The Design of Food Villain, a Serious Game to Influence Healthy Eating Habits Among African International Students
Bibliographic record
Abstract
The transition from Africa to Western countries poses significant challenges for African International Students, particularly in maintaining traditional healthy eating habits and active lifestyles. Factors such as the unavailability and high cost of familiar foods, coupled with sedentary habits influenced by cold weather conditions, contribute to the development of unhealthy behaviors and increased health risks among this demographic. In response to these challenges, there is a growing interest in leveraging technology, specifically serious games, to promote healthy behaviors. This article explores the design and development of Food Villain, a serious game aimed at influencing healthy eating habits among African International Students in Western countries. We discussed the design of two versions of the game: a web-based version that can be played on any device with a browser and a Virtual Reality version for people with access to VR headsets. By addressing the cultural, environmental, and behavioral factors influencing dietary choices, Food Villain seeks to educate and motivate players toward healthier behaviors. Through an analysis of its design principles and educational content, this article highlights the potential of serious games as effective tools for health promotion and behavior change interventions targeting culturally diverse populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".