Educators’ Knowledge of Exceptionalities and Its Relationship with Their Use of Universal Design for Learning and Differentiated Instruction
Bibliographic record
Abstract
Ontario teachers work with students of varying strengths and needs profiles. Consequently, they must develop the competencies to support students with different exceptionalities. The current study examined elementary and secondary in-service teachers’ (N = 95) knowledge and confidence in teaching students with different exceptionalities and how this confidence was related to their use of Universal Design for Learning and Differentiated Instruction. Findings revealed that teachers felt less confident in teaching students with low prevalence exceptionalities (i.e., deaf/hard of hearing and blind/low vision) and more confident working with students with high prevalence conditions (i.e., learning disabilities and behavioural exceptionalities). Teachers also reported feeling more confident in supporting writing, organisation, and time management skills but less so in memory, executive functioning, and fine motor skills. Weak but positive correlations were observed between their use of UDL and DI and their confidence in their ability to teach students with different exceptionalities. The study underscores key challenges and opportunities for improving teacher training and professional development and enhancing educational outcomes for all students.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".