10.1007/BF03217390

Stillman, G. A. (2011). Applying metacognitive knowledge and strategies in applications and modelling tasks at secondary school. In G. Kaiser, W. Blum, R. Borromeo Ferri, & G. A. Stillman (Eds.), Trends in teaching and learning of mathematical modelling (pp. 165–180). Dordrecht, Netherlands: Springer. https://doi.org/10.1007/978-94-007-0910-2

Stillman, G. A., & Galbraith, P. L. (1998). Applying mathematics with real world connections: Metacognitive characteristics of secondary students. Educational Studies in Mathematics, 36(2), 157–194. https://doi.org/10.1023/A:1003246329257

Vauras, M., Iiskala, T., Kajamies, A., Kinnunen, R., & Lehtinen, E. (2003). Shared-regulation and motivation of collaborating peers: A case analysis. PSYCHOLOGIA – An International Journal of Psychology in the Orient, 46(1), 19–37. https://doi.org/10.2117/psysoc.2003.19

Veenman, M. V. J. (2011a). Alternative assessment of strategy use with self-report instruments: A discussion. Metacognition and Learning, 6(2), 205–211. https://doi.org/10.1007/s11409-011-9080-x

Veenman, M. V. J. (2011b). Learning to self-monitor and self-regulate. In P. A. Alexander & R. E. Mayer (Eds.), Handbook of research on learning and instruction (pp. 197–218). New York: Routledge. https://doi.org/10.4324/9780203839089.ch10

Veenman, M. V. J., & Elshout, J. J. (1999). Changes in the relation between cognitive and metacognitive skills during the acquisition of expertise. European Journal of Psychology of Education, 14(4), 509–523. https://doi.org/10.1007/BF03172976

Volet, S., Summers, M., & Thurman, J. (2009). High-level co-regulation in collaborative learning: How does it emerge and how is it sustained? Learning and Instruction, 19(2), 128–143. https://doi.org/10.1016/j.learninstruc.2008.03.001

Vorhölter, K. (2017). Measuring metacognitive modelling competencies. In G. A. Stillman, W. Blum, & G. Kaiser (Eds.), Mathematical modelling and applications. Crossing and researching boundaries in mathematics education (pp. 175–185). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-62968-1_15

Vorhölter, K. (2018). Conceptualization and measuring of metacognitive modelling competencies: Empirical verification of theoretical assumptions. ZDM – Mathematics Education, 50(1-2), 343–354. https://doi.org/10.1007/s11858-017-0909-x

Vorhölter, K. (2019). Enhancing metacognitive group strategies for modelling. ZDM – Mathematics Education, 51, 703 – 716. https://doi.org/10.1007/s11858-019-01055-7

Vorhölter, K., Krüger, A., & Wendt, L. (2019). Metacognition in mathematical modeling – An overview. In S. A. Chamberlin & B. Sriraman (Eds.), Advances in mathematics education. Affect in mathematical modeling (pp. 29–51). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-04432-9_3

Weinert, F. E. (1984). Metakognition und motivation als determinanten der lerneffektvität: Einführung und überblick. In F. E. Weinert, R. H. Kluwe, & A. L. Brown (Eds.), Metakognition, motivation und lernen (pp. 9–21). Stuttgart, Germany: Kohlhammer.

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2021-06-30

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Vorhölter, K., & Krüger, A. (2021). Estratégias metacognitivas em modelação: comparação de resultados alcançados através de diferentes métodos. Quadrante, 30(1), 178–197. https://doi.org/10.48489/quadrante.23653

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