Virtual Reality in the Language Classroom: Strategies to Make it Work
Bibliographic record
Abstract
The present investigation aimed to assess the use of scaffolding strategies by young English as a second language learners who carried out communicative tasks in the context of high-immersive virtual reality (HVR) ( Kaplan-Rakowski & Gruber, 2019 ). More specifically, 24 students enrolled in an intensive program in the province of Quebec, Canada, were recorded as they carried out four communicative tasks while wearing head-mounted displays and navigating virtual worlds. Based on the work of Gagné and Parks (2013) and García Mayo and Imaz Agirre (2019) , the analysis consisted in the identification of strategies as they pertained to language and other aspects of the tasks, such as technological challenges. Using language-related episodes (LRE) as a unit of analysis, the investigation revealed that participants produced 247 LREs that were generally triggered and resolved by the student themselves and that focused on the vocabulary found in the tasks and the virtual world. The analysis of the recordings for strategies other than language-related revealed that students used 14 different strategies, five of which were specific to the HVR context. Implications for the use of HVR in the context of language learning are also discussed.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".