International Peer Collaboration to Learn about Global Climate Changes.
Bibliographic record
Abstract
Climate change is not local; it is global. This means that many environmental issues related to climate change are not geographically limited and hence concern humans in more than one location. There is a growing body of research indicating that today’s increased climate change is caused by human activities and our modern lifestyle. Consequently, climate change awareness and attention from the entire world’s population needs to be a global priority and we need to work collaboratively to attain a sustainable future. A powerful tool in this process is to develop an understanding of climate change through education. Recognizing this, climate change has been included in many science curricula as a part of science education in schools. However, teaching such a complex and global topic as climate change is not easy. The research in this paper has been driven by this challenge. In this paper, we will present our online science module called Global Climate Exchange, designed with inquiry activities for international peer collaboration to teach climate change. In this study, we engaged 157 students from four countries (Canada, China, Sweden, and Norway) to collaborate in Global Climate Exchange. To explore the opportunities that international peer collaboration in Global Climate Exchange gives, we have analyzed how students develop their explanations about climate change issues over time. Our analysis showed that the students increased the proportion of relevant scientific concepts in relation to the total number of words in their explanations and that they improved the quality of links between concepts over a six-week period. The analysis also revealed that the students explained more perspectives relating to climate change issues over time. The outcomes indicate that international peer collaboration, if successfully supported, can be an effective approach to climate change education.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".