Effects of Higher Order Questioning in Prekindergarten for School Readiness
Bibliographic record
Abstract
This study investigated whether the strategy of higher order questioning during interest area time would have a positive effect on kindergarten school readiness (specifically focusing on mathematical and language concepts) for students in a low-socioeconomic area school. Evidence from the Developmental Indicators for the Assessment of Learning/ Third Edition (DIAL-3) scores (completed upon entering kindergarten) establishes that the students in this low-socioeconomic area school were not kindergarten ready. Statistical analyses concurred that increasing the frequency of higher order questioning during interest area time significantly improves the test performance of students within the mathematical and language concepts area of the DIAL-3 assessment. The student achievement results of providing teacher training in higher-order questioning techniques during interest area time (the most vital learning time of the prekindergarten day) has provided evidence of increased cognitive development, ultimately increasing student achievement in mathematical and language skills. High-quality prekindergarten services involving best practices are the precursors for kindergarten; therefore, improving teacher-child verbal interactions in prekindergarten ultimately addresses the issue of kindergarten school readiness. Additional findings included a correlation indicating that the students who did well in Language skills also did well in Mathematics and a statistically significant correlation existed between better scores and positive behavior. It is anticipated that the contributions of the present study will encourage future research that will continue to elaborate upon the effects of higher order questioning at the prekindergarten level on kindergarten school readiness.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".