Abstract
Ushbu maqolada oliy ta’lim muassasalarida talabalar akademik o‘zlashtirishini mashinaviy o‘rganish algoritmlari yordamida bashoratlash masalalari tadqiq etiladi. Tadqiqot obyekti sifatida talabalarning davomati oraliq baholari topshiriqlarni bajarish faolligi va o‘quv platformasidagi harakatlari kabi ko‘rsatkichlar tanlandi. An’anaviy baholashda talabaning qiyinchiliklari ko‘pincha kech ya’ni yakuniy nazorat bosqichida ma’lum bo‘ladi. Mashinaviy o‘rganishga asoslangan bashoratlash modellari mavjud ma’lumotlardan qonuniyatlarni o‘rganib talabaning kelgusi natijasini yoki xavf darajasini erta aniqlash imkonini beradi. Maqolada belgilarni tanlash model turlari baholash mezonlari (aniqlik precision recall F1-ko‘rsatkich) model talqini hamda ma’lumotlar maxfiyligi va algoritmik adolat masalalari tahlil qilindi. Natijalar shuni ko‘rsatadiki bashoratlash tizimi xavf ostidagi talabalarni erta aniqlab o‘z vaqtida yordam ko‘rsatishga xizmat qiladi biroq u talabaga qat’iy yorliq qo‘ymasdan o‘qituvchining qaror qabul qilishini qo‘llab-quvvatlovchi vosita sifatida qo‘llanilishi maqsadga muvofiqdir.
References
Hastie T. Tibshirani R. Friedman J. The Elements of Statistical Learning. 2nd edition. Springer 2009.
2. Bishop C.M. Pattern Recognition and Machine Learning. Springer 2006.
3. Breiman L. Random Forests. Machine Learning 2001.
4. Chen T. Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of KDD 2016.
5. Goodfellow I. Bengio Y. Courville A. Deep Learning. MIT Press 2016.
6. Romero C. Ventura S. Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems Man and Cybernetics 2010.
7. Baker R.S.J.d. Inventado P.S. Educational Data Mining and Learning Analytics. Springer 2014.
8. Kotsiantis S.B. Use of Machine Learning Techniques for Educational Purposes: A Decision Support System for Forecasting Students’ Grades. Artificial Intelligence Review 2012.
9. Marbouti F. Diefes-Dux H.A. Madhavan K. Models for Early Prediction of At-Risk Students in a Course Using Standards-Based Grading. Computers & Education 2016.
10. Pardo A. Siemens G. Ethical and Privacy Principles for Learning Analytics. British Journal of Educational Technology 2014.
11. Pedregosa F. va boshqalar. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 2011.
12. European Union. General Data Protection Regulation (GDPR). Official Journal of the European Union 2016.