Identifying Chemistry Students’ Baseline Systems Thinking Skills When Constructing System Maps for a Topic on Climate Change
Why this work is in the frame
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Bibliographic record
Abstract
New resources have recently been emerging for educators to implement systems thinking (ST) in chemistry education, including a proposed set of ST skills. While these efforts aim to make ST implementation easier, little is known about how to assess these skills in a chemistry context. In this study, we investigated ST skills employed by students who constructed system maps of a topic related to climate change. Eighteen undergraduate chemistry students from first- to third-year participated in this study. We designed and implemented a ST intervention to capture how students engaged with three ST tasks, performed individually and collaboratively. In our analysis, we focused on 11 ST skills that aligned with five characteristics proposed in a recent study. We found that participants demonstrated most of these ST skills when engaging with the ST tasks, with nuances. Participants' system maps: (1) lacked concepts and connections at the submicroscopic level, (2) included multiple types of connections but few circular loops and causal connections, (3) lacked causal reasoning, although participants did predict how their system maps changed over time, (4) demonstrated the breadth of connections but did not describe human connections to the underlying chemistry of climate change topics. These findings identify aspects of ST where chemistry educators need to place emphasis when teaching ST skills to chemistry students and when guiding learning activities and other assessments. Using our findings, we created an adaptable ST rubric for the chemistry community as a tool for assessing ST skills.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it