Bibliographic record
Abstract
Asking questions is a powerful learning tool that children take full advantage of, as they are well-known to be prolific and determined question-askers. But do children ask good questions? In this chapter, we review and discuss qualitative and quantitative studies to trace the developmental trajectory of children’s question–asking strategies, focusing on their effectiveness and adaptiveness. Previous research has so far established three milestones: children’s question–asking abilities evolve from being able to identify effective questions, but not being able to spontaneously generate them at the age of five, to beginning to generate effective questions from scratch at age seven, to implementing efficient and adaptive question–asking strategies by the age of ten, echoing adult–level patterns of performance. We discuss how the cognitive and environmental factors driving these developmental changes still remain unclear, and how taking a multidisciplinary approach might be necessary to fill these gaps. We argue that the results from research on question-asking have the potential to inform educational policies, and to help design targeted training interventions and educational curricula that exploit the early emergence of these skills and support their further development, providing children with a toolbox of strategies and concepts they can use to effectively navigate the world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".