THE ROLE OF DIGITAL TECHNOLOGIES IN BLENDED LEARNING: FOREIGN EXPERIENCE AND CHALLENGES FOR UKRAINIAN HIGHER EDUCATION INSTITUTIONS
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".