“Teach Your Classmates About the Behavior of Water with School-Level Science Models”: An Experience in Initial Preschool Teacher Education
Bibliographic record
Abstract
Abstract Prospective preschool teachers (PPTs) need to have learning experiences with the practice of scientific modelling to be able to design appropriate lessons as teachers. In the literature on research in science education, scarce experiences of PPTs in scientific modelling can be found. This study aims to fill the knowledge gap about PPTs’ representations of water and its states by means of models. To this end, an analysis is made of the models designed by PPTs and the difficulties they found in such a design process. The participants were 47 PPTs, working in groups of 2 or 3, forming 19 groups in total. The data source for analysis was the report written by each group. The oral presentation of these reports in class also served to clarify any doubts about the models elaborated by the PPTs. The models were analysed and categorized using qualitative content analysis methods, by combining inter- and intra-rater evaluation strategies. The results reveal that PPTs in general used a variety of resources to make models about the water molecule. Nonetheless, they found it harder to model the differences between the three aggregation states of water from a molecular perspective. The PPTs also acknowledged having had difficulties, such as when choosing and handling the materials they used to create the models or when thinking how to adapt them for the explanations to their peers. It is concluded with a discussion and implications of this study towards the PPTs’ training in scientific modelling and its didactics.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".