A study of future physics teachers’ knowledge for teaching: A case of a decibel sound level scale
Bibliographic record
Abstract
This study examines the knowledge for teaching of prospective secondary physics teachers as related to the subject of sound waves, specifically the topics of sound level and sound intensity. The data is comprised from future teachers’ responses to the task in which they had to compose a script for an imaginary dialogue between a teacher and a group of students and provide a commentary elaborating on their instructional choices. The topics selected for the task were chosen intentionally as they provide authentic and rich opportunities to bridge mathematics and science concepts while challenging future teachers to consider logarithmic measurement scale and its role in science. The task provided the beginning of the dialogue, that featured a student’s confusion related to the measurement of the sound level using a decibel scale. Future physics teachers were asked to extend this dialogue through describing envisioned instructional interactions that could have ensued. The instructional interchange related to the relationship between sound intensity and sound level, and particular teachers’ responses to the student ideas related to the meaning of a decibel sound level scale were categorized as featuring superficial or deep, conceptual or procedural knowledge for teaching. We describe each category using illustrative excerpts from the participants’ scripts. We conclude with highlighting the affordances of scriptwriting for teachers, teacher educators, and researchers.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".