Gleaning Strategies for Knowledge Sharing and Collective Assessment in the Art Classroom from the Videogame, “Little Big Planet’s Creator Spotlights”
Bibliographic record
Abstract
This chapter examines the notion of videogames as a resource for teaching practice. Games are often used as teaching tools, but not often used as resources for informing pedagogical practice. Media Molecule’s game, Little Big Planet (LBP) for the Playstation 3, is a constructivist game with a niche online community of practice known as LBP Central. The game, along with the community, exemplifies multiple learning strategies in a constructivist environment, lending itself as a potentially powerful resource for studying constructivist teaching/learning strategies. In this chapter, the authors look closely at a community assessment and knowledge sharing strategy known as the “creator spotlight” and, based on the premise that art classrooms tend to be more constructivist by nature than other subject areas and because LBP has strong links to visual art, they suggest ways in which this process could be explored and applied with secondary visual arts students within a constructivist learning environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".