Integrating Mobile Learning into Nomadic Education Programme in Nigeria: Issues and perspectives
Bibliographic record
Abstract
The establishment of the National Commission for Nomadic Education (NCNE) in Nigeria in 1989 created a wider opportunity for the estimated population of 9.3 million nomads in Nigeria to acquire literacy skills. The coming of the Commission arose due to the massive illiteracy figure of the pastoral nomads and migrant fishermen put at 0.02% and 2.0% (Federal Ministry of Education, 2003; UNESCO, 1998) respectively. To improve the literacy rate of the nomads, the NCNE employed various approaches such as on-site schools, the shift system, schools with alternative intake and Islamiyya schools to provide literacy education to the nomads. However, a critical appraisal of these approaches by the Commission shows that very few of the schools were viable. This portrays the fact that these approaches have not actually helped to improve the literacy rate among nomads in Nigeria. There is, therefore, the need for alternative approach to be adopted. With the revolutionary trend of ICT in Nigeria, there is the need to bring in mobile learning through the use of mobile technologies ( such as handset, simple text message etc. which is predominantly in many parts of Nigeria) to enhance the literacy learning process in the Nomadic Education Programme of Nigeria. This paper, therefore, explores the need and advantages of integrating mobile learning into Nomadic Education programme in Nigeria so as to ensure a successful implementation and achievement of the goals of the programme.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".