Demand of eLearning for Professional Education and Learners' Preparation: Bangladesh Perspective
Bibliographic record
Abstract
Abstract: This paper demonstrates the demand of elearning that needs to cope with and possible technology introduction through Bangladesh Open University as a lone government agency of distance education in Bangladesh for its learners who are doing Commonwealth Executive MBA (CEMBA)/Commonwealth Executive MPA (CEMPA) offered in cooperation with Commonwealth of Learning (COL) and MBA program of its own. Web based learning or eLearning is a new genre for providing services in the arena of education. Though in most developed countries such USA, Canada, UK, web based learning becomes popular to learners of their country, that is a very new to a third country like Bangladesh. Students of Bangladesh are also partaking courses through online programmes offered by those developed countries. These courses are expensive too, so it is necessary to establish our own elearning system in Bangladesh. As the demand of training and education is huge in Bangladesh, it is necessary to adapt the mobile and flexible system. Keeping the eye on view, a survey was conducted among the learners of those professional academic programmes. The informants were selected both from the capital city of Bangladesh as well as divisional cities and district town. The survey demonstrates that this technology can be appropriately used to meet the demands of the professional programmes. However, this survey also shows that the professional programmes require careful planning and sound technological infrastructures. The study will clarify how far prospective learners have the appreciation of the technology and how the learners have comparatively more preparation to participate in an eLearning programme rather than the organizations. The survey also focuses the primary and secondary education through collected data from websites of Bangladeshi government and private organization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".