The Resilient Generation of UIN Malang Mathematics Education: Perspective on the Cognitive Load of Students
Main Article Content
Abstract
Deep learning needs to be applied in learning, so that the learning process is not limited to cognitive aspects alone. Moreover, the cognitive load that humans can accept is limited. This load does not only come from within the classroom during learning, but also from other factors such as emotions, time constraints, and existing tasks. Cognitive load theory needs to be applied to deal with this, so that the learning portion is in accordance with the conditions required by students. This study conducted on first-semester students of the Mathematics Education study program at UIN Malang aims to identify and analyze existing cognitive load. The study used a quantitative approach with 54 students as sample respondents. Data were collected through a semi-structured guided questionnaire covering aspects of intrinsic load, extraneous load, emotional load, time and task load, and resource load. Data analysis used descriptive statistics with Rasch Model validity testing. The results showed that cognitive load was in the moderate to high category, with intrinsic load, extraneous load, emotional load, time load, and task load in the moderate category, and resource load in the high category. These findings indicate that students are able to manage their cognitive load quite well. However, the high time and task load needs to be addressed as it has the potential to cause cognitive fatigue and reduce learning effectiveness. This study recommends the development of OBE-based adaptive learning strategies that take into account the balance between academic activities and dormitory life.
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How to Cite
ILMI, Ilham Imtiyazil; APRILLIA, Yustika Berlian Cindy; MUKTI, Taufiq Satria.
The Resilient Generation of UIN Malang Mathematics Education: Perspective on the Cognitive Load of Students.
Proceeding of International Conference on Islamic Education (ICIED), [S.l.], v. 10, n. 1, p. 203-210, dec. 2025.
ISSN 2613-9804.
Available at: <https://conferences.uin-malang.ac.id/index.php/icied/article/view/3593>. Date accessed: 03 feb. 2026.
doi: https://doi.org/10.18860/icied.v10i1.3593.
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Articles
References
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Brockbank, R. B., & Feldon, D. F. (2024). Cognitive Reappraisal: The Bridge between Cognitive Load and Emotion. Education Sciences, 14(8). https://doi.org/10.3390/educsci14080870
Chi, M. T. H., Glaser, R., & Rees, E. (1981). Expertise in Problem Solving. University of Pittsburgh.
Chotimah, D. N., Kirom, M., Roziki, K., & Abidin, M. (2025). Outcome-Based Education (OBE) Curriculum: Implementation in the Arabic Language and Literature Study Program. Tadris: Jurnal Keguruan Dan Ilmu Tarbiyah, 10(1), 369–382. https://doi.org/10.24042/tadris.v10i1.25279
Deng, Q., & Wang, Z. (2022). Using Cognitive Loads for Practical uses to Adjust the Task Arrangement in High School Students. International Conference on Mental Health, Education and Human Development (MHEHD).
