Sandra Agustina (1), Nizlel Huda (2), Syamsir Sainnudin (3)
Background: Mathematics education fosters logical and critical thinking, where metacognition—students’ awareness and regulation of their thinking—plays a pivotal role. Specific background: Despite the growing number of studies, a comprehensive mapping of research patterns in student metacognition remains limited. Knowledge gap: Prior works emphasize classroom applications rather than systematic bibliometric visualization. Aims: This study aims to analyze global trends, thematic clusters, and temporal developments in student metacognition research within mathematics education. Results: From 362 Scopus-indexed articles (2015–2025), publication growth is dominated by mathematics (42.2%) and social sciences (41.7%). Keyword mapping identifies four clusters: metacognitive strategies and skills, conceptual frameworks, affective and individual factors, and metacognitive dimensions. Temporal mapping shows a thematic shift from conceptual foundations to technology integration, motivation, and collaboration. Novelty: The study integrates systematic literature review and bibliometric visualization using VOSviewer to provide a holistic picture of metacognition research. Implications: Findings offer insights for educators and researchers to design reflective, self-regulated, and collaborative learning strategies grounded in metacognitive awareness.
Publication trends show increasing attention to metacognition in mathematics learning.
Four major research clusters reveal multidimensional themes in metacognitive studies.
Recent studies emphasize motivation, technology, and collaboration in metacognition research.
Metacognition, Self-Regulation, Problem Solving, Mathematics Learning, Bibliometric Analysis
Rafiqoh, S. (2020). Arah Kecenderungan dan Isu Dalam Pembelajaran Matematika Sesuai Pembelajaran Abad 21 Untuk Menghadapi Revolusi Industri 4.0. Jurnal MathEducation Nusantara, 3(1), 58–73.
Simanjuntak, J. (2021). Perkembangan Matematika dan Pendidikan Matematika Di Indonesia. Sepren, 2(2), 32–39. https://doi.org/10.36655/sepren.v2i2.512
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003066X.34.10.906
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033
Heyes, C., Bang, D., Shea, N., Frith, C. D., & Fleming, S. M. (2020). Knowing ourselves together: The cultural origins of metacognition. Trends in Cognitive Sciences, 24(5), 349–362. https://doi.org/10.1016/j.tics.2020.02.007
Tri Aniah, Dwi Oktaviana, & Hartono, H. (2022). Pengembangan Media Pembelajaran Ludo Statistika Pada Pembelajaran Matematika Untuk Meningkatkan Keterampilan Metakognitif Siswa. Jurnal Riset Rumpun Matematika Dan Ilmu Pengetahuan Alam, 1(2), 51–65. https://doi.org/10.55606/jurrimipa.v1i2.441
Swartz, R. J., & Perkins, D. N. (1995). Teaching thinking: Issues and approaches. Pacific Grove, CA: Midwest Publications.
Puji Pramana, A., Ika Purwaningsih, W., & Budi Darmono, P. (2024). Analisis Kemampuan Metakognitif Siswa Dalam Pemecahan Masalah Matematika Pada Siswa SMP Kelas VIII. Mathematic Education Journal (MathEdu), 7(2), 7. http://journal.ipts.ac.id/index.php/
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Marc, W. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(March), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Aitchison, L., Bang, D., Bahrami, B., & Latham, P. E. (2015). Doubly Bayesian analysis of confidence in perceptual decision-making. 1–23. https://doi.org/10.1371/journal.pcbi.1004519
Czocher, J. A. (2018). How does validating activity contribute to the modeling process? 137–159.
Desoete, A., Baten, E., Vercaemst, V., Busschere, A. De, Baudonck, M., & Vanhaeke, J. (2019). Metacognition and motivation as predictors for mathematics performance of Belgian elementary school children. ZDM, 51(4), 667–677. https://doi.org/10.1007/s11858-018-01020-w
Geiger, V., Stillman, G., Brown, J., Galbriath, P., & Niss, M. (2017). Using mathematics to solve real world problems: The role of enablers. https://doi.org/10.1007/s13394-017-0217-3
Lingel, K., Lenhart, J., & Schneider, W. (2019). Metacognition in mathematics: Do different metacognitive monitoring measures make a difference? ZDM, 51(4), 587–600. https://doi.org/10.1007/s11858-019-01062-8
Lucangeli, D., Chiara, M., Martina, F., Annamaria, P., Valeria, P., Kenneth, P., Maria, H., & Penna, P. (2019). Metacognition and errors: The impact of self-regulatory trainings in children with specific learning disabilities. ZDM, 0(0), 0. https://doi.org/10.1007/s11858-019-01044-w
Özcan, Z. Ç. (2015). International Journal of Mathematical Education in the relationship between mathematical problem-solving skills and self-regulated learning through homework behaviours, motivation, and metacognition. 5211(September). https://doi.org/10.1080/0020739X.2015.1080313
Schukajlow, S., Kaiser, G., & Stillman, G. (2023). Modeling from a cognitive perspective: Theoretical considerations and empirical contributions. 25(3), 259–269.
Smith, J. M., Mancy, R., & Smith, J. M. (2018). Research in mathematics education exploring the relationship between metacognitive and collaborative talk during group mathematical problem-solving – what do we mean by collaborative metacognition? Research in Mathematics Education, 0(0), 1–23. https://doi.org/10.1080/14794802.2017.1410215
Vorhölter, K. (2019). Enhancing metacognitive group strategies for modelling. ZDM, 51(4), 703–716. https://doi.org/10.1007/s11858-019-01055-7