40

Айни пайтга келиб инсоният улкан илмий-амалий муавфаққиятларга эришди ва эришишда давом этмоқда. Шулардан бири бу сунъий интеллект (СИ) ҳисобланади ва у инсон фаолиятининг кўплаб соҳаларида кенг қўлланилмоқда. Ҳозирги кунда СИ кўплаб соҳаларда жадал ривожланмоқда, хусусан олий таълим тизими бундан мустасно эмас. Мазкур мақола талаба академик фаолиятни башоратлашга таъсир кўрсатувчи омиллар ва уни назоратга олиш ҳамда талаба салоҳиятини янада ошириш учун амалда қўлланган ва қўлланиб келинаётган усуллар таҳлилига бағишланган бўлиб, унда мавжуд таснифлаш усулларини қандай ҳолатларда оптимал эканлиги кўрсатиб берилган.

  • Ссылка в интернете
  • DOI
  • Дата создание в систему UzSCI 17-09-2024
  • Количество прочтений 40
  • Дата публикации 30-03-2024
  • Язык статьиO'zbek
  • Страницы143-148
English

By this time, humanity has achieved and continues to achieve great scientific and practical achievements. One of them is artificial intelligence (AI), which is widely used in many areas of human activity. Currently, SI is rapidly developing in many areas, and the higher education system is no exception. This article is devoted to the analysis of the factors affecting the prediction of the student's academic activity and the methods used in practice to control it and further increase the student's potential.

Русский

К этому времени человечество достигло и продолжает добиваться великих научных и практических достижений. Одним из них является искусственный интеллект (ИИ), который широко используется во многих сферах человеческой деятельности. В настоящее время СИ бурно развивается во многих сферах, и система высшего образования не является исключением. Данная статья посвящена анализу факторов, влияющих на прогнозирование учебной деятельности студента, и используемых на практике методов ее контроля и дальнейшего повышения потенциала студента.

Ўзбек

Айни пайтга келиб инсоният улкан илмий-амалий муавфаққиятларга эришди ва эришишда давом этмоқда. Шулардан бири бу сунъий интеллект (СИ) ҳисобланади ва у инсон фаолиятининг кўплаб соҳаларида кенг қўлланилмоқда. Ҳозирги кунда СИ кўплаб соҳаларда жадал ривожланмоқда, хусусан олий таълим тизими бундан мустасно эмас. Мазкур мақола талаба академик фаолиятни башоратлашга таъсир кўрсатувчи омиллар ва уни назоратга олиш ҳамда талаба салоҳиятини янада ошириш учун амалда қўлланган ва қўлланиб келинаётган усуллар таҳлилига бағишланган бўлиб, унда мавжуд таснифлаш усулларини қандай ҳолатларда оптимал эканлиги кўрсатиб берилган.

Название ссылки
1 1. Alyahyan, E., Düştegör, D. Predicting academic success in higher education: literature review and best practices. Int J Educ Technol High Educ 17, 3 (2020). https://doi.org/10.1186/s41239-020-0177-7
2 2. Sotiris Kotsiantis, Christos Pierrakeas, and Panagiotis Pintelas, "Predicting students performance in distance learning using machine learning techniques," Applied Artificial Intelligence, vol. 18, no. 5, pp. 411--426, 2010.
3 3. Hoti, A. H., Zenuni, X., Hamiti, M., & Ajdari, J. (2023, June). Student Performance Prediction Using AI and ML: State of the Art. In 2023 12th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-6). IEEE.
4 4. Amirah Mohamed Shahiria, Wahidah Husaina, and Nur’aini Abdul Rashid, "A Review on Predicting Student’s Performance using Data Mining Techniques," Procedia Computer Science, vol. 72, pp. 414-- 422, 2015.
5 5. Farshid Marbouti, Heidi A. Diefes-Dux, and Krishna Madhavan, "Models for early prediction of at-risk students in a course using standards-based grading," Computers & Education, vol. 103, pp. 1-- 15, 2016.
6 6. Pavel Kiselev, Boris Kiselev, Valeriya Matsuta, Artem Feshchenko, Irina Bogdanovskaya, and Alexandra Kosheleva, "Career guidance based on machine learning: social networks in professional identity construction," Procedia Computer Science, vol. 169, pp. 158--163, 2020.
7 7. Pauziah Mohd Arsad, Norlida Buniyamin, and Jamalul-lail Ab Manan, "Neural network model to predict electrical students' academic performance," in 2012 4th International Congress on Engineering Education, Georgetown, Malaysia, IEEE, 2012, pp. 1-- 5.
8 8. D. Kabakchieva, "Predicting student performance by using data mining methods for classification," Cybernetics and information technologies, vol. 13, no. 1, pp. 61--72, 2013.
9 9. Amal Alhassan, Bassam Zafar, and Ahmed Mueen, "Predict students’ academic performance based on their assessment grades and online activity data," International Journal of Advanced Computer Science and Applications, vol. 11, no. 4, pp. 185--194, 2020.
10 10. Amjad Abu Saa, Mostafa Al-Emran, and Khaled Shaalan, "Mining student information system records to predict students’ academic performance," in International conference on advanced machine learning technologies and applications, Springer International Publishing, 2019, pp. 229--239.
11 11. Solomia Fedushko, and Taras Ustyianovych, "Predicting Pupil’s Successfulness Factors Using Machine Learning Algorithms and Mathematical Modelling Methods," in Advances in Computer Science for Engineering and Education II, Springer International Publishing, 2020, pp. 625--636
12 12. Shah Hussain, and Muhammad Qasim Khan, "Student-performulator: predicting students’ academic performance at secondary and intermediate level using machine learning," Annals of data science, pp. 1--19, 2021.
В ожидании