Pseudo-Evaluative Behavior in Mathematical Problem Solving: An Analysis of Students’ Metacognitive Processes in Matrix Addition
DOI:
10.29303/jm.v8i1.11007Published:
2026-04-13Downloads
Abstract
Metacognitive regulation is widely acknowledged as a key component that supports students in managing their thinking during mathematical problem solving. This study investigates the metacognitive processes demonstrated by a student while completing matrix addition tasks, with a specific focus on the components of planning, monitoring, and evaluating. The research was conducted in a public senior high school in North Halmahera during November and December 2025. One eleventh grade student was purposively selected to provide an in-depth illustration of metacognitive regulation. A qualitative case study design was employed, and data was gathered through written work, direct observation, and semi structured interviews. Analysis was carried out using metacognitive process indicators to capture how the participant organized, controlled, and reviewed each stage of problem solving. The findings show that the participant performed planning effectively by identifying relevant information and determining an approach to the task, and monitoring was evident throughout the solution process. The evaluating phase appeared limited because the participant mainly checked the final answer without engaging in deeper reflection such as analyzing sources of error or comparing alternative procedures. This pattern is characterized in the study as pseudo evaluative behavior, indicating evaluation that is present yet incomplete. The study underscores the need for instructional practices that intentionally strengthen student’s evaluative metacognition during mathematical problem solving.
Keywords:
evaluating matrix addition metacognitive regulation monitoring planning pseudo-evaluati pseudo-evaluative behavior qualitative case studyReferences
Çini, A., Järvelä, S., Dindar, M., & Malmberg, J. (2023). How multiple levels of metacognitive awareness operate in collaborative problem solving. Metacognition and Learning, 18(3), 891–922. https://doi.org/10.1007/S11409-023-09358-7
Craig, K., Hale, D., Grainger, C., & Stewart, M. E. (2020). Evaluating metacognitive self-reports: systematic reviews of the value of self-report in metacognitive research. Metacognition and Learning, 15(2), 155–213. https://doi.org/10.1007/S11409-020-09222-Y
DeJonckheere, M., & Vaughn, L. M. (2019). Semistructured interviewing in primary care research: A balance of relationship and rigour. Family Medicine and Community Health, 7(2). https://doi.org/10.1136/FMCH-2018-000057
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906
Güner, P., & Erbay, H. N. (2021). Metacognitive Skills and Problem-Solving. International Journal of Research in Education and Science, 7(3), 715–734. https://doi.org/10.46328/IJRES.1594
Hidayat, R., Hermandra, & Ying, S. T. D. (2023). The sub-dimensions of metacognition and their influence on modeling competency. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/S41599-023-02290-W
Jupri, A., & Sispiyati, R. (2020). Students’ algebraic proficiency from the perspective of symbol sense. Indonesian Journal of Science and Technology, 5(1), 86–94. https://doi.org/10.17509/IJOST.V5I1.23102
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. https://doi.org/10.1037/0022-3514.77.6.1121
Miles, Matthew B.; Huberman, A. Michael; Saldaña, J. (2014). Qualitative Data Analysis: A Methods Sourcebook. In SAGE Publications, Inc. (3rd ed.). SAGE Publications, Inc.
Muncer, G., Higham, P. A., Gosling, C. J., Cortese, S., Wood-Downie, H., & Hadwin, J. A. (2022). A Meta-Analysis Investigating the Association Between Metacognition and Math Performance in Adolescence. Educational Psychology Review, 34(1), 301–334. https://doi.org/10.1007/S10648-021-09620-X
Poth, C. N., Bulut, O., Aquilina, A. M., & Otto, S. J. G. (2021). Using Data Mining for Rapid Complex Case Study Descriptions: Example of Public Health Briefings During the Onset of the COVID-19 Pandemic. Journal of Mixed Methods Research, 15(3), 348–373. https://doi.org/10.1177/15586898211013925
Santos, K. da S., Ribeiro, M. C., de Queiroga, D. E. U., da Silva, I. A. P., & Ferreira, S. M. S. (2020). The use of multiple triangulations as a validation strategy in a qualitative study. Ciencia e Saude Coletiva, 25(2), 655–664. https://doi.org/10.1590/1413-81232020252.12302018
Scheibe, D. A., Was, C. A., Dunlosky, J., & Thompson, C. A. (2023). Metacognitive Cues, Working Memory, and Math Anxiety: The Regulated Attention in Mathematical Problem Solving (RAMPS) Framework. Journal of Intelligence, 11(6). https://doi.org/10.3390/JINTELLIGENCE11060117
Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational Psychology Review, 7(4), 351–371. https://doi.org/10.1007/BF02212307/METRICS
Sutama, S., Anif, S., Prayitno, H. J., Narimo, S., Fuadi, D., Sari, D. P., & Adnan, M. (2021). Metacognition of Junior High School Students in Mathematics Problem Solving Based on Cognitive Style. Asian Journal of University Education, 17(1), 134–144. https://doi.org/10.24191/AJUE.V17I1.12604
Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66. https://doi.org/10.1016/J.CEDPSYCH.2021.101976
License
Copyright (c) 2026 Dediromario Wasahua, Surya Sari Faradiba
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
