An exploratory analysis of the impact of learners’ first language on vocabulary recall using immersive technologies
Bibliographic record
Abstract
This exploratory post-hoc analysis examined the impact of learners’ first language (L1) on learning vocabulary annotated in immersive 360º pictures. This analysis is a part of a larger, between-subjects study (Papin & Kaplan-Rakowski, 2020) in which learners (N=63) of French as a second language (L2) studied vocabulary annotated in (1) Two-Dimensional (2D) pictures viewed on a desktop monitor, (2) 360º pictures viewed on a desktop monitor, and (3) 360º pictures viewed using a Virtual Reality (VR) headset. A multiple regression linear model revealed that native speakers of English benefited significantly more from immersive technologies compared with L1 Chinese speakers. When low-immersion and high-immersion technologies were used, Chinese L1 speakers were significantly disadvantaged by high-immersion VR. This study has implications in the field of L2 vocabulary research and learning materials design.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".