Understanding Transitions for Disabled Students from Secondary to Post-Secondary Education Using Ecological Systems Theory: A Mixed Methods Approach
Bibliographic record
Abstract
This thesis explored the transition planning experiences from secondary to post-secondary education of disabled students. Transition planning is essential for disabled students to access accommodations in higher education (Newman et al., 2016). Recent changes have been proposed by the Government of Ontario Education Technical Sub-committee to help improve transitions for disabled students by working to remove barriers to transition planning (Government of Ontario, 2021). To date, transition planning processes typically follow an individual model (Small et al., 2013). However, this model has yielded limited results in successfully removing barriers. Further research suggests that an ecological systems approach may be more promising for supporting disabled students in their transition planning process (Small et al., 2013). Based on the literature on transition planning and ecological systems theory, this thesis followed a mixed methods explanatory design using initial surveys and follow-up interviews to contextualize students' experiences. Using the Government of Ontario's recommendations, an initial survey was developed and distributed to disabled first and second-year students at Brock University (n=16). Follow-up interviews were also conducted with participants to contextualize their experiences and discuss recommendations (n=4). A descriptive analysis of quantitative survey results as well as a thematic analysis of qualitative survey responses and follow-up interview responses provides understating on barriers students face and how the government of Ontario’s recommendations may reduce these barriers. The findings of this research demonstrate the need for interdependent supports in transition planning for disabled students when transitioning from secondary to post-secondary education.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".