O‘quv jarayonida “Data Science” elementlarini integratsiyalashning didaktik imkoniyatlari
DOI:
https://doi.org/10.5281/zenodo.18058386Ключевые слова:
Data Science, didaktik integratsiya, ma’lumotlar tahlili, ta’lim texnologiyalari, analitik fikrlash, raqamli savodxonlikАннотация
Zamonaviy ta’lim tizimida ma’lumotlar fani (Data Science) elementlarini o‘quv jarayoniga integratsiyalash
muhim ahamiyat kasb etmoqda. Ushbu tadqiqot Data Science asoslarini turli ta’lim bosqichlarida qo‘llashning didaktik
imkoniyatlari, metodologik yondashuvlari va amaliy tatbiq strategiyalarini tahlil qiladi. Maqolada talabalarning analitik
fikrlash qobiliyatlarini rivojlantirishda ma’lumotlar tahlili, vizualizatsiya va matematik modellashtirish usullarining ta’sir
darajasi ko‘rib chiqilgan. Tadqiqot natijalari Data Science integratsiyasining ta’lim sifatini oshirishdagi samaradorligini
tasdiqlaydi.
Библиографические ссылки
1. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education.
London: Pearson Education.
2. Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate? Laying out the skills,
knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011
3. Piety, P. J. (2019). Educational data use: Practices, policies, and possibilities. New York: Routledge.
4. Siemens, G., & Long, P. (2019). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 54(2),
30–40.
5. Anderson, C., & Gantz, J. F. (2019). Data-driven decision making in education: Opportunities and challenges. Educational
Technology Research and Development, 67(5), 1123–1138. https://doi.org/10.1007/s11423-019-09654-1
6. Berland, L., & Reisman, A. (2020). Cultivating data literacy in teacher education. Journal of Teacher Education, 71(4),
410–423. https://doi.org/10.1177/0022487119896101
7. Brynjolfsson, E., & McElheran, K. (2019). Data in action: Data-driven decision-making in U.S. manufacturing. Management
Science, 65(5), 2000–2019. https://doi.org/10.1287/mnsc.2018.3270
8. Ferguson, R. (2017). Learning analytics: Drivers, developments and challenges. International Journal of Technology
Enhanced Learning, 9(2), 124–144. https://doi.org/10.1504/IJTEL.2017.085676
9. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching
and learning. Boston: Center for Curriculum Redesign.
10. Khalil, M., & Ebner, M. (2020). Learning analytics in higher education: A systematic literature review. Computers in
Human Behavior, 101, 104–121. https://doi.org/10.1016/j.chb.2019.07.009
Загрузки
Опубликован
Выпуск
Раздел
Лицензия
Copyright (c) 2025 MAKTABGACHA VA MAKTAB TA’LIMI JURNALI

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