The Impact of Dyslexia Legislation: An Analysis of Knowledge and Preparedness Among In-Service Educators
Bibliographic record
Abstract
The primary purpose of this study was to compare the dyslexia knowledge and misconceptions and perceived preparedness for teaching students with dyslexia between New Jersey in-service educators (n = 706) and in-service educators from states without dyslexia code (n = 219). A second purpose was to compare the factors that predict dyslexia knowledge between these two groups. Educators in both groups completed a survey about their dyslexia knowledge and perceived preparedness. Findings indicated no significant overall differences in total dyslexia knowledge scores or the three subscales (i.e., general knowledge, characteristics, interventions, treatments) despite mandated dyslexia professional development in New Jersey. Further descriptive analysis of each survey item on the scale was conducted to determine if any differences were present between the groups of educators. Survey question analysis revealed that both groups share accurate knowledge on 45% of items, emphasizing a common understanding of dyslexia. However, differences emerged in 33% of the questions, highlighting nuanced knowledge differences between groups. Both groups share misconceptions about visual aspects of dyslexia, highlighting the challenge of dispelling visual processing neuromyths of dyslexia. The main effects of the multiple regression revealed that dyslexia knowledge is significantly influenced by both years of education experience and feelings of preparedness. New Jersey educators expressed significantly higher overall feelings of preparedness than educators in states without dyslexia code, suggesting potential positive impacts of mandated professional development on preparedness, though not necessarily on knowledge. Implications for in-service educators’ professional development, dyslexia legislation, and future research directions are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".