Making Sense of Virtual Risks: A Quasi-Experimental Investigation into Game-Based Training
Bibliographic record
Abstract
Along with the rise of digital games over the past decades came an increased interest for using games for other purposes than entertainment. Although a few successes are known, much research seems to suggest little evidence for games’ advantages. Existing literature claims that more studies are needed that investigate the effective design and use of games and especially studies that are comprehensive, rigorous, and innovative. To contribute to this emerging field, the author investigated the case of Levee Patroller. The target audience of the game, levee patrollers, are considered the “eyes and ears” of the Dutch water authorities. They inspect levees and report any risks they encounter. Similarly, in the game players have to find all virtual failures in a region and report these. If they do not find the failures in time or report them incorrectly, it could result in a levee breach that floods the whole virtual region. Using this game an innovative game-based training was set up to prove its effectiveness in training inspection knowledge and skills, and to understand the contributing factors. In total 147 levee patrollers from 3 water authorities in the Netherlands participated in a structured 3-week training which was evaluated using a quasi-experimental design with a mix of quantitative and qualitative methods. The results highlight a successful training. Clear evidence was found that the patrollers improved on their inspection knowledge and skills. But because how players performed in the game is most crucial for the game’s success as a training tool, future research should consider game design, data, and performance more elaborately.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".