Konsep Pemperibadian Kognitif dan Maklum Balas Adaptif untuk Menyokong Pembelajaran Kanak-Kanak

Cognitive Personalization and Adaptive Feedback for Supporting Children’s Learning

Authors

  • Nor Hidayah Hussain Faculty of Creative Multimedia & Computing, Universiti Islam Selangor
  • Nadiah Yusof Faculty of Computing & Multimedia, University Poly-Tech Malaysia, Cheras, Malaysia
  • Tengku Siti Meriam Tengku Wook Universiti Kebangsaan Malaysia (UKM)
  • Siti Fadzilah Mat Noor Universiti Kebangsaan Malaysia (UKM)
  • Hazura Mohamed Universiti Kebangsaan Malaysia (UKM)

DOI:

https://doi.org/10.53840/myjict10-2-231

Keywords:

Pembelajaran aktif, maklum balas adaptif, kecerdasan buatan, antara muka multimodal, kanak-kanak

Abstract

Kemunculan aplikasi pembelajaran multimodal yang memperkenal pelbagai input mod interaksi seperti sentuhan, suara, isyarat mata dan gerak isyarat menawarkan pengalaman pembelajaran yang dinamik dan interaktif kepada kanak-kanak di peringkat prasekolah. Namun begitu, penggunaan mekanisme maklum balas statik yang tidak disesuaikan mengikut tahap kemahiran kanak-kanak yang berbeza menyebabkan mereka masih perlu bergantung kepada orang dewasa untuk melakukan interaksi multi sentuh. Hal ini menimbulkan cabaran isu kognitif, kurang keterlibatan secara aktif dalam proses pembelajaran, seterusnya menjejaskan kefahaman dalam pencapaian objektif pembelajaran. Keadaan ini menghalang perkembangan pembelajaran kendiri yang merupakan elemen penting dalam pendidikan abad ke-21. Objektif kajian adalah meneroka persekitaran pembelajaran adaptif melalui gabungan konsep pemperibadian (personalized) kognitif dan maklum balas dinamik pada aplikasi untuk menyokong pembelajaran kanak-kanak. Kajian ini menggunakan pendekatan tinjauan literatur melibatkan empat fasa iaitu pencarian literatur, kriteria pemilihan, analisis dan sintesis, serta pembentukan kerangka konseptual. Hasil kajian adalah kerangka konseptual yang mengetengahkan potensi Kecerdasan Buatan (AI) dalam menyediakan pengalaman pembelajaran yang diperibadikan dan interaktif di peringkat awal kanak-kanak. Ini selaras dengan prinsip yang digariskan dalam Pelan Hala Tuju Kecerdasan Buatan Negara 2021–2025, khususnya aspek pembangunan ciri AI yang bermanfaat untuk kepelbagaian pelajar

Downloads

Download data is not yet available.

Author Biographies

  • Nor Hidayah Hussain, Faculty of Creative Multimedia & Computing, Universiti Islam Selangor

    Department of Computing, Faculty of Creative Multimedia and Computing, Universiti Islam Selangor (UIS)

  • Nadiah Yusof, Faculty of Computing & Multimedia, University Poly-Tech Malaysia, Cheras, Malaysia

    Faculty of Computing & Multimedia, University Poly-Tech Malaysia, Cheras, Malaysia

  • Tengku Siti Meriam Tengku Wook, Universiti Kebangsaan Malaysia (UKM)

    Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
    Selangor, Malaysia

  • Siti Fadzilah Mat Noor, Universiti Kebangsaan Malaysia (UKM)

    Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
    Selangor, Malaysia

  • Hazura Mohamed, Universiti Kebangsaan Malaysia (UKM)

    Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
    Selangor, Malaysia

References

Alshammari, M., Anane, R., & Hendley, R. (2015). Adaptivity in e-learning systems. Smart Learning Environments, 2(1), 1–19.

Ayeni, O. O., Al Hamad, N. M., Chisom, O. N., Osawaru, B., & Adewusi, O. E. (2024). AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261-271.

Baradari, F., Wang, C., & Liu, J. (2025). NeuroChat: A neuroadaptive AI chatbot for customizing learning experiences. arXiv preprint arXiv:2503.07599. https://arxiv.org/abs/2503.07599

Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web: Methods and strategies of web personalization (pp. 3–53). Springer. https://doi.org/10.1007/978-3-540-72079-9_1

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.

Hussain, N. H., Wook, T. S. M., Mat Noor, S. F., & Mohamed, H. (2017). Multi-touch gestures in multimodal systems interaction among preschool children. 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI), 1–6.

