Moderating Effects of Educational Level and Mathematical Competence on the Effectiveness of Cognitive Conflict Strategy: A Meta-Analysis
DOI:
https://doi.org/10.32938/jpm.v7i1.9531Keywords:
cognitive conflict strategy, educational level, mathematical competence, mathematics learning, meta-analysisAbstract
This study aims to analyze the effectiveness of the cognitive conflict strategy in mathematics learning and to examine whether educational level and mathematical competence act as moderators in this strategy. A meta-analysis approach was conducted on 41 effect sizes. Data were analyzed using a random-effects model, with effect sizes calculated using Hedges' g. The data analysis was performed using the Comprehensive Meta-Analysis (CMA) software to ensure the accuracy of effect size calculations, heterogeneity tests, and moderator analyses. The results show that the cognitive conflict strategy has a significant effect (g = 1.047; p < 0.05). Additionally, mathematical competence was found to be a significant moderator (p < 0.05), with the highest effect sizes observed in conceptual understanding (g = 1.712) and critical thinking (g = 1.355). However, the educational level did not serve as a significant moderator (p = 0.092), indicating that the cognitive conflict strategy is beneficial across various educational levels. This study concludes that the cognitive conflict strategy is effective in enhancing mathematics learning, especially in conceptual understanding and critical thinking. Its novelty lies in identifying mathematical competence, not educational level, as a significant moderator. This implies that implementation should be adjusted based on the targeted competence. Future research may explore other potential moderators, including learner traits, instructional design, and technology use, to refine its application.
References
Azevedo, R., & Aleven, V. (2013). Metacognition and Learning Technologies: An Overview of Current Interdisciplinary Research. In Springer International Handbooks of Education (Vol. 28, pp. 1–16). Springer Nature. https://doi.org/10.1007/978-1-4419-5546-3_1
Berlin, J. A., & Golub, R. M. (2014). Meta-analysis as evidence: Building a better pyramid. Jama, 312(6), 603–605. https://doi.org/10.1001/jama.2014.8167
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta‐Analysis. In Principles and Practice of Clinical Trials. Wiley. https://doi.org/10.1002/9780470743386
Brusilovsky, P., & Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems. In The Adaptive Web (pp. 3–53). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_1
Chi, M. T. H., & Wylie, R. (2014). The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823
Chow, T.-C. F., & Treagust, D. F. (2013). An Intervention Study Using Cognitive Conflict to Foster Conceptual Change. Journal of Science and Mathematics, 36(1), 44–64.
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003
Duval, R. (2017). Understanding the Mathematical Way of Thinking – The Registers of Semiotic Representations. Springer International Publishing. https://doi.org/10.1007/978-3-319-56910-9
Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629
Fitri, S., Syahputra, E., & Syahputra, H. (2019). Blended Learning Rotation Model Of Cognitive Conflict Strategy To Improve Mathematical Resilience In High School Students. Article in International Journal of Scientific & Technology Research, 8. www.ijstr.org
Goldin, G., & Kaput, J. (1996). A joint perspective on the idea of representation in learning and doing mathematics (pp. 397–430).
Hasanah, M., & Suharso, A. (2023). Algoritma Haversine pada Sistem Informasi Geografis: Tinjauan Literatur Sistematis. Nuansa Informatika, 17(2), 135–143. https://doi.org/10.25134/ilkom.v17i2.10
Hedges, L. V, & Olkin, I. (1985). Statistical Methods for Engineers. Academic Pres, Inc: New york.
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. In British Medical Journal (Vol. 327, Issue 7414, pp. 557–560). https://doi.org/10.1136/bmj.327.7414.557
Ismaimuza, D. (2010). Pengaruh Pembelajaran Berbasis Masalah Dengan Strategi Konflik Kognitif Terhadap Kemampuan Berpikir Kritis Matematis Dan Sikap Siswa SMP. Jurnal Pendidikan Matematika, 4(1).
