TA’LIMDA RAQAMLI TRANSFORMATSIYA VA INNOVATSION TEXNOLOGIYALAR: INTELLEKTUAL TAHLIL VA ADAPTIV O‘QITISH TIZIMI
DOI:
https://doi.org/10.5281/zenodo.20227778Keywords:
raqamli ta’lim platformasi, CNN, LSTM, PCA, mustahkamlash o‘rganishi, adaptiv o‘qitish, akademik anomaliya, talabalar o‘zlashtirishi.Abstract
Ushbu maqolada zamonaviy raqamli ta'lim platformalarida talabalar o'quv faoliyatini real vaqt rejimida kuzatish va intellektual tahlil qilish tizimi taqdim etiladi. Tizim platformadagi faollik vaqti, topshiriqlar bajarilishi va test natijalari kabi o'quv faoliyati ma'lumotlarini qayta ishlash uchun konvolyutsion neyron tarmoqlar (CNN), uzoq muddatli qisqa xotira tarmoqlari (LSTM) va asosiy komponentlar tahlili (PCA) usullarini qo'llaydi. Bundan tashqari, individual o'qitish metodikasi va adaptiv o'quv rejalarini optimallashtirish maqsadida mustahkamlash o'rganishi (Reinforcement Learning) algoritmidan foydalaniladi. Turli o'quv yuklamasi darajalarida o'tkazilgan eksperimental natijalar tizimning akademik natijalarni bashorat qilish aniqligini 94,7% ga yetkazishini ko'rsatdi. Taklif etilayotgan yondashuv ta'lim jarayoni samaradorligini va talabalar o'zlashtirishini sezilarli darajada oshirish imkonini beradi.References
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, 40(6), 601–618.
Piech, C., Spencer, J., Huang, J., et al. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 28.
Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
Huang, X., et al. (2019). Exploring adaptive learning in digital education with reinforcement learning. Computers & Education, 140, 103–119.
Yu, J., et al. (2022). Anomaly detection in e-learning platforms using deep autoencoders. Expert Systems with Applications, 195, 116567.
Alimboyeva, Z., Musurmonqulova, S., & Axmadjonov, I. (2023). Digital transformation in higher education: Opportunities and challenges. Farg'ona davlat texnika universiteti ilmiy axboroti, 5(2), 112–124.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Innovative Academy RSC
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
