“Students are Once Again ‘Numbers’ Instead of Actual Human Beings”: Teacher Performance Assessment and The Governing of Curriculum and Teacher Education.
Bibliographic record
Abstract
This paper will examine the educational experiences of teacher candidates and the use of teacher performance assessment (edTPA) to measure their quality, competence, and impact. It will situate edTPA within the national, politically-charged debate between the defenders and reformers of teacher education who advocate for the professionalization versus deregulation of the field, respectively. Their positions converge, however, in the collective belief and reliance on testing to measure educational inputs and outputs. Even the defenders are caught in a reactive stance to show through testing data the value and relevance of teacher preparation. The paper will also investigate the perspectives on edTPA of teacher candidates at a medium-sized, public university in the US Midwest. Using a survey of candidates who completed edTPA during the 2014-15 academic year, it will highlight candidate resistance to edTPA, even though they have been disciplined and immersed in a culture of testing throughout their K-12 and university education. Their resistance foregrounds three themes: (a) time and stress; (b) outsourcing of teacher evaluation; and (c) contradictions between curriculum and assessment in teacher education. Moreover, it will mobilize Michel Foucault’s concepts of governmentality and critique to analyze the ruling logic and practices in education and the candidates’ resistance under difficult conditions.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".