Extraneous Cognitive Load as a Moderator of the Correlation Between Mathematical Literacy and Mathematical Reasoning in Indonesian Junior High School Mathematics
DOI:
https://doi.org/10.31949/dm.v8i2.18674Abstract
Mathematical literacy and mathematical reasoning are widely recognized as essential competencies for meaningful engagement with mathematics; however, the cognitive conditions shaping their relationship remain insufficiently understood. Drawing on Cognitive Load Theory, this study examined the association between mathematical literacy and mathematical reasoning and investigated whether Extraneous Cognitive Load (ECL) significantly moderates this association among Indonesian junior high school students. A quantitative ex post facto correlational design was employed involving 275 eighth-grade students selected through proportionate stratified random sampling. Data were collected using a mathematical literacy test, a mathematical reasoning test, and an ECL questionnaire adapted from the Cognitive Load Component Questionnaire. The data were analyzed using descriptive statistics, simple linear regression, and Moderated Regression Analysis (MRA). The findings revealed a significant positive association between mathematical literacy and mathematical reasoning, with mathematical literacy explaining 18.2% of the variance in mathematical reasoning (R² = .182). After the inclusion of Extraneous Cognitive Load and the interaction term, the explained variance increased to 19.4% (R² = .194), representing a modest increase in explanatory power (ΔR² = .012). Although ECL did not show a significant direct association with mathematical reasoning, the interaction between mathematical literacy and ECL was statistically significant and negative (β = −0.371, p = .048), indicating that higher levels of ECL were associated with a weaker positive association between mathematical literacy and mathematical reasoning. These findings provide empirical evidence that the association between mathematical literacy and mathematical reasoning varies according to students' perceived levels of Extraneous Cognitive Load and highlight the importance of fostering mathematical literacy while minimizing unnecessary extraneous cognitive demands to better support students' mathematical reasoning
Keywords:
Mathematical literacy, Mathematical reasoning, Extraneous cognitive load, Cognitive load theory, Moderated regression analysisDownloads
References
Abah, J. A., & Vilakazi, Z. F. (2026). The interactive learning revolution: A systematic review on balancing memorization and conceptual understanding in mathematics. International Journal of Educational Qualitative Quantitative Research, 5(1), 31–44. https://doi.org/10.58418/ijeqqr.v5i1.176
Asmara, A. S., Waluya, S. B., Suyitno, H., & Junaedi, I. (2020). The roles of cognitive load theory in mathematics learning in Indonesia. In Proceedings of the International Conference on Science and Education and Technology (ISET 2019) (pp. 110–114). Atlantis Press. https://doi.org/10.2991/assehr.k.200620.022
Asmara, A. S., Waluya, S. B., Suyitno, H., Junaedi, I., & Ardiyanti, Y. (2024). Developing patterns of students' mathematical literacy processes: Insights from cognitive load theory and design-based research. Infinity Journal, 13(1), 197–214. https://doi.org/10.22460/infinity.v13i1.p197-214
Asmara, A. S., Waluya, S. B., Suyitno, H., Junaedi, I., Suparman, T., & Prawiyogi, A. G. (2019). Development of mathematical literacy ability through the learning tools based on cognitive load theory. Journal of Physics: Conference Series, 1321(2), Article 022109. https://doi.org/10.1088/1742-6596/1321/2/022109
Beege, M., Nebel, S., Schneider, S., & et al. (2021). The effect of signaling in dependence on the extraneous cognitive load in learning environments. Cognitive Processing, 22, 209–225. https://doi.org/10.1007/s10339-020-01002-5
Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224–226. https://doi.org/10.1177/0890334420906850
Brockbank, R. B., Feldon, D. F., & Litson, K. (2026). Cognitive load, affect, and regulatory strategies: A more integrated model. Frontiers in Psychology, 17, Article 1774619. https://doi.org/10.3389/fpsyg.2026.1774619
Chen, O., Castro-Alonso, J. C., Paas, F., Sweller, J., & Kalyuga, S. (2018). Extending cognitive load theory to incorporate working memory resource depletion: Evidence from the spacing effect. Educational Psychology Review, 30(2), 483–501. https://doi.org/10.1007/s10648-017-9426-2
Chu, H. C., Chen, J. M., & Tsai, C. L. (2017). Effects of an online formative peer-tutoring approach on students’ learning behaviors, performance and cognitive load in mathematics. Interactive Learning Environments, 25(2), 203–219. https://doi.org/10.1080/10494820.2016.1276085
Falani, F., & Sainuddin, S. (2026). Mathematical literacy assessment: A scalable mobile adaptive blueprint for mapping proficiency across PISA domains. AlphaMath: Journal of Mathematics Education, 12(1), 281–308. https://doi.org/10.30595/alphamath.v12i1.30279
Ferguson-Patrick, K., & Liebech-Lien, B. (2025). Implementing collaborative problem-solving in primary school mathematics: Action research. Educational Research, 67(4), 461–482. https://doi.org/10.1080/00131881.2025.2571226
Hawthorne, B. S., Slemp, G. R., Vella-Brodrick, D. A., & Hattie, J. (2025). The relationship between positive and painful emotions and cognitive load during an algebra learning task. Learning and Individual Differences, 117, Article 102597. https://doi.org/10.1016/j.lindif.2024.102597
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). The Guilford Press.
