A Kaleidoscope of Understanding: Comparing Real with Random Data, Using Binary-Choice Items, to Study Preservice Elementary Teachers’ Knowledge of Climate Change*
Bibliographic record
Abstract
Abstract The authors used a 59-item survey to probe the understanding of climate change by 89 Ontario preservice teachers. The study investigated the usefulness of comparing real survey data from closed, binary choice items, with randomly generated data. Climate change was chosen to be the topic because it is a new emphasis in K–12 science curricula. If teachers had answered the survey randomly, according to Monte Carlo simulations, a normal distribution would result, with 56 of the 59 items answered correctly by 40%–60% of the respondents. A bimodal distribution resulted, however, with 34 items answered correctly by more than 60% and 18 items by less than 40%. Apparently, the teachers knew a lot about climate change, but also had many misconceptions, some identified here for the first time. Item discrimination indices and correlation coefficients, however, were the same for the real versus Monte Carlo data, suggesting that preservice teachers’ knowledge was a “kaleidoscope of understanding,” rather than a coherent picture. This may be because their understanding of climate change came primarily from unconnected sources in the media, or because climate change science involves many different fields of study including astronomy, biology, chemistry, ecology, oceanography, and physics. In conclusion, the analysis herein demonstrates the benefit of comparing real and random data for binary-choice item surveys in multidiscipline topics such as climate change. For those interested in climate change education, these results suggest the importance of emphasizing the difference between reliable and unreliable sources of information and giving careful attention to how to draw on concepts from different scientific fields.
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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 | 0.031 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".