Il problema potenziale nell'esplorazione della regolazione metacognitiva nel problem-solving collaborativo
DOI:
https://doi.org/10.6092/issn.1970-2221/16086Parole chiave:
Problem-solving collaborativo, metacognizione, regolazione metacognitiva, strumenti di ricercaAbstract
La regolazione metacognitiva è un'abilità importante che gli studenti universitari devono avere nella risoluzione di problemi collaborativi. Tuttavia, prima di ricercare l'esplorazione della regolazione metacognitiva nella risoluzione collaborativa dei problemi, deve essere effettuato lo sviluppo di strumenti adeguati. Lo strumento giusto produrrà i dati corretti. In questo studio sono stati sviluppati due strumenti, ovvero compiti contenenti problemi matematici e linee guida per colloqui basati su compiti. Inoltre, questo studio ha anche identificato criteri problematici appropriati utilizzati per esplorare la regolazione metacognitiva nella risoluzione collaborativa dei problemi. I risultati hanno mostrato che i problemi che possono innescare la regolazione metacognitiva nella risoluzione collaborativa dei problemi soddisfano i seguenti criteri: problemi da dimostrare, problemi non di routine, problemi aperti, problemi geometrici e senza DGE. L'uso di linee guida per interviste semi-strutturate può anche aiutare ad approfondire l'esplorazione da parte degli studenti della regolazione metacognitiva nella risoluzione collaborativa dei problemi. I risultati di questo studio sono particolarmente importanti per i ricercatori che svilupperanno strumenti per esaminare la regolazione metacognitiva, specialmente nella risoluzione collaborativa dei problemi.
Riferimenti bibliografici
Artz, 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), 131–175. https://doi.org/10.1207/s1532690xci0902_3
Artzt, A. F., & Armour-Thomas, E. (1997). Mathematical problem solving in small groups: Exploring the interplay of students’ metacognitive behaviors, perceptions, and ability levels. Journal of Mathematical Behavior, 16(1), 63–74. https://doi.org/10.1016/s0732-3123(97)90008-0
Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe, (Eds.) Metacognition, motivation, and understanding (pp. 65-116). Lawrence Erlbaum.
De Backer, L., Van Keer, H., Moerkerke, B., & Valcke, M. (2016). Examining evolutions in the adoption of metacognitive regulation in reciprocal peer tutoring groups. Metacognition and Learning, 11(2), 187–213. https://doi.org/10.1007/s11409-015-9141-7
De Backer, L., Van Keer, H., & Valcke, M. (2014). Socially shared metacognitive regulation during reciprocal peer tutoring: Identifying its relationship with students’ content processing and transactive discussions. Instructional Science, 43(3), 323-344. https://doi.org/10.1007/s11251-014-9335-4
De Backer, L., Van Keer, H., & Valcke, M. (2022). The functions of shared metacognitive regulation and their differential relation with collaborative learners’ understanding of the learning content. Learning and Instruction, 77, 101527. https://doi.org/10.1016/j.learninstruc.2021.101527
Firmansyah, F. F., Sa, C., Subanji, S., & Qohar, A. (2022). Characterizations of students’ metacognition in solving geometry problems through positioning group work. 11(3), 1391–1398. https://doi.org/10.18421/TEM113
Flavell, J. H. (1979). Metacognition and cognitive monitoring a new area of cognitive-development inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
Fleming, S. M., & Lau, H. C. (2014). How to measure Metacognition. In Frontiers in Human Neuroscience, 8, 1-9. https://doi.org/10.3389/fnhum.2014.00443
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(2), 193–223. https://doi.org/10.1023/A:1016209010120
Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared Metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21(3), 379–393. https://doi.org/10.1016/j.learninstruc.2010.05.002
Iiskala, T., Volet, S., Jones, C., Koretsky, M., & Vauras, M. (2021). Significance of forms and foci of metacognitive regulation in collaborative science learning of less and more successful outcome groups in diverse contexts. Instructional Science, 49(5), 687-718. https://doi.org/10.1007/s11251-021-09558-1
Ismail, A. D., Jamil, A. F., & Putri, O. R. U. (2017). Pengembangan modul trigonometri bercirikan open-ended problem. AdMathEdu: Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika Dan Matematika Tera-pan, 7(1), 687-718. https://doi.org/10.12928/admathedu.v7i1.7396
Izzati, L. R., & Mahmudi, A. (2018). The influence of Metacognition in mathematical problem solving. Journal of Physics: Conference Series, 1097(1), 1-7. https://doi.org/10.1088/1742-6596/1097/1/012107
Jin, Q., & Kim, M. (2018). Metacognitive regulation during elementary students’ collaborative group work. Interchange, 49(2), 263–281. https://doi.org/10.1007/s10780-018-9327-4
Kim, Y. R., Park, M. S., Moore, T. J., & Varma, S. (2013). Multiple levels of Metacognition and their elicitation through complex problem-solving tasks. Journal of Mathematical Behavior, 32(3), 377–396. https://doi.org/10.1016/j.jmathb.2013.04.002
Krulik, S., & Rudnick, J. (1995). Teaching reasoning and problem solving in elementary school. London Allyn and Bacon.
