APPLYING DEEP LEARNING TO SUPPORT EARLY COGNITIVE DEVELOPMENT IN PRIMARY STUDENTS: INSIGHTS FROM AN INDONESIAN ISLAMIC SCHOOL CONTEXT
DOI:
https://doi.org/10.62567/micjo.v2i4.1532Keywords:
Deep learning, cognitive development, primary education, artificial intelligence, adaptive learning, Islamic schoolAbstract
The rapid advancement of artificial intelligence (AI) offers new opportunities to enhance teaching and learning in early education. This study applies deep learning approaches to support early cognitive development among primary students in an Indonesian Islamic school. A hybrid Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) model was designed to analyse students’ cognitive patterns, attention, and engagement. Using a mixed-methods design, the research involved 60 students aged 8–10 at MIS Terpadu Alhijrah Bintuju. The model processed multimodal classroom data to generate adaptive feedback and personalized learning pathways. Results showed significant improvements in attention (+18%), memory recall (+22%), and problem-solving (+25%) after eight weeks of AI-assisted learning. Qualitative findings revealed higher motivation, engagement, and self-regulation. The study demonstrates that culturally aligned AI systems can effectively enhance early cognitive development and promote learner autonomy in Islamic primary education.
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References
Abdullah, M., & Rahman, N. (2022). Artificial intelligence in Islamic education: Opportunities and ethical challenges. International Journal of Educational Technology in Higher Education, 19(1), 54–69. https://doi.org/10.1186/s41239-022-00343-1
Alamri, A., & Al-Shehri, M. (2021). Deep learning models for adaptive learning systems: A systematic review. Computers & Education: Artificial Intelligence, 2, 100035. https://doi.org/10.1016/j.caeai.2021.100035
Basri, H., & Nugroho, D. (2023). Integrating AI-assisted learning in Indonesian primary schools: A pilot study on digital literacy and engagement. Education and Information Technologies, 28(7), 7653–7671. https://doi.org/10.1007/s10639-023-11589-0
Chen, X., Wang, Y., & Li, L. (2020). Deep learning in education: A state-of-the-art survey. IEEE Access, 8, 181892–181904. https://doi.org/10.1109/ACCESS.2020.3028890
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Han, J., & Chen, C. (2022). Deep learning analytics for early childhood education: Predicting learning trajectories and engagement. British Journal of Educational Technology, 53(4), 892–908. https://doi.org/10.1111/bjet.13227
Kaur, P., & Singh, R. (2023). AI-driven adaptive feedback to enhance primary students’ learning outcomes. Education and Information Technologies, 28(3), 2561–2584. https://doi.org/10.1007/s10639-022-11297-2
Mursyid, A., & Fatimah, S. (2021). The role of deep learning frameworks in supporting cognitive growth in early learners. Journal of Educational Computing Research, 59(6), 1245–1264. https://doi.org/10.1177/07356331211045197
Rahimi, M., & Wang, J. (2022). A multimodal deep learning approach to assess student attention and engagement in classroom environments. Computers & Education, 185, 104542. https://doi.org/10.1016/j.compedu.2022.104542
Santos, M., & Rodrigues, L. (2019). Machine learning for personalised learning: Models, methods, and challenges. Computers in Human Behavior, 92, 604–616. https://doi.org/10.1016/j.chb.2018.04.002
Sun, Q., Zhao, H., & Zhang, Y. (2024). Cognitive pattern recognition in primary classrooms using CNN-LSTM hybrid networks. IEEE Transactions on Learning Technologies, 17(2), 225–237. https://doi.org/10.1109/TLT.2024.3341193
Zhou, L., & Brown, D. (2020). Educational learning theories: A comprehensive review. Educational Psychology Review, 32(4), 1103–1127. https://doi.org/10.1007/s10648-020-09585-2
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Copyright (c) 2025 Laswardi, Asmila Damayanti, Rizki Hamonangan Dalimunthe, Anita Adinda
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