The effectiveness of the Foodbot Factory serious game on increasing nutrition knowledge in children
Bibliographic record
Abstract
Background: The interactive nature of serious games (i.e., video games designed for educational purposes) enable deeper learning and facilitate behavior change; however, there is limited data on their impact on child nutrition knowledge. The objective of this study was to determine if Foodbot Factory effectively improves children???s knowledge of 2019 Canada???s Food Guide. Methods: Study was a single-blinded, parallel randomized controlled trial conducted among children ages 8-10 years attending Ontario Tech University summer day camps. Results: Compared to the control group (n=34), children who used Foodbot Factory (n=39) had significant increases in overall nutrition knowledge (10.3 ?? 2.9 to 13.5 ?? 3.8 versus 10.2 ?? 3.1 to 10.4 ?? 3.2, p<0.001), and in Vegetables and Fruits (p<0.001), Protein Foods (p<0.001), and Whole Grain Food (p=0.040) sub-scores. No significant difference in knowledge was observed in Drinks sub-score. Conclusion: Foodbot Factory is an effective educational tool to support children learning about nutrition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".