Implementation of Education for Sustainability in Turkish Pre-Service Teachers’ Practicum
Bibliographic record
Abstract
In this study, we explored early childhood education pre-service teachers’ (ECEPTs’) understanding and pedagogical application of education for sustainability (EfS) by critically analyzing EfS implementation during ECEPTs’ practicum. The study examined the challenges and critical aspects of EfS practices in the practicum, and the relationships among mentor teachers, academic mentors, and ECEPTs. A multiple case study methodology was employed involving two purposefully distinct universities with 14 participant students across the two university case study sites. Initially, 22 practicum activity plans for each ECEPT were examined via content analysis; then, the students were individually interviewed about the plans and their implications. Subsequent analysis indicated negligible differences between the two universities’ student activity plans regarding quality (aspects of EfS) and quantity (frequency of EfS activities). The students self-reflected about their EfS understandings and implementation. Additionally, they remarked that academic mentors’ and mentor teachers’ stances influenced them either positively or negatively during their workplace-based learning. They also pointed to the absence of a whole-institution approach, not only at the practicum school but also at the university level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".