Roles of teacher assistants in elementary school special education settings / by William Anthony Hanlon.
Bibliographic record
Abstract
While the occupation of teacher assistant has become rooted in the \nOntario School System no research exists concerning the roles these teacher \nassistants perform in Ontario. There is also a paucity of research which \nexamines teacher assistant roles from the point of view of teacher assistants \nthemselves. \nThis research investigated the roles of teacher assistants in two \nelementary school special education classrooms in Ontario. Qualitative \nmethods were used to gain an insider?s view of what teacher assistants do in \nthe classroom. These methods included interviews with teacher assistants, \nteachers, principals and a superintendent as well as observations of teacher \nassistants and teachers working together in the classroom and document \nanalysis of the Site School Board Teacher Assistant job description and \nrelevant Ontario Teachers? Federation documents. \nBased on the data a conceptual model of the roles of the teacher \nassistant was developed. This model consisted of two categories of roles. \nCategory I was called Working with Children and consisted of five roles: \ninstructor, behaviour manager, observer, caregiver and team player. Category II \nwas called Non-Contact with Children and consisted of two roles: technician \nand clerk. \nImplications of this research include the development of curriculum for \npreservice and inservice programs for teacher assistants as well as augmenting \ncurriculum in teacher preservice special education training in order to address \nhow to fully utilize the teacher assistants in the classroom. Further research is \nrecommended to investigate the caregiver role in greater depth and to examine \nthe relationship between the roles of the teacher and the teacher assistant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".