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Arxiv / №10 · 2026 / Maqola
13.00.00 – PEDAGOGIKA FANLARI

ELEKTRON TA’LIM MUHITIDA FORMATIV VA SUMMATIV BAHOLASHNI FUZZY MANTIQ ASOSIDA TAKOMILLASHTIRISH

Annotatsiya
O'ZBEKCHA

Maqolada elektron ta’lim muhitida formativ va summativ baholash jarayonini fuzzy mantiq asosida takomillashtirish masalasi ko‘rib chiqiladi. O‘quvchilarning bilim va faoliyatini ko‘p me-zonli baholash, noaniqliklarni hisobga olish hamda yakuniy elektron bahoni shakllantirish modeli taklif etiladi. Model fuzzifikatsiya, fuzzy inferensiya va defuzzifikatsiya bosqichlariga asoslanadi.

Kalit so'zlar: elektron ta’lim, formativ baholash, summativ baholash, fuzzy mantiq, noaniqlik, elek-tron baholash, ko‘p mezonli baholash
РУССКИЙ

В статье рассматривается вопрос совершенствования формативного и сумма-тивного оценивания в электронной образовательной среде на основе нечеткой логики. Предлагает-ся модель многокритериального оценивания знаний и учебной деятельности обучающихся с учетом неопределенности. Модель включает этапы фаззификации, нечеткого вывода и дефаззификации.

Ключевые слова: электронное обучение, формативное оценивание, суммативное оценивание, нечеткая логика, неопределенность, электронное оценивание, многокритериальное оценивание.
ENGLISH

The article considers the improvement of formative and summative assessment in an electronic learning environment based on fuzzy logic. A model for multicriteria assessment of students’ knowledge and learning activities is proposed, taking into account uncertainty. The model includes fuzzification, fuzzy inference, and defuzzification stages.

Keywords: e-learning, formative assessment, summative assessment, fuzzy logic, uncertainty, electronic assessment, multicriteria assessment.

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Foydalanilgan adabiyotlar

  1. Zadeh L.A. Fuzzy Sets // Information and Control. 1965, vol. 8, № 3, p. 338–353.
  2. Zadeh L.A. The Concept of a Linguistic Variable and its Application to Approximate Reasoning. Information Sciences. 1975, vol. 8, № 3, p. 199–249.
  3. Zimmermann H.J. Fuzzy Set Theory–and Its Applications. 4th ed. Dordrecht: Springer, 2001, 514 p.
  4. Pedrycz W., Gomide F. Fuzzy Systems Engineering: Toward Human-Centric Computing. Hoboken, Wiley -Interscience, 2007, 526 p.
  5. Kosko B. Fuzzy Thinking: The New Science of Fuzzy Logic. New York, Hyperion, 1993, 336 p.
  6. Chen S.M., Lee C.H. New Methods for Students’ Knowledge Assessment Based on Fuzzy Logic. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2006.
  7. Bai S.M., Chen S.M. Evaluating Students’ Learning Achievement Using Fuzzy Membership Functions and Fuzzy Rules // Expert Systems with Applications. 2008
  8. Siemens G., Long P. Penetrating the Fog: Analytics in Learning and Education // EDUCAUSE Review. 2011, vol. 46, № 5, p. 30 – 40.
  9. Romero C., Ventura S. Educational Data Mining and Learning Analytics: An Updated Survey // Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020, vol. 10, № 3.
  10. Ferguson R. Learning Analytics: Drivers, Developments and Challenges. International Journal of Technology Enhanced Learning. 2012, vol. 4, № 5/6, p. 304–317.
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