Serious Game Development for Public Health: Participatory Design Approach to COVID-19 Quarantine Policy Education
Bibliographic record
Abstract
Background: Public health education plays a crucial role in effectively addressing infectious diseases such as COVID-19. However, existing educational materials often provide only foundational information, and traditional group education faces challenges due to social distancing policies. Objective: Addressing these gaps, our study introduces a serious game called "Flattening the Curve." This interactive experience immerses learners in the role of quarantine policy managers, offering unique insights into the effects and challenges of social distancing policies. Methods: The development of the game adhered to the SERES framework, ensuring a scientifically designed foundation. To achieve its learning objectives, the game incorporated learning and game mechanics including an agent-based infection model, a social distancing policy model, and an economic model, which were developed based on previous literature. After defining a broad concept of scientific and design foundations, we used a participatory design process. This study included 16 undergraduates and took place over one semester. Participants played the game, gave feedback, and answered surveys. The game was improved based on participants' feedback throughout the process. Participants' feedback was analyzed based on the Design, Play, and Experience framework. Surveys were conducted before and after the activity and analyzed to assess participants' evaluation of and satisfaction with the game. Results: The game successfully achieved its learning objectives, encompassing a comprehensive understanding of infectious disease characteristics; the disease transmission process; the necessity and efficacy of quarantine policies and their delicate balance with economic factors; and the concept of flattening the curve. To achieve this, the game includes the following: (1) an agent-based infection model based on the modified Susceptible-Exposed-Infectious-Hospitalized-Recovered (SEIHR) model with five infectious disease scenarios; (2) a quarantine policy model with social distancing, travel control, and intensive care unit management; and (3) an economic model that allows users to consider the impact of quarantine policies on a community's economy. In response to participatory design feedback, the game underwent meticulous modifications, including refining game systems, parameters, design elements, the user interface, and interactions. Key feedback included requests for more scenarios and engaging yet simple game elements, as well as suggestions for improving the scoring system and design features. Notably, concerns about the fairness of the outcome evaluation system (star rating system), which could incentivize prioritizing economic activity over minimizing casualties, were raised and addressed by replacing the star rating system with a progress-based vaccine development system. Quantitative evaluation results reflect participants' positive assessments of the game through the learner-centric approach. Conclusions: The serious game "Flattening the Curve," developed through a participatory design approach, emerges as a valuable tool for public health education, particularly concerning social distancing policies. The game and its source code are openly accessible online, enabling widespread use for research and educational purposes.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".