Exploring Open and Distance Learning Reform at the National University of Lesotho: A Managerial Perspective
Bibliographic record
Abstract
This study investigated how open and distance learning (ODL) reform was managed within the Institute of Extramural Studies (IEMS), at the National University of Lesotho (NUL). The reform was introduced during the 2017/18 academic year with first-year programmes in three departments: (a) Adult Education; (b) Business and Management Development; and (c) Research, Evaluation, and Media. The study employed interviews and analysis of institutional documents as data collection techniques. Interviews were held with eight programme coordinators, four department heads, and the director of IEMS. Purposive sampling was used to select the participants to the study given their strategic position in the management and implementation of the reform. Qualitative content analysis was used to interpret the data. The findings suggested that the ODL programmes were introduced without a policy and comprehensive plan. The implementation faced several challenges such as finance, as well as infrastructural and human resources. Evidence from the literature has suggested that compared to face-to-face strategy, ODL as an educational strategy requires special resources, support, and funding. Thus, curricular materials should be adapted for the ODL context, taking into account students’ characteristics. The study found that these pertinent requirements were not considered, and implementation continued as if the reform still constituted face-to-face or campus-based instruction.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".