2025 №3 / Болашақтың технологиялары мен білім беру ресурстары: сыныптағы және сыныптан тыс генеративті ЖИ мен оқытудағы аналитика
Болашақтың технологиялары мен білім беру ресурстары: сыныптағы және сыныптан тыс генеративті ЖИ мен оқытудағы аналитика
Автор: Райан Шон Бэйкер
DOI: 10.62670/2308-7668.2025.53.3.007
Дереккөз: 53 шығарылым, № 3, 13 қазан 2025 ж.
Басып шығарушы: "Педагогикалық шеберлік орталығы" ЖМ
Құжат түрі: Шолу-талдау мақаласы
Аннотация
Оқытудағы аналитика – XXI ғасырда білім берудегі зерттеулер мен тәжірибенің аса маңызды аспектілерінің бірі болып табылады. Мақалада оқудағы аналитика қолданылатын негізгі салалар, оқыту процесін, оқушылардың белсенді қатысуын, олардың өмірде табысты болуын қолдауға бағытталған жаңа мүмкіндіктер қарастырылады. Сонымен қатар генеративті жасанды интеллект саласындағы соңғы жетістіктердің оқытудағы аналитика әдістеріне және оның қолданылуына қалай әсер ететініне тоқталамыз. Генеративті ЖИ-дің кеңінен таралуы білім беру саласына жаңа мүмкіндіктермен қатар бірқатар сын-талаптарды да алып келуде. Мақалада осы жаңа сын-талаптар және оларды еңсеру жолдары, сондай-ақ зерттеу және білім беру тәжірибесін жетілдірудегі әлеуеті талқыланады.
Түйін сөздер: генеративті ЖИ, оқытудағы аналитика, білім беру деректерін интеллектуалдық талдау
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and Knowledge Conference (pp. 260–271).
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24. Levonian, Z. & Henkel, O. (2024). Safe generative chats in a WhatsApp intelligent tutoring system. In Educational Datamining ’24 Human-Centric eXplainable AI in Education and Leveraging Large Language Models for Next-Generation Educational Technologies Workshop Joint Proceedings.
25. Baker, R. S., Liu, X., Shah, M., Pankiewicz, M., Kim, Y. J., Lee, Y., & Porter, C. (in press) Generative AI as a teaching assistant. To appear in Lancrin, S (Ed.) OECD Digital Education Outlook 2025. Paris, France: OECD.
26. Pankiewicz, M. & Baker, R. S. (2023) Large language models (GPT) for automating feedback on programming assignments. Proceedings of the 31st International Conference on Computers in Education.
27. Koutcheme, C., Dainese, N., Sarsa, S., Hellas, A., Leinonen, J., & Denny, P. (2024). Open source language models can provide feedback: Evaluating LLMs’ ability to help students using GPT-4-as-a-judge. In Proceedings of the 2024 on Innovation and Technology in Computer
Science Education V. 1 (pp. 52–58).
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Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., Hallit, S., & Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14(1), 1983.
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31. McKnight, S. W., Civelekoglu, A., Gales, M., Bannò, S., Liusie, A., & Knill, K. M. (2023). Automatic assessment of conversational speaking tests. In Proc. SLaTE 2023 (pp. 99–103).
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37. Holstein, K., McLaren, B. M., & Aleven, V. (2018, June). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In International Conference on Artificial Intelligence in Education (pp. 154-168). Cham: Springer International Publishing.
38. Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43.
39. Craig, S. D., Hu, X., Graesser, A. C., Bargagliotti, A. E., Sterbinsky, A., Cheney, K. R., & Okwumabua, T. (2013). The impact of a technology-based mathematics after-school program using ALEKS on student’s knowledge and behaviors. Computers & Education, 68, 495–504.
40. Rajendran, R. & Muralidharan, A. (2013, December). Impact of Mindspark’s adaptive logic on student learning. In 2013 IEEE Fifth International Conference on Technology for Education (t4e 2013) (pp. 119–122). IEEE.