Training Pre-service Teachers to Fulfill Special Education Responsibilities in Ontario, Canada: A Content Analysis
Bibliographic record
Abstract
As inclusive education becomes the norm in Kindergarten to Grade 12 classrooms, general education teachers have a growing need for knowledge and skills in inclusive and special education (ISE) practice. While many early-career teachers pursue professional development in special education, pre-service teacher training plays an essential role in preparing educators to support the diverse needs of exceptional learners. In this study, we examined ISE-related course content from pre-service–teacher-education programs in Ontario, Canada. We analyzed a total of 1,011 course descriptions from 14 provincially accredited pre-service–teacher-education programs using summative content analysis. Our findings have shed light on what might be missing in Ontario pre-service–teacher-education programs: (a) a need for exceptionality-related content in core curriculum courses, (b) consistency in ISE training across grade-level divisions, and (c) training specific to effective collaboration with educational assistants. We highlight the need for policymakers to mandate strong ISE integration into pre-service–teacher-education programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".