Attitudes and Perceptions of Students to Open and Distance Learning in Nigeria
Bibliographic record
Abstract
In the West African Region of Africa, the National Open University of Nigeria (NOUN) is the first full fledged university that operates in an exclusively open and distance learning (ODL) mode of education. NOUN focuses mainly on open and distance teaching and learning system, and delivers its courses materials via print in conjunction with information and communication technology (ICT), when applicable. This 'single mode' of open education is different from the integration of distance learning system into the face- to- face teaching and learning system, which is more typical of conventional Universities in Nigeria and other parts of the world. Thus, NOUN reflects a novel development in the provision of higher education in Nigeria. This study assesses the attitudes and perceptions of distance teaching and learning by students enrolled in the NOUN and of the National Teachers' Institute (NTI) compared to their experiences at conventional universities. One hundred and twenty (n = 120) randomly selected NOUN and NTI students of NOUN were the subjects of the study. The Students' Attitude and Perception Rating of Open and Distance Learning Institutions Inventory (SAPRODLII), developed by the researchers, was administered to the subjects to measure their attitudes and experiences. Results of the study showed that students generally have a positive perception and attitude towards ODL, compared to traditional forms of higher education.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".