RAQAMLI TA’LIM MUHITIDA TALABALAR O‘QUV NATIJALARINI BAHOLASHNING PEDAGOGIK-DIAGNOSTIK IMKONIYATLARI
PDF

Keywords

raqamli ta’lim muhiti
o‘quv natijalari
pedagogik diagnostika
baholash
sun’iy intellekt
learning analytics
teskari aloqa
individual ta’lim trayektoriyasi
diagnostik feedback
adaptiv baholash.

How to Cite

RAQAMLI TA’LIM MUHITIDA TALABALAR O‘QUV NATIJALARINI BAHOLASHNING PEDAGOGIK-DIAGNOSTIK IMKONIYATLARI. (2026). Qo‘qon DPI. Ilmiy Xabarlar Jurnali, 8(6), 737-747. https://doi.org/10.70728/c.series.ped.v08.i06.114

Abstract

Maqolada raqamli ta’lim muhitida talabalar o‘quv natijalarini baholashning pedagogik-diagnostik imkoniyatlari tahlil qilinadi. An’anaviy baholashning yakuniy natijani qayd etish va ball qo‘yish bilan cheklanishi talabaning individual qiyinchiliklari, xatolari hamda rivojlanish ehtiyojlarini to‘liq aniqlashga imkon bermasligi asoslanadi. Raqamli muhit baholashni uzluksiz kuzatish, tahlil, diagnostika va individual teskari aloqa jarayoni sifatida tashkil etishga xizmat qiladi. Maqolada baholashning diagnostik, adaptiv, tahliliy, prognostik va rivojlantiruvchi funksiyalari yoritiladi. Sun’iy intellekt, learning analytics, educational data mining va avtomatlashtirilgan feedback vositalari o‘qituvchining pedagogik qarorlarini dalillangan ma’lumotlar bilan qo‘llab- quvvatlovchi didaktik vosita sifatida talqin etiladi.
PDF

References

1. UNESCO. Guidance for generative AI in education and research. Paris: United Nations Educational, Scientific and Cultural Organization, 2023.

2. UNESCO. AI competency framework for teachers. Paris: United Nations Educational, Scientific and Cultural Organization, 2024.

3. Hattie J., Timperley H. The Power of Feedback // Review of Educational Research.

2007. Vol. 77, No.

1. P. 81–112. DOI: 10.3102/003465430298487.

4. 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. DOI: 10.1007/978-1-4614-3305-7_4.

5. 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.

6. 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. DOI: 10.1007/s40593-014-0024-x.

7. 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.