Holenstein, M., Bruckmaier, G., & Grob, A. (2021). Transfer effects of mathematical literacy: An integrative longitudinal study. European Journal of Psychology of Education, 36(3), 799–825. https://doi.org/10.1007/s10212-020-00491-4
Huang, Y.-H. (2018). Influence of instructional design to manage intrinsic cognitive load on learning effectiveness. Eurasia Journal of Mathematics, Science and Technology Education, 14(6), 2653–2668. https://doi.org/10.29333/ejmste/90264
Hwang, J., & Ham, Y. (2021). Relationship between mathematical literacy and opportunity to learn with different types of mathematical tasks. Journal on Mathematics Education, 12(2), 199–222. https://doi.org/10.22342/jme.12.2.13625.199-222
Innocenti, F., Candel, M. J., Tan, F. E., & van Breukelen, G. J. (2024). Sample size calculation and optimal design for multivariate regression-based norming. Journal of Educational and Behavioral Statistics, 49(5), 817–847. https://doi.org/10.3102/10769986231210807
Klepsch, M., & Seufert, T. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instructional Science, 48, 45–77. https://doi.org/10.1007/s11251-020-09502-9
Latpate, R., Kshirsagar, J., Gupta, V. K., & Chandra, G. (2021). Stratified random sampling. In Advanced sampling methods (pp. 53–78). Springer. https://doi.org/10.1007/978-981-16-0622-9_3
Leppink, J., Paas, F., Gog, T. Van, Vleuten, C. P. M. Van Der, & Merriënboer, J. J. G. Van. (2014). Effects of pairs of problems and examples on task performance and different types of cognitive load. Learning and Instruction, 30, 32–42. https://doi.org/10.1016/j.learninstruc.2013.12.001
Lespiau, F., & Tricot, A. (2024). Reasoning more efficiently with primary knowledge despite extraneous cognitive load. Evolutionary Psychology, 22(2), 14747049241252694. https://doi.org/10.1177/14747049241252694
Mao, X., Dai, Y., Liu, Y., Jiang, Y., & Zhang, Y. (2025). Optimizing cognitive load in digital mathematics textbooks: A mixed-methods study on content organization and application models. Journal of Educational Technology and Innovation, 7(3), 44–59. https://doi.org/10.61414/pxn87q66
Nardi, P. M. (2018). Doing survey research: A guide to quantitative methods (4th ed.). Routledge. https://doi.org/10.4324/9781315172231
Nurmala, I., Sumliyah, S., & Rohaeti, T. (2025). The effectiveness of PBL assisted by H5P interactive video in improving mathematics literacy of junior high school students. Journal of General Education and Humanities, 4(4), 1561–1570. https://doi.org/10.58421/gehu.v4i4.688
Organisation for Economic Co-operation and Development. (2019). PISA 2018 results (Volume I): What students know and can do. OECD Publishing. https://doi.org/10.1787/5f07c754-en
Organisation for Economic Co-operation and Development. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing.
