Entornos virtuales de aprendizaje (EVA): Una revisión del estado del arte en dieciséis países
Bibliographic record
Abstract
The document compiles research on the impact of virtual environments on education in various countries. In China, the focus is on assessing training through virtual reality. Taiwan emphasizes that virtual reality enhances motivation and participation in learning. Germany demonstrates the potential of immersive fitness games for postoperative recovery. Italy highlights the importance of visuospatial skills in virtual navigation. In Spain, multiple topics are addressed, from learning strategies in engineering to neuro marketing and teaching perspectives through virtual reality. In Portugal, the positive influence of TIC on higher education is emphasized, while in Villa Lousada, gamification and augmented reality are implemented to enhance reading comprehension. In summary, it is concluded that technological tools enhance academic performance when adapted correctly to the context and learning style of students. The study also addresses other countries. In Canada, the use of virtual reality in medicine is highlighted. In the United States, the focus is on bilingualism through virtual classrooms. In Mexico, the widespread use of Learning Environments, especially at the university level, is emphasized. In Cuba, the focus is on the implementation of virtual classrooms as a complement to face-to-face teaching. In Panama, the importance of Virtual Environments for the comprehensive education of students is highlighted. Overall, the relevance of technology in adapting to changes and improving the teaching-learning process is emphasized.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".