Reducing Extraneous Cognitive Load to Improve Analytic Geometry Problem Solving among Undergraduate Mathematics Students

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Published: Jun 2, 2026
Digital Object Identifier: 10.58578/mikailalsys.v4i2.10376
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Authors:
Damar Rais1*, Yue Wen Yan2, Silvie Rama Yuliane3, Deswandhana AS4
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Damar Rais
Universitas Negeri Makassar, Indonesia
Yue Wen Yan
East China University of Technology, China
Silvie Rama Yuliane
Nalanda University, Bihar, India
Deswandhana AS
Universitas Negeri Makassar, Indonesia

Abstract

Mathematical problem solving in higher education requires students to coordinate symbolic, graphical, and conceptual information within limited cognitive resources. This study examines the effect of extraneous cognitive load on undergraduate mathematics students’ problem-solving performance in an Analytic Geometry course and determines whether cognitively optimized task presentation can improve performance by reducing unnecessary mental effort during mathematical reasoning. A within-subject explanatory mixed-methods design was employed with 25 mathematics department students. Participants completed equivalent Analytic Geometry problem-solving tasks under two presentation conditions: a conventional format and a cognitively optimized format. Data were collected through problem-solving tests, perceived extraneous cognitive load ratings, time-on-task records, written solution analysis, and semi-structured interviews. The results showed that students achieved higher problem-solving scores in the cognitively optimized condition than in the conventional condition. They also reported lower extraneous cognitive load and completed the tasks in less time. Correlation and regression analyses indicated that higher extraneous cognitive load was associated with lower problem-solving performance, suggesting that unnecessary processing demands constrained students’ mathematical reasoning. Qualitative findings supported these results by showing that students benefited from integrated diagrams, clearer symbolic representations, and reduced information search. The study concludes that extraneous cognitive load is a critical factor in undergraduate mathematical problem solving and that optimizing instructional presentation can improve cognitive efficiency without reducing mathematical rigor. These findings contribute to research on cognitive load, mathematical thinking, and educational technology, while offering practical implications for designing Analytic Geometry learning materials that support efficient and meaningful mathematical reasoning.

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How to Cite
Rais, D., Yan, Y. W., Yuliane, S. R., & AS, D. (2026). Reducing Extraneous Cognitive Load to Improve Analytic Geometry Problem Solving among Undergraduate Mathematics Students. Journal of Multidisciplinary Science: MIKAILALSYS, 4(2), 1700-1720. https://doi.org/10.58578/mikailalsys.v4i2.10376

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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

References

Aprillia, F., & Retnowati, E. (2025). Do goal-free problems with guided questions affect retention, transfer, and cognitive load? Edumatica: Jurnal Pendidikan Matematika, 15(3), 296–309. https://doi.org/10.22437/edumatica.v15i3.45657

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

Baiduri, Holisin, I., Inganah, S., & Hidayati, W. S. (2024). The impact of cognitive load on learning achievement and semester level in mathematics education students. Journal of Hunan University Natural Sciences, 51(8), 19–35. https://doi.org/10.55463/issn.1674-2974.51.8.2

de Bruin, A. B. H., & van Merriënboer, J. J. G. (2017). Bridging cognitive load and self-regulated learning research: A complementary approach to contemporary issues in educational research. Learning and Instruction, 51, 1–9. https://doi.org/10.1016/j.learninstruc.2017.06.001

Funny, R. A., & Rahmawati, F. K. (2025). Mapping cognitive load profiles in realistic mathematics education: A study with aerospace engineering students. Jurnal Elemen, 11(4), 1018–1029. https://doi.org/10.29408/jel.v11i4.32104

Ginns, P., Hu, F.-T., & Bobis, J. (2020). Tracing enhances problem-solving transfer, but without effects on intrinsic or extraneous cognitive load. Applied Cognitive Psychology, 34(6), 1522–1529. https://doi.org/10.1002/acp.3732

Khan, M. A., Asadi, H., Zhang, L., Qazani, M. R. C., Oladazimi, S., Loo, C. K., Lim, C. P., & Nahavandi, S. (2024). Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review. Expert Systems with Applications, 249, Article 123717. https://doi.org/10.1016/j.eswa.2024.123717

Lavy, I., & Shriki, A. (2023). On reading mathematical texts, question-asking and cognitive load. Education Sciences, 13(7), Article 678. https://doi.org/10.3390/educsci13070678

