Heuristic Evaluation of an African-centric Mobile Persuasive Game for Promoting Safety Measures Against COVID-19
Why this work is in the frame
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Bibliographic record
Abstract
The COVID-19 pandemic affected the whole world, including the African Population. As the lockdown rules against the spread of COVID-19 pandemic are being eased off and Africans have begun going about their normal daily activities, there is a need for interventions and measures to ensure that they continue to observe the safety guidelines to prevent a second wave of the pandemic. While several interventions are emerging, there is a limited number of games or gamified interventions aimed at raising awareness about these safety guidelines with a specific focus on Africans. Games and gamified applications are popular among Africans especially young people due to their entertainment value. Therefore, we present the design, implementation and heuristic evaluation of a mobile persuasive game, titled COVID Dodge, aimed at raising awareness on the importance of social distancing and other precautionary measures against the spread of the COVID-19. This persuasive game strategically employs popular persuasive features and strategies to increase the attention of Africans towards social distancing and other precautionary measures. The results of the heuristic evaluation (Heuristic Evaluation for Playability) revealed that the game possessed a high level of playability which implies that it would be engaging and enjoyed by users. The result of the persuasive strategy evaluation revealed that 13 out of the 15 strategies we implemented were strongly present in the game. Based on the evaluators' comments, we provided some design consideration and insights for developing persuasive games.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it