O‘QUV JARAYONIDA DATA-TAHLIL VA MONITORING TIZIMLARIDAN FOYDALANISHNING JAHON TAJRIBASI VA GLOBAL HOLATI

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Аннотация:

Bugungi kunda ta’lim jarayoni global va mahalliy miqyosda turli algoritmlar, analitik vositalar va monitoring tizimlari orqali optimallashtirilmoqda. Jumladan, talabalarning faoliyati, ularning yutuqlari va qiyinchiliklari, professor-o‘qituvchilarning dars olib borish jarayonlari hamda boshqaruv organlarining qaror qabul qilish jarayonlari uchun katta hajmdagi ma’lumotlar (“data”) tahlili muhim omilga aylangan. Shu munosabat bilan o‘quv jarayonida data‑tahlil (ma’lumotlarni yig‘ish, qayta ishlash, tahlil qilish) va monitoring tizimlari (o‘zgarishlarni kuzatish, baholash, koordinatsiya qilish) ning samaradorligini tadqiq qilish ilmiy muhim ahamiyatga ega. Maqolada zamonaviy ta’lim tizimida data-tahlil va monitoring texnologiyalarining o‘rni, ularning o‘qitish jarayonidagi samaradorlikni oshirishdagi roli hamda jahon mamlakatlarida qo‘llanilayotgan ilg‘or tajribalar tahlil qilingan. Shuningdek, AQSh, Yevropa, Osiyo va Avstraliya davlatlarining ta’limda raqamli analitika yondashuvlari qiyosiy o‘rganilib, O‘zbekiston uchun istiqbolli yo‘nalishlar taklif etilgan..

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Tohirov , Z. ., Murodullayev , S. ., & Otaxonova , M. (2025). O‘QUV JARAYONIDA DATA-TAHLIL VA MONITORING TIZIMLARIDAN FOYDALANISHNING JAHON TAJRIBASI VA GLOBAL HOLATI. Центральноазиатский журнал академических исследований, 3(10 Part 2), 140–144. извлечено от https://in-academy.uz/index.php/cajar/article/view/63208

Библиографические ссылки:

Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 267–270.

Baker, T. (2022). EduData Canada: National education data for informed policy-making. Canadian Journal of Higher Education, 52(4), 45–61.

Ferguson, R. (2012). The state of learning analytics in 2012: A review and future challenges. Technical Report KMI-12-01, The Open University.

Hill, C. (2021). Predictive analytics and student success at Georgia State University. EDUCAUSE Review, 56(3), 22–30.

JISC. (2022). Learning Analytics Service Overview. Joint Information Systems Committee. Retrieved from https://www.jisc.ac.uk

Kennedy, G., Corrin, L., & de Barba, P. (2020). Learning analytics at scale: Implications for Australian higher education. Australasian Journal of Educational Technology, 36(2), 1–15.

Kim, J., & Lee, H. (2020). Data-driven higher education in South Korea: K-MOOC and AI analytics. Asian Education Review, 8(1), 23–40.

Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400.

Tan, S. (2019). SkillsFuture Singapore and the role of data analytics in lifelong learning. International Journal of Education Policy, 8(3), 85–101.

UNESCO. (2024). Responsible Data Framework for Education Systems. Paris: UNESCO Publishing.

Zhao, L., Wang, Q., & Liu, S. (2021). Smart campus and AI-based monitoring in Chinese universities. Journal of Educational Technology Development, 39(2), 78–95.