If at First You Don’t Succeed: Helping Players Make Progress in Games with Breaks and Checkpoints
Bibliographic record
Abstract
Developing skill and overcoming in-game challenges is of great interest to both players and game designers. Players can improve through repetition, but sometimes practice does not lead to improvement and progress stalls. It would be useful if designers could help players make progress without compromising their long-term skill development. We carried out a study to investigate how two techniques—checkpoints and breaks—affect in-game progress and player skill. Checkpoints allow multiple attempts at a challenge without having to repeat earlier sections; this aids progress, but could potentially hinder skill development. Second, breaks in gameplay have been shown to accelerate skill development, but their effectiveness is unknown when the breaks are integrated into the game's design. Our study evaluated the effects of game-integrated breaks and checkpoints on players' in-game progress (when the techniques were present) as well as two test sessions (with all techniques removed). Our results showed that both checkpoints and breaks aid progress (combining both had the largest effect) and that neither technique reduced performance in the transfer task, suggesting that skill development was not hindered. Our work provides evidence that checkpoints and breaks are valuable techniques that can assist both player progress and skill.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".