Exclusion Reloaded: The Chronicles of Covid-19 on Students with Disabilities in a South African Open Distance Learning Context
Bibliographic record
Abstract
Students with disabilities have been going through different forms of discrimination and exclusion. These include inaccessible learning materials and learning platforms, negative attitudes from lecturers, fellow students and more. This paper comes from a qualitative study that sought to explore how Covid-19 deepened these educational inequalities at an Open Distance Learning institution in South Africa. The results of the study reveal that institutions of higher learning had to quickly adjust their teaching and assessment to online mode. This led to heightened exclusion of students with disabilities as their examinations had to be postponed to second semester due to lack of preparations for special examinations. Students also reported experiencing extra pressure as they had to write double the examinations at the end of the year. Some students reported lack of access to assistive technologies which they normally borrow from the library, this was because the Post Office was not operating during the National Lockdown Level 5. The novel nature of Covid-19 is such that the real barriers it caused on people and students with disabilities in particular and it will keep revealing itself gradually. This paper ends by making recommendations on how an ODL institution could accommodate the needs of students with disabilities to enhance their learning experiences during pandemics or natural disasters.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".