Education for Sustainable Development through Socioscientific Issues:Pre-service Teachers’ Pedagogical Design Capacity
Bibliographic record
Abstract
Even though the importance of sustainability has been recognized in education, teachers struggle with identifying an appropriate method to implement education for sustainable development (ESD) in STEM subjects. There is a relatively large literature on ESD, and the implementation of socioscientific issues (SSI) separately. However, there is limited research on using SSI for ESD. For this reason, this empirical study aims at characterizing STEM teacher candidates’ pedagogical design capacity (PDC) to address what resources they use, and how they interact with these resources to design SSI-based instruction to facilitate ESD. The qualitative data is collected through field notes, reflection reports and semi-structured interviews. The results reveal that during their design, pre-service teachers referred to teacher resourcesthe most, followed by collaborative resources, and instructional resources. Even though their use of resources shows strong connections between SSI and their pedagogical content knowledge, pre-service teachers’ consideration regarding assessment remains inadequate. Furthermore, this study shows that professional development sessions have the potential to foster pre-service teachers’ use of PDC resources to address ESD.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".