Teachers’ Engagement with Individual Education Plans for Secondary Students with Autism
Bibliographic record
Abstract
The Individual Education Plan (IEP) is widely considered the main mechanism for guiding individualized instruction and supports for students with disabilities—a right under the United Nations Convention on the Rights of Persons with Disabilities (2004). In Ontario, students with autism are among those believed to benefit from the use of an IEP as it facilitates their inclusion in general education classrooms. This includes secondary-aged students with autism, who have historically received less research attention compared to their younger counterparts. Despite the importance of teachers’ role in developing, revising, and implementing IEPs, few studies have investigated how teachers use the IEP in their instructional planning. Therefore, the purpose of this study was to examine how general education teachers engage with IEPs for secondary students with autism in their planning from a variety of angles. Using a multimethod design, this study addressed four research questions: (1) What are general education teachers’ perceptions of the IEP for secondary students with autism in inclusive classrooms? (2) How do general education teachers perceive their preparation to implement an IEP for secondary students with autism in inclusive classrooms? (3) What relationships, if any, exist between general education teachers’ demographic characteristics (e.g., years of experience), perceptions of the IEP, and preparation to implement it for secondary students with autism in inclusive classrooms? And (4) How do general education teachers use IEPs in their planning to support secondary students with autism in inclusive classrooms? Data were collected in two phases: Phase 1 was quantitative and used a survey, while Phase 2 was qualitative and combined a concurrent think-aloud protocol with semi-structured interviews. Findings revealed which IEP components teachers found most and least relevant to their planning, and their perceived preparation to enact various IEP-related actions and responsibilities. Qualitative analysis led to the construction of ten themes, which were re-organized into a conceptual model for understanding how teachers engage with IEPs in their planning to support students with autism in inclusive classrooms. Findings from this research contribute to the literature on teachers’ engagement with IEPs for students with autism and, more broadly, teacher planning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".