360 virtual tour and escape room design as a video games-based learning process for diagnosis and strengthening of the English language
Bibliographic record
Abstract
Nowadays we know it, in these last times the return to the classroom wasn´t the return to the classroom. Some students when was studying the last year, they were waiting for the time to finish class and go out to the university, now because the pandemic they and much more students didn´t return to the classroom in a normal way. The Universidad Politécnica de Juventino Rosas and the University of Guelph closed its installation in the same way that any school, college or university in the world, and nowadays we have new students that for first time in the history they started class by online mode, and they didn’t know their scholar environment. In this project we proposed a 360 virtual tour of the UPJR and we decided make more immersive and educational the experience through the escape room design as a video game-based learning process to diagnosis the English language in the first stage. The novelty of the project is to include the new TIC´s showed too in the manufacture 4.0 as the VR technology to replay in virtual way the main scholar buildings, classrooms, laboratories, library, soccer field and the cafe of the University.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".