Sun’iy intellekt texnologiyalari asosida talabalar o‘quv natijalarini baholashning pedagogik modeli
DOI:
https://doi.org/10.5281/zenodo.20919389Ключевые слова:
raqamli ta’lim muhiti, sun’iy intellekt, pedagogik model, o‘quv natijalari, baholash, diagnostik feedback, o‘qituvchi moderatsiyasi, dinamik baholash mezonlari, learning analytics, individual ta’lim trayektoriyasiАннотация
Mazkur maqolada raqamli ta’lim muhitida talabalar o‘quv natijalarini sun’iy intellekt texnologiyalari asosida
baholashning pedagogik modeli yoritilgan. Oliy ta’lim tizimida baholash jarayonining xolisligi, shaffofligi, moslashuvchanligi
hamda diagnostik-rivojlantiruvchi funksiyasini kuchaytirish zarurati ushbu modelni ishlab chiqishning nazariy-metodik
asosini tashkil etadi. Maqolada sun’iy intellekt vositalari o‘qituvchini almashtiruvchi texnologik tizim sifatida emas, balki
uning pedagogik qaror qabul qilish faoliyatini qo‘llab-quvvatlovchi raqamli-didaktik mexanizm sifatida talqin qilinadi.
Tadqiqotda pedagogik modelning maqsadli, mazmuniy, texnologik, tashkiliy-pedagogik, baholash-diagnostik va natijaviy
komponentlari ochib berilgan. Shuningdek, “o‘qituvchi - talaba - raqamli ta’lim muhiti - sun’iy intellekt vositasi” o‘rtasidagi
didaktik aloqadorlik, baholash jarayonida inson nazorati, o‘qituvchi moderatsiyasi, diagnostik feedback, dinamik baholash
mezonlari hamda individual rivojlanish trayektoriyasining pedagogik ahamiyati asoslangan. Maqolada taklif etilgan
model raqamli ta’lim muhitida baholashni yakuniy nazorat vositasidan talabaning o‘quv faoliyatini tahlil qiluvchi, xatolarini
aniqlovchi va rivojlanishini yo‘naltiruvchi pedagogik-diagnostik tizimga aylantirish imkonini berishi ko‘rsatib berilgan
Библиографические ссылки
1. O‘zbekiston Respublikasi Prezidentining 2019-yil 8-oktabrdagi PF-5847-son “O‘zbekiston Respublikasi oliy ta’lim
tizimini 2030-yilgacha rivojlantirish konsepsiyasini tasdiqlash to‘g‘risida”gi Farmoni.
2. O‘zbekiston Respublikasi Prezidentining 2020-yil 5-oktabrdagi PF-6079-son “Raqamli O‘zbekiston - 2030” strategiyasini
tasdiqlash va uni samarali amalga oshirish chora-tadbirlari to‘g‘risida”gi Farmoni.
3. O‘zbekiston Respublikasi Prezidentining 2021-yil 17-fevraldagi PQ-4996-son “Sun’iy intellekt texnologiyalarini jadal
joriy etish uchun shart-sharoitlar yaratish chora-tadbirlari to‘g‘risida”gi Qarori.
4. O‘zbekiston Respublikasi Prezidentining 2024-yil 14-oktabrdagi PQ-358-son “Sun’iy intellekt texnologiyalarini 2030-
yilga qadar rivojlantirish strategiyasini tasdiqlash to‘g‘risida”gi Qarori.
5. UNESCO. Guidance for generative AI in education and research. Paris: United Nations Educational, Scientific and
Cultural Organization, 2023.
6. UNESCO. AI competency framework for teachers. Paris: United Nations Educational, Scientific and Cultural Organization,
2024.
7. Hattie J., Timperley H. The Power of Feedback // Review of Educational Research. 2007. Vol. 77, No. 1. P. 81-112.
DOI: 10.3102/003465430298487.
8. Baker R.S., Inventado P.S. Educational Data Mining and Learning Analytics // Learning Analytics: From Research to
Practice / ed. by J.A. Larusson, B. White. New York: Springer, 2014. P. 61-75.
9. Luckin R., Holmes W., Griffiths M., Forcier L.B. Intelligence Unleashed: An argument for AI in Education. London:
Pearson Education, 2016.
10. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning.
Boston: Center for Curriculum Redesign, 2019.
11. Koedinger K.R., Corbett A.T. Cognitive Tutors: Technology bringing learning sciences to the classroom // The Cambridge
Handbook of the Learning Sciences / ed. by R.K. Sawyer. Cambridge: Cambridge University Press, 2006. P.
61-78.
12. Heffernan N.T., Heffernan C.L. The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers
Together for Minimally Invasive Research on Human Learning and Teaching // International Journal of Artificial Intelligence
in Education. 2014. Vol. 24. P. 470-497.
13. Siemens G., Long P. Penetrating the Fog: Analytics in Learning and Education // EDUCAUSE Review. 2011. Vol. 46,
No. 5. P. 30-40.
14. Shute V.J. Focus on Formative Feedback // Review of Educational Research. 2008. Vol. 78, No. 1. P. 153-189.
15. Black P., Wiliam D. Assessment and Classroom Learning // Assessment in Education: Principles, Policy & Practice.
1998. Vol. 5, No. 1. P. 7-74.
16. Ferguson R. Learning analytics: drivers, developments and challenges // International Journal of Technology Enhanced
Learning. 2012. Vol. 4, No. 5/6. P. 304-317.
17. Romero C., Ventura S. Educational Data Mining: A Review of the State of the Art // IEEE Transactions on Systems,
Man, and Cybernetics, Part C: Applications and Reviews. 2010. Vol. 40, No. 6. P. 601-618.
18. Cukurova M., Luckin R., Kent C. Impact of an Artificial Intelligence Research Frame on the Perceived Credibility of
Educational Research Evidence // International Journal of Artificial Intelligence in Education. 2020. Vol. 30. P. 205-235.
19. Zawacki-Richter O., Marín V.I., Bond M., Gouverneur F. Systematic review of research on artificial intelligence applications
in higher education - where are the educators? // International Journal of Educational Technology in Higher
Education. 2019. Vol. 16, Article 39.
Загрузки
Опубликован
Выпуск
Раздел
Лицензия
Copyright (c) 2026 MAKTABGACHA VA MAKTAB TA’LIMI JURNALI

Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.