Preschool Literacy and the Common Core: A Professional Development Model
Bibliographic record
Abstract
Many states have adopted the Common Core Standards for literacy and math and have begun enacting these standards in school curriculum. In states where these standards have been adopted, professional educators working in K-12 contexts have been working to create transition plans from existing state-based standards to the Common Core standards. A part of this process has included re-aligning professional development models to support implementation of these new standards. While K-12 professional educators have been hard at work in this changeover, little attention has been paid to early childhood contexts and the need of pre-school curriculum to support learners in moving toward new kindergarten goals in the Common Core. This study examines the alignment between an existent professional development model for preschool literacy widely employed in one Southern state and the new Common Core Standards. The researcher’s goal was to examine the existent professional development model to determine if the offered curriculum supported teachers in supporting learners’ knowledge and skills expected in a kindergarten classroom preparing students for the common core. The researchers sought to determine where the curriculum supported learners in this new standards environment as well as to recommend revising the professional development content as necessary in light of the new standards. The overarching goal of the study was to support preschool teachers’ abilities to prepare their students for the new expectations for school-based literacy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".