THE ROLE OF DIGITAL TECHNOLOGIES IN BLENDED LEARNING: FOREIGN EXPERIENCE AND CHALLENGES FOR UKRAINIAN HIGHER EDUCATION INSTITUTIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the article, the author analyzes the definitions of “blended learning” and the role of digital technologies in its implementation. The author analyzes the experience of using blended learning in countries such as Canada, the Czech Republic and the Federal Republic of Germany, which demonstrate the effective implementation of digital platforms and technologies to improve the quality of the educational process. In particular, it focuses on the use of learning management systems (Moodle, MS Teams, Google Classroom), recording and distribution of video lectures, interactive simulations and forums to maintain communication between teachers and students. The study identifies key aspects that Ukrainian higher education institutions can adapt from international experience: a flexible combination of synchronous and asynchronous activities, providing technical support for teachers, creating centers of pedagogical excellence, as well as state support for the digitalization of education. The main challenges, such as the lack of a unified digitalization strategy, the need to develop infrastructure and prepare teachers for the effective use of digital technologies and innovations, are outlined. It has been determined that the application of innovative approaches to blended learning in Ukraine will improve the quality of educational services, ensure the flexibility of the educational process and promote the integration of national education into the world educational space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it