Phan, H. P., Ngu, B. H., & Yeung, A. S. (2017). Achieving optimal best: Instructional efficiency and the use of cognitive load theory in mathematical problem solving. Educational Psychology Review, 29(4), 667–692. https://doi.org/10.1007/s10648-016-9373-3
Prabawati, M., Herman, T., & Turmudi. (2019). Mathematical literacy skills of junior high school students in terms of gender differences. Journal of Physics: Conference Series, 1315(1), Article 012084. https://doi.org/10.1088/1742-6596/1315/1/012084
Purpura, D. J., Schmitt, S. A., & Ganley, C. M. (2017). Foundations of mathematics and literacy: The role of executive functioning components. Journal of Experimental Child Psychology, 153, 15–34. https://doi.org/10.1016/j.jecp.2016.08.010
Rahmawati, W. A., Usodo, B., & Fitriana, D. L. (2021). Mathematical literacy skills of junior high school students in solving PISA-like mathematical problems. Journal of Physics: Conference Series, 1808(1), Article 012045. https://doi.org/10.1088/1742-6596/1808/1/012045
Rasyid, M. R. (2024). The effectiveness of GeoGebra-based learning in enhancing students' mathematical reasoning skills in secondary schools. Aksioma Education Journal, 1(3), 42–53. https://doi.org/10.62872/aej.v1i3.512
Rohwer, D. (2022). Designing ex post facto and experimental studies. In C. Conway (Ed.), Inquiry in music education: Concepts and methods for the beginning researcher (2nd ed., pp. 230–252). Routledge. https://doi.org/10.4324/9781003057703-15
Safiro, N., Sari, N., Nuraeni, Z., Sukmaningthias, N., & Ramadhan, M. H. (2026). E-modules assisted learning: Mathematical reasoning of junior high school students in solving mathematical literacy problems. Jurnal Elemen, 12(2), 428–446. https://doi.org/10.29408/jel.v12i2.33128
Saleh, R. M., Nurlaila, H. T., & Ageng, T. (2025). Students' thought process in solving inverse proportion problems in terms of cognitive style and gender based on information processing theory. Jurnal Pedagogi dan Pembelajaran, 8(3), 550–559. https://doi.org/10.23887/jp2.v8i3.101444
Sana, F., & Fenesi, B. (2025). Working memory and instructional fit: Reintroducing aptitude–treatment interaction in education research. Behavioral Sciences, 15(6), Article 765. https://doi.org/10.3390/bs15060765
Singh, H. P., & Gorey, S. M. (2019). Estimation of population proportion of a qualitative character using randomized response technique in stratified random sampling. Communications in Statistics—Theory and Methods, 48(4), 794–809. https://doi.org/10.1080/03610926.2017.1417436
Susanta, A., Sumardi, H., Susanto, E., & Retnawati, H. (2023). Mathematics literacy task on number pattern using Bengkulu context for junior high school students. Journal on Mathematics Education, 14(1), 85–102. https://doi.org/10.22342/jme.v14i1.pp85-102
Syutaridho, S., Putri, R. I. I., Zulkardi, Z., & Darmawijoyo. (2025). Instructional strategies for fostering mathematical literacy in junior high school: A systematic literature review. Mosharafa: Jurnal Pendidikan Matematika, 14(3), 791–802. https://doi.org/10.31980/mosharafa.v14i3.3557
van Lieshout, E. C. D. M., & Xenidou-Dervou, I. (2020). Simple pictorial mathematics problems for children: Locating sources of cognitive load and how to reduce it. ZDM–Mathematics Education, 52(1), 73–85. https://doi.org/10.1007/s11858-019-01091-3
Verschaffel, L., Schukajlow, S., Star, J. R., & Van Dooren, W. (2020). Word problems in mathematics education: A survey. ZDM–Mathematics Education, 52(1), 1–16. https://doi.org/10.1007/s11858-020-01130-4
Wang, C. C., Cheng, P. K. H., & Wang, T. H. (2022). Measurement of extraneous and germane cognitive load in the mathematics addition task: An event-related potential study. Brain Sciences, 12(8), Article 1036. https://doi.org/10.3390/brainsci12081036
Wang, T., & Lajoie, S. P. (2023). How does cognitive load interact with self-regulated learning? A dynamic and integrative model. Educational Psychology Review, 35, Article 69. https://doi.org/10.1007/s10648-023-09794-6
Zheng, X., Lai, B., Zhang, Y., Liang, W., & Wu, X. (2026). From motivation to literacy: Chain mediation of learning habits and mathematical reasoning in mathematical literacy development. Acta Psychologica, 268, Article 107195. https://doi.org/10.1016/j.actpsy.2026.107195
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Putri Nur Aeni, Hanifah Nurus Sopiany, Nur Aziza

This work is licensed under a Creative Commons Attribution 4.0 International License.
