Equipped to Teach: An Examination of Pre-Service Teacher Competencies in Supporting the Needs of Exceptional Learners
Bibliographic record
Abstract
This exploratory study examines elementary pre-service teachers' (PSTs) perceived competencies in supporting students with exceptionalities. Previous global research on teacher competencies has found that PSTs often feel unprepared to meet the diverse needs of students in the classroom. Additionally, inconsistencies exist in defining and measuring competence. Notably, no research has been conducted on this topic in the Canadian context. This study addresses this gap by exploring PSTs' perceived competencies in Canada. Data was collected from two stakeholder groups—pre-service teachers and principals—to examine potential differences between PSTs' self-perceived competencies and principals' expectations of novice teachers. The findings indicate that PSTs enrolled in a special education-related concentration report the highest levels of perceived competence in supporting students with exceptionalities. Additionally, this group aligns most closely with principals’ expectations. However, results also highlight a potential disconnect between the skills developed in teacher education programs and the competencies expected by school principals. This research identifies specific areas of strength and areas requiring further development in novice teachers' competencies. By doing so, it provides a clear set of skills that should be emphasized in future training and professional development initiatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".