Designing a Technique-Oriented Sport Training Game for Motivating a Change in Running Technique
Bibliographic record
Abstract
Athletes often learn suboptimal techniques that place a ceiling on their performance or put them at risk of injury. Adopting a new technique can lead to a short-term dip in performance while learning it, which can be demotivating and cause an athlete to revert to their previous, suboptimal technique. To address the challenge of demotivation in adopting new techniques, we explore technique-oriented sport training games, which aim to improve sport skills by capturing behaviour and providing feedback that motivates adopting a new technique. As of yet, previous work has provided little information on how to design sports training games, and some early research suggests that immersive games may distract players, preventing them from being able to learn a new technique effectively. We designed a study comparing a baseline non-game training system with three versions of a running game, ranging from a simplistic text-based game to a 3D audiovisual game with motion feedback. We find that the games with more immersive elements were as effective as the baseline system for adopting a new technique but were preferred by players and improved their intrinsic motivation. We propose design considerations from these findings and provide new directions for researching effective sport training games.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".