The Design and Evaluation of Food Villain, A Serious Game to Promote Healthy Nutrition
Bibliographic record
Abstract
The rise of diet-related health issues among African international students in Western countries necessitates innovative interventions to promote healthy eating. Food Villain, a serious game designed to address this challenge, combines educational content with engaging gameplay to encourage better nutritional choices. The current paper extends previous work on the development of Food Villain. In the current paper, we present the evaluation of the game with a focus on metrics such as ease of use, perceived usefulness, engagement, quality of information, and aesthetics. Developed through a literature review of persuasive strategies and serious game design, Food Villain incorporates elements like points, levels, feedback, rewards, and authority to educate and motivate players. The game spans four levels, featuring activities like adventure missions, quizzes, food categorization tasks, and recipe creation. The evaluation involved 24 African international students, assessing key metrics via a survey. Our results indicated that participants found the game useful and informative, with suggestions on improving the user interface and interactivity. These findings provide valuable insights for future iterations, highlighting the importance of ongoing user feedback.
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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 | 0.002 | 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".