Identifying, Rating, and Categorizing Elements of Systems Thinking in Chemistry Education
Bibliographic record
Abstract
The chemistry education community has made considerable progress developing a foundation for chemistry educators to begin infusing a systems thinking approach into their course design. As researchers and practitioners seek to explore possibilities from systems thinking in chemistry education, an understanding of the range of strategies and motivations is needed. Herein, we report on a four-stage process for identifying and classifying elements of systems thinking in chemistry education. First: A systematic review of the literature on systems thinking in chemistry education was used to identify systems thinking elements in peer-reviewed published works. Second: We thematically clustered these elements. This resulted in three categories based on what system thinking techniques are being taught, how they are being taught, and why they are being taught. Third: We collected questionnaire responses from published scholars on systems thinking in chemistry education to assess how scholars rate the requirement of the elements. Fourth: We asked questionnaire participants to categorize the systems thinking elements using the framework that emerged from our thematic clustering. We found that 90% of participants agreed that 14 of the 34 elements are required for the effective implementation of a systems thinking approach in chemistry education. Notably, participants did not report a clear delineation between the “what”, “how”, and “why” for some elements. Together, the findings in this manuscript highlight areas of agreement and uncertainty for the implementation of systems thinking in chemistry 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 | 0.052 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".