Achieving transformative sustainability learning: engaging head, hands and heart
Bibliographic record
Abstract
Purpose The current UN Decade of Education for Sustainable Development echoes many scholars' calls to re‐envision education for sustainability. Short of a complete overhaul of education, the paper seeks to propose learning objectives that can be integrated across existing curricula. These learning objectives are organized by head, hands and heart – balancing cognitive, psychomotor and affective domains. University programs and courses meeting these learning objectives exhibit an emergent property here termed transformative sustainability learning (TSL). Design/methodology/approach Theoretically, TSL grew from traditions of sustainability education and transformative education. Practically, TSL emerged from experimental learning collaborations sponsored by the University of British Columbia in 2003 and 2004 in an effort to enable explicit transitions to sustainability‐oriented higher education. Primarily through action research, these community‐based, applied learning experiences constituted cyclical processes of innovation, implementation and reflection. Findings The paper finds: advancement of head, hands and heart as an organizing principle by which to integrate transdisciplinary study (head); practical skill sharing and development (hands); and translation of passion and values into behaviour (heart); development of a cognitive landscape for understanding TSL as a unifying framework amongst related sustainability and transformative pedagogies that are inter/transdisciplinary, practical and/or place‐based; creation of learning objectives, organized to evaluate a course or program's embodiment of TSL. Originality/value By enabling change within existing structures of higher education, the paper complements and contributes to more radical departures from the institution. The work to date demonstrates potential in applying this learning framework to courses and programs in higher education.
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 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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".