University Students' Perceptions of Learning Disabilities
Bibliographic record
Abstract
Special education has been widely explored as the prevalence of learning disabilities (LD) has become extremely relevant (Statistics Canada, 2016). Therefore, it is important to gain a deeper understanding of the terminology special education and inclusive education, to promote further awareness of how it can be impactful to students with LDs learning. Special education exists to support individuals with LDs who benefit from specialized learning plans, to help facilitate and enhance their learning (Street, 2004). The essence of inclusive education is to ensure that diversity is supported and celebrated by each unique learner (UNESCO, 2008). An LD can be referred to as a group of heterogeneous disorders which may impact an individual's organization skills, retention, comprehension, or use of verbal and non-verbal information (Learning Disabilities Association of Canada, 2016). This qualitative narrative inquiry approach investigated how students within Southern Ontario with a learning disability diagnosis story and perceive their learning and their access to support at the university level. Semi-structured interviews were implemented to gather meaningful data (van den Hoonaard, 2018). The results revealed the challenges that participants faced due to their LDs, such as stigmas associated with LDs and the obstacles they overcame while receiving accommodations. To conclude, policies and regulations need to be implemented to not only help educate faculty regarding accommodations but also find ways to enhance the services provided to students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".