Design and Implementation of a Blended Learning System for Higher Education in the Democratic Republic of Congo as a Response to Covid-19 Pandemic
Bibliographic record
Abstract
Until now, the higher education system in the Democratic Republic of Congo has relied on the traditional face-to-face teaching method, which consists in the real physical presence of students and teachers during classes and lectures. Thus, the United Nations Educational, Scientific and Cultural Organization (UNESCO) is currently advocating e-learning as the only alternative for education in the COVID-19 era. It goes without saying that this requires specific frameworks and appropriate resources, including access to a good quality internet connection. Several countries around the world have implemented this recommendation since the first quarter of 2020 to protect their populations from the significant risks of Covid-19 contamination. In educational environment however, given the disadvantageous realities of the Democratic Republic of Congo, including the cost and quality of internet, the low rate of electrification, and the lack of experience of the educational stakeholders involved, the migration to e- learning remains a challenge. Thus, we propose in this paper a blended learning model that can smoothly introduce e-learning through a platform specially designed to integrate with the traditional way of delivering courses in Congolese higher education by combining the old method and e-learning based on ICT.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".