Rethinking the Role of Educational Assistants to Better Support Inclusive Education in Ontario Secondary Schools
Bibliographic record
Abstract
The purpose of this project was to review how educational assistants support students with significant support needs (SSN), such as, developmental disabilities, autism, and multiple disabilities in inclusive secondary school classrooms, and to create learning guides for administrators to use when providing professional development to educational assistants. The learning guides focus on collaboration and research-based practices that close student learning gaps, improve student achievement, and foster inclusive classrooms. Research has shown the role of the school principal to be pivotal for fostering new meaning, promoting inclusive school cultures and instructional programs as well as building relationships between schools and communities (Riehl, 2000). Educational assistants support the emotional, social, and academic success of all students, but particularly for those with special education needs. Administration and educational assistants both play important roles in student success and in creating inclusive classrooms; therefore, it is essential that they both learn alongside each other with a shared goal of student success. This project is in response to my work within the Upper Canada District School Board as a classroom teacher, special education teacher, secondary learning resource coach, student engagement teacher, vice principal, and principal. Throughout my career I have been dedicated to creating a positive, caring, and inclusive learning environment where all students are encouraged, supported, and given the opportunity to reach their full potential. I have had the privilege of working with many highly effective educational assistants who shared my vision, and working together, along with the help of other dedicated staff, parents, and families, many of my students with significant support needs went on to earn their Ontario Secondary School Diploma. When I moved to administration nine years ago, I made listening to and honouring educational assistants’ voices and their professional development a priority. I have made it a practice to have weekly team meetings with educational assistants with professional development as the focus. I discovered that this practice is not widely adopted throughout our school district, nor are there resources to support this practice. This resource guide was designed, edited, and revised based on informal feedback from many stakeholders, including administrators, classroom teachers, special education teachers, student success teachers, guidance teachers, and educational assistants. This project may be valuable to administrators who are interested in providing professional development to their school’s educational assistants and are looking for resources to support them. These learning guides are based on five topics, that include current research and how to put learning into action compiled into easily accessible documents with links to resources.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".