Examining the Correlation and Predictive Power of Metacognitive Domains on Mathematics Performance Among Senior High School Students

Authors

  • Jose A. Catador Jr. Kapayapaan Integrated School, Division of Calamba City, Laguna, Philippines

DOI:

https://doi.org/10.69569/jip.2024.0192

Keywords:

Mathematics performance, Metacognitive domains, Senior high school

Abstract

This study aims to determine the correlation and predictors of students’ performance in mathematics based on the eight domains of metacognition: declarative, procedural, conditional, planning, information management strategy, comprehension monitoring, debugging strategy, and evaluation. This study employed a descriptive-correlational research design conducted with 272 Senior High School Grade 11 students enrolled in the academic track. The research instruments utilized were the Metacognitive Awareness Inventory to assess students’ metacognitive awareness while the multiple-choice test was used to measure students’ performance in mathematics, with reliability coefficients of .853 and .790, respectively. Descriptive and inferential statistics, such as Chi-square and multiple regression analysis, were employed to interpret and analyze the data. Utilizing the descriptive statistics, results reported that metacognitive knowledge attained a mean score of 3.69 while the metacognitive control/regulation obtained a mean score of 3.65 with an overall mean score of 3.67 interpreted as aware, respectively. Furthermore, the results revealed a significant association between students’ performance in mathematics and their metacognitive awareness. The study highlighted that high-performing students in mathematics were those who effectively utilized and managed their metacognitive awareness. Moreover, it was found that greater awareness of metacognitive thinking correlated with better performance in mathematics. Additionally, the results indicated that 75.3% of metacognitive domains contributed to students’ success in mathematics. However, only declarative, procedural, conditional, and debugging strategies significantly predicted students’ success in mathematics. This suggests that students who effectively use and manage these specific metacognitive skills are more likely to excel in mathematics. In essence, this study highlights the crucial role of metacognition in mathematics learning. By fostering students’ awareness and utilization of these powerful thinking strategies, teachers can empower learners to excel in mathematics and beyond.

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References

Abari, T. & Tyovenda, T. (2021). Effect of Metacognition on Secondary School Students' Interest in Mathematics in Gwer-East Local Government Area of Benue State. International Journal of Advance in Engineering and Management, 3(12):297-302. https://rb.gy/8fxdgm

Abdelrahman, R.M. (2020). Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon 6(9). doi: 10.1016/j.heliyon.2020.e04192

Ayodele, C.S.& Adeoye, O. (2022). Meta-Cognitive Ability and Students’ Academic Performance. International Research Journal of Modernization in Engineering Technology and Science, Vol. 4, Issue: 05/May-2022.

Belet, S. D. & Guven, M. (2011). Meta-cognitive Strategy: Usage and Epistemological Beliefs of Primary School Teacher Trainees. Educational Sciences Theory & Practice, 11(1):51-57. https://tinyurl.com/5a26rwd2

Chytrý, V.; Říčan, J.; Eisenmann, P.; Medová, J. (2020). Metacognitive Knowledge and Mathematical Intelligence—Two Significant Factors Influencing School Performance. Mathematics 8, 969. https://doi.org/10.3390/math8060969

Department of Education. (2020). Interim Guidelines for Assessment and Grading in Light of the Basic Education Learning Continuity Plan. In https://www.deped.gov.ph/wp-content/uploads/2020/10/DO_s2020_031.pdf

Du Toit, S. D., & Kotze, G. (2009). Metacognitive strategies in the teaching and learning of mathematics. Pythagoras, 70, 57-67. DOI:10.4102/pythagoras.v0i70.39

Efklides, A. (2008). Metacognition: Defining its facets and levels of functioning in relation to self-and co-regulation. European Psychologist, 13, 277-287. DOI:10.1027/1016-9040.13.4.277

Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46, 6-25. DOI:10.1080/00461520.2011.538645

Flavell, J. (1979). Metacognition and Cognitive Monitoring. A New Era of Cognitive-Developmental Inquiry. American Psychologist, 34(10), 906–911. DOI:10.1037/0003-066X.34.10.906

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. DOI:10.1023/A:1016209010120

Ibabe, I., Jauregizar, J. (2010). Online Self-assessment with Feedback and Metacognitive Knowledge. International Journal of Higher Education Research, 59(2):243-258. DOI:10.1007/s10734-009-9245-6

Jacobse, A. E., & Harskamp, E. G. (2009). Student-controlled metacognitive training for solving word problems in primary school mathematics. Educational Research and Evaluation, 15(5), 447–463. DOI:10.1080/13803610903444519

