Pre-service Teachers' Misconceptions Regarding Three Environmental Issues.
Bibliographic record
Abstract
This study identifies and describes misconceptions held by preservice elementary teachers about three environmental issues: the greenhouse effect, atmospheric ozone, and acid precipitation. One hundred and thirteen students participated in this study. A 29-statement survey was used to probe students on the causes, effects, and interactions of the three issues. Responses were obtained both quantitatively and qualitatively. The major misconceptions found in the study include: • the increased greenhouse effect may cause skin cancer, • ozone depletion may cause global warming, • ozone is a multifunctional layer, and • pollutants evaporate with water, later come down as acid rain. Recommendations regarding the changes in the curriculum and the classroom teaching practices are made for elementary teacher education programs to address the deficiencies identified in this study. Resume Cette etude cerne et decrit des interpretations erronees entretenues par des stagiaires en enseignement primaire a propos de trois enjeux environnementaux : l’effet de serre, l’ozone atmospherique et les precipitations acides. Vingt-neuf enonces ont servi a sonder 113 etudiants sur les causes, les effets et les interactions de ces trois enjeux. Des reponses tant quantitatives que qualitatives ont ete obtenues. D’apres l’etude, les principales idees fausses sont, notamment :
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".