Assessing students’ learning about fundamental concepts of climate change under two different conditions
Bibliographic record
Abstract
Students from three different British Columbia grade six classes were followed through two weeks of instruction on climate change. Pre, post, and follow-up surveys were used to determine the differences in knowledge gained and retained by students that received direct instruction from their science teacher, and by those who received equivalent content instruction from outside presenters. The teacher participant also completed a survey on her experience with the researcher-designed lesson plans. Students’ results on the surveys were compared to results from a control group with no intervention. The teacher-based setting resulted in significantly higher knowledge gain, although no difference was found between the groups’ rate of knowledge decline thereafter. Highest gains in knowledge were for the carbon cycle and the human impacts topic, followed by understanding the difference between climate and weather. The students and teacher alike appeared to struggle with the topic of global warming and the greenhouse effect. The research suggests that with the appropriate background information the classroom teacher is likely to be more effective at conveying the science of climate change, particularly when it is taught through an understanding of the carbon cycle and its human impacts. It also suggests that those non-governmental organizations engaged in climate change education might be better served by investing their limited resources in the development of learning materials and subsequent professional development for teachers rather than focusing on in-school presentations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".