Beyond “Inert” Ideas to Teaching General Chemistry from Rich Contexts: Visualizing the Chemistry of Climate Change (VC3)
Bibliographic record
Abstract
Abstract As one approach to moving beyond transmitting ?inert? ideas to chemistry students, we use the term ?teaching from rich contexts? to describe implementations of case studies or context-based learning based on systems thinking that provide deep and rich opportunities for learning crosscutting concepts through contexts. This approach nurtures the use of higher-order cognitive skills to connect concepts and apply the knowledge gained to new contexts. We describe the approach used to design a set of resources that model how rich contexts can be used to facilitate learning of general chemistry topics. The Visualizing the Chemistry of Climate Change (VC3) initiative provides an exemplar for introducing students in general chemistry courses to a set of core chemistry concepts, while infusing rich contexts drawn from sustainability science literacy. Climate change, one of the defining sustainability challenges of our century, with deep and broad connections to chemistry curriculum and crosscutting concepts, was selected as a rich context to introduce four topics (isotopes, acids?bases, gases, and thermochemistry) into undergraduate general chemistry courses. The creation and assessment of VC3 resources for general chemistry was implemented in seven steps: (i) mapping the correlation between climate literacy principles and core first-year university chemistry content, (ii) documenting underlying science conceptions, (iii) developing an inventory of chemistry concepts related to climate change and validating instruments that make use of the inventory to assess understanding, (iv) articulating learning outcomes for each topic, (v) developing and testing peer-reviewed interactive digital learning objects related to climate literacy principles with particular relevance to undergraduate chemistry, (vi) piloting the materials with first-year students and measuring the change in student understanding of both chemistry and climate science concepts, and (vii) disseminating the interactive resources for use by chemistry educators and students. A novel feature of the approach was to design resources (step v) based on tripartite sets of learning outcomes (step iv) for each chemistry and climate concept, with each knowledge outcome accompanied by an outcome describing the evidential basis for that knowledge, and a third outcome highlighting the relevance of that knowledge for students.
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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 | 0.001 | 0.005 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".