Kang, H., Scharmann, L. C., Kang, S., & Noh, T. (2010). Cognitive conflict and situational interest as factors influencing conceptual change. International Journal of Environmental and Science Education, 5(4), 383–405. http://www.ijese.com/
Kapur, M. (2014). Productive failure in learning math. Cognitive Science, 38(5), 1008–1022. https://doi.org/10.1111/cogs.12107
Kapur, M. (2016). Examining Productive Failure, Productive Success, Unproductive Failure, and Unproductive Success in Learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457
Khairunnisa, K., Sari, F. F., Anggelena, M., Agustina, D., & Nursa’adah, E. (2022). Penggunaan Effect Size Sebagai Mediasi dalam Koreksi Efek Suatu Penelitian. Jurnal Pendidikan Matematika (Judika Education), 5(2), 138–151. https://doi.org/10.31539/judika.v5i2.4802
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2), 75–86. https://doi.org/10.1207/s15326985ep4102_1
Lee, G., & Kwon, J. (2001). What Do We Know about Students’ Cognitive Conflict in Science Classroom : A Theoretical Model of Cognitive Conflict Process. AETS Annual Meeting, 309–325.
Maulana, R., & Eliasan, E. I. (2024). Eksplorasi Ciri Khas dan Tugas Perkembangan Anak Usia Dini (2-6 Tahun): Implikasi Fisik, Kognitif, dan Sosio-Emosi dalam Pendidikan dan Pengasuhan. EDUCATIONAL : Jurnal Inovasi Pendidikan & Pengajaran, 4(4), 239–252. https://doi.org/10.51878/educational.v4i4.3404
Nakagawa, S., Lagisz, M., Jennions, M. D., Koricheva, J., Noble, D. W. A., Parker, T. H., Sánchez‐Tójar, A., Yang, Y., & O’Dea, R. E. (2022). Methods for testing publication bias in ecological and evolutionary meta‐analyses. Methods in Ecology and Evolution, 13(1), 4–21. https://doi.org/10.1111/2041-210X.13724
Ngicho, D. O., Karuku, S., & King’endo, M. (2020). Manifestations and meanings of cognitive conflict among mathematics students in Embu, Kenya. Educational Research and Reviews, 15(11), 690–699. https://doi.org/10.5897/ERR2020.4061
Nugraha, T., & Suparman, S. (2021). Heterogeneity of Indonesian primary school students’ mathematical critical thinking skills through problem-based learning: A meta-analysis. Al-Jabar : Jurnal Pendidikan Matematika, 12(2), 315–328. https://doi.org/10.24042/ajpm.v12i2.9645
Onesimus Laia, H., & Dasari, D. (2025). Meta-Analysis: Effectiveness of Reciprocal Teaching Model on Mathematics Learning Outcomes. Mathline : Jurnal Matematika Dan Pendidikan Matematika, 10(1), 29–43. https://doi.org/10.31943/mathline.v10i1.688
Onesimus Laia, H., Martadiputra, B. A. P., & Dahlan, J. A. (2025). The Effect of Logical-Mathematical Intelligence on Mathematics Learning and Moderator Analysis : A Meta- Analysis. Al-Jabar: Jurnal Pendidikan Matematika, 16(01), 129–149. https://doi.org/10.24042/ajpm.v16i1.25539
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372. https://doi.org/10.1136/bmj.n71
Palinscar, A. S., & Brown, A. L. (2009). Reciprocal Teaching of Comprehension-Fostering and Comprehension Monitoring Activities. Cognition and Instruction, 1(2), 117–175. https://doi.org/10.1207/s1532690xci0102
Piaget, J. (1977). The Development of Thought. Equilibration of Cognitive Structures. Oxford: Basil Blackwell.
