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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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