Dasturlash ko‘nikmalarini baholashda generativ sun’iy intellekt va gamifikatsiya integratsiyalashgan arxitektura modelini ishlab chiqish
DOI:
https://doi.org/10.5281/zenodo.21242642Ключевые слова:
dasturlash ta’limi, avtomatlashtirilgan baholash (AAT), generativ sun’iy intellekt (LLM), gamifikatsiya, ta’lim analitikasi (Learning Analytics), ko‘p mezonli baholash, Python, formativ qayta aloqa, CodeLearn ProАннотация
Ushbu maqolada oliy ta’lim muassasalarida dasturlash tillarini o‘qitish samaradorligini oshirish, talabalarning
amaliy ko‘nikmalarini ko‘p mezonli (multikriterial) diagnostika qilish va o‘qituvchining monitoring yuklamasini
optimallashtirish muammolari tizimli tahlil qilingan. An’anaviy avtomatlashtirilgan baholash tizimlarining faqat funksional
to‘g‘rilikka qaratilgan cheklovlarini bartaraf etish maqsadida generativ sun’iy intellekt (Large Language Models) va gamifikatsiya
mexanizmlari integratsiyalashgan “CodeLearn Pro” interaktiv ta’lim platformasining mantiqiy-funksional arxitekturasi
ishlab chiqilgan. Tadqiqotda modelning to‘rt qatlamli arxitekturasi, ma’lumotlar oqimi hamda ABC (Affective,
Behavioral,
Cognitive) pedagogik modeliga asoslangan 5 bosqichli baholash matritsasi ilmiy asoslab berilgan. Dastlabki
empirik natijalar sun’iy intellekt asosidagi formativ qayta aloqa (feedback) talabalarning xatolarni tushunish tezligini
oshirishini va gamifikatsiya elementlarining o‘quv faolligini kuchaytirishini ko‘rsatdi.
Библиографические ссылки
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