What Makes Them the Best? An Analysis of the Relationship between State Education Quality and Principal Preparation Practices
Bibliographic record
Abstract
This paper examines the relationship between principals’ training experiences and perceived school quality in seven U.S. states. Current school principals were surveyed regarding their perceptions of the comparative effectiveness of field experiences in the principal preparation program (PPP) each attended. States were selected to represent high, middle, and low scorers in the annual Education Week “Quality Counts” report. Surveys were emailed to school principals in Kentucky, Maine, Maryland, Massachusetts, Mississippi, Nebraska, and South Dakota; the response rate was over 17%. Most respondents completed field experiences as part of their PPPs and considered many of those experiences to have been valuable learning tools. Principals from the highest-ranked states identified data-driven analysis as having helped prepare them the most, while principals from two of the three lowest-ranked states mentioned working with curriculum, data analysis, & involvement in teacher observations and/or evaluations as field experiences that helped prepare them the most. This research found strong support for expanding the use of field experiences in principal training, especially as part of a longer PPP period or internship. It also indicates a need for more budget and finance training; teacher observation and evaluation training; curriculum training; and student discipline training.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".