Piaget, J., & Inhelder, B. (2000). The Psychology of the Child. Basic Books, 1290 Avenue of the Americas, New York, NY 10104. https://doi.org/10.2307/j.ctv19fvxpz.8
Pratiwi, E., Nanna, A. W. I., & Wulandari, A. E. (2022). Peran Strategi Konflik Kognitif pada Proses Penyeleaian Masalah Geometri. SIGMA, 8(1), 68. https://doi.org/10.53712/sigma.v8i1.1743
Puspasari, R. (2017). Strategi Konflik Kognitif (Cognitive Conflict) dalam Mengatasi Miskonsepsi Siswa. JP2M (Jurnal Pendidikan Dan Pembelajaran Matematika), 3(1), 1. https://doi.org/10.29100/jp2m.v3i1.285
Putra, R., Fauzan, A., & Habibi, M. (2020). The Impact of Cognitive Conflict Based Learning Tools on Students` Mathematical Problem Solving Ability. International Journal of Educational Dynamics, 2(1), 209–218. https://doi.org/10.24036/ijeds.v2i1.247
Rabab’ah, Y. (2024). Effect of Cognitive Conflict Strategy and Motivation on Conceptual Change in Algebra. International Journal of Academic Research in Progressive Education and Development, 13(1). https://doi.org/10.6007/IJARPED/v13-i1/21207
Retnawati, H., Apino, E., Kartianom, Djidu, H., & Anazifa, R. D. (2018). Pengantar Analisis Meta. In Yogyakarta : Parama Publishing (Issue July).
Ruppar, T. (2020). Meta-analysis: How to quantify and explain heterogeneity? European Journal of Cardiovascular Nursing, 19(7), 646–652. https://doi.org/10.1177/1474515120944014
Sedgwick, P. (2015). Meta-analyses: what is heterogeneity? BMJ, 350(mar16 1), h1435–h1435. https://doi.org/10.1136/bmj.h1435
Sfard, A. (2008). Thinking as Communicating: Human development, the growth of discourses, and mathematizing. Cambridge University Press. https://doi.org/10.1017/CBO9780511499944
Shandy, A. N. (2023). Kemampuan Berpikir Kritis dan Kepercayaan Diri Siswa pada Pembelajaran dengan Strategi Konflik Kognitif. Jurnal Ilmu Pendidikan Dan Psikologi (JIPP), 1(4), 176–183. https://doi.org/10.61116/jipp.v1i4.259
Sholihah, D. A., & Shanti, W. N. (2018). Pembelajaran Konflik Kognitif Untuk Meningkatkan Kemampuan Berpikir Kritis Matematis Siswa. UNION: Jurnal Ilmiah Pendidikan Matematika, 6(1), 71–82. https://doi.org/10.30738/.v6i1.1999
Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rücker, G., Harbord, R. M., Schmid, C. H., Tetzlaff, J., Deeks, J. J., Peters, J., Macaskill, P., Schwarzer, G., Duval, S., Altman, D. G., Moher, D., & Higgins, J. P. T. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ (Online), 343(7818), 1–8. https://doi.org/10.1136/bmj.d4002
Sujana, A., Rifa’i, R., & Astuti, N. (2019). Penerapan Strategi Konflik Kognitif untuk Meningkatkan Kemampuan Berpikir Kritis Matematis Siswa SMP. Jurnal Penelitian Dan Pembelajaran Matematika, 12(1). https://doi.org/10.30870/jppm.v12i1.4864
Tall, D., & Vinner, S. (1981). Concept Image and Concept Definition in Mathematics with Particular Reference to Limits and Continuity.
Thalheimer, W., & Cook, S. (2002). How to calculate effect sizes from published research. Work-Learning Research, 1(August), 1–9. www.work-learning.com
Verawati, N. N. S. P., & Afifah, G. (2018). Efek Penggunaan Strategi Konflik Kognitif terhadap Hasil Belajar Kognitif Siswa. Prisma Sains : Jurnal Pengkajian Ilmu Dan Pembelajaran Matematika Dan IPA IKIP Mataram, 6(2), 113. https://doi.org/10.33394/j-ps.v6i2.1081
Widia, W., Suhirman, S., Suhardi, M., Prayogi, S., Yamin, M., Salahuddin, M., Haryanto, L., Ewisahrani, E., E Nursa’ban, E. N., Ilyas, I., & Mujitahid, M. (2022). The Effect of Cognitive Conflict Strategies on Students’ Cognitive Learning Outcomes. Jurnal Penelitian Pendidikan IPA, 8(1), 388–392. https://doi.org/10.29303/jppipa.v8i1.1308
Zhou, M., & Brown, D. (2017). Educational Learning Theory. Education Open Textbooks. https://doi.org/10.4324/9780203062920-11
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