Knowledge, urgency and agency: reflections on climate change education course outcomes
Bibliographic record
Abstract
This research arose out of conversations among climate change education instructors teaching at Lakehead University and our shared interest to better understand student experiences in our courses, with the intention of informing pedagogical decisions around course design and content.Data were collected from students at the end of seven courses through a mixed methods approach consisting of an online questionnaire (n = 55), which allowed participant segmentation using the Six Americas Framework, and follow-up semi-structured interviews (n = 22).The questionnaire collected students' self-reported levels of knowledge and understanding, sense of urgency, and sense of agency related to climate change, which are shared learning goals across our courses, as well as responses to open-ended questions on student experiences within the courses.In the interviews, participants were asked to elaborate on these themes.Participants reported increased knowledge, a heightened sense of urgency and strengthened sense of agency-including describing individual and collective changes they made following the course.We engage with the empirical data and present our critical reflections as instructors on course elements and design, encouraging others to teach climate change education in initial teacher 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".