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Record W2471420471 · doi:10.19173/irrodl.v17i4.2409

Increasing Access to Higher Education Through Open and Distance Learning: Empirical Findings From Mzuzu University, Malawi

2016· article· en· W2471420471 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationRemunerationHigher educationPaymentFlexibility (engineering)Medical educationOpen universityPedagogySociologyPsychologyBusinessPolitical scienceManagementComputer scienceMedicineEconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

<p>Slowly but surely, open and distance learning (ODL) programmes are being regarded as one of the most practical ways that universities across the world are increasingly adopting in order to increase access to university education. Likewise, Mzuzu University (MZUNI) set up the Centre for Open and Distance Learning (CODL) to oversee the running of these programmes in 2011. In this study, we adopted the Transactional Distance Theory (Moore, 1997) to investigate the modes of instructional systems, benefits or opportunities, and the challenges associated with the delivery of ODL programmes at MZUNI. By self-administering a questionnaire to 350 ODL students and 9 Heads of Department in the Faculty of Education whose programmes are offered through ODL, we found that instructions are mostly delivered to students through print-based instructional materials. The major benefits noted include increased access to quality higher education, affordable tuition fees, and flexibility in payment of fees. However, we established some challenges which need to be addressed by the University which include, delayed feedback of assignments and release of end of semester examination results, absence of information for courses of study, poor communication between the Centre and departments, and poor remuneration for lecturers.</p>

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.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0040.005
Research integrity0.0000.001
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 head0.160
GPT teacher head0.479
Teacher spread0.319 · 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