Learning science concepts through prompts to consider alternative possible worlds
Bibliographic record
Abstract
We investigated whether prompting children to think counterfactually when learning a complex science concept (planetary habitability) would promote their learning and transfer. In Study 1, children (N = 102 6- and 7-year-olds) were either prompted to think counterfactually about Earth (e.g., whether it is closer to or farther from the sun) or prompted to think about examples of different planets (Venus and Neptune) during an illustrated tutorial. A control group did not receive the tutorial. Children in the counterfactual and examples groups showed better comprehension and transfer of the concept than those in the control group. Moreover, children who were prompted to think counterfactually showed some evidence of better transfer to a novel planetary system than those who were prompted to think about different examples. In Study 2, we investigated the nature of the counterfactual benefit observed in Study 1. Children (N = 70 6- and 7-year-olds) received a tutorial featuring a novel (imaginary) planet and were either prompted to think counterfactually about the planet or prompted to think about examples of additional novel planets. Performance was equivalent across conditions and was better than performance in the control condition on all measures. The results suggest that prompts to think about alternative possibilities-both in the form of counterfactuals and in the form of alternative possible worlds-are a promising pedagogical tool for promoting abstract learning of complex science concepts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".