Strategy for a sustained quality delivery mode of ODL programmes for massive enrollments and e-learning: The Case for Zimbabwe Open University
Bibliographic record
Abstract
The market dynamics in distance education has precipitated phenomenal growth opportunities in enrollments and e-learning. The purpose of the paper was to develop a strategy for sustained quality delivery mode of distance education progammes that precipitate massive enrollments and e-learning in an open and distance learning (ODL) institution using Zimbabwe Open University (ZOU) as a case study. There is an increase in public accountability for higher education which compels institutions to demonstrate quality within the programmes and processes, including those provided online. The strategy for massive enrollments and e-learning is developed and this includes a mobile strategy and mobile web framework. How the landscape of quality assurance has been changed by the emergence of MOOCs is discussed. The methodology used is qualitative and focus groups were used as research designs in the case study of Zimbabwe Open University (ZOU). Triangulation and peer review was used to test the validity of the data. Strategic directions were developed to inform the new key result areas, goals, objectives, strategies and priorities for the university for the period 2015-2020.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".