The Sketchbook as a Learning Tool to Support Student Well-Being: Examining the Perspectives and Practices of Visual Art Teachers
Bibliographic record
Abstract
This study explores the impact of the COVID-19 pandemic on art education in Ontario, focusing on the sketchbook pedagogies of six secondary Visual Art teachers in the District School Board of Niagara (DSBN). The study investigates whether teachers have modified their sketchbook practices due to the pandemic, and asks for their perspectives on how sketchbooks influence student learning and well-being. Findings indicated that teachers perceived sketchbooks as flexible, holistic, and instrumental to effective art instruction. A majority of teachers noticed an increase in sketchbook use to support student well-being during the pandemic, and many maintained efforts to use sketchbooks this way during the return to in-school instruction. Concerns presented by teachers included a lack of engagement from students when asked to complete practice and planning-based sketchbook tasks, as well as difficulties assessing sketchbooks. Data analysis identified implications for future research regarding sketchbook assessment practices, how to promote student buy-in in light of decreasing motivation, and how to develop purposeful adaptive strategies via sketchbook use for student well-being. The results of this study influenced a companion project in the form of a manual titled: Sketchbooks: A Reference for New Art Educators.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".