Climate Change in Teaching Chemistry: Focusing on the Use of Charts
Bibliographic record
Abstract
This paper aims to present research focused on fostering digital skills and data literacy in grammar schools through the teaching of the topic “Climate Change” during chemistry lessons. In the first phase, a set of four methodologies was developed – Earth’s Climate System, Monitoring air quality, Acid rains and their impact on the environment, Greenhouse effect and global warming. Each methodology included a teacher’s guide and a student worksheet. The methodologies focused on developing data literacy; to process the data, students worked with charts and data from well-known climate atlases, such as Copernicus Interactive Climate Atlas Climate (https://atlas.climate.copernicus.eu/), Atlas of Canada (https://climateatlas.ca/) as well as with the data from the Slovak Hydrometeorological Institute (SHMI, https://www.shmu.sk), which provides up-to-date meteorological, hydrological, and climatological information in Slovakia. These methodologies were designed to help students develop their ability to seek and analyse data on air quality at both national and regional levels, identify greenhouse gases with a high global warming potential, understand their role in atmospheric processes, explore causality, and propose potential solutions to global problems based on the knowledge acquired. A pilot testing of these methodologies involved 50 3rd-year grammar school students. To assess the effectiveness of teaching using these methodologies, students completed exit cards. The evaluation of students’ responses indicated that they gained knowledge about the causes of air pollution, substances that pollute the environment (including their threshold concentration values), appreciated working with climate atlases and SHMI, and learned how to seek and analyse data based on various criteria. Students showed interest in learning about combating climate change and improving air quality.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".