Resource teachers' perceptions of the utilization of educational assistants and implications for future practice
Bibliographic record
Abstract
Changing demographics, resulting from immigration and broadening eligibility criteria for special needs categorical funding has led school divisions to rely more heavily on the utilization of educational assistants to meet the diverse educational needs of rapidly growing student populations.The accepted practice of hiring and assigning educational assistants to students with complex educational needs was questioned.The purpose of this study was to detennine resource teachers' perspectives of the utilization of educational assistants with students with disabilities in a rural Manitoba school division.The intent was to gain a better understanding of existing practices and how these practices compared to roles and responsibilities of educational assistants as outlined in current educational research.Resource teachers in the school division were surveyed using a24 item questionnaire on the cument practices in the utilization of educational assistants in their school.The data identified some commonalities in the reliance on educational assistants among the respondents.The results identified revisions needed in policy and general practice to better serve students with disabilities.Implications for improved future practice include clearly defined employment policies, improved training opportunities for educational assistants, and professional development for resource and classroom teachers on effective management of educational assistants.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".