Integrating Mobile Learning into Nomadic Education Programme in Nigeria: Issues and perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it