A Thirty State Analysis of Teacher Supervision and Evaluation Systems in the ESSA Era
Bibliographic record
Abstract
We analyzed teacher supervision and evaluation policy systems in 30 states since the passage of the Every Student Succeeds Act (ESSA) of 2015 in the United States (US). This qualitative study of state ESSA policy documents and legislation examined how teacher supervision and evaluation systems (TSES) models have been developed under ESSA, specifically regarding how the construction of TSES models conflated formative feedback with summative evaluation. Despite evolving federal-level and state-level education accountability policies spurred by No Child Left Behind (NCLB) in 2001, we argue that TSES systems are influenced by state-level historical political culture (Elazar, 1994; Fowler, 2013), workplace behaviorism (Hazi, 2019), decision-making structures (Hazi & Arredondo Rucinski, 2009; Ruff, 2019), and policy rationalism (Louis et al., 2008; Orr, 2007). Data were analyzed inductively (Wolcott, 2009) to investigate how 30 states developed TSES models and from this we analyze the messages conveyed about improvement. Thus, while ESSA intended to provide states and local districts with more political control to develop and implement TSES models, our analyses shows how ESSA has extended and reinforced state-level TSES policy development and reduced districts’ local control and authority to supervise and evaluate instruction.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".