Art Education as Environmental Activism in Pre-service Teacher Education
Bibliographic record
Abstract
This presentation explores how art and design education can contribute to the imperative of climate change and help societies adapt to living more sustainably. Drawing on methods from arts-based research and qualitative case study, it reports on a study that investigates what can be learned from creating environmental art installations with pre-service teachers (those training to be K-12 teachers) as part of environmental art education at the Ontario Institute for Studies in Education at the University of Toronto. Data collection methods used in this study are two-fold. The arts-based research methods include journaling, photography and the creation of installations from environmental art-making experiences. Traditional qualitative methods include anonymous online surveys, semi-structured interviews and feedback forms on art workshops. Findings suggest that preservice teachers experience attitudinal and behavioural shifts towards sustainability after engaging in the processes of creating environmental art. Involvement in the workshops also provided opportunities for building community, engaging multiple domains of learning, modeling sustainable art-making practices and prompting environmental activism. Overall, connecting environmental issues with arts-based pedagogy through environmental and sustainability education (ESE) may inspire art educators to reflect on their responsibility to advance climate action and consider what role(s) they can play in environmental activism inside and outside of their educational institutions. This presentation explores how the study adds a new dimension to the current literature because of its focus on generalist pre-service teachers and pedagogical strategies that engage those with little background in art education. The results of this study inform a developing pedagogy for environmental art education in higher education settings.
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.
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 | 0.000 | 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 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".