Design and Implementation of a Blended Learning System for Higher Education in the Democratic Republic of Congo as a Response to Covid-19 Pandemic
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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
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.
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 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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