Lessons from a Pandemic: Educating for Complexity, Change, Uncertainty, Vulnerability, and Resilience
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has fundamentally changed many aspects of our world including the way we teach chemistry. Our emergence from the pandemic provides an opportunity for deep reflection and intentional action about what we teach, and why, as well as how we facilitate student learning. Focusing on foundational postsecondary chemistry courses, we suggest that we cannot simply return to "normal? practice but need to design and implement new ways of teaching and learning based on fundamentally reimagined learning outcomes for our courses that equip students for life after the rupture they have experienced. We recommend that new learning objectives should be guided both by an analysis of existing global challenges and the types of understandings and practices needed to confront them, and by research-based frameworks that provide insights into important areas of knowledge, skill, and attitude development. We identify a core set of competencies along three major dimensions (crosscutting reasoning, core understandings, and fundamental practices) that we believe should guide the design, implementation, and evaluation of chemistry curricula, teaching practices, and assessments in foundational courses for science and engineering majors. The proposed framework adopts systems thinking as the underpinning form of reasoning that students should develop to analyze and comprehend complex global systems and phenomena.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".