Updating the Foodbot Factory serious game with new interactive engaging features and enhanced educational content
Bibliographic record
Abstract
Serious games (i.e., digital games designed for educational purposes) can foster positive learning attitudes and are increasingly used as educational tools. Foodbot Factory is a serious game application (app) that helps children learn about healthy eating based on Canada’s Food Guide principles and has demonstrated to increase nutrition knowledge among this group. This paper describes the process followed to expand Foodbot Factory’s educational content and integrate immersive technologies and innovative features into the app. The revision process, which was guided by the Obesity-Related Behavioral Intervention Trials model, included the following phases: first, an interdisciplinary team of nutrition scientists, education experts, and computer scientists analyzed data from the original pilot study, recently published literature, and feedback from stakeholders to define areas to improve Foodbot Factory. The five original Foodbot Factory modules were evaluated by the team during weekly meetings, where the educational content, interactive features, and other elements that required updates (e.g., aesthetics and accessibility) were identified. Second, prototypes were created and refined until a final version of Foodbot Factory was approved. Nineteen children tested the updated Foodbot Factory and found it “easy to use” (89%) and “fun” (95%). The new version of Foodbot Factory contains 19 learning objectives, including 13 original and six new objectives. Interactive engagement features in the updated Foodbot Factory included augmented reality incorporated into two learning modules; new mini-games were created, including a memory game; an overhaul of the aesthetics; (e.g., new food images); and accessibility features were included to support users with cognitive and vision disabilities.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".