Conceptualising and (re)modelling ESE teaching quality
Bibliographic record
Abstract
Building on symposia at the ECER network 30 in 2023 (Lysgaard & Gericke, 2023) and 2024 (Lysgaard & Tryggvason, 2024) this presentation attempts to synthesize findings and conceptualize and model the qualities of green transition teaching within the broader field of ESE, ESD and eco-literacy research. The point of departure is an elaboration of prior research and policy work on ESE/ESD teaching quality frameworks (Mogensen, Breiting, & Mayer, 2005; UN, 2015) and their imagined and real practical implications for local school systems across the globe. We critically discuss whether one universal model for ESE teaching could be established (Laugesen & Elf, 2023; Lysgaard & Bengtsson, 2020) and argue that a more situated and pluralistic understanding of ESE teaching sensitive to local school traditions, potentials and constraints is probably more viable. Based on a large-scale empirical research project in Northern Europe (Laugesen & Elf, 2023) as well as a range of smaller, linked international case studies, the presentation, focuses on how different concepts and practices of environmental and sustainability education (ESE) can be identified, understood and further developed in schools and in and across school subjects while also considering ecosystemic aspects. A key feature of this presentation is that it focuses on exploring qualities in actual teaching in an empirical sense (i.e. drawing on substantial qualitative and quantitative data) in conversation with what is and has been considered both historically and theoretically ‘good’ quality teaching, in a more normative sense. Throughout the development of the field of Environmental and Sustainability Education (ESE) there has been a steady influx of implicit and explicit understandings of quality as a way of deliberate on the core, emphases and parameters of associated education and teaching (Poeck & Lysgaard, 2016; Poeck, Öhman, & Östman, 2019). From the foregrounding of ‘facts, knowledge and behavior’ via critiques drawing on a German-Nordic Bildung-infused focus on critical thinking and democratic participation (Mogensen et al., 2005) to more recent post-anthropocentric perspectives (Lysgaard, Bengtsson, & Laugesen, 2019; Paulsen, 2021), ESE theory and practice continue to be both highly contextualized and contested in relation to local and national educational structures and environmental and sustainability concerns (Greer, Walshe, Kitson, & Dillon, 2024). Equally, the ongoing mainstreaming tendencies within the field, particularly within the context of the Sustainable Development Goals, including SDG4 on Quality Education (United Nations, 2015), highlight the importance of developing a more nuanced language of which notions of quality are relevant and prioritized, how these might imperil or strengthen policy, practice and research-based understandings of the field, and how they could be taken up in by practitioners and resonate with practice. The presentation focuses on how a diversity of concepts and practices of quality in teaching and education within the field of ESE can be identified, understood and developed further. This presentation builds on the earlier ECER NW 30 symposia in order to explore the key question of: How can we understand quality education and, more specifically, quality teaching in light of environmental and sustainability education, and what might be the qualities of green transition teaching in educational practice?
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".