Virtual Realities as Optimal Learning Environments in Sport - A Transfer Study of Virtual and Real Dart Throwing
Bibliographic record
Abstract
Virtual realities offer a safe and repeatable learning environment, which is optimal for skills that are difficult to replicate in real-world settings. Previous research has demonstrated transfer of motor skill between basketball and darts but not of perceptual performance (Rienhoff et al., 2013). Our study considered the transferability of a specific skill between virtual and real learning environments - in our case throwing accuracy (TA) and quiet eye duration (QED) in dart throwing. Participants (n = 38) were separated into three groups (virtual training, real training, & control) and completed 15 throws in pre- and post-tests on a real and on a virtual (Microsoft XBox Kinect) dartboard. The training groups performed three sessions of 50 throws each. QED was measured using SMI eye tracking glasses and TA was defined as radial distance from the bull’s eye. Results showed significant differences in TA for group and condition; the real training group outperformed the control group and TA was better in the virtual group. The interaction of test and group was significant. Both training groups improved between tests while the control group performed worst. Results for QED showed a significant increase between tests. Furthermore, significant differences for condition and a significant interaction of condition and test were measured. QED was longer and enhanced in the virtual group. Our results generally showed the efficiency of both training modalities and the slight difference in training effects between groups suggests transferability between tasks.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".