A call for robust evaluations of the impacts of serious games for climate change mitigation: example with The Climate Fresk conducted among 2 million participants from 150 countries
Bibliographic record
Abstract
Serious games and gamified workshops are increasingly used in sustainability education, yet their actual cognitive, emotional, attitudinal, and behavioral impacts remain under-evaluated. The Climate Fresk is a widely implemented example, with over two million participants in more than 150 countries. Designed to raise climate awareness through collaborative learning and emotional engagement, its growing popularity contrasts with the limited scientific assessment of its effectiveness. This perspective paper uses The Climate Fresk as a case study to examine the broader challenges of evaluating serious games in climate education. Drawing on insights from environmental psychology, educational and behavioral sciences, we analyze its potential mechanisms of action, identify key moderating factors, such as participant characteristics, facilitator attributes, and implementation context, and highlight limitations in current evaluation practices. We conclude by outlining a research agenda that emphasizes the need for rigorous, theory-driven experimental designs, including randomized controlled trials focused on relevant psychological determinants of behavior change. Such efforts are essential to establish the evidence base required to improve the effectiveness, reproducibility, and scalability of gamified climate education interventions.
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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 | 0.211 | 0.392 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".