Assessing School Readiness: Validity and Bias in Preschool and Kindergarten Teachers' Ratings
Bibliographic record
Abstract
As a part of efforts to evaluate and monitor the increasing public investment in early childhood education, teachers are being asked to assess children's school readiness. In this study, preschool teachers and kindergarten teachers rated children's skills in three areas (kindergarten readiness, academic skills, and communication skills), and these ratings were compared with direct assessments of the children's skills. Ratings by both groups of teachers tended to be more highly related to basic skills, such as counting and number naming, than to abilities such as solving applied problems and using expressive and receptive vocabulary. Preschool teachers' ratings had a lower association with children's observed skills and abilities than kindergarten teachers' ratings. Ratings of children attending Head Start were systematically inflated, but this relationship was mediated to a significant extent by the teachers' levels of education. More educated teachers rated children in a manner consistent with the children's directly assessed skills. Implications of these findings for informing future efforts to assess school readiness by using teacher ratings are discussed.
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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 | 0.034 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".