Is a Change as Good as a Rest? Comparing BreakTypes for Spaced Practice in a Platformer Game
Bibliographic record
Abstract
The development of skill in games is of interest to players and designers. Spaced practice in games, i.e., adding breaks to core gameplay, has been shown to improve performance over playing continuously; however, it is unclear if the benefits of spaced practice apply in complex games that combine several skills and elements. Further, many break-like activities are already present in games (e.g., cutscenes, mini-games, leaderboards, loading screens) and we do not know whether engaging with these as breaks reduces the benefits of spaced practice. We built a custom 2D platform game in which players wall-jump, swing, via a grapple hook and double-jump through an obstacle course and used it as the core gameplay activity in two experiments---one to test if spaced practice improves performance in a complex game, and another to determine how spaced practice is affected by the choice of in-game break activity. We show that spaced practice significantly improves skill development in a complex platformer game; that spaced practice is effective across several types of ecologically-valid break activities; and that the use of short breaks does not subvert flow states during play.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".