Transforming Teaching: Integrating Competency-Based Frameworks in Sustainability Education
Bibliographic record
Abstract
This symposium critically explores the transformative potential of competency-based learning frameworks in advancing Sustainable Development Goals. Led by the ONE Distinguished Educator Award and ONE Early Career Teaching Award winners, the session will provide educators with innovative strategies to integrate sustainability competencies into their teaching practices. The symposium emphasizes the alignment of competency-based learning with SDG, focusing on curriculum frameworks that cultivate practical, ethical, and critical competencies essential for sustainable practices. Participants will engage in cutting-edge pedagogical methodologies, including project-based learning, experiential opportunities, and collaborative approaches. Panelists will share empirical insights and experiences that highlight effective strategies for embedding the principles of sustainability and social responsibility into educational programs. A key focus is on the development and implementation of assessment tools that accurately measure the acquisition and application of sustainability competencies. Discussions address the challenges of adapting these approaches to diverse educational contexts while considering cultural and institutional constraints. This symposium provides a platform for exchanging best practices, fostering collaboration, and advancing the role of educators as facilitators of impactful competency-based education. Participants are equipped to inspire the next generation of leaders to address the complexities of sustainability through responsible business practices.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".