Le Cunff, A. L., Giampietro, V., & Dommett, E. (2024). Neurodiversity and cognitive load in online learning: A systematic review with narrative synthesis. Educational Research Review, 43, Article 100604. https://doi.org/10.1016/j.edurev.2024.100604

Leppink, J. (2017). Cognitive load theory: Practical implications and an important challenge. Journal of Taibah University Medical Sciences, 12(5), 385–391. https://doi.org/10.1016/j.jtumed.2017.05.003

Lin, J.-H., & Lin, S. S. J. (2014). Cognitive load for configuration comprehension in computer-supported geometry problem solving: An eye movement perspective. International Journal of Science and Mathematics Education, 12(3), 605–628. https://doi.org/10.1007/s10763-013-9479-8

Looi, C.-K., Liu, Z., & Sun, D. (2026). Taming the black box: Design principles for rule-integrated LLM tutoring systems in primary school mathematical problem solving. Computers and Education: Artificial Intelligence, 10, Article 100586. https://doi.org/10.1016/j.caeai.2026.100586

Murtianto, Y. H., Herlambang, B. A., & Muhtarom. (2022). Cognitive load theory on virtual mathematics laboratory: Systematic literature review. KnE Social Sciences, 7(19), 413–420. https://doi.org/10.18502/kss.v7i19.12461

Murtianto, Y. H., Retnowati, E., & Hanham, J. (2025). Reducing cognitive load using social persuasion prompts in mathematics multimedia learning. Jurnal Pendidikan Matematika, 19(3), 465–488. https://doi.org/10.22342/mej.v19i3.pp465-488

Ngu, B. H., & Phan, H. P. (2022). Developing problem-solving expertise for word problems. Frontiers in Psychology, 13, Article 725280. https://doi.org/10.3389/fpsyg.2022.725280

Oktaviyanthi, R., Agus, R. N., & Khotimah. (2024). Exploring the link between cognitive load and brain activity during calculus learning through electroencephalogram (EEG): Insights from visualization and cluster analysis. Journal on Mathematics Education, 15(4), 1383–1408. https://doi.org/10.22342/jme.v15i4.pp1383-1408

Oktaviyanthi, R., Agus, R. N., Garcia, M. L. B., & Lertdechapat, K. (2024). Cognitive load scale in learning formal definition of limit: A Rasch model approach. Infinity Journal, 13(1), 99–118. https://doi.org/10.22460/infinity.v13i1.p99-118

Sholihah, D. A. (2022). Strategi Pembelajaran Matematika berdasarkan Cognitive Load Theory untuk Meminimalkan Extraneous Cognitive Load. EQUALS: Jurnal Ilmiah Pendidikan Matematika, 5(1), 13–23. https://doi.org/10.46918/equals.v5i1.1197

Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171–196. https://doi.org/10.1007/s10648-021-09624-7

Sonnatthanon, N., & Choocharukul, K. (2025). Crash severity prediction using a virtual geometry-group-based deep learning approach with images-based feature representation. Results in Engineering, 27, Article 106155. https://doi.org/10.1016/j.rineng.2025.106155

Sweller, J. (2024). Cognitive load theory and individual differences. Learning and Individual Differences, 110, Article 102423. https://doi.org/10.1016/j.lindif.2024.102423

Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5

Zeitlhofer, I., Zumbach, J., & Schweppe, J. (2024). Complexity affects performance, cognitive load, and awareness. Learning and Instruction, 94, Article 102001. https://doi.org/10.1016/j.learninstruc.2024.102001

Zhang, S., Zhao, S., Zhang, Q., & Song, Y. (2026). HyDRA: Hyperbolic dual-geometry representation alignment for knowledge-aware recommendation. Neurocomputing, 693, Article 133848. https://doi.org/10.1016/j.neucom.2026.133848

Zhong, Y., Li, Y., Luo, P., & Jie, S. (2026). Effects of visual reference information on solution quality in crowdsourcing contests: A cognitive load perspective. International Journal of Information Management, 88, Article 103048. https://doi.org/10.1016/j.ijinfomgt.2026.103048

Zhu, J., Zhu, M., Wu, P., He, Q., Wang, J., Niu, X., & Wang, Z. (2026). The geometry of neural representation: From neural tuning to manifold geometry. Chaos, Solitons & Fractals, 208, Article 118193. https://doi.org/10.1016/j.chaos.2026.118193