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Jurnal Riset Pendidikan Matematika

Keywords

cognitive style, field dependence, field independence, metacognitive failure, problem-solving

Document Type

Article

Abstract

This study aims to reveal the metacognitive failures experienced by mathematics pre-service teachers based on their mistakes when solving problems in online learning during the pandemic era. This case study involved 29 participants who attended the mathematical problem test and cognitive style test, the two participants were categorized based on their cognitive style: Field Dependence (FD) and Field Independence (FI). The instrument used was a mathematical problem test to collect data on metacognitive that adapted from Stewart and a cognitive styles test to categorize the cognitive style that adapted from the Group Embedded Figures Test (GEFT). An interview was conducted to determine the nature of mathematical error based on metacognitive failure. The description of data analysis and interpretation of the meaning of the findings applied the text analysis. The results showed the different metacognitive failures of the two participants. The metacognitive failure of FI student was categorized as metacognitive blindness and the FD student was categorized as metacognitive stagnation, a new condition of metacognitive failure that was found in this study.

Page Range

179-190

Issue

2

Volume

8

Digital Object Identifier (DOI)

10.21831/jrpm.v8i2.43366

Source

https://journal.uny.ac.id/index.php/jrpm/article/view/43366

References

Abdullah, A. H., Mokhtar, M., Halim, N. D. A., Ali, D. F., Tahir, L. M., & Kohar, U. H. A. (2017). Mathematics teachers' level of knowledge and practice on the implementation of higher-order thinking skills (HOTS). Eurasia Journal of Mathematics, Science and Technology Education, 13(1), 3-17. https://doi.org/10.12973/eurasia.2017.00601a

Amin, I., & Sukestiyarno, Y. L. (2015). Analysis metacognitive skills on learning mathematics in high school. International Journal of Education and Research, 3(3), 213-222. http://www.ijern.com/journal/2015/March-2015/18.pdf

Faradiba, S. S., Sa'dijah, C., Parta, I. N., & Rahardjo, S. (2019). Looking without seeing: The role of metacognitive blindness of student with high math anxiety. International Journal of Cognitive Research in Science, Engineering and Education, 7(2), 53-65. https://doi.org/10.5937/IJCRSEE1902053F

Gagatsis, A., & Kyriakides, L. (2000). Teachers' attitudes towards their pupils' mathematical errors. Educational Research and Evaluation, 6(1), 24-58. https://doi.org/10.1076/1380-3611(200003)6:1;1-i;ft024

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

Hidayah, I. N., Sa'dijah, C., Subanji, & Sudirman. (2021). The students' cognitive engagement in online mathematics learning in the pandemic Covid-19 era. AIP Conference Proceedings, 2330(March). https:/doi.org/10.1063/5.0043567

Kashefi, H., Ismail, Z., & Yusof, Y. M. (2012). Overcoming students obstacles in multivariable calculus through blended learning: A mathematical thinking approach. Procedia - Social and Behavioral Sciences, 56(Ictlhe), 579-586. https://doi.org/10.1016/j.sbspro.2012.09.691

Kashefi, H., Ismail, Z., Yusof, Y. M., & Rahman, R. A. (2012). Fostering mathematical thinking in the learning of multivariable calculus through computer-based tools. Procedia - Social and Behavioral Sciences, 46, 5534-5540. https://doi.org/10.1016/j.sbspro.2012.06.471

Kozhevnikov, M., Evans, C., & Kosslyn, S. M. (2014). Cognitive style as environmentally sensitive individual differences in cognition: A modern synthesis and applications in education, business, and management. Psychological Science in the Public Interest, Supplement, 15(1), 3-33. https://doi.org/10.1177/1529100614525555

Lester, F. K. (2013). Thoughts about research on mathematical problem-solving instruction. The Mathematics Enthusiast, 10(1), 245-278. https://doi.org/10.54870/1551-3440.1267

Li, S., Lajoie, S. P., Zheng, J., Wu, H., & Cheng, H. (2021). Automated detection of cognitive engagement to inform the art of staying engaged in problem-solving. Computers and Education, 163, 104114. https://doi.org/10.1016/j.compedu.2020.104114

Ling, C., & Salvendy, G. (2009). Effect of evaluators' cognitive style on heuristic evaluation: Field dependent and field independent evaluators. International Journal of Human Computer Studies, 67(4), 382-393. https://doi.org/10.1016/j.ijhcs.2008.11.002

