Investigating Augmented Reality for Adaptive Motor-Skill Training
Bibliographic record
Abstract
Adaptive training of motor-skills, where the difficulty level of the training task is adapted optimally based on the learner’s skill levels, has been shown to enable higher learning gains compared to non-adaptive training. However, prior approaches rely on adapting physical tools that are tedious to design and build. This work investigates using augmented reality (AR) to achieve a similar objective of maintaining functional task difficulty – the difficulty experienced by the learner – at an optimal challenge point during adaptive training. A study prototype of an AR adaptive basketball training system was developed, wherein the learners train to throw a physical ball into a virtual AR hoop seen through a head-mounted device. Results from the study (N=16) aimed to measure the learning gains showed higher learning gains after adaptive AR training compared to non-adaptive AR training. An analysis of participant feedback, however, highlighted challenges with AR-based adaptive training, pointing to the need for a different design approach compared to the physical adaptive tools. Collectively, this exploratory study investigates the use of AR for adaptive motor-skill learning and lays the foundation for future research directions for the AR-tool design.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".