Aesthetic Design For Learning With Games
Bibliographic record
Abstract
This paper presents a design framework for educational games utilizing the notion of game aesthetics. Aesthetics in games is presently defined by all the facets of gaming experienced by players either directly through audio and graphics or indirectly through rules, geography, temporal characteristics and number of players. Researchers have observed that learning depends on the aesthetic qualities of an instructional environment and therefore the design of effective learning environments is dependent on its aesthetic considerations. Using the aesthetic principles of instructional design and the Design/Creativity loop model as the overall framework this paper elucidates how a game can be aesthetically conceived to reveal the core learning concepts and complexities for a deeper engagement with the content. To emphasize the role of creativity, we conceptualize the design process through a comparative analysis between choreography and the aesthetic configuration of a game based learning environment. We present and discuss the parallel processes of these two creative and iterative design activities, using various exemplary educational games and West African dance forms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".