An Analysis of Principal Perceptions of the Primary Teaching Evaluation System Used in Eight U.S. States
Bibliographic record
Abstract
This research examines how public school principals in eight U.S. states perceive their teacher evaluation systems which are based on Charlotte Danielson’s Framework for Teaching (FfT). States were selected to represent high, middle, and low scorers in the annual Education Week “Quality Counts” report (Education Week, 2016). 1,142 out of over 8,100 working principals in the eight states responded to an online survey, yielding a response rate of over 14%. Most principals were not satisfied with FfT and found implementing the system too cumbersome. Responses suggested an average of two changes to FfT desired by each principal; few wanted to keep their FfT as is. Targets for improvement included overhauling software used to enter teacher evaluations; eliminating student growth goals and student test scores (VAMs) as part of evaluations; reducing the time and paperwork required; and wanting more training for administrators and teachers on the use of FfT. Some states’ principals wanted to return control over teacher evaluation systems to local school districts. Most respondents agreed that their version of FfT has improved their school’s instructional program, and they prefer the new instrument over their previous evaluation instrument.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".