PELATIHAN PEMBUATAN MODUL AJAR BERBASIS DEEP LEARNING BAGI GURU SDIT AL-AZHAR MADANI CENTER
DOI:
https://doi.org/10.59407/jpki2.v3i3.2130Abstract
Tujuan dari kegiatan pengabdian ini adalah untuk memberikan pelatihan kepada guru sekolah dasar dalam pembuatan modul ajar berbasis deep learning yang dapat mengintegrasikan konsep mindful learning, meaningful learning, dan joyful learning. Kegiatan dilakukan melalui lima tahapan, yaitu studi pendahuluan, sosialisasi, pelatihan, evaluasi, dan pemberian umpan balik. Tahap studi pendahuluan mengidentifikasi tantangan dan kebutuhan guru terkait penerapan teknologi pembelajaran, khususnya deep learning, melalui wawancara dengan kepala sekolah dan guru. Hasil wawancara menunjukkan bahwa meskipun guru memiliki pemahaman dasar tentang deep learning, penggunaannya dalam pembelajaran masih terbatas karena keterbatasan perangkat dan akses internet. Tahap sosialisasi dilakukan kepada 44 guru dengan dominasi peserta perempuan (82%), dilanjutkan pelatihan intensif selama dua hari yang mencakup pengenalan konsep deep learning dan pendampingan penyusunan modul ajar untuk berbagai mata pelajaran. Tim PKM Universitas Majalengka memberikan materi sesuai kepakaran masing-masing. Evaluasi menunjukkan bahwa pelatihan ini memberikan pemahaman baru serta keterampilan praktis bagi guru dalam mengembangkan modul ajar adaptif berbasis teknologi. Kegiatan ini diharapkan menjadi langkah awal bagi implementasi deep learning di sekolah dasar serta membuka peluang kolaborasi lanjutan antara mitra dan perguruan tinggi.
Kata Kunci: Pengabdian Kepada Masyarakat, Deep Learning, Modul Ajar, Kurikulum Merdeka, Pendidikan Dasar
References
Ahamed, H. R., & Hanirex, D. K. (2024). A Deep Learning-Enabled Approach for Real-Time Monitoring of Learner Activities in Adaptive E-Learning Environments. 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), 1, 846–851. https://doi.org/10.1109/ICCPCT61902.2024.10673041
Ajani, O. A., Gamede, B., & Matiyenga, T. C. (2024). Leveraging artificial intelligence to enhance teaching and learning in higher education : Promoting quality education and critical engagement. 7(1).
Al Husaeni, D. F., Al Husaeni, D. N., Nandiyanto, A. B. D., Rokhman, M., Chalim, S., Chano, J., Al Obaidi, A. S. M., & Roestamy, M. (2024). How Technology Can Change Educational Research? Definition, Factors for Improving Quality of Education and Computational Bibliometric Analysis. ASEAN Journal of Science and Engineering, 4(2), 127–166. https://doi.org/10.17509/ajse.v4i2.62045
Asad, M. M., & Suleman, N. (2025). Impact of technology-supported personalized learning 5.0 on instructional quality: insights from the higher education institutions of Pakistan. Quality Assurance in Education, ahead-of-print(ahead-of-print). https://doi.org/10.1108/QAE-10-2024-0200
Bryce, T. G. K., & Blown, E. J. (2024). Ausubel’s meaningful learning re-visited. Current Psychology, 43(5), 4579–4598. https://doi.org/10.1007/s12144-023-04440-4
Chen, C. (2025). Entertainment social media based on deep learning and interactive experience application in English e-learning teaching system. Entertainment Computing, 52, 100846. https://doi.org/https://doi.org/10.1016/j.entcom.2024.100846
Chen, X., Hu, X., Huang, Y., Jiang, H., Ji, W., Jiang, Y., Jiang, Y., Liu, B., Liu, H., Li, X., Lian, X., Meng, G., Peng, X., Sun, H., Shi, L., Wang, B., Wang, C., Wang, J., Wang, T., … Zhang, L. (2025). Deep learning-based software engineering: progress, challenges, and opportunities. In Science China Information Sciences (Vol. 68, Issue 1). https://doi.org/10.1007/s11432-023-4127-5
Chen, Y., & Sun, Y. (2024). The Usage of Artificial Intelligence Technology in Music Education System under Deep Learning. IEEE Access, 12(September), 130546–130556. https://doi.org/10.1109/ACCESS.2024.3459791
Cronqvist, M. (2024). Enhanced student joy in learning environment; understanding and influencing the process. European Journal of Education, 59(3), 1–12. https://doi.org/10.1111/ejed.12671
Du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Data to support scoping review on: Personalized adaptive learning in higher education - key characteristics and impact on academic performance and engagement. Mendeley Data, 1(March).
