University-level teaching of Anthropogenic Global Climate Change (AGCC) via student inquiry
Bibliographic record
Abstract
This paper reviews university-level efforts to improve understanding of anthropogenic global climate change (AGCC) through curricula that enable student scientific inquiry. We examined 152 refereed publications and proceedings from academic conferences and selected 26 cases of inquiry learning that overcome specific challenges to AGCC teaching. This review identifies both the strengths and weaknesses of each of these case studies. It is the first to go beyond examining the impact of specific inquiry instructional approaches to offer a synthesis of cases. We find that inquiry teaching can succeed by concretising scientific processes, providing access to global data and evidence, imparting critical and higher order thinking about AGCC science/policy and contextualising learning with places and scientific facts. We recommend educational researchers and scientists collaborate to create and refine curricula that utilise geospatial technologies, climate models and communication technologies to bring students into contact with scientists, climate data and authentic AGCC research processes. Many available science education technologies and curricula also require further research to maximise trade-offs between implementation and training costs and their educational value.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".