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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".