Service-Learning as a niche innovation in higher education for sustainability
Bibliographic record
Abstract
Education for Sustainable Development (ESD) is a framework proposed by UNESCO to develop knowledge, skills, values, and behaviors in youth for sustainable development. As part of the global development agenda, higher educational institutions are expected to integrate ESD into their curricula. Service-Learning is a type of experiential learning in which students combine academic coursework with community service which is aligned with the learning objectives of their academic program. In light of the global trend, our paper investigates how universities are responding to this call through the introduction of Service-Learning programs. First, a comprehensive review of UN documents presents the background and structure of ESD. Second, a systematic review of the academic literature analyses how Service-Learning is being introduced in higher educational institutes. Key findings are that Service-Learning programs align with most of the UNESCO framework components, but higher education institutions are finding it challenging to implement them. Educators play a pivotal role in implementation, and unless they are trained and incentivized and this is systematized, not only Service-Learning but also ESD may fail to transform learning environments. Furthermore, there is a need for impact evaluation, particularly in terms of key sustainability competences. The three major challenges are insufficient educator capacity, funding, and educator attitudes. These challenges can be addressed through university-based projects addressing local problems that have a visible impact, as well as collaboration with local communities, other institutions and, social enterprises.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".