AMALIY MATEMATIKA FANLARINI INTELLEKTUAL ADAPTIV TA’LIM TEXNOLOGIYALARI ASOSIDA O‘QITISHNING METODIK MODELI VA UNING ALGORITMIK TA’MINOTI

Mualliflar

  • Dilfuzaxon Nishonova Farg‘ona davlat texnika universiteti 1-son akademik litseyi ##default.groups.name.author##

Kalit so‘zlar:

amaliy matematika, metodik model, intellektual adaptiv ta’lim texnologiyasi, bilim komponenti, bilimlarni Bayes usulida kuzatish, test topshiriqlari nazariyasi, aralash ta’lim, differensial tenglamalar.

Abstrak

Maqolada oliy ta’lim muassasalarida amaliy matematika fanlarini intellektual 
adaptiv ta’lim texnologiyalari asosida o‘qitishning metodik modeli taklif etilgan. Model 
maqsadli, mazmuniy, tashkiliy-texnologik va baholash-natijaviy bloklardan iborat. Fan 
mazmunini bilim komponentlari va tayanch bog‘lanishlar grafi ko‘rinishida 
tuzilmalashtirish «Oddiy differensial tenglamalar» moduli misolida ko‘rsatilgan. Talaba 
modelini yangilash uchun bilimlarni Bayes usulida kuzatish, topshiriq tanlash uchun 
test topshiriqlari nazariyasining ikki parametrli modeli qo‘llangan; algoritmning 
ishlashi hisoblash misolida namoyish etilgan. Talabaning bilim holatiga mos pedagogik 
qaror qoidalari, aralash ta’lim sikli bosqichlari hamda metodika samaradorligini 
baholash mezonlari va statistik usullari belgilangan. 

Библиографические ссылки

1. O‘zbekiston Respublikasi Prezidentining 2019-yil 8-oktabrdagi “O‘zbekiston Respublikasi oliy ta’lim tizimini 2030-

yilgacha rivojlantirish konsepsiyasini tasdiqlash to‘g‘risida”gi PF-5847-son Farmoni. – URL: https://lex.uz.

2. O‘zbekiston Respublikasi Prezidentining 2020-yil 7-maydagi “Matematika sohasidagi ta’lim sifatini oshirish va

ilmiy-tadqiqotlarni rivojlantirish chora-tadbirlari to‘g‘risida”gi PQ-4708-son Qarori. – URL: https://lex.uz.

3. Corbett A. T., Anderson J. R. Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge // User

Modeling and User-Adapted Interaction. – 1995. – Vol. 4, № 4. – P. 253–278.

4. VanLehn K. The Behavior of Tutoring Systems // International Journal of Artificial Intelligence in Education. –

2006. – Vol. 16, № 3. – P. 227–265.

5. Doignon J.-P., Falmagne J.-C. Knowledge Spaces. – Berlin: Springer, 1999. – 333 p.

6. Lord F. M. Applications of Item Response Theory to Practical Testing Problems. – Hillsdale, NJ: Lawrence Erlbaum

Associates, 1980.

7. Koedinger K. R., Corbett A. T., Perfetti C. The Knowledge-Learning-Instruction Framework: Bridging the Science-

Practice Chasm to Enhance Robust Student Learning // Cognitive Science. – 2012. – Vol. 36, № 5. – P. 757–798.

8. Vygotsky L. S. Mind in Society: The Development of Higher Psychological Processes. – Cambridge, MA: Harvard

University Press, 1978. – 159 p.

9. Bloom B. S. The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring

// Educational Researcher. – 1984. – Vol. 13, № 6. – P. 4–16.

10. Kulik J. A., Fletcher J. D. Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review // Review of

Educational Research. – 2016. – Vol. 86, № 1. – P. 42–78.

11. Hake R. R. Interactive-Engagement versus Traditional Methods: A Six-Thousand-Student Survey of Mechanics

Test Data for Introductory Physics Courses // American Journal of Physics. – 1998. – Vol. 66, № 1. – P. 64–74.

12. Cohen J. Statistical Power Analysis for the Behavioral Sciences. – 2nd ed. – Hillsdale, NJ: Lawrence Erlbaum

Associates, 1988.

13. Baker R. S., Corbett A. T., Koedinger K. R., Wagner A. Z. Off-Task Behavior in the Cognitive Tutor Classroom: When

Students “Game the System” // Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2004).

– New York: ACM, 2004. – P. 383–390.

14. Piech C., Bassen J., Huang J., Ganguli S., Sahami M., Guibas L., Sohl-Dickstein J. Deep Knowledge Tracing //

Advances in Neural Information Processing Systems. – 2015. – Vol. 28. – P. 505–513.

15. Aleven V., McLaughlin E. A., Glenn R. A., Koedinger K. R. Instruction Based on Adaptive Learning Technologies //

Handbook of Research on Learning and Instruction / Ed. by R. E. Mayer, P. A. Alexander. – 2nd ed. – New York: Routledge,

2017. – P. 522–560.

16. Растригин Л. А., Эренштейн М. Х. Адаптивное обучение с моделью обучаемого. – Рига: Зинатне, 1988. –

160 с.

17. Беспалько В. П. Образование и обучение с участием компьютеров (педагогика третьего тысячелетия). –

М.: МПСИ; Воронеж: МОДЭК, 2002. – 352 с.

18. Yunusova D. I. Matematikani o‘qitishning zamonaviy texnologiyalari. – Toshkent: Fan va texnologiya, 2011.

19. Freeman S., Eddy S. L., McDonough M., Smith M. K., Okoroafor N., Jordt H., Wenderoth M. P. Active Learning

Increases Student Performance in Science, Engineering, and Mathematics // Proceedings of the National Academy of

Sciences. – 2014. – Vol. 111, № 23. – P. 8410–8415.

20. Graham C. R. Blended Learning Systems: Definition, Current Trends, and Future Directions // The Handbook of

Blended Learning: Global Perspectives, Local Designs / Ed. by C. J. Bonk, C. R. Graham. – San Francisco: Pfeiffer, 2006. – P.

3–21.

21. Wilson R. C., Shenhav A., Straccia M., Cohen J. D. The Eighty Five Percent Rule for Optimal Learning // Nature

Communications. – 2019. – Vol. 10. – Art. 4646.

Загрузки

Nashr qilingan

2026-10-05

Nashr

Bo'lim

13.00.00 Pedagogika fanlari

Как цитировать

AMALIY MATEMATIKA FANLARINI INTELLEKTUAL ADAPTIV TA’LIM TEXNOLOGIYALARI ASOSIDA O‘QITISHNING METODIK MODELI VA UNING ALGORITMIK TA’MINOTI . (2026). Zamonaviy Ilmiy-Texnik Tadqiqotlar, 2(4), 57-63. https://ijcst.uz/index.php/journal/article/view/291