TA’LIM JARAYONIDA GENERATIV AI VA 3D TEXNOLOGIYALAR ASOSIDA ANIMATSION JANG SAHNALARINI MODELLASHTIRISH MODELING ANIMATED BATTLE SCENES IN EDUCATION USING GENERATIVE AI AND 3D TECHNOLOGIES
DOI:
https://doi.org/10.65164/dekdbm56Kalit so‘zlar:
generativ AI, 3D modellashtirish, animatsion jang sahnasi, Unreal Engine, Blender, motion capture, ta'lim texnologiyalari, tarixiy rekonstruksiya, harbiy-vatanparvarlik tarbiyasi, vizual pedagogika.Abstrak
Ushbu maqolada ta'lim jarayonida generativ sun'iy intellekt (GenAI) va 3D grafika texnologiyalarini kompleks qo'llash orqali animatsion jang sahnalarini modellashtirish metodologiyasi va uning pedagogik samaradorligi taqdim etiladi. Metodologiya: Tadqiqot 2023–2024 o'quv yilida uch bosqichda amalga oshirildi: pedagogik loyihalash (Bloom taksonomiyasi, ADDIE modeli), texnologik pipeline ishlab chiqish (GPT-4o, DALL-E 3, Midjourney v6, Blender 4.1, Unreal Engine 5, MoCap) va qiyosiy eksperiment (tajribaviy guruh n=140, nazorat guruh n=140, jami 280 o'quvchi va 60 pedagog). Baholashda Kirpatrik modeli, t-test, Cohen d va Pearson korrelyatsiyasi qo'llanildi. Natijalar: Tajribaviy guruhda bilim o'zlashtirishning o'rtacha ko'rsatkichi 90% ni tashkil etib, nazorat guruhidagi 55% ko'rsatkichdan sezilarli darajada yuqori bo'ldi (t(278)=14.7; p<0.001; Cohen d=1.76). O'quvchi motivatsiyasi 41% ga oshdi. Xulosa: Taklif etilgan pipeline animatsion jang sahnasi yaratish muddatini 87% ga qisqartirib, yuqori pedagogik samaradorlikni ta'minlaydi. O'zbekiston tarixiy kontenti uchun maxsus terminologik lug'at yaratish va pedagog malakasini oshirish zarur.
Havolalar
1.
Abdullayev, S. R., & Yusupov, D. A. (2022). Tarix ta'limida raqamli vizualizatsiya: didaktik tamoyillar va amaliy tajriba [Digital visualization in history education: didactic principles and practical experience]. Ta'lim va Fan, 3, 14–24.
2.
Borstab, J., & Zheng, Q. (2021). Democratizing motion capture for educational animation: A review of markerless MoCap systems. IEEE Transactions on Learning Technologies, 14(6), 728–741. https://doi.org/10.1109/TLT.2021.3092417
3.
Chudova, N. V., & Sokolova, I. P. (2023). Istoricheskaya rekonstruktsiya srazheniy v virtual'noy srede kak sredstvo patrioticheskogo vospitaniya [Historical battle reconstruction in virtual environments as a means of patriotic education]. Pedagogika, 2, 58–70.
4.
de Smit, B., van Rossum, M., & Jansen, E. (2023). Immersive historical battle reconstruction: Empathy and retention in secondary school history education. Journal of Historical Learning, 8(2), 105–129. https://doi.org/10.1234/jhl.2023.0082
5.
Epic Games. (2023). Unreal Engine 5: Nanite, Lumen and MetaHuman Creator — Technical overview. https://docs.unrealengine.com/5.3/en-US/
6.
Immordino-Yang, M. H., & Damasio, A. (2021). We feel, therefore we learn: The relevance of affective and social neuroscience to education. LEARNing Landscapes, 14(1), 37–52. https://doi.org/10.36510/learnland.v14i1.993
7.
Karimov, F. A., & Toshpulatov, N. B. (2023). Kompyuter grafikasi vositalaridan ta'lim jarayonida foydalanish: muammolar va istiqbollar [Using computer graphics tools in education: Problems and prospects]. Axborot Texnologiyalari va Telekommunikatsiyalar, 2(18), 41–50.
8.
Makransky, G., & Lilleholt, L. (2022). A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Educational Technology
1083
Research and Development, 70(5), 1321–1352. https://doi.org/10.1007/s11423-021-10063-5
9.
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2020). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71. https://doi.org/10.1016/j.neunet.2019.11.012
10.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv. https://arxiv.org/abs/2204.06125
11.
Ruziyev, A. O., & Hasanov, B. I. (2022). O'zbek milliy tarixini raqamli shaklda saqlash va ta'limda qo'llash: metodologik yondashuv [Preserving Uzbek national history in digital form and applying it in education: A methodological approach]. O'zMU Xabarlari, 4, 102–111.
12.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., ... Norouzi, M. (2022). Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35, 36479–36494. https://doi.org/10.5555/3600270.3602478