Iterations on a Transmedia Game Design Experience for Youth’s Autonomous, Collaborative Learning
Bibliographic record
Abstract
Transmedia design, which involves extending a narrative from one medium to another, offers a context for potentially rich, interdisciplinary learning. We explored these opportunities by creating a week-long workshop to guide 7th-grade student teams in designing games based on comic books about viruses. This design case describes the framework and rationale behind our design choices. It illustrates our experiences by drawing on field note observations and audio recordings, student-generated design artifacts, student and facilitator interviews, and planning documentation from across two iterations of the workshop. We reflect on our experiences in attempting to balance (1) the dual focus of the workshop on science learning and game design through our choices of comic and game genres; and (2) the ability for students to be both autonomous and to receive necessary guidance through our enforcement of design constraints and interdependent team roles. We also reflect on the contextual factors that mediated our work, including students’ existing interests and peer relations, their teachers’ involvement, and our own team’s shifting expertise as membership changed from one iteration to the next. Among other things, our experiences highlight the importance of designing to allow for change, particularly as learning through collaborative transmedia game design can occur in unanticipated ways. Finally, we reflect on plans for future iterations of this workshop.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".