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Record W2797214593 · doi:10.1080/01587919.2018.1457944

Opportunities and challenges for campus-based universities in Africa to translate into dual-mode delivery

2018· article· en· W2797214593 on OpenAlexaff
A. S. Kanwar, A. Carr, Kayla Ortlieb, Romeela Mohee

Bibliographic record

VenueDistance Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationDual (grammatical number)Dual modeHigher educationBusinessMode (computer interface)Public relationsKnowledge managementMarketingEconomic growthPolitical scienceSociologyComputer scienceEngineeringPedagogyEconomics

Abstract

fetched live from OpenAlex

With the growing demand for tertiary education, especially in Africa, the transformation of contact universities to dual-mode institutions is critical. However, conventional universities have had limited success establishing the dual-mode delivery stream. This paper assesses barriers to adoption and implementation of open and distance learning (ODL) in conventional higher education institutions in Cameroon, Kenya and Rwanda by applying a framework on innovation adoption to case studies of ODL in higher education in these countries. This qualitative meta-study shows that the transition to dual-mode is not systematic and that there are various barriers, ranging from national policy and funding; infrastructure, organisational structure and capacity; complexity and cost of ODL; as well as student and staff skills and perceptions, which have impeded adoption. Based on the findings, this paper makes recommendations for implementing ODL in existing conventional universities. Cases of successful transition to dual-mode are 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0060.006
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.058
GPT teacher head0.348
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations23
Published2018
Admission routes1
Has abstractyes

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