K-MEANS KLASTERLASH ALGORITMI YORDAMIDA TALABALAR MA'LUMOTLARINI TAHLIL QILISH
Kalit so'zlar
https://doi.org/10.47390/ts-v3i12y2025N07Kalit so'zlar
ta'lim sohasida ma'lumotlarni qazib olish (EDM), K-means klasterlash, talabalar segmentatsiyasi, shaxsiylashtirilgan ta'lim, o‘quv tahlili, mashinali o‘rganish, xulq-atvor tahlili.Annotasiya
Ushbu maqolada ta’lim sohasida ma’lumotlarni qazib olishning (Educational Data Mining - EDM) muhim yo‘nalishi bo‘lgan klasterlash tahlilidan foydalanilgan. Maqolada K-means klasterlash algoritmi yordamida talabalarning o‘quv faoliyati va xulq-atvoriga oid ma'lumotlarni tahlil qilish metodologiyasi taklif etilgan. Tadqiqot doirasida talabalar o‘zlarining o‘xshash xususiyatlariga ko‘ra turli guruhlarga (klasterlarga) ajratilgan. Har bir klaster o‘ziga xos xususiyatlari, jumladan, akademik o‘zlashtirish, onlayn platformalardagi faollik va o‘quv resurslaridan foydalanish intensivligi bilan tavsiflangan. Natijalar shuni ko‘rsatadiki, K-means algoritmi talabalarni aniq segmentlarga ajratish imkonini beradi.
Tadqiqot natijalari ta'lim muassasalari ma'muriyati uchun ma'lumotlarga asoslangan qarorlar qabul qilishda va o‘quv jarayonini shaxsiylashtirishda amaliy ahamiyatga ega.
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