Learning Changes Following a Structured Metacognitive Intervention among Lower-Performing Undergraduate Students in Calculus Limits

Xiaoxia Liu, Sian Hoon Teoh, Geethanjali Narayanan, Walton Wider, Xiaoai Zeng

Abstract


This study examined within-student learning changes during a six-week structured metacognitive intervention among 46 lower-performing undergraduate students studying calculus limits. Participants were identified using a pragmatic, cohort-relative bottom 30% screening rule and completed content-matched 100-point achievement forms in a one-group pretest-posttest design. Mean achievement increased from 34.70 (SD = 19.56) to 59.54 (SD = 18.12), representing a mean gain of 24.85 points, t(45) = 8.27, p < .001, dz = 1.22. Absolute point gains differed significantly across low-, medium-, and high-difficulty categories, F(2, 90) = 13.87, p < .001, partial η² = .24. Medium-difficulty absolute gains exceeded low- and high-difficulty gains, whereas low- and high-difficulty gains did not differ significantly. Because response-format score maxima differed between forms, scores were normalized to percentages of the available maximum. Percentage-point gains differed significantly across multiple-choice, true-false, and short-answer formats after Greenhouse-Geisser correction, F(1.74, 78.18) = 8.07, p = .001, partial η² = .15. Short-answer gains exceeded those for multiple-choice and true-false formats, whereas the latter two did not differ significantly. Completion time increased significantly by 16.87 minutes, whereas the score-time ratio showed no statistically significant change. These findings characterize within-student performance changes rather than causal treatment effects, as maturation, repeated testing, concurrent instruction, instructor-related influences, and regression to the mean remain plausible alternative explanations. Planning, monitoring, evaluation, cognitive load, and implementation fidelity were not directly measured. Controlled studies incorporating direct process measures, prospective fidelity assessment, and psychometrically calibrated achievement forms are needed to test efficacy and proposed mechanisms.


Keywords


Metacognitive Scaffolding; Calculus Limits; Lower-Performing Students; Self-Regulated Learning; Mathematics Achievement; Time-On-Task; Education Quality

Full Text:

PDF

References


Aydın, U., & Özgeldi, M. (2024). What’s metacognition got to do with the relationship between test anxiety and mathematics achievement? European Journal of Psychology of Education, 39(3), 2509–2529. https://doi.org/10.1007/s10212-024-00797-7

Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231. https://doi.org/10.1111/j.1464-0597.2005.00205.x

Brown, A. L. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65–116). Lawrence Erlbaum Associates.

Dignath, C., Büttner, G., & Langfeldt, H.-P. (2008). How can primary school students learn self-regulated learning strategies most effectively? Educational Research Review, 3(2), 101–129. https://doi.org/10.1016/j.edurev.2008.02.003

Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645

Flavell, J. H. (1976). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231–235). Erlbaum.

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

Garofalo, J., & Lester, F. K. (1985). Metacognition, cognitive monitoring, and mathematical performance. Journal for Research in Mathematics Education, 16(3), 163–176. https://doi.org/10.2307/748391

Hanifa, M., Nursalam, N., Kusumayanti, A., & Suharti, S. (2025). Understanding the influence of metacognition on mathematical literacy: A literature review. Pedagonal: Jurnal Ilmiah Pendidikan, 9(2), 188–200. https://doi.org/10.55215/pedagonal.v9i2.42

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Hildenbrand, L., & Wiley, J. (2025). Supporting comprehension: The advantages of multiple-choice over true-false practice tests. Memory & Cognition, 53(8), 2580–2596. https://doi.org/10.3758/s13421-025-01726-9

Huda, N., Anwar, K., Novferma, & Kurniawan, W. (2025). ChatGPT scaffolding in supporting metacognition for limit concepts in guided inquiry mathematics learning. Journal of Information Technology Education: Research, 24, Article 38.

Kramarski, B., & Mevarech, Z. R. (2003). Enhancing mathematical reasoning in the classroom: The effects of metacognitive training. Journal for Research in Mathematics Education, 34(1), 24–58.

