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COLLABORATIVE METACOGNITIVE ACTIVITIES, STUDENTS’ SOCIALLY MOTIVATED METACOGNITIVE EXPERIENCES, AND STOICHIOMETRIC PROBLEM-SOLVING
Corresponding Author(s) : Fitzgerald L. Fabelico
Humanities & Social Sciences Reviews,
Vol. 8 No. 4 (2020): July
Abstract
Purpose of the study: This study investigates the effect of collaborative metacognitive activities (CMA) on students’ socially motivated metacognitive experiences (SMME) during stoichiometric problem-solving.
Methodology: This descriptive research employed mixed methods. To document and analyze students’ CMA, dual coding process, discourse analysis, and social network analyses were used. There were 18 participants selected purposively and grouped homogeneously based on their academic ability.
Main Findings: The findings revealed that CMA affects students’ SMME quantitatively in stoichiometric problem-solving across ability groups and chemistry tasks. Successful collaboration occurs when feedback requests and other monitoring responses on the assessment of understanding and strategy influences students’ estimates of solution correctness and feeling of satisfaction across ability groups and affects the feeling of difficulty across chemistry tasks.
Applications of this study: This study will help teachers design student activities that could capitalize on the advantages of collaborative metacognitive activities to help students achieve successful collaboration during problem-solving activities not only in chemistry but also in the allied fields.
Novelty/Originality of this study: This study explains clearly the relevance of CMA and SMME during stoichiometric problem-solving. Moreover, this study elucidated the mechanism of successful and unsuccessful collaboration used by the different groups of students in solving the algorithmic and conceptual chemistry tasks.
Keywords
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Efklides, A. (2006). Metacognition and affect: What can metacognitive experiences tell us about the learning process? Educational Research Review, 1: 3-14. https://doi.org/10.1016/j.edurev.2005.11.001 DOI: https://doi.org/10.1016/j.edurev.2005.11.001
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Etokeren, S., Ibemenji, K., & Alamina, J. (2019). Effect of problem-solving teaching technique on students’ stoichiometry academic performance in senior secondary school chemistry in Nigeria. Asian Journal of Advanced Research and Reports, 4 (3): 1 - 11. https://doi.org/10.9734/ajarr/2019/v4i330110 DOI: https://doi.org/10.9734/ajarr/2019/v4i330110
Fabelico, F.L. (2014). Social interaction and ability grouping: Their effects on students’ metacognitive experiences in stoichiometric problem-solving. Asia Pacific Journal of Education, Arts and Sciences, 1 (4): 129-136. https://www.academia.edu/8553503
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Gulacar, O., Tan, A., Cox, C. Jr., Bloomquist, J., Jimmy, O. & Cao, N. (2019). Analyzing characteristics of experts in the context of stoichiometric problem-solving. Education Sciences, 9 (2019): 1-11. https://doi.org/10.3390/educsci9030219 DOI: https://doi.org/10.3390/educsci9030219
Gupta, T. (2020). Promoting mathematical reasoning and problem solving through inquiry-based relevance focused computer simulations: A stoichiometry lab. Chemistry Teacher International, 1 (1): 1-12. https://doi.org/10.1515/cti-2018-0008 DOI: https://doi.org/10.1515/cti-2018-0008
Hurme, TR., Merenluoto, K., & Jarvela, S. (2009). Socially shared metacognition of pre-service primary teachers in a computer-supported mathematics course and their feelings of task difficulty: A case study. Educational Research and Evaluation: An International Journal on Theory and Practice, 15 (5): 503-524. https://doi.org/10.1080/13803610903444659 DOI: https://doi.org/10.1080/13803610903444659
Hurme, TR., Palonen, T., & Jarvela, S. (2006). Metacognition in joint discussions: An analysis of the patterns of interaction and the metacognitive content of the networked discussions in mathematics. Metacognition Learning, 1: 181 - 200. https://doi.org/10.1007/s11409-006-9792-5 DOI: https://doi.org/10.1007/s11409-006-9792-5
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Ijirana & Supriadi. (2018). Metacognitive skills profiles of chemistry education students in solving at low ability level. Journal Pendidikan IPA Indonesia, 7 (2): 239-245, https://doi.org/10.15294/jpii.v7i2.14266 DOI: https://doi.org/10.15294/jpii.v7i2.14266
Iriani, R., Norjanah I., & Kusasi M. (2019). The development of electronic publication module integrated with a means-end analysis learning model to improve students’ analytical thinking skills in stoichiometry materials. Advances in Social Sciences, Education and Humanities Research, 407: 1 - 4. https://doi.org/10.2991/assehr.k.200219.056 DOI: https://doi.org/10.2991/assehr.k.200219.056
Jagals, D. & Van der Walt, M. (2016). Exploiting metacognitive networks embedded in narrative focus group interviews using NodeXL. The Qualitative Report, 21 (10): 1868 -1880. https://search.proquest.com/openview/df220566b53a685314586efec445eaea/
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