Co-constructing collaborative classrooms: novice and veteran teachers perceptions of working with educational assistants.
Bibliographic record
Abstract
This research study documents the perceptions of Manitoba teachers working with educational assistants as schools comply with the Appropriate Educational Programming Amendment to the Public Schools Act (Manitoba Education, Citizenship and Youth, 2005). Eight teachers who work with educational assistants in rural and urban Manitoba were asked about four aspects of this emerging role: (i) What are their experiences working with educational assistants in the classroom (what roles do they perceive educational assistants to perform), (ii) the competencies they think they need for their work, (iii) how they have been prepared for this responsibility, and (iv) how they think teachers should or could be (better) prepared for their work with educational assistants. The study invited four novice teachers (less than two years of teaching experience) and four veteran teachers (more than 10 years of teaching experience) to participate in one-on-one face-to-face interviews. Open-ended questions based in current research prompted the participants to reflect on their own practice. The study reveals some of the perceived issues teachers report as challenges in their changing role to meet new legislative mandates. It examines the need to introduce collaboration with educational assistants during pre-service training and access to professional in-service learning opportunities to facilitate teachers understanding of the role of educational assistants in Manitoba schools. The study also reveals some of the competencies required for managing the activities 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".