Kuzle, A. (2013). Patterns of metacognitive behavior during mathematics problem-solving in a dynamic geometry environment. International Electronic Journal of Mathematics Education, 8(1), 20-40. https://doi.org/10.29333/iejme/272
Lester Jr, F. K. (1994). Musings about mathematical problem-solving research: 1970-1994. Journal for Research in Mathematics Education, 25(6), 660-675. https://doi.org/10.2307/749578
Magiera, M. T., & Zawojewski, J. S. (2011). Characterizations of social-based and self-based contexts associated with students’ awareness, evaluation, and regulation of their thinking during small-group mathematical modeling. Journal for Research in Mathematics Education, 42(5), 486–520. https://doi.org/10.5951/jresematheduc.42.5.0486
Mahanal, S., Zubaidah, S., Setiawan, D., Maghfiroh, H., & Muhaimin, F. G. (2022). Empowering college students’ problem-solving skills through RICOSRE. Education Sciences, 12(3), 1-17. https://doi.org/10.3390/educsci12030196
Mayer, R. E. (1989). Models for Understanding. Review of Educational Research, 59(1), 43–64. https://doi.org/10.3102/00346543059001043
Nancarrow, M. (2004). Exploration of Metacognition and non-routine problem based mathematics instruction on undergraduate student problem solving success. The Florida State University.
Nelson, T. O. (1990). Metamemory: A theoretical framework and new findings. Psychology of Learning and Motivation - Advances in Research and Theory, 26(C), 125-173. https://doi.org/10.1016/S0079-7421(08)60053-5
Olivares, D., Lupiáñez, J. L., & Segovia, I. (2021). Roles and characteristics of problem solving in the mathematics curriculum: A review. International Journal of Mathematical Education in Science and Technology, 52(7), 1079-1096. https://doi.org/10.1080/0020739X.2020.1738579
Özcan, Z. Ç., & Eren Gümüş, A. (2019). A modeling study to explain mathematical problem-solving performance through Metacognition, self-efficacy, motivation, and anxiety. Australian Journal of Education, 63(1), 116-134. https://doi.org/10.1177/0004944119840073
Polya, G. (1945). How to solve it. Princeton University Press.
Rhodes, S. J. (2019). Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling. In Journal of Theoretical Biology,465, 51-55. https://doi.org/10.1016/j.jtbi.2019.01.017
Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In C. O'Mall (ed.). Computer supported collaborative learning. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-85098-1_5
Salminen-Saari, J. F. A., Garcia Moreno-Esteva, E., Haataja, E., Toivanen, M., Hannula, M. S., & Laine, A. (2021). Phases of collaborative mathematical problem solving and joint attention: A case study utilizing mobile gaze tracking. ZDM - Mathematics Education, 53(4), 771–784. https://doi.org/10.1007/s11858-021-01280-z
Schoenfeld, A. H. (1985). Mathematical problem solving. Academia Press.
Schoenfeld, A. H. (2013). Reflections on problem solving theory and practice. Mathematics Enthusiast, 10(1–2), 8-34. https://doi.org/10.54870/1551-3440.1258
Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics (reprint). Journal of Education, 196(2), 1-38. https://doi.org/10.1177/002205741619600202
Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7(4), 351-371. https://doi.org/10.1007/BF02212307
Stacey, K. (2005). The place of problem solving in contemporary mathematics curriculum documents. Journal of Mathematical Behavior, 24(3–4), 341-350. https://doi.org/10.1016/j.jmathb.2005.09.004
Stephanou, G., & Mpiontini, M.-H. (2017). Metacognitive knowledge and metacognitive regulation in self-regulatory learning style, and in its effects on performance expectation and subsequent performance across diverse school subjects. Psychology, 08(12), 1941–1975. https://doi.org/10.4236/psych.2017.812125
Sternberg, R. J., & Sternberg, K. (2012). Cognitive psychology Sixth Edition. Wadsworth.
Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. In Metacognition and Learning, 1(1), 3-14. https://doi.org/10.1007/s11409-006-6893-0
Yusuf, M., Murshid, S. F., Abdul Rahim, S. S., & Kwan Eu, L. (2021). Solving mathematical problems among college students: Process or strategy?. International Journal of Academic Research in Business and Social Sciences, 11(4), 1144–1152. https://doi.org/10.6007/ijarbss/v11-i4/9777
Zhao, N., Teng, X., Li, W., Li, Y., Wang, S., Wen, H., & Yi, M. (2019). A path model for Metacognition and its relation to problem-solving strategies and achievement for different tasks. ZDM - Mathematics Education, 51(4), 641-653. https://doi.org/10.1007/s11858-019-01067-3
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