The Relationships Between Internal Program Measures and a High-Stakes Teacher Licensing Measure in Mathematics Teacher Preparation: Program Design Considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accountability measures have quickly entered into formal teacher-preparation programs. As a response, we introduce the use of structural equation modeling vis-à-vis path analysis in secondary-grade mathematics teacher preparation as a methodology to test models to understand the strength of relationships to recommendations of prominent professional organizations and standards for entering the teaching profession. This longitudinal, 6-year, five-cohort study examines the relationship of program design sequencing and core components (internal measures) to an externally scored high-stakes teacher licensing examination portfolio intended to measure pedagogical content knowledge and first-year teacher readiness. The internal measures and program sequencing model explains 49.2% of the variance in relation to the standardized outcome teaching portfolio examination with high-power and medium- to large-effect statistics. We provide implications for teacher preparation with respect to recommendations of professional organizations, governments, and accreditation standards. Results should stimulate discussions and fuel future research efforts.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it