Academic and Social Challenges Facing Students with Developmental and Learning Disabilities in Higher Institutions: Implications to African colleges and universities
Bibliographic record
Abstract
African societies have much to learn from the exemplary programs and projects on disabilities, adult literacy and special education provisions in developed societies, like the United States, where effective legislations, curriculum and support services are provided at all levels for individuals with disabilities. This paper discusses the academic and social challenges facing students with developmental and learning disabilities in higher institutions; including available services in institutions of higher learning for such individuals-something that is yet to be introduced or effectively conducted in most African nations unlike in the United States. This paper noted the challenges which developmental and learning disabilities pose to students in general; as well as recognize the potentials, talents, and individual abilities of such students in contemporary institutions of higher learning which could be applicable to African universities and colleges. In this regards, recommendations on understanding student developmental and learning disabilities; application of universal design for learning (UDL), and the institutional roles needed to ensure that such students cope in class and achieve success on campus, were provided.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".