Fan, G. (2025). The Reconfiguration of Human Education in an Uncertain World. ECNU Review of Education. https://doi.org/10.1177/20965311241266856
Ferri, F., Grifoni, P., & Guzzo, T. (2020). Online learning and emergency remote teaching: Opportunities and challenges in emergency situations. Societies, 10(4), 1–18. https://doi.org/10.3390/soc10040086
Gao, Y. (2025). Deep learning-based strategies for evaluating and enhancing university teaching quality. Computers and Education: Artificial Intelligence, 8, 100362. https://doi.org/https://doi.org/10.1016/j.caeai.2025.100362
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3(May), 275–285. https://doi.org/10.1016/j.susoc.2022.05.004
Hava, K. (2021). The effects of the flipped classroom on deep learning strategies and engagement at the undergraduate level. Participatory Educational Research, 8(1), 379–394. https://doi.org/10.17275/per.21.22.8.1
Hussain, T., Yu, L., Asim, M., Ahmed, A., & Wani, M. A. (2024). Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education. Information (Switzerland), 15(5). https://doi.org/10.3390/info15050277
Kruk, M., & Kałużna, A. (2025). Investigating the Role of AI Tools in Enhancing Translation Skills, Emotional Experiences, and Motivation in L2 Learning. European Journal of Education, 60(1), e12859. https://doi.org/https://doi.org/10.1111/ejed.12859
Ma, Q., Lee, H. T. H., Gao, X., & Chai, C. sing. (2024). Learning by design: Enhancing online collaboration in developing pre-service TESOL teachers’ TPACK for teaching with corpus technology. British Journal of Educational Technology, March, 2639–2667. https://doi.org/10.1111/bjet.13458
Namaziandost, E., & Rezai, A. (2024). Interplay of academic emotion regulation, academic mindfulness, L2 learning experience, academic motivation, and learner autonomy in intelligent computer-assisted language learning: A study of EFL learners. System, 125, 103419. https://doi.org/https://doi.org/10.1016/j.system.2024.103419
Naseer, F., Khan, M. N., Tahir, M., Addas, A., & Aejaz, S. M. H. (2024). Integrating deep learning techniques for personalized learning pathways in higher education. Heliyon, 10(11), e32628. https://doi.org/10.1016/j.heliyon.2024.e32628
Onker, V., Singh, K. K., Lamkuche, H. S., Kumar, S., Sharma, V. S., Chowdhary, C. L., & Kumar, V. (2025). Harnessing machine learning for academic insight: A study of educational performance in Bhopal, India. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13357-3
Pan, Z., & Wang, Y. (2025). From Technology-Challenged Teachers to Empowered Digitalized Citizens: Exploring the Profiles and Antecedents of Teacher AI Literacy in the Chinese EFL Context. European Journal of Education, 60(1), 1–16. https://doi.org/10.1111/ejed.70020
Rehman, T. U. (2024). The transformative impact and evolving landscape: A comprehensive exploration of the globalization of higher education in the 21st century. Journal of Further and Higher Education, 1–16. https://doi.org/10.1080/0309877X.2025.2457537
Rizwan, S., Nee, C. K., & Garfan, S. (2025). Identifying the Factors Affecting Student Academic Performance and Engagement Prediction in MOOC using Deep Learning: A Systematic Literature Review. IEEE Access, 13(December 2024), 18952–18982. https://doi.org/10.1109/ACCESS.2025.3533915
Sa’odah, S., Yuniasih, N., & Haryanti, Y. D. (2022). Learning Technology in Elementary School. AL-ISHLAH: Jurnal Pendidikan, 14(4), 6739–6744. https://doi.org/10.35445/alishlah.v14i4.1488
Sitthiworachart, J., Joy, M., King, E., Sinclair, J., & Foss, J. (2022). Technology-Supported Active Learning in a Flexible Teaching Space. Education Sciences, 12(9). https://doi.org/10.3390/educsci12090634
Sun, D., Looi, C.-K., Yang, Y., & Jia, F. (2025). Exploring students’ learning performance in computer-supported collaborative learning environment during and after pandemic: Cognition and interaction. British Journal of Educational Technology, 56(1), 128–149. https://doi.org/https://doi.org/10.1111/bjet.13492
Sun, Y., Zhou, G., & Deng, X. (2024). Emotional Intelligence and Joyful Teaching: Pathways to Enhanced Student Engagement and Achievement. Lecture Notes in Education Psychology and Public Media, 52(1), 163–168. https://doi.org/10.54254/2753-7048/52/20241533
Timm, J. M., & Barth, M. (2021). Making education for sustainable development happen in elementary schools: the role of teachers. Environmental Education Research, 27(1), 50–66. https://doi.org/10.1080/13504622.2020.1813256
Wang, Y., Liu, W., Yu, X., Li, B., & Wang, Q. (2024). The impact of virtual technology on students’ creativity: A meta-analysis. Computers & Education, 215, 105044. https://doi.org/https://doi.org/10.1016/j.compedu.2024.105044
Wijnen, F., Walma van der Molen, J., & Voogt, J. (2023). Primary teachers’ attitudes towards using new technology and stimulating higher-order thinking in students: A profile analysis. Education and Information Technologies, 28(6), 6347–6372. https://doi.org/10.1007/s10639-022-11413-w
Wu, X. Y. (2024). Exploring the effects of digital technology on deep learning: a meta-analysis. In Education and Information Technologies (Vol. 29, Issue 1). Springer US. https://doi.org/10.1007/s10639-023-12307-1
Zhu, Q., & Niyozov, S. (2024). Towards Deep Learning in Online Courses : A Case Study in Cross-pollinating Universal Design for Learning and Dialogic Teaching. Journal of the Scholarship of Teaching and Learning, 24(3), 87–104. https://doi.org/10.14434/josotl.v24i3.35331






















