Mapping cognitive load profiles in realistic mathematics education: A study with aerospace engineering students

Authors

  • Rindu Alriavindra Funny Institut Teknologi Dirgantara Adisutjipto
  • Fajar Khanif Rahmawati Institut Teknologi Dirgantara Adisutjipto

DOI:

https://doi.org/10.29408/jel.v11i4.32104

Keywords:

aerospace engineering, cognitive load theory, engineering mathematics, mental effort, realistic mathematics education

Abstract

Although Realistic Mathematics Education (RME) promotes deeper conceptual learning, empirical evidence mapping different types of cognitive loads in university engineering mathematics is limited. This mixed-methods study profiled intrinsic, extraneous, and germane cognitive loads among 76 first-year aeronautical engineering students working on RME-based task, a contextualized double integral problems modelling aircraft wing surface. We measured load components with a CLT questionnaire that adapted from Leppink et.al and mental effort with the Paas scale, then triangulated findings with student reflections and observations. Correlations showed intrinsic and germane load related to students’ mental effort, while extraneous load was minimal, suggesting clear task design. Multiple regression analysis clarified that the germane load was the main unique predictor of mental effort, whereas intrinsic complexity and extraneous factors contributed little uniquely. Qualitative data confirmed that students used strategies such as breaking tasks into sub-steps, activating prior knowledge, and peer explanation to manage effort. We propose an RME–CLT alignment framework that scaffolds intrinsic difficulty, minimizes extraneous processing, and cultivates germane engagement through reflective context-rich tasks. The findings also inform the design of cognitively efficient engineering-mathematics curricula. Thus, it offers practical guidance for designing cognitively efficient engineering mathematics instruction and recommends future studies using longitudinal and real-time measures.

Author Biography

Fajar Khanif Rahmawati, Institut Teknologi Dirgantara Adisutjipto

Teknik Dirgantara

References

Aditomo, A. (2014). Cognitive load theory and mathematics learning: A. Systematic Review, 24(June), 207–217.

Du, X., Dai, M., Tang, H., Hung, J. L., Li, H., & Zheng, J. (2023). A multimodal analysis of college students’ collaborative problem solving in virtual experimentation activities: a perspective of cognitive load. Journal of Computing in Higher Education, 35(2), 272–295. https://doi.org/10.1007/s12528-022-09311-8

Goold, E., & Devitt, F. (2020). The role of mathematics in engineering practice and in the formation of engineers. Proceedings of the 40th SEFI Annual Conference 2020 - Engineering Education 2020: Meet the Future, 1(June).

Gravemeijer, K. (1994). Educational development and developmental research in mathematics education. Journal for Research in Mathematics Education, 25(5), 443–471. https://doi.org/10.2307/749485

Gravemeijer, K., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics ·. https://doi.org/10.1023/A

Greenberg, K., & Zheng, R. (2023). Revisiting the debate on germane cognitive load versus germane resources. Journal of Cognitive Psychology, 35(3), 295–314. https://doi.org/10.1080/20445911.2022.2159416

Gupta, U., & Zheng, R. Z. (2020). Cognitive load in solving mathematics problems: Validating the role of motivation and the interaction among prior knowledge, worked examples, and task difficulty. European Journal of STEM Education, 5(1), 1–14. https://doi.org/10.20897/ejsteme/9252

Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134. https://doi.org/10.1007/s11251-009-9110-0

Juandi, D., Kusumah, Y. S., & Tamur, M. (2022). A meta-analysis of the last two decades of realistic mathematics education approaches. International Journal of Instruction, 15(1), 381–400. https://doi.org/10.29333/iji.2022.15122a

Kalyuga, S. (2006). Instructing and testing for expertise: A cognitive load perspective. Advances in Psychology Research, 46, 75–127.

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539. https://doi.org/10.1007/s10648-007-9054-3

Klepsch, M., & Seufert, T. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Instructional Science, 48(ue 1)). https://doi.org/10.1007/s11251-020-09502-9

Leppink, J., Paas, F., Vleuten, C. P. M., Gog, T., & Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1

Mayer, R. E., & Moreno, R. M. (2016). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist: A, 38(1), 43–52. https://doi.org/10.4324/9780203764770-6

Orru, G., & Longo, L. (2019). The evolution of cognitive load theory and the measurement of its intrinsic, extraneous and germane loads: A review. Communications in Computer and Information Science, 1012(February), 23–48. https://doi.org/10.1007/978-3-030-14273-5_3

Paas, F. G., Merriënboer, J. J., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79(1 Pt 2), 419–430. https://doi.org/10.2466/pms.1994.79.1.419

Santoso, F. G. I., & Sari, A. E. R. (2025). Mathematical literacy of prospective mathematics teacher based on cognitive style. Jurnal Elemen, 11(2), 427–446. https://doi.org/10.29408/jel.v11i2.28494

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7

Sweller, J. (2011). Cognitive load theory. In Psychology of Learning and Motivation - Advances in Research and Theory (Vol. 55). Elsevier Inc. https://doi.org/10.1016/B978-0-12-387691-1.00002-8

Zimmerman, B. J. (2010). Becoming a self-regulated learner: An overview. Theory into practice, 5841(2002), 64–70. https://doi.org/10.1207/s15430421tip4102

Downloads

Published

09-11-2025

How to Cite

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

Issue

Section

Articles

Similar Articles

<< < 4 5 6 7 8 9 10 11 12 13 > >> 

You may also start an advanced similarity search for this article.