Using Brief Teacher Interviews to Assess the Extent of Inquiry in Classrooms
Bibliographic record
Abstract
Inquiry-based instruction is common to nearly every model of gifted education. Six teachers of 14 secondary classes were briefly interviewed about their teaching and learning methods, use of inquiry-based strategies, classroom descriptions, a typical day, student expectations, and inquiry-instruction outcomes. A criterion-referenced checklist of 25 qualities of inquiry classrooms was used in a protocol analysis of the transcribed interviews. The classes were previously categorized as Most, Middle, and Least Inquiry with a modification of Llewellyn’s simplified rubric for inquiry teaching complemented by teacher and student interviews and a teacher questionnaire. Extent of inquiry was well identified using only the teacher interviews and checklist. Teachers of Most Inquiry classrooms mentioned 21 or 25 of the 25 inquiry items. Middle Inquiry teachers mentioned 17 and 18 items. Least Inquiry teachers noted 6 and 9. Brief teacher interviews with a relatively straightforward coding system can assess the extent of classroom inquiry students experience.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".