KATTA MA'LUMOTLAR ASOSIDA ADAPTIV TA'LIM MODELLARI VA ALGORITMLARINI ISHLAB CHIQISH
Keywords:
Kalit so'zlar: Adaptiv ta'lim, Katta ma'lumotlar, Mashinali o'qitish, Gipergrafikli klasterlash, Bayes modeli, Shaxsiylashtirilgan ta'lim, Real vaqt tahliliAbstract
Annotatsiya: Ushbu maqolada katta ma'lumotlar (Big Data) texnologiyalaridan
foydalangan holda adaptiv ta'lim tizimlarini yaratish uchun yangi matematik modellar
va algoritmlar taqdim etiladi. Biz o'quvchilarning xatti-harakatlari, o'quv natijalari va
kognitiv xususiyatlarini real vaqtda tahlil qilish asosida shaxsga moslashtirilgan o'quv
yo'llarini avtomatik tarzda shakllantiruvchi gipergrafikli klasterlash algoritmi (HCA —
Hypergraph Clustering Algorithm) va Bayes adaptiv modeli (BAM — Bayesian
Adaptive Model) ni birgalikda qo'llaymiz. 50,000 dan ortiq o'quvchi ma'lumotlari
ustida o'tkazilgan eksperimental sinovlar ko'rsatdiki, taklif etilgan yondashuv o'quv
samaradorligini 34.7% ga oshiradi va bilim saqlanish darajasini 41.2% ga yaxshilaydi.
Matematik asoslanmalar, algoritmik murakkablik tahlili va qiyosiy natijalar batafsil
keltirilgan.
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