Empowering youth for sustainability in universities: service-learning and the willingness to act
Bibliographic record
Abstract
Purpose Service-learning (SL) shows potential to respond to the global policy agenda of education for sustainable development (ESD) by increasing pro-sustainability competences through direct involvement of students in projects that satisfy identified community needs. Nevertheless, there is a scarcity of studies that attempt to measure the impact of SL on students’ sustainability competences, especially the action competence. This study aims to address this gap by examining the experiences of higher education students. Design/methodology/approach A pre-post survey design based on the Self-Perceived Action Competence for Sustainability Questionnaire was conducted on an interdisciplinary group of 219 students of two courses (Sustainable Development and Ecology) in Medellin, Colombia, half of which (109) participated in SL projects. Findings Sufficient empirical evidence was found to suggest that SL boosts the impact of academic courses regarding action competences in students (specially their willingness to act). Research limitations/implications The statistical analysis shows some contradictions that should be addressed in further research. Practical implications These results can encourage more educators and universities to implement strategies such as SL to move forward with ESD and thus help overcome the current socioecological crisis. Originality/value This paper not only discusses the theoretical potential of SL but also contrasts theory with empirical observations of 13 SL projects assessed in terms of self-perceived action competence for sustainability.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".