The voices of high school students with learning disabilities: Focusing on challenges, barriers, and supports
Bibliographic record
Abstract
High school students with learning disabilities face more difficulties completing their\neducation successfully and acquiring competent life skills compared to others without\nlearning disabilities. In Canada, few studies have collected and analyzed the experiences of\nhigh school students with learning disabilities and none focused on those attending Prince\nEdward Island (PEI) high schools. This study, as the first research conducted on this topic in\nPEI schools, provides important and necessary data towards bridging that gap. The purpose\nof this study was to explore the experiences of a sample of PEI high school students\ndiagnosed with learning disabilities, focusing on how their challenges, barriers, and support\naffect their academic and social lives. Phenomenological design guided by Vygotsky’s\nsocio-cultural theory of human learning was adopted for this study. Students with learning\ndisabilities in this study encountered many academic challenges and barriers, such as note\ntaking, comprehension ability, difficulties applying for post-secondary education, and\nenrollment in French Immersion classes. More importantly, the lack of understanding about\nlearning disabilities as expressed by the students themselves, as well as teachers, peers, and\nothers became apparent as the biggest academic and social barrier. Advice from these\nstudents to future students, teachers and staffs were also presented and discussed. This study\nprovides valuable information for parents, educators, policy makers, and researchers on the\nsupports identified as important for students with learning disabilities to be successful\nacademically and socially in high school.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".