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Abstract
This thesis analyzes issues related to improving criteria for assessing the quality of education in a digital learning environment. It substantiates that the widespread implementation of digital platforms, distance and blended learning, electronic resources, and artificial intelligence tools in the modern educational process requires new approaches to assessing educational quality. Alongside traditional assessment criteria, the study highlights the need to consider learners’ digital competence, independent learning skills, activity on educational platforms, the dynamics of learning material acquisition, level of interactivity, quality of feedback, and effectiveness of using digital resources. Particular attention is also paid to learning outcomes, transparency, individual development trajectories, and the use of analytical data in assessing educational quality. The thesis interprets the development of comprehensive assessment criteria adapted to the digital learning environment as an important factor in improving the effectiveness of the educational process, identifying learners’ individual needs, and enhancing management decisions.
References
1. UNESCO. Global Education Monitoring Report 2023: Technology in Education – A Tool on Whose Terms? – Paris: UNESCO, 2023. DOI: 10.54676/UZQV8501. Mazkur hisobot raqamli texnologiyalarning ta’lim sifati, tenglik, samaradorlik va boshqaruvga ta’sirini kompleks yoritadi. (UNESCO Documentation)
2. OECD. OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. – Paris: OECD Publishing, 2023. DOI: 10.1787/c74f03de-en. Hisobotda raqamli ta’lim ekotizimi, raqamli baholash platformalari, ta’lim ma’lumotlari va boshqaruv mexanizmlari tahlil qilingan. (OECD)
3. Redecker C., Punie Y. European Framework for the Digital Competence of Educators: DigCompEdu. – Luxembourg: Publications Office of the European Union, 2017. – EUR 28775 EN. DOI: 10.2760/159770. Ushbu model pedagoglarning raqamli kompetensiyalarini 6 yo‘nalish va 22 kompetensiya asosida baholash uchun muhim metodologik asos bo‘lib xizmat qiladi. (JRC Publications)
4. Siemens G., Long P. Penetrating the Fog: Analytics in Learning and Education // EDUCAUSE Review. – 2011. – Vol. 46. – No. 5. – P. 30–40. Maqolada learning analytics ta’lim jarayonini tahlil qilish, o‘quvchilarning rivojlanishini kuzatish va dalillarga asoslangan qarorlar qabul qilish vositasi sifatida asoslangan. (ERIC)
5. UNESCO. Technology in Education: A Tool on Whose Terms? – Global Education Monitoring Report, 2023. Unda raqamli texnologiyalarni baholashda moslik, tenglik, kengaytirish imkoniyati va barqarorlik mezonlariga e’tibor qaratish zarurligi ta’kidlanadi. (2023 GEM Report)
6. OECD. OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. – Paris: OECD Publishing, 2021. Ushbu manbada sun’iy intellekt, avtomatlashtirilgan baholash va raqamli ma’lumotlardan ta’lim sifatini oshirishda foydalanish imkoniyatlari tahlil qilingan. (OECD)
7. European Commission. Digital Education Action Plan 2021–2027. – Brussels: European Commission. Mazkur strategiyada raqamli kompetensiya ta’lim tizimining muhim tarkibiy qismi sifatida belgilanib, pedagoglarning raqamli tayyorgarligi va ta’lim infratuzilmasini takomillashtirish zarurati qayd etilgan. DigCompEdu modeli ham ushbu siyosiy yo‘nalish bilan integratsiyalashgan. (JRC Publications)
8. UNESCO Global Education Monitoring Report Team. Technology and Education: Digital Skills, Assessment and System Management. – Paris: UNESCO, 2023. Hisobot materiallarida raqamli savodxonlikni baholash, ta’lim ma’lumotlaridan foydalanish va raqamli ta’lim sifatini monitoring qilish masalalari alohida yoritilgan. (UNESCO)