Comparative Analysis of Operational Structures in Single- and Dual-Mode Distance Learning Institutions in Nigeria
Bibliographic record
Abstract
This study examined the similarities and differences in the processes and facilities for distance education at National Open University of Nigeria (NOUN), a single-mode distance learning institution, and Obafemi Awolowo University (OAU), Ile-Ife, a dual-mode distance learning institution. The study adopted a case study research design, with a population of administrators/facilitators and distance learning students at both NOUN and OAU. The sample for the study consisted of 38 key informants (30 administrators/facilitators and 8 students) selected using a purposive sampling technique. All the administrators/facilitators responded to a key informant questionnaire; 8 of the administrators/facilitators and all 8 students were also interviewed. The 16 interviewees were selected based on gender, institution, educational role, and mode of distance learning. The collected data were analysed using tabular juxtaposition and phenomenological analysis techniques. Results showed that similarities in the operational structures at NOUN and OAU included the use of blended learning approaches. Differences in operations included compulsory tutorial attendance at OAU and the deployment of part-time and quasi part-time facilitators at NOUN and OAU, respectively. The study recommended an increase in the use of information and communications technology (ICT).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".