An Accessible Version of the <i>Foodbot Factory</i> Serious Game for Nutrition Education
Bibliographic record
Abstract
TheFoodbot Factoryserious game was developed to be used in Canadian classrooms and for online learning to teach students in grades 4–6 about nutrition. However, as with most video games and serious games,Foodbot Factorywas not developed with accessibility in mind and, therefore, cannot be played by individuals with, for example, visual impairments. Following the Game Accessibility Guidelines and the Web Content Accessibility Guidelines, we converted a portion ofFoodbot Factoryinto an audio game for visually impaired and blind players. In this article, we highlight the process required for converting theFoodbot Factoryserious game into an accessible audio game. We also present the results of a preliminary user study that was conducted to examine the usability of theFoodbot Factoryaudio game. Although all participants were sighted individuals and our results are limited and preliminary, theFoodbot Factoryaudio game is usable. Based on our experience in developing theFoodbot Factoryaudio game in addition to our usability study results, we have shown that an existing serious game that lacks adherence to accessibility guidelines can be converted into the accessible version of the game.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.253 | 0.076 |
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".