Knowledge of Late-Emerging Reading Disabilities Amongst Current and Future Ontario Educators
Bibliographic record
Abstract
Children with Late Emerging Reading Disabilities (LERD) are believed to represent a significant proportion of children with reading disabilities (Badian, 1999; Leach et al., 2003; Shaywitz et al., 1992). Yet, these disorders have gone unnoticed or are minimally discussed in educational settings (Chugh, 2011; Catts et al., 2012). Little is known about the extent of teachers’ LERD knowledge, which is problematic given their role in supporting students with reading difficulties. In the first study of this dissertation, in-service teachers’ knowledge, and perceptions of LERD was investigated. Results showed that teachers had little knowledge of LERD and limited confidence in their abilities to identify and provide interventions for these students in the classroom. Based on these findings, a web-based, self-paced workshop was developed with the purpose of increasing educators’ conceptual and practical knowledge of LERD. Participants in Study 2 were pre-service teachers at the University of Western Ontario. Participants watched three module videos, completed associated quizzes, and completed pre- and post-workshop questionnaires. The findings of this study supported the utility of the short web-based workshop for significantly improving pre-service teachers’ conceptual knowledge of LERD. There was some support for its impact on practical knowledge acquisition. Implications for current and future educators’ professional development as well as limitations and next steps for this area of research 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".