Observing Instructional Practice: Can We Consistently Measure Teaching Quality Constructs?
Bibliographic record
Abstract
Classroom observation systems can be an important tool for understanding teaching quality. However, the wide range of possible lessons that can be observed raises concerns about whether fixed observation rubrics can measure the intended teaching quality constructs equally well in each lesson. This paper argues for exploring measurement invariance across lessons in observation systems, adopting an understanding of measurement invariance that emphasises the alignment between a theoretical construct and the measurement of that construct. We conceptualise teaching quality using the Protocol for Language Arts Teaching Observation (PLATO). PLATO’s focus on individual items is misaligned to traditional measurement invariance approaches, so we emphasise the importance of detailed, qualitative considerations of how rubric operationalisations of a construct may or may not capture the intended teaching quality construct equally well across lessons with different characteristics. We discuss the affordances and limitations of this way of considering measurement invariance and argue for the importance of ensuring measurement invariance across lessons.
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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 | 0.095 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".