Climate Change as an Integrating Context for Learning
Bibliographic record
Abstract
As the effects of global climate change are being observed but not yet fully understood, how can we best teach our K-12 students to examine and respond to this planet-sized problem? This research report describes evaluation results from the National Science Foundation- sponsored SPRINTT [Student Polar Research with National [and International] Teacher Training project, administered by U.S. Satellite Laboratory, Inc. In SPRINTT, students study standards-based science concepts in the context of Earth’s Polar Regions and conduct their own research projects in which they analyze authentic data, collected by both western and indigenous scientists, and present their findings in the form of an online research paper. A random sample of research papers from more than 1000 students was analyzed using the program rubric to examine students’ understanding of science concepts (e.g., adaptations of organisms, weather and climate); demonstration of process skills (e.g., citing evidence, drawing conclusions); and making connections to indigenous scientific knowledge and Native peoples of the Arctic. Students at the upper elementary, middle, and high school levels illustrated strong evidence of understandings of polar concepts and science process skills. These understandings and skills may help students as they become voters and decision-makers faced with socioscientific issues such as climate change.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".