Potentials of E-learning as a Study Tool in Business Education in Nigerian Schools
Bibliographic record
Abstract
With advancement in information technology in the 21st century, e-learning has become an invaluable technology for teaching, learning and research in education. E-learning involves the use of technology to enhance learning including digital collaboration, satellite broadcasting, CD-ROMS amongst others. E-learning has so many advantages over the traditional method of teaching and learning. Besides, so many e-learning tools are available for teaching and learning. However, despite these numerous advantages, a careful look at Nigerian educational system shows that the use of e-learning is still rather slow or imaginary. One of the reasons for this may be due to ignorance, negative perceptions amongst students and teachers and; non-availability of e-learning facilities in the system. This paper critically examined the potentials and immense benefits of e-learning in education generally and business education in particular. The paper also looked into the Nigeria policy on information and communication technology (ICT) as it relates to education. It further presented various methods that may be employed in delivery business education lessons via e-learning. Recommendations were made that there should be awareness creation about the potentials and prospects of e-learning in Nigerian schools amongst others.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".