Engaged in Learning: The ArtsSmarts Model
Bibliographic record
Abstract
Approximately a dozen internal research studies into student learning and program effectiveness were conducted during ArtsSmarts' first eight years. In the spring of 2006, we compiled the results of those studies, along with a like number of reports by outside researchers, to create a synthesis of possible directions for future work. Although we used a small sample of available outside studies, it was immediately and glaringly evident that the arts and educational communities are hungering for research that will "help us understand what the arts learning experience is for children, and what characteristics of that experience are likely to travel across domains of learning" (Deasy, 2002:99). It was equally evident to all ArtsSmarts partners that, while future ArtsSmarts research could be taken in any number of directions, it made the most sense to identify and build from ArtsSmarts' own strengths and successes. We also felt the need to align the research direction and the methods of data collection with our intended audiences.Different groups would find different aspects of ArtsSmarts compelling, and distinctly different types of data would be required for each. Partners identified educators (teachers, administrators, and senior Board office personnel) as the audience they most wanted to reach.With that in mind, the decision was made to develop a theory of learning that would serve the dual purposes of explaining ArtsSmarts' impact in Canadian classrooms and framing the research work of the next few years. We felt that establishing an ArtsSmarts theory of learning would help to answer the question, "If ArtsSmarts didn't exist, what would be lost?" Further, a theory of learning would assist teachers, artists and partners in identifying key, essential components of the ArtsSmarts experience, and would also prevent ArtsSmarts from being viewed as a pleasant but unnecessary add-on to classroom activity. The paper that follows develops an ArtsSmarts theory of learning centred on the concept of student engagement.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".