A study of vocabulary learning using annotated 360° pictures
Bibliographic record
Abstract
Second language (L2) learning research suggests that virtual reality (VR) has the potential to enhance the development of language skills due to its immersive nature and its situated learning opportunities. This quantitative, between-subjects study compared the effectiveness of three learning conditions. University students (N = 63) studied vocabulary annotated on: (1) 360° pictures viewed using a VR headset, (2) 360° pictures viewed on a desktop monitor, and (3) standard two-dimensional (2D) pictures viewed on a desktop monitor (control condition). After the experiment, the students completed productive and receptive posttests measuring vocabulary recall. Through multiple analyses of variance (MANOVA), the study revealed that learning new vocabulary with annotated 360° pictures viewed on a desktop monitor is associated with significantly higher posttests scores, compared with learning using a VR headset or standard 2D pictures. Kruskal-Wallis H tests showed vocabulary learning with 360° to be engaging, but effective only when studied on the 2D monitor. This study has practical implications for VR-assisted language learning and for the design of teaching materials to enhance L2 vocabulary learning.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".