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Record W1954045813 · doi:10.21083/ajote.v1i1.1585

Academic and Social Challenges Facing Students with Developmental and Learning Disabilities in Higher Institutions: Implications to African colleges and universities

2011· article· en· W1954045813 on OpenAlexvenueno aff
Williams Emeka Obiozor, V. C. Onu, Ugwoegbu Ifeanyi

Bibliographic record

VenueAfrican Journal of Teacher Education · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumHigher educationLearning disabilityPedagogyPsychologyMathematics educationPolitical sciencePublic relationsMedical educationDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.123
GPT teacher head0.398
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2011
Admission routes1
Has abstractyes

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