The Design of Disciplinarily-Integrated Games as Multirepresentational Systems
Bibliographic record
Abstract
Disciplinarily-integrated games represent a generalizable genre and template for designing games to support science learning with a focus on bridging across formal and phenomenological representations of core science relationships (Clark, Sengupta, Brady, Martinez-Garza, and Killingsworth, 2015; Clark, Sengupta, & Virk, 2016; Sengupta & Clark, 2016). By definition, disciplinarily-integrated games (DIGs) are therefore multirepresentational systems with the affordances and challenges associated with that medium. The current paper analyzes the DIG structure through the focal parameters framed by the DeFT framework (Ainsworth, 2006) to synthesize effective design considerations for DIGs in terms of the specific design and intended functions of the representations themselves as well as the overarching environment and activity structures. The authors leverage the literatures on embodied cognition, adaptive scaffolding, representations in science education, and learning from dynamic visualizations to address the challenges, tradeoffs, and questions highlighted by the framework. They apply these research-derived design considerations to an existing DIG (SURGE Symbolic) and to hypothetical examples of other DIGs in other domains to explore generalizability of the design considerations and the genre.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".