Fang, G., Chan, P. W. K., & Kalogeropoulos, P. (2020). Social Support and Academic Achievement of Chinese Low-Income Children: A Mediation Effect of Academic Resilience. International Journal of Psychological Research, 13(1), 19–28. https://doi.org/10.21500/20112084.4480
Feriyanto, F., & Anjariyah, D. (2024). Deep Learning Approach Through Meaningful, Mindful, and Joyful Learning: A Library Research. Electronic Journal of Education, Social Economics and Technology, 5(2), 208–212. https://doi.org/10.33122/ejeset.v5i2.321
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025). Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203
Goretzko, D., Siemund, K., & Sterner, P. (2024). Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis. Educational and Psychological Measurement, 84(1), 123–144. https://doi.org/10.1177/00131644231163813
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), 05. https://doi.org/10.20897/ejsteme/9252
Hasibuan, J., & Harahap, F. I. N. (2024). Integrating an Outcome-Based Education (OBE) Framework in Andragogy: A Case Study in the Community Education Department. AL-ISHLAH: Jurnal Pendidikan, 16(4), 4853–4863. https://doi.org/10.35445/alishlah.v16i4.5377
Kirschner, P. A. (2002). Cognitive load theory: implications of cognitive load theory on the design of learning. In Learning and Instruction (Vol. 12). www.elsevier.com/locate/learninstruc
Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van 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
Marzuki. (2024). The Effect of Dormitory-Based Coaching and Religiosity on Islamic Education Learning Outcomes. Educazione: Journal of Education and Learning, 1(2), 116–129. https://doi.org/10.61987/educazione.v1i2.498
Mystakidis, S. (2021). Deep Meaningful Learning. Encyclopedia, 1(3), 988–997. https://doi.org/10.3390/encyclopedia1030075
Paas, F., Renkl, A., & Sweller, J. (2003b). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1
Rahman, M. (2022). How does social media marketing information influence purchase decisions? A study on online and offline markets. International Journal of Scientific Engineering and Applied Science (IJSEAS), 8(5), 225–239. www.ijseas.com
Rahmania, F., & Islaha, S. (2023). Adaptation of Organizational Commitment Questionnaire in Indonesia. International Journal of Polistics and Sociology Research (IJOBSOR), 11(3), 349–357. www.ijobsor.pelnus.ac.id
Rahmawati, A. D., Ardianzah, F., & Novitasari, P. (2024). Penerapan Teori Beban Kognitif dalam Pengajaran Matematika Dalam Mengurangi Beban Kognitif Tak Esensial. JURNAL JENDELA PENDIDIKAN, 4(04), 463–472. https://doi.org/10.57008/jjp.v4i04.1112
Richardo, R., & Aksen Cahdriyana, R. (2021). Strategi meminimalkan beban kognitif eksternal dalam pembelajaran matematika berdasarkan load cognitive theory. 21(1), 17–32. https://doi.org/10.21831/hum.v21i1
Richardo, R., & Cahdriyana, R. A. (2021). Strategi meminimalkan beban kognitif eksternal dalam pembelajaran matematika berdasarkan load cognitive theory. HUMANIKA, 21(1), 17–32. https://doi.org/10.21831/hum.v21i1.38228
Sari, F. F., Pujiarti, T., Hidayat, H., & Anjosa, A. (2024). Pembelajaran Matematika Diskrit Mengacu pada Teori Beban Kognitif (Cognitive Load Theory). DIKSI: Jurnal Kajian Pendidikan Dan Sosial, 5(1), 10–17. https://doi.org/10.53299/diksi.v5i1.370
Sari, F. F., Sudatha, I. G. W., Santoso, M. H., & Suartama, I. K. (2025). Mengurangi Beban Kognitif dalam Pembelajaran Matematika: Tinjauan Sistematis Strategi dan Intervensi Pembelajaran. Jurnal Pendidikan Dan Pembelajaran Indonesia (JPPI), 5(3), 1630–1644. https://doi.org/10.53299/jppi.v5i3.2190
Schnotz, W., & Kürschner, C. (2007b). A Reconsideration of Cognitive Load Theory. Educational Psychology Review, 19(4), 469–508. https://doi.org/10.1007/s10648-007-9053-4
Sujarwo, & Herwin. (2023). Parental Involvement and Student Achievement: A Meta-analysis of Publications in the Scopus Database. International Journal of Instruction, 16(2), 107–124. https://doi.org/10.29333/iji.2023.1627a
Sunawan, S., Yani, S. Y. A., Anna, C. T., Kencana, T. I., Mulawarman, -, & Sofyan, A. (2017). Dampak Efikasi Diri terhadap Beban Kognitif dalam Pembelajaran Matematika dengan Emosi Akademik sebagai Mediator. Jurnal Psikologi, 44(1), 28–38. https://doi.org/10.22146/jpsi.22742
Suyadi, S. (2022). Type developing an Islamic education curriculum on outcomes-based education as a defensive strategy facing the challenges of industry revolution 4.0. International Journal of Education and Learning, 4(1), 41–57. https://doi.org/10.31763/ijele.v4i1.440