MEMANFAATKAN TEORI BELAJAR KOGNITIVISME UNTUK MEMPERKUAT PEMBELAJARAN DEEP LEARNING
Keywords:
Cognitivism; Deep Learning; Cognitive Load; Adaptive Learning; Instructional Design
Abstract
This study aims to examine the role of cognitivist learning theory in enhancing the effectiveness of Deep Learning, both as a pedagogical approach and as a form of artificial intelligence (AI) technology. Cognitivism emphasizes the importance of internal mental processes, knowledge structures, and cognitive load management strategies in understanding and retaining information. Meanwhile, Deep Learning in education demands higher-order thinking skills, conceptual understanding, and the ability to connect knowledge across contexts. Using a literature review method, this study analyzes scholarly works published between 2010 and 2025 that discuss the integration of cognitive theory into digital learning design and adaptive AI systems. The findings indicate that strategies such as worked examples, fading, chunking, advance organizers, and metacognition-based active learning effectively improve learners’ comprehension in Deep Learning contexts. Furthermore, the use of AI-powered adaptive technologies developed based on cognitivist principles—such as neural cognitive diagnosis—can enhance learning personalization and instructional effectiveness. This study concludes that the synergy between cognitivist theory and Deep Learning can shape a more meaningful, reflective, and sustainable learning framework.
References
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Bruner, J. S. (1966). Toward a Theory of Instruction. Harvard University Press.
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Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive Apprenticeship: Teaching the Crafts of Reading, Writing, and Mathematics. Cognition and Instruction (konsep scaffolding)
DeWolfe, T. E. (2024). Jean Piaget’s theory of cognitive development. EBSCO Research Starters.
Editors (2025). Enhancing the cognitive load theory and multimedia learning framework with AI insight. Discover Education, 4:160. doi:10.1007/s44217-025-00592-6 SpringerLink
Fauziati, E. (2022). Deep Learning: a Theoretical Review. Suar Betang, Kemdikbud Indonesia.
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Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N. ..., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, National Academy of Sciences. meta-analisis active learning approx +0.47 SD
Frontiers in Education (2024). Analysis of learner centered software education and support strategies.
Geirhos, R., Jacobsen, J. H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F. A. (2020). Shortcut Learning in Deep Neural Networks. arXiv preprint arxiv.org
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Jose, B. et al. (2025). The cognitive paradox of AI in education: between enhancement and erosion. Frontiers in Psychology, 16:1550621. doi:10.3389/fpsyg.2025.1550621
Kadarismanto & Puspita Sari (2025). Konsep Deep Learning sebagai Pilar dalam Strategi Pendidikan Berkualitas. doi:10.010125/dweh6m45 journal.metanusantara.com
Khasawneh, Y. J. A. et al. (2024). Cognitive load analysis of adaptive learning technologies in special education classrooms. IJAAS, 11(12):34 41. doi:10.21833/ijaas.2024.12.004 science-gate.com
Koć Januchta, M. M. et al. (2022). “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI enriched textbook. Int. Journal of Educational Technology in Higher Education, 19:11. doi:10.1186/s41239-021-00317-3 SpringerOpen
Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Neuron, 100(2), 334-348. https://doi.org/10.1016/j.neuron.2018.10.008
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Liu, Q., Lin, C., Chen, G., & Yu, C. (2019). Exploiting Cognitive Structure for Adaptive Learning. IEEE Transactions on Learning Technologies, 12(1), 62–75. https://doi.org/10.1109/TLT.2018.2885704
Macedo, H. d. S. et al. (2023). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv
Ausubel, D. P. (1967). The Psychology of Meaningful Verbal Learning: An Introduction to School Learning.
Macedo, H. d. S., dos Santos, I. T. & Oliveira da Silva, E. L. (2023). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv preprint.
Macedo, H. d. S., et al. (2023). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv preprint. https://arxiv.org/abs/2311.06586
McLeod, S. (2018). Instructional scaffolding & Vygotsky and Bruner constructs. Wikipedia / Wikiversity.
Nadhifah, I., Shifa, L., Al Hasan, F. T., & Anbiya, B. F. (2025). Peran Teori Kognitivisme dalam Meningkatkan Proses Pembelajaran PAI. Jurnal Teknologi Pendidikan, 5(1). https://doi.org/10.37304/jtekpend.v5i1.17405
Ni’amah, K. et al. (2021). Teori pembelajaran kognitivistik dan aplikasinya. Jurnal Ilmiah Mahasiswa Raushan Fikr, 10(2), 204–217. DOI:10.24090/jimrf.v10i2.4947
Putri, R. P., Ardhiansyah, S. S., Kurnia, H., Sari, M. I., & Putri, M. F. J. L. (2024). Penerapan Deep Learning dalam Pendidikan di Indonesia, Generasi Pancasila. doi: (tersedia di sumber) Open Journal
Putri, R. P., Ardhiansyah, S. S., Kurnia, H., Sari, M. I., & Putri, M. F. J. L. (2022). Penerapan Deep Learning dalam Pendidikan di Indonesia. Generasi Pancasila, 2, 97–102. https://doi.org/10.1016/j.iheduc.2015.03.001? (catatan DOI fix menurut metadata) openjournal.unpam.ac.id
Saomi, M. R. (2025). Penerapan Model Kognitivisme dalam Desain Pembelajaran. Iftitah: Jurnal Ilmiah Pendidikan Islam Anak Usia Dini, 1(1). https://doi.org/10.55656/ijpiaud.v1i1.366 Jurnal IAI PDK Indramayu
Storrs, K. R. & Kriegeskorte, N. (2019). Deep Learning for Cognitive Neuroscience. arXiv preprint.
Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37-76. https://doi.org/10.1016/B978-0-12-387691-1.00002-8
Sweller, J.; Paas, F.; Renkl, A. et al. Cognitive Load Theory and Worked Example Effect (dalam berbagai publikasi antara 1988–2006). Learning and Instruction.
Wang, F., Liu, Q., Chen, E., et al. (2019). Neural Cognitive Diagnosis for Intelligent Education Systems. arXiv preprint arxiv.org
Wasfy et al. (2024). Optimizing learning through Gagné’s Theory (PMC). Microlearning via Nine Events.
Zikrulloh, M., Srihartini, Y., Humairo, S. S., & Yulistiani, S. A. (2025). Konsep Dasar Mengenai Teori Belajar Kognitif serta Implikasinya Dalam Pembelajaran. At Tadris: Journal of Islamic Education, 4(1). https://doi.org/10.56672/attadris.v4i1.452
Artikel Wikipedia tentang Deep Learning (2025, Desember): Relation to human cognitive and brain development. en.wikipedia.org
Ausubel, D. P. (1963). The Psychology of Meaningful Verbal Learning. Grune & Stratton.
Ausubel, D. P. (1968). Educational Psychology: A Cognitive View. Holt, Rinehart and Winston.
Basyir, M. S., Dinana, A., & Devi, A. D. (2022). Kontribusi Teori Belajar Kognitivisme David P. Ausubel dan Robert M. Gagné dalam Proses Pembelajaran. Jurnal Pendidikan Madrasah, 7(1). https://doi.org/10.14421/jpm.2022.71.12 ejournal.uin-suka.ac.id
Beckmann, P., Köstner, G. & Hipólito, I. (2023). Rejecting Cognitivism: Computational Phenomenology for Deep Learning. arXiv arxiv.org
Bruner, J. (1967/15 Jun 2025 update). Jerome Bruner – scaffolding, spiral curriculum, representation modes. Wikipedia.
Bruner, J. S. (1966). Toward a Theory of Instruction. Harvard University Press.
Chaplot, D. S., MacLellan, C., Salakhutdinov, R., & Koedinger, K. (2018). Learning Cognitive Models using Neural Networks. arXiv preprint arxiv.org
Cherry, K. (2024). Piaget’s Stages of Cognitive Development Explained. Verywell Mind.
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive Apprenticeship: Teaching the Crafts of Reading, Writing, and Mathematics. Cognition and Instruction (konsep scaffolding)
DeWolfe, T. E. (2024). Jean Piaget’s theory of cognitive development. EBSCO Research Starters.
Editors (2025). Enhancing the cognitive load theory and multimedia learning framework with AI insight. Discover Education, 4:160. doi:10.1007/s44217-025-00592-6 SpringerLink
Fauziati, E. (2022). Deep Learning: a Theoretical Review. Suar Betang, Kemdikbud Indonesia.
Fitriana Sari, F., Pujiarti, T., Hidayat, H., & Anjosa, A. (–). Pembelajaran Matematika Diskrit Mengacu pada Teori Beban Kognitif. Jurnal DIKSI. doi:10.53299/diksi.v5i1.370
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N. ..., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, National Academy of Sciences. meta-analisis active learning approx +0.47 SD
Frontiers in Education (2024). Analysis of learner centered software education and support strategies.
