7. Negotiating Shared Understandings of Our Work Through a Collaborative Curriculum: Exploring the Experience of Creativity in Cross Discipline Visual Arts Projects
Bibliographic record
Abstract
In 1994, the National Arts Education Association created a research agenda to address major research issues in the field of visual arts education for the purpose of examining, negotiating, and modifying commonly held beliefs in the field of art education. Research by arts educators has done much to inform visual arts education theory and practice, but largely through studies by individuals with few collaborative efforts. In 1991, Neil Owen Houser proposed a collaborative processing model for arts education, which reflects the experiential or constructivist nature of instruction. In this paper, we present our reflections on our shared work where we explored the benefits of interdisciplinary collaboration, the role of play in the process of problem solving, and how experiential learning strategies and techniques could be applied to the teaching of various subjects through visually-mediated arts projects.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".