Hussain, N. H., Wook, T. S. M., Mat Noor, S. F., & Mohamed, H. (2018). Speech input as an alternative mode to perform multi-touch gestures. TELKOMNIKA (Telecommunication Computing Electronics and Control), 16(3), 1367–1375.

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001

Kobsa, A. (2007). Generic user modeling systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web: Methods and strategies of web personalization (pp. 136–154). Springer. https://doi.org/10.1007/978-3-540-72079-9_4

Li, Y., Zhang, L., & Chen, X. (2025). Design and evaluation of children’s education interactive learning system based on human computer interaction technology. Scientific Reports, 15, 90800.

Mao, P., Cai, Z., Wang, Z., Hao, X., Fan, X., & Sun, X. (2024). The effects of dynamic and static feedback under tasks with different difficulty levels in digital game-based learning. The Internet and Higher Education, 60, 100923.

Mayer, R. E. (2020). Multimedia Learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781108894333

Mejeh, M., Sarbach, L., & Hascher, T. (2024). Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learning. Education and Information Technologies, 29, 12510–12510. https://doi.org/10.1007/s10639-024-12510-8

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326. https://doi.org/10.1007/s10648-007-9047-2

MOSTI. (2021). Pelan Hala Tuju Kecerdasan Buatan Negara 2021–2025. Putrajaya: Kementerian Sains, Teknologi dan Inovasi.

Nacher, V., & Jaen, J. (2015). Evaluating the accuracy of pre-kindergarten children multi-touch interaction. International Federation for Information Processing, 9297(11), 159–176.

Nacher, V., Jaen, J., Catala, A., Navarro, E., & González, P. (2015). Multi-touch gestures for pre-kindergarten children. International Journal of Human–Computer Studies, 73, 37–51. https://doi.org/10.1016/j.ijhcs.2014.08.004

Neumann, M. M., & Neumann, D. L. (2017). The use of touch-screen tablets at home and pre-school to foster emergent literacy. Journal of Early Childhood Literacy, 17(2), 203–220. https://doi.org/10.1177/1468798415619773

Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational psychologist, 50(4), 258-283. https://doi.org/10.1080/00461520.2015.1122533

Shute, V. J., & Rahimi, S. (2017). Review of computer‐based assessment for learning in elementary and secondary education. Journal of Computer Assisted Learning, 33(1), 1-19. https://doi.org/10.1111/jcal.12172

Shute, V. J., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist, 38(2), 105–114. https://doi.org/10.1207/S15326985EP3802_5

Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68, 1–16. https://doi.org/10.1007/s11423-019-09701-3

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4

Tan, S. (2025). Artificial intelligence-enabled adaptive learning platforms: A review. Education and Information Technologies, 30(1), 55–72.

Tang, K. S., Murcia, K., Brown, J., Cross, E., Mennell, S., Seitz, J., ... & Sabatino, D. (2024). Exploring the multimodal affordances of digital coding devices in fostering creative thinking in early childhood education. Thinking Skills and Creativity, 53, 101602.

The Society of Artificial Intelligence (THESAI). (2024). Adaptive AI-based personalized learning for accelerated vocabulary and syntax learning. International Journal of Advanced Computer Science and Applications, 15(4), 587–596. https://thesai.org/Downloads/Volume16No4/Paper_67-Adaptive_AI_Based_Personalized_Learning.pdf

Undheim, M. (2022). Children and teachers engaging together with digital technology in early childhood education and care institutions: A literature review. European early childhood education research journal, 30(3), 472-489.

UNESCO. (2022). Reimagining our futures together: A new social contract for education. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000379707

Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12(1), 1-21.

Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2022). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 169, 104214. https://doi.org/10.1016/j.compedu.2021.104214

Yaseen, H., Mohammad, A. S., Ashal, N., Abusaimeh, H., Ali, A., & Sharabati, A.-A. A. (2025). The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy. Sustainability, 17(3), 1133. https://doi.org/10.3390/su17031133

Yaseen, Z., Banihashem, S. K., & Afshari, M. (2025). The role of AI in sustainable and adaptive education systems. Sustainability, 17(3), 1133.

Published

17-12-2025

Issue

Section

Articles

How to Cite

Hussain, N. H., Yusof, N. ., Tengku Wook, T. S. M., Mat Noor, S. F. ., & Mohamed, H. . (2025). Konsep Pemperibadian Kognitif dan Maklum Balas Adaptif untuk Menyokong Pembelajaran Kanak-Kanak: Cognitive Personalization and Adaptive Feedback for Supporting Children’s Learning. Malaysian Journal of Information and Communication Technology (MyJICT), 10(2), 40-47. https://doi.org/10.53840/myjict10-2-231

Share