Experiential learning for sustainability leadership in higher education
Bibliographic record
Abstract
Purpose – The purpose of this paper is to compare the program design of a sustainability leadership certificate to participants’ perceptions of their in-program learnings and competencies development. The authors present the results from the analysis of one program evaluation component, a survey, which was delivered before the program start and at the program end. Design/methodology/approach – The authors describe key design elements of a sustainability leadership certificate, which was framed around five key sustainability competencies. Using a pre/post self-assessment, participants ( n = 32) selected their level of confidence and competence in each of the key sustainability competencies and completed open-ended questionnaires. Quantitative data were analyzed using a Mann–Whitney U test, and qualitative data were transcribed and coded using a grounded theory approach in NVivo 10. Findings – Based on the survey feedback, the program participants were generally excited by the program’s experiential format and supportive community. They felt that they had improved their confidence and competence in the key sustainability competencies. Three themed clusters, community, future and personal development, emerged from the participants’ open-ended responses. This supports the program design and can inform further program development. Practical implications – The third theme, personal development, is notable, as it is not a typical focus of sustainability in higher education, but held high importance to participants. This strong resonance with participants suggests that sustainability programs should consider the role of the self to foster the development of key sustainability competencies. Originality/value – The program’s focus on “personal” was intentional in the program design. Based on participants’ feedback, the inclusion of personal development exercises was a critical element for successful sustainability leadership development.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".