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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".