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Record W4290988889 · doi:10.26522/brocked.v31i2.941

Exploring Effective Pedagogies in Environmental and Sustainability Education for Teachers: A Story of New Zealand Pre-Service Teachers’ Learning Experiences

2022· article· en· W4290988889 on OpenAlexvenueno aff
Sally Birdsall

Bibliographic record

VenueBrock Education Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningFlourishingPedagogySociologySustainabilityEnvironmental educationQualitative researchMathematics educationPsychologySocial scienceSocial psychology

Abstract

fetched live from OpenAlex

Although teachers have been identified as key change agents in the shift towards a flourishing planet for all, research into effective pedagogies for embedding environmental and sustainability education (ESE) into teacher education courses is an emerging area. Understandings about the most effective approaches and activities are needed, along with theories that could underpin teachers’ learning. This study explores 21 pre-service primary school teachers’ learning following their engagement in an elective course designed to help them embed ESE into their future practice. Qualitative data were gathered using reflections as well as peer and individual interviews. An analysis showed that the activities considered most valuable for learning were those that gave pre-service teachers space and time to think more deeply and in different ways. Mezirow’s transformative learning theory was used to explore their learning. The use of its three elements and six components identified that transformative learning took place for about half of these pre-service teachers. While it seems this theory has potential to underpin ESE teacher education courses, further research is needed to explore how transformation can occur for more teachers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.292
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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