Are Prompts Provided by Electronic Books as Effective for Teaching Preschoolers a Biological Concept as Those Provided by Adults?
Bibliographic record
Abstract
Research Findings: Prior research indicates that shared book reading is an effective method for teaching biological concepts to young children. Adult questioning during reading enhances children’s comprehension. We investigated whether adult prompting during the reading of an electronic book enhanced children’s understanding of a biological concept. Ninety-one 4-year-olds read about camouflage in 3 conditions. We varied how prompts were provided: (a) read by the book, (b) read by a researcher, or (c) given face to face by the researcher. There was an interaction between children’s initial vocabulary level and condition. Children with low vocabulary scores gave fewer camouflage responses than their high-vocabulary peers, and this effect was particularly pronounced in the book-read condition. Children’s executive function was also measured and discussed. Practice or Policy: Our findings indicate that under some circumstances electronic prompts built into touchscreen books can be as effective at supporting conceptual development as the same prompts provided by a coreading adult. However, children with low vocabulary skills may be particularly supported by adult-led prompting. We suggest that adult prompting be used to motivate children to test and revise their own biological theories. Once children have learned strategies for updating their concepts, electronic prompting may be useful for scaffolding children’s transition to using the strategies when reading alone.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".