Playing on the Same Team: Collaboration between Teachers and Educational Assistants for Inclusive Physical Education
Bibliographic record
Abstract
Together, teachers and educational assistants have an opportunity to ensure physical education (PE) is inclusive, accessible, and meaningful to all students. This article discusses collaboration for teaching inclusive PE (IPE), where students with disabilities participate within the context of the general PE environment with their peers. With the diversity we see in practitioners' backgrounds, school contexts, and student populations, it can be difficult for them to “get on the same page” and work together without having a strategic plan. This article highlights a process for collaboration and suggests tasks for teachers and educational assistants to implement to achieve a collaborative approach to teaching and planning for IPE. It is essential for teachers and educational assistants to work through this plan and create an inclusive environment together to ensure appropriate and meaningful opportunities of IPE are being facilitated for students with disabilities. Specifically, this article discusses ways for teachers to better connect and work collaboratively with educational assistants in the IPE environment. Teachers and educational assistants can ensure the best IPE experiences by (1) starting the conversation, (2) unpacking experiences, (3) setting expectations, (4) discussing students, (5) planning for success, (6) pursuing professional development, and (7) engaging in collaborative reflection. Make time to communicate, build a relationship, support each other, and plan for successful IPE.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".