Geirhos, R., Jacobsen, J. H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F. A. (2020). Shortcut Learning in Deep Neural Networks. arXiv preprint arxiv.org
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2020). Shortcut learning in deep neural networks. Nature Communications, 11(1), 1-10. https://doi.org/10.1038/s41467-020-19815-7
Haditia, M., Sinaga, M. N. A., Soepriyanto, Y., Purnomo, P., & Ma’ruf, R. A. (2025). A Systematic Review on Deep Learning in Education: Concepts, Factors, Models and Measurements, Journal of Education and Educational Research, 7(1), 125–129. doi:10.54097/gzk2yd38 journal.ipts.ac.id
Harefa, D. (2025). The Application Of Hombo Batu Local Wisdom-Based Learning In Enhancing Student Discipline And Cooperation In The Nias Islands. Ndrumi : Jurnal Ilmu Pendidikan Dan Humaniora, 8(1), 14-27. Https://Doi.Org/10.57094/Ndrumi.V8i1.2565
Harefa, D., I Made Sutajaya, I Wayan Suja, & Ida Bagus Made Astawa. (2024). Lowalangi Dalam Konsep Tri Hita Karana Dalam Kearifan Lokal NIAS. Ndrumi : Jurnal Ilmu Pendidikan Dan Humaniora, 7(2), 51-61. Https://Doi.Org/10.57094/Ndrumi.V7i2.2226
Harefa, D., I Made Sutajaya, I Wayan Suja, & Ida Bagus Made Astawa. (2024). Nilai Moral Tri Hita Karana Dalam Album “Keramat” Ciptaan H. Rhoma Irama. Ndrumi : Jurnal Ilmu Pendidikan Dan Humaniora, 7(2), 1-15. Https://Doi.Org/10.57094/Ndrumi.V7i2.2117
Jose, B. et al. (2025). The cognitive paradox of AI in education: between enhancement and erosion. Frontiers in Psychology, 16:1550621. doi:10.3389/fpsyg.2025.1550621
Kadarismanto & Puspita Sari (2025). Konsep Deep Learning sebagai Pilar dalam Strategi Pendidikan Berkualitas. doi:10.010125/dweh6m45 journal.metanusantara.com
Khasawneh, Y. J. A. et al. (2024). Cognitive load analysis of adaptive learning technologies in special education classrooms. IJAAS, 11(12):34 41. doi:10.21833/ijaas.2024.12.004 science-gate.com
Koć Januchta, M. M. et al. (2022). “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI enriched textbook. Int. Journal of Educational Technology in Higher Education, 19:11. doi:10.1186/s41239-021-00317-3 SpringerOpen
Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Neuron, 100(2), 334-348. https://doi.org/10.1016/j.neuron.2018.10.008
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Liu, Q., Lin, C., Chen, G., & Yu, C. (2019). Exploiting Cognitive Structure for Adaptive Learning. IEEE Transactions on Learning Technologies, 12(1), 62–75. https://doi.org/10.1109/TLT.2018.2885704
Macedo, H. d. S. et al. (2023). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv
Ausubel, D. P. (1967). The Psychology of Meaningful Verbal Learning: An Introduction to School Learning.
Macedo, H. d. S., dos Santos, I. T. & Oliveira da Silva, E. L. (2023). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv preprint.
Macedo, H. d. S., et al. (2023). The Power of Attention: Bridging Cognitive Load, Multimedia Learning, and AI. arXiv preprint. https://arxiv.org/abs/2311.06586
McLeod, S. (2018). Instructional scaffolding & Vygotsky and Bruner constructs. Wikipedia / Wikiversity.
Nadhifah, I., Shifa, L., Al Hasan, F. T., & Anbiya, B. F. (2025). Peran Teori Kognitivisme dalam Meningkatkan Proses Pembelajaran PAI. Jurnal Teknologi Pendidikan, 5(1). https://doi.org/10.37304/jtekpend.v5i1.17405
Ni’amah, K. et al. (2021). Teori pembelajaran kognitivistik dan aplikasinya. Jurnal Ilmiah Mahasiswa Raushan Fikr, 10(2), 204–217. DOI:10.24090/jimrf.v10i2.4947
Putri, R. P., Ardhiansyah, S. S., Kurnia, H., Sari, M. I., & Putri, M. F. J. L. (2024). Penerapan Deep Learning dalam Pendidikan di Indonesia, Generasi Pancasila. doi: (tersedia di sumber) Open Journal
Putri, R. P., Ardhiansyah, S. S., Kurnia, H., Sari, M. I., & Putri, M. F. J. L. (2022). Penerapan Deep Learning dalam Pendidikan di Indonesia. Generasi Pancasila, 2, 97–102. https://doi.org/10.1016/j.iheduc.2015.03.001? (catatan DOI fix menurut metadata) openjournal.unpam.ac.id
Saomi, M. R. (2025). Penerapan Model Kognitivisme dalam Desain Pembelajaran. Iftitah: Jurnal Ilmiah Pendidikan Islam Anak Usia Dini, 1(1). https://doi.org/10.55656/ijpiaud.v1i1.366 Jurnal IAI PDK Indramayu
Storrs, K. R. & Kriegeskorte, N. (2019). Deep Learning for Cognitive Neuroscience. arXiv preprint.
Sweller, J. (2011). Cognitive load theory. Psychology of Learning and Motivation, 55, 37-76. https://doi.org/10.1016/B978-0-12-387691-1.00002-8
Sweller, J.; Paas, F.; Renkl, A. et al. Cognitive Load Theory and Worked Example Effect (dalam berbagai publikasi antara 1988–2006). Learning and Instruction.
Wang, F., Liu, Q., Chen, E., et al. (2019). Neural Cognitive Diagnosis for Intelligent Education Systems. arXiv preprint arxiv.org
Wasfy et al. (2024). Optimizing learning through Gagné’s Theory (PMC). Microlearning via Nine Events.
Zikrulloh, M., Srihartini, Y., Humairo, S. S., & Yulistiani, S. A. (2025). Konsep Dasar Mengenai Teori Belajar Kognitif serta Implikasinya Dalam Pembelajaran. At Tadris: Journal of Islamic Education, 4(1). https://doi.org/10.56672/attadris.v4i1.452
Published
2025-08-02
