Instructional Practices in K-12 Climate Change Education Across Disciplines: A Study of Early Adopters from New Jersey
Bibliographic record
Abstract
The United Nations’ 2030 Agenda for Sustainable Development centers on the 17 Sustainable Development Goals (SDGs). Among these goals, two address climate change education: Goal 13, Climate Action, and Goal 4, Quality Education. In order to build a more sustainable future, climate change education is critical. In 2022, New Jersey became the first state in the US to integrate climate change into learning standards across subjects and grade levels K-12. In an effort to better understand the way in which teachers began to include climate change in their instruction, 50 teachers were observed implementing a lesson of their choosing that included climate change throughout the 2023–2024 academic year. Though most of the observed lessons featured science, many subject areas were included in the dataset, such as art, technology, history, and physical education. Teachers engaging in climate change instruction tended to use a variety of instructional practices. In nearly all cases, a multitude of methodologies were used in each lesson. However, small group instruction was featured in nearly all observed lessons. Quantitative descriptions of the findings are followed by three vignettes of exemplar instruction to provide a clearer understanding of the context of this work. These findings provide a scope for how climate change can be integrated in instructional settings at scale and suggestions for leveraging the experiences of early adopters of this innovation to support widespread implementation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".