SUN'IY INTELLEKT YORDAMIDA O'QUVCHILARGA INDIVIDUAL MATEMATIK MASALALAR TAVSIYA QILISH TIZIMI: GIBRID ALGORITMNING ADAPTIV SAMARADORLIGI
Keywords:
adaptive learning, tavsiya tizimlari, individual ta'lim, matematik masalalar, mashina o'rganish, kontentga asoslangan filtrlash, hamkorlikli filtrlash, gibrid algoritm, cold start, o'quvchi profili.Abstract
Ushbu tadqiqotda o'quvchilarning individual o'rganish xususiyatlarini real vaqt rejimida hisobga olgan holda matematik masalalar tavsiya qilishning gibrid tizimi ishlab chiqildi va eksperimental baholandi. Tizim kontentga asoslangan filtrlash (content-based filtering) va hamkorlikli filtrlash (collaborative filtering) usullarining dinamik kombinatsiyasidan foydalanadi. Taklif etilgan adaptiv vazn mexanizmi yangi foydalanuvchilar uchun "cold start" muammosini hal qiladi va tajribali o'quvchilar uchun hamkorlikli tavsiyalarning ulushini avtomatik oshiradi. 80 nafar 9-11 sinf o'quvchilari ishtirokida o'tkazilgan 16 haftalik kvazi-eksperimental tadqiqot natijasida tizim 87.3% aniqlik (Precision@10), 82.1% to'liqlik (Recall@10) va 84.6% F1-score ko'rsatkichlariga erishdi. Tajriba guruhi o'quvchilari nazorat guruhiga nisbatan o'rtacha 23.3% yuqori o'sish namoyish etdi (p < 0.001, Cohen's d = 1.29). O'quvchilar subyektiv qoniqish darajasi 4.3/5 ballni tashkil etdi. Natijalar sun'iy intellekt yordamida individual ta'lim samaradorligini oshirishning imkoniyatlarini tasdiqlaydi.
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