Kramarksi, B., & Friedman, S. (2014). Solicited versus unsolicited metacognitive prompts for fostering mathematical problem-solving using multimedia. Journal of Educational Computing Research, 50(3), 285–314. DOI:10.2190/EC.50.3.a

Kuhn, D. & Dean, D. (2004). A Bridge Between Cognitive Psychology and Educational Practice. Theory into Practice, 43(4), 268-273. DOI:10.1207/s15430421tip4304_4

Lai, Emily R. (2011). Metacognition: A Literature Review. Psychology, Education. https://api.semanticscholar.org/CorpuzID:146606759

Menz, P., & Cindy Xin (2016). Making Students’ Metacognitive Knowledge Visible through Reflective Writing in a Mathematics-for-Teachers Course. Collected Essays on Learning and Teaching, 9, 155-166. DOI:10.22329/celt.v9i0.4426

Nongtodu, S., & Bhutia, Y. (2017). Metacognition and its Relation with Academic Achievement among college-going Students of Meghalaya. International Journal of Education and Psychological Research (IJEPR), 6(2), 54-60. https://tinyurl.com/4n2suk3h

Ozçakmak, H., Köroğlu, M., Korkmaz, C. & Bolat, Y. (2021). The Effect of Metacognitive Awareness on Academic Success. African Educational Research Journal Vol. 9(2), pp. 434-448. DOI: 10.30918/AERJ.92.21.020

Ozsoy, G. (2011). An investigation of the relationship between metacognition and mathematics achievement. Asia Pacific Educational Review, 12(2):227-235. DOI:10.1007/s12564-010-9129-6

Ozsoy, G. & Ataman, A. (2009). The effect of metacognitive strategy training on problem solving achievement. International Electronic Journal of Elementary Education, 1(2), 67–82. https://files.eric.ed.gov/fulltext/ED508334.pdf

Radmehr, F. & Drake, M. (2020). Exploring Students’ Metacognitive Knowledge: The Case of Integral Calculus. Education Sciences, 10, 55. https://doi.org/10.3390/educsci10030055

Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26(1-2), 113-125. DOI:10.1023/A:1003044231033

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460-475. https://doi.org/10.1006/ceps.1994.1033

Sousa, V., Driessnack, M. & Mendes, I. (2007). Part 1: Quantitative Research Designs. Revista Latino-Americana de Enfermagem, 15(3): 502-7. DOI:10.1590/S0104-11692007000300022

Stanton, J. D., Sebesta, A. J. & Dunlosky, J. (2021). Fostering Metacognition to Support Student Learning and Performance. CBE Life Sci Educ, 20(2). https://doi.org/10.1187/cbe.20-12-0289

Swanson, H. L. (1990). Influence of metacognitive knowledge and aptitude on problem solving. Journal of Educational Psychology, 82(2), 306-314. DOI:10.1037/0022-0663.82.2.306

Tarricone, P. (2011). The taxonomy of metacognition. US: Psychology Press, New York. eBook: DOI:10.4324/9780203830529

Van der Stel, M., Veenman, M. V. J., Deelen, K., & Haenen, J. (2010). The increasing role of metacognitive skills in math: A cross-sectional study from a developmental perspective. ZDM Mathematics Education, 42(2), 219–229. DOI:10.1007/s11858-009-0224-2

Veenman, M. V. J., Kok, R., & Blote, A. W. (2005). The relation between intellectual and metacognitive skills in early adolescence. Instructional Science, 33(3), 193–211. DOI:10.1007/s11251-004-2274-8

Wilson, D. & Conyers, M. (2016). Teaching students to drive their brains: Metacognitive strategies, activities, and lesson ideas. Alexandria, Virginia, USA: ASCD, [2016]. https://searchworks.stanford.edu/view/11749372

Yunus, M., Suraya, A., Zah, W. & Ali, W. (2009). Motivation in the Learning of Mathematics. European Journal of Social Sciences, 7(4). Retrieved from http://bit.ly/2yNhZBu

Zafari, Y., Meskini, H. (2015). The Effect of Metacognitive Instruction on Problem Solving Skills in Iranian Students of Health Sciences. Global Journal of Health Science, 8(1). DOI:10.5539/gjhs.v8n1p150

Zulkiply, N. (2008). Metacognition and its Relationship with Students’ Academic Performance. Semantic Scholar. https://api.semanticscholar.org/CorpusID:141162315

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Published

2024-05-28

How to Cite

Catador , J. J. (2024). Examining the Correlation and Predictive Power of Metacognitive Domains on Mathematics Performance Among Senior High School Students. Journal of Interdisciplinary Perspectives, 2(7), 446–454. https://doi.org/10.69569/jip.2024.0192