European Perspectives on Innovative Educational Practices in the Age of Globalization and Digitalization
Bibliographic record
Abstract
The potential of innovative learning technologies and digitalization is a key factor in the progressive development of the academic space within the context of educational environment integration. This article aims to study the potential of innovative learning technologies in the context of globalization and the digitalization of education, based on successful European experiences. It also pays special attention to immersive digital media that involve virtual and mixed-reality possibilities. To achieve these goals, a combined approach was applied, including the analysis of existing case studies and the formation of conclusions based on secondary data analysis. This research suggests ways to integrate immersive-learning tools into practice-oriented learning. These types of tools contribute to improving the processes of learning by students, allowing them to visualize complex and abstract definitions, motivate active participation in the learning process, develop creativity and practical skills of problem-based thinking, and provide realistic experiences of solving educational tasks in practice. This article substantiates that these innovations successfully used in developed countries of the European community will allow for the effective development of the national educational system in the post-war reconstruction of Ukraine. This article establishes the need to upgrade the existing educational development strategy to delineate powers between different management institutions, create a competitive environment in the educational sector, and expand opportunities for financing state-of-the-art projects in this area. The prospects for educational development are focused on the potential of immersive learning technologies, practice-oriented education, and interactive communication tools for competence-based learning.
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.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".