Envisioning quality education for sustainability transformation in teacher education: perspectives from an international dialogue on Sustainable Development Goal 4
Bibliographic record
Abstract
Purpose The explanatory study aimed to understand the global perspective of quality education and describe transformative strategies that empower teacher educators and equip the education system and future teachers to act as change agents by fostering learning processes that support students towards a sustainable future. Design/methodology/approach The explanatory study used a descriptive qualitative design. The researchers conducted four semi-structured group interviews of the 23 participants including pre- and in-service teachers, teacher educators, and policymakers from Austria, Germany, Italy, Canada, the United States, and South Africa during the Teach4Reach 1.0 project on SDG4. The inductive thematic analysis of the data was guided by Kuckartz’s six-phase approach for constructing data-driven categories. Findings The study identified SDG4 as pivotal in supporting the overall UN Agenda 2030. Furthermore, three distinct themes unequivocally emerged and were developed concerning the imperative nature of envisioning transformation for sustainability: (1) collaboration, (2) well-being and context of individuals, and (3) strategies for skill development. Research limitations/implications Due to the qualitative nature of the study, the participants’ comprehensive understanding of the SDGs and their perceptions remained conditional to methodological subjectivity. Practical implications The study enabled a dialogue between educational stakeholders (pre- and in-service teachers, teacher educators, and policymakers), whereby an awareness and encouragement of SDG4 regarding quality education and training of quality future teachers occurred. A further implication includes increased collaboration and dialogue regarding the well-being of individuals (educational stakeholders), the context of individuals, and strategies for skill development. Originality/value The study elaborates on reaching the SDGs and identifies themes essential for sustainability transformation in teacher education. It also highlights further research and clarity about the responsibilities of educational stakeholders for quality education, for instance, (1) how to increase the supply of quality teachers by focusing on collaboration, well-being, and the contexts of individuals and (2) how value-embedded skills can support quality education 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 | 0.032 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.015 |
| 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".