Bibliographic record
Abstract
The way the world moves forward forces us to prepare ourselves through 21st-century educational processes: those that are active, realistic, systemic, and cross-disciplinary. However, facilitating such processes is not trivial as they require skills and resources that educators can seldom possess. Narratives, primarily in the form of interactive digital narratives (IDNs), are potent tools for 21st-century education, but they too require significant resources from educators. Transmedia learning, which is the use of educational media revolving around a story universe, can potentially support educators in applying IDNs, primarily if it lets them and their students add new narratives to a shared story universe, thus freeing them from having to create narratives from scratch. However, how to conduct this co-creation process is underexplored. Through a theoretical investigation of the literature on 21st-century education, IDNs, transmedia learning, narrative co-creation, and shared universes, we propose a framework for co-designing a transmedia learning ecosystem. The framework divides a transmedia learning ecosystem into learning content, narrative, and interactivity aspects, to be handled collaboratively by educators, narrative experts, and interactivity experts. Furthermore, each aspect is systemic and hierarchical, which allows for designing educational IDNs that expand upon each other, thus letting each educator work on one that suits their expertise while ensuring that the end products provide a comprehensive learning experience. We applied the framework in a case study of co-designing a transmedia learning ecosystem for 21st-century informatics education. The case study’s preliminary result is a general design of the ecosystem, which shows how co-design tasks can be delegated efficiently to produce IDN media that deliver informatics subjects hierarchically and engagingly. We also discuss our future research directions, including developing the ecosystem further to produce actual IDN media and expanding the framework to support other IDN creation activities, such as design implementation and IDN media validation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".