Digital Games as Multirepresentational Environments for Science Learning: Implications for Theory, Research, and Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environments in which learning involves coordinating multiple external representations (MERs) can productively support learners in making sense of complex models and relationships. Educational digital games provide an increasing popular medium for engaging students in manipulating and exploring such models and relationships. This article applies cognitive science research on MERs to a range of popular educational and recreational games that focus on the interpretation and manipulation of models. We leverage the literatures on embodied cognition, adaptive scaffolding, science education, and dynamic visualizations to address the challenges, trade-offs, and questions highlighted by the research. We apply these research-derived design considerations to analyze (a) the extent and forms through which the design considerations are reflected in the design of the games, (b) the implications for designing effective model-based games for learning, and (c) the implications for future research on MERs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.002 |
| 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 it