Early Childhood Educators’ Perceptions of Dyslexia and Ability to Identify Students At-Risk
Bibliographic record
Abstract
This study primarily explored the perceptions of dyslexia held by early childhood educators teaching in Head Start centers. A secondary purpose was to investigate how early childhood educators in Head Start centers perceive the notion of risk for dyslexia and how they identify at-risk students in ways that are consist with the results of a research-based assessment instrument. A case study approach was used for this study of two Head Start centers, one in the state of New Jersey and one in the state of Pennsylvania. Two teachers in each center (n = 4) and a total of 19 preschoolers participated in the study. Data were gathered using semi-structured interviews, observations, a teacher rating scale, and the Preschool Early Literacy Indicator (PELI) assessment. Findings indicate that the Head Start teachers held the prevailing misconception that dyslexia is a visual processing disorder rather than a phonological processing disorder. The Head Start teachers did not view phonemic awareness as a key factor in identifying children at-risk for dyslexia. Participants had a high success rate in identifying students at-risk in the areas of alphabet knowledge and oral language, but not in phonemic awareness and vocabulary. The results suggest that the stereotypes of dyslexia are hard to dispel and that professional development for pre-service and in-service teachers in early literacy practices and dyslexia are needed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".