Increasing Access to Higher Education Through Open and Distance Learning: Empirical Findings From Mzuzu University, Malawi
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".