Informed-Decision Regarding Global Warming and Climate Change Among High School Students in the United Kingdom
Bibliographic record
Abstract
Abstract Global warming and climate change are serious issues facing humanity at present and education needs to focus on including informed-decision in classroom practices. The conceptual framework used in this study has provided interconnections that influence beliefs and understandings in providing a knowledge base for making “informed-decision” among high school students. This study was conducted in three year 9 classes in two high schools in the UK and among 65 students. An inquiry intervention model was developed using the 5E instructional model (Engage, Explore, Explain, Elaborate, and Evaluate) to identify beliefs and understanding and to strengthen students’ knowledge base. This study used a design-based research setting and utilised a mixed methodology. The Wilcoxon signed-rank tests were computed to examine the pre-post-difference among questionnaire items, and structural equation modelling (SEM) was utilised to explore the relationship between belief, understanding, and intention. Data analysis of the intervention revealed that students developed a strong understanding of the causes and effects of global warming. There is evidence that students used that knowledge to “inform-decision” in relation to global warming and climate change. Promoting informed decision-making through science teaching can encourage responsible action in the future. The real gap identified in this study is that the regular school curriculum does not engage socio-scientific issues in the real world and has no opportunity to organise an inquiry-based instructional sequence for informed decision-making.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".