Luo, Z. (2013). A framework mathematics as for examining knowledge teacher analysis used. Knowing and Using Mathematics in Teaching, 29(3), 22-25. https://www.jstor.org/stable/25594562

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

Mefoh, P. C., Nwoke, M. B., Chukwuorji, J. B. C., & Chijioke, A. O. (2017). Effect of cognitive style and gender on adolescents' problem solving ability. Thinking Skills and Creativity, 25, 47-52. https://doi.org/10.1016/j.tsc.2017.03.002

Murni, A., Sabandar, J., Kusumah, Y. S., & Kartasamita, B. G. (2013). The enhancement of junior high school student's skill-based metacognitive learning. Journal on Mathematics Education, 4(2), 194-203. https://doi.org/10.22342/jme.4.2.554.194-203

Nizlel, H., Subanji, Toto, N., Susiswo, Akbar, S., & Swasono, R. (2016). University students metacognitive failures in mathematical proving investigated based on the framework of assimilation and accommodation. Educational Research and Reviews, 11(12), 1119-1128. https://doi.org/10.5897/err2016.2721

Nosratinia, M., & Adibifar, S. (2014). The effect of teaching metacognitive strategies on field-dependent and independent learners' writing. Procedia - Social and Behavioral Sciences, 98, 1390-1399. https://doi.org/10.1016/j.sbspro.2014.03.557

Oh, E., & Lim, D. (2005). Cross relationships between cognitive styles and learner variables in online learning environment. Journal of Interactive Online Learning, 4(1), 53-66. https://eric.ed.gov/?id=EJ1066791

Palmer, E. C., David, A. S., & Fleming, S. M. (2014). Effects of age on metacognitive efficiency. Consciousness and Cognition, 28(1), 151-160. https://doi.org/10.1016/j.concog.2014.06.007

Patricia Aguilera-Hermida, A. (2020). College students' use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011. https://doi.org/10.1016/j.ijedro.2020.100011

Rahiem, M. D. H. (2021). Remaining motivated despite the limitations: University students' learning propensity during the COVID-19 pandemic. Children and Youth Services Review, 120, 105802. https://doi.org/10.1016/j.childyouth.2020.105802

Richardson, J. C., & Newby, T. (2006). The role of students' cognitive engagement in online learning. International Journal of Phytoremediation, 21(1), 23-37. https://doi.org/10.1207/s15389286ajde2001_3

Riding, R., & Rayner, S. (2020). Cognitive style and learning. Cognitive Styles and Learning Strategies, March, 145-168. https://doi.org/10.4324/9781315068015-13

Shekhar, M., & Rahnev, D. (2021). Sources of metacognitive inefficiency. Trends in Cognitive Sciences, 25(1), 12-23. https://doi.org/10.1016/j.tics.2020.10.007

Shukor, N. A., Tasir, Z., Van der Meijden, H., & Harun, J. (2014). A predictive model to evaluate students' cognitive engagement in online learning. Procedia - Social and Behavioral Sciences, 116(2006), 4844-4853. https://doi.org/10.1016/j.sbspro.2014.01.1036

Son, A. L., Darhim, & Fatimah, S. (2020). Students' mathematical problem-solving ability based on teaching models intervention and cognitive style. Journal on Mathematics Education, 11(2), 209-222. https://doi.org/10.22342/jme.11.2.10744.209-222

Stewart, J. (2005). Multivariable calculus: Concepts and contexts. Belmont CA: Thomson Brooks/Cole.

Vula, E., Avdyli, R., Berisha, V., Saqipi, B., & Elezi, S. (2017). The impact of metacognitive strategies and self-regulating processes of solving math word problems. International Electronic Journal of Elementary Education, 10(1), 49-59. https://doi.org/10.26822/iejee.2017131886

Wilson, D., & Conyers, M. (2016). Teaching students to drive their brains: Metacognitive strategies, activities, and lesson ideas. Alexandria, VA: ASCD.

Zheng, F., Khan, N. A., & Hussain, S. (2020). The COVID 19 pandemic and digital higher education: Exploring the impact of proactive personality on social capital through internet self-efficacy and online interaction quality. Children and Youth Services Review, 119, 105694. https://doi.org/10.1016/j.childyouth.2020.105694

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