Designing and Applying Project Fidelity Assessment for a Teacher Implemented Middle School Instructional Improvement Pilot Intervention
Bibliographic record
Abstract
Abstract: This article argues that intervention pilot test evaluations have focused insufficient attention on the measurement of project fidelity and the subsequent use of fidelity results for (a) interpreting variations in project outcomes and (b) understanding the rationale for teachers’ deviations from implementation protocols. The authors report on the establishment and application of an evaluation methodology for measuring and analyzing implementation fidelity for a middle school instructional improvement pilot project. The authors found that the highest implementation fidelity scores were not correlated with the most desirable project outcomes, as lower fidelity scores—in the 70–79% range—produced the most favourable gains on pre-post student outcomes. Moreover, application of the fidelity evaluation methodology provided insight into teacher deviation from implementation protocols; such deviation from the implementation protocols typically reflected meaningful professional classroom judgements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".