Question Asking During Reading Comprehension Instruction: A Corpus Study of How Question Type Influences the Linguistic Complexity of Primary School Students’ Responses
Bibliographic record
Abstract
Abstract The authors examined teachers’ ( N = 19) use of different question types during small‐group comprehension instruction for 6–11‐year‐olds ( N = 115). The authors tagged the corpus of 40 hours of guided reading sessions to enable computer‐based searches for syntactic forms of questions. Teachers frequently asked high‐challenge wh ‐ word questions (e.g., “How does that fit in with what you just read?”), and this was more pronounced in schools located in regions of low socioeconomic status, a finding associated with recency of completion of teacher training. Students’ responses were more linguistically complex when teacher questions comprised a high frequency of high‐challenge questions, particularly wh ‐ word adverb questions (predominantly why and how ). These findings applied across the wide age and ability range of the sample, indicating that high‐challenge questions are effective in small‐group comprehension instruction for students in different age groups and at various levels of reading ability. The authors conclude that teachers benefit from being informed about the effect of various syntactic forms of questions, particularly the nuances of wh ‐ word questions. The findings also highlight the advantages of using corpus search methods to examine the influence of teacher question‐asking strategies during classroom interactions.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".