Ma, Y. (2024). The effect of metacognition on university students’ performance in mathematics. Journal of Education, Humanities and Social Sciences, 30, 56–61. https://doi.org/10.54097/85m7fy24

Oficiar, J. R. G., Ibañez, E. D., & Pentang, J. T. (2024). Metacognitive awareness as a predictor of mathematical modeling competency among preservice elementary teachers. International Journal of Educational Methodology, 10(2), 279–292. https://doi.org/10.12973/ijem.10.1.1079

Özcan, Z. Ç., & Erktin, E. (2015). Enhancing mathematics achievement of elementary school students through homework assignments enriched with metacognitive questions. EURASIA Journal of Mathematics, Science and Technology Education, 11(6). https://doi.org/10.12973/eurasia.2015.1402a

Permadi, H. (2023). Metacognitive intervention: Can it solve suspension of sense-making? Mathematics Teaching Research Journal, 15(1), 44–66.

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Rasmussen, C., Marrongelle, K., & Borba, M. C. (2014). Research on calculus: What do we know and where do we need to go? ZDM, 46(4), 507–515. https://doi.org/10.1007/s11858-014-0615-x

Schoenfeld, A. H. (1985). Mathematical problem solving. Academic Press.

Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics (Reprint). Journal of Education, 196(2), 1–38. https://doi.org/10.1177/002205741619600202

Sercenia, J. C., & Prudente, M. S. (2023). Effectiveness of the metacognitive-based pedagogical intervention on mathematics achievement: A meta-analysis. International Journal of Instruction, 16(4), 561–578.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Tak, C. C., Zulnaidi, H., & Eu, L. K. (2025). Mediating role of metacognitive awareness between attitude and mathematics reasoning in pre-service teachers. European Journal of Science and Mathematics Education, 13(2), 90–102. https://doi.org/10.30935/scimath/16116

Tall, D., & Vinner, S. (1981). Concept image and concept definition in mathematics with particular reference to limits and continuity. Educational Studies in Mathematics, 12(2), 151–169. https://doi.org/10.1007/BF00305619

Varveris, D., Saltas, V., & Tsiantos, V. (2023). Exploring the role of metacognition in measuring students’ critical thinking and knowledge in mathematics: A comparative study of regression and neural networks. Knowledge, 3(3), 333–348.

Veenman, M. V. J., Van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: Conceptual and methodological considerations. Educational Psychology Review, 18(1), 3–14.

Vo, K., Sarkar, M., White, P. J., & Yuriev, E. (2025). Metacognitive problem solving: Exploration of students’ perspectives through the lens of multi-dimensional engagement. Chemistry Education Research and Practice, 26(1), 141–157. https://doi.org/10.1039/D4RP00096J

Xie, Y., Zeng, F., & Yang, Y. (2024). A meta-analysis of the relationship between metacognition and academic achievement in mathematics: From preschool to university. Acta Psychologica, 249, 104486. https://doi.org/10.1016/j.actpsy.2024.104486

Yang, X., & Guo, W. (2023). Improving learning effectiveness by adding metacognitive questions in mathematics homework. Advances in Education, 13(6), 3195–3200. https://doi.org/10.12677/AE.2023.136503

Zhang, J., Zhou, Y., Jing, B., Pi, Z., & Ma, H. (2024). Metacognition and mathematical modeling skills: The mediating roles of computational thinking in high school students. Journal of Intelligence, 12(6), 55. https://doi.org/10.3390/jintelligence12060055

Zhang, W., & Lian, R. (2024). The impact of reading metacognitive strategies on mathematics learning efficiency and performance: An analysis using PISA 2018 data in China. Acta Psychologica, 246, 104247. https://doi.org/10.1016/j.actpsy.2024.104247

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.




DOI: https://doi.org/10.47738/jads.v7i4.1481

Refbacks

  • There are currently no refbacks.



Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0