Related Data for: Developing a learning progression for climate change in geography education
Bibliographic record
Abstract
Climate change is taught explicitly as a topic in the Singapore school geography curriculum. In responding to the city state’s desired outcomes of education and meeting its standards of twenty-first century competencies, it is important for learners to develop criticality and dispositions to engage climate change issues. Based on previous studies conducted by the PI over the last four years, it has been found that geography students have misconceptions about this topic that are similar to those found in other students around the world. In reviewing the literature on methodologies that examine how best geography can be learned, the Learning Progression (LP) approach offers an empirics-based roadmap for building students’ holistic knowledge base and in confronting the fragmented and often incomplete understanding of the climate change issue. The study endeavours to answer the key question of how school geography curriculum can be designed for learning about climate change and how it can be enacted in the classroom based on the outcomes of this research study. The methodology is adapted from the common practice of establishing a hypothetical learning progression (HLP), testing and validating the HLP to develop the empirical learning progression (ELP) before determining intervention strategies to test if students can learn climate change better through this approach. The findings will contribute towards the curriculum design and development of the climate change topic, offer a case study in geography teaching and learning informed by the OER’s instructional core model, provide opportunities for evidenced-informed delivery of NIE’s pre-service and in-service programmes on geography education, and foster deeper professional collaborations between NIE, MOE-HQ and schools. More importantly, the research study will inform the teaching and learning of climate change within the wider context of geographical and environmental education in the international community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".