Connecting Competences and Pedagogical Approaches for Sustainable Development in Higher Education: A Literature Review and Framework Proposal
Bibliographic record
Abstract
Research into and practice of Higher Education for Sustainable Development (HESD) have been increasing during the last two decades. These have focused on providing sustainability education to future generations of professionals. In this context, there has been considerable progress in the incorporation of SD in universities’ curricula. Most of these efforts have focussed on the design and delivery of sustainability-oriented competences. Some peer-reviewed articles have proposed different pedagogical approaches to better deliver SD in these courses; however, there has been limited research on the connection between how courses are delivered (pedagogical approaches) and how they may affect sustainability competences. This paper analyses competences and pedagogical approaches, using hermeneutics to connect these in a framework based on twelve competences and twelve pedagogical approaches found in the literature. The framework connects the course aims to delivery in HESD by highlighting the connections between pedagogical approaches and competences in a matrix structure. The framework is aimed at helping educators in creating and updating their courses to provide a more complete, holistic, and systemic sustainability education to future leaders, decision makers, educators, and change agents. To better develop mind-sets and actions of future generations, we must provide students with a complete set of sustainability competences.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".