Epidemic: Learning Games Go Viral
Bibliographic record
Abstract
In this case study, we document the development and user-testing of Epidemic: Self-care for Crisis, an online educational resource that invites users (aged 14-20) to develop game-based knowledge and practices around prevention, self-care and (mis)information in the face of contagious diseases - a timely project, given the ongoing anxieties, and false (and not so false) alarms, over SARS, Avian Flu, and H1N1. The game Contagion, the forerunner to Epidemic, mobilized the conventions and mechanics of single-player adventure games to engage players 'experientially' with health- and disease-related understandings: we configured the same basic self-care information as "narrative knowledge", intended to mobilize players' attention and intelligence voluntarily, using narrative as a rhetorical strategy. We were using narrative's traditional, paradigmatic function within literate cultural forms of interpellation - stories of playful, pleasurable persuasion designed to engage players, Epidemic takes a decidedly different tack towards delivering the same educational content. Reconfiguring digital play within social networking conventions affords us a design-based platform for fundamental theory development in game-based learning. Epidemic's modular, Flash and XML-based design allows for accessible and straightforward creation and editing of educational content, both textual and visual: players can generate and publish their own virus-like avatars, stop-motion animations, and disease-related public service announcements. Some interesting divergences in play-based education on community health/self care, between interactive narrative and social-networking configurations for ludic knowledge representation, appear noteworthy. Our user-testing, we argue, signifies a further innovation in the field of educational game design, leaving behind the clichéd concern over 'what did you learn today' in favor of focusing on when and how laughter, engagement and attention are most at work. Taken together, these innovations instantiate an approach to digitally-mediated learning that construes and practices assessment differently than in traditional education (and in educational technology design), which are more concerned with propositionally identifiable learning outcomes. In the case of Epidemic, however, we are more concerned with how play-based learning design can best support the cultivation of responsible and critically-informed attitudes towards public health.
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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 | 0.009 |
| 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".