The Relationships Between Internal Program Measures and a High-Stakes Teacher Licensing Measure in Mathematics Teacher Preparation: Program Design Considerations
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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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 | 0.098 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".