Impacts of the Migration of Cross-Cutting Courses of a Traditional University in Distance Learning
Bibliographic record
Abstract
Public universities in Senegal are facing huge issues related to the student reception and training. Given the budget deficit in Senegal and in African countries in general, our leaders struggle to continuously invest in the human and material resources within the universities. In addition, the courses taught in traditional programs are most often repetitive and sometimes not up-to-date, which does not allow universities to optimize time, space, human resources, and finance. As a result, it become acute to adapt the learning models and teaching methods within the traditional universities. Distance learning seems to be an essential solution. However, existing distance learning offers in these universities are in general geared towards certifying and degree-based distance learning. This does not solve the problems encountered in classical pedagogical training. Starting from these limits, we first propose a new efficient model of distance education at the service of a classical university, then we demonstrate its positive financial, material and pedagogical impacts. The proposed model has been implemented and successfully evaluated at Alioune Diop University of Bambey, a classical university in Senegal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".