E-Learning implementation in higher education in response to the covid-19 pandemic: a review
Bibliographic record
Abstract
Through the use of technology, various sectors have shifted as a result of the covid-19 pandemic, including the education system. The educational paradigm evolves by leveraging information technology as a vehicle for scientific growth, and online learning has become a part of our life. Face-to-face sessions in traditional classrooms are transformed into live face-to-face sessions in virtual classrooms. The purpose of this study was to examine the implementation carried out by universities in selected research subject countries using the correspondence method of data collection. Correspondence was performed with contacts at several universities and the Ministry of Education (MOE) in each of the countries that became the focus of this research, including Singapore, Hong Kong, Australia, and Canada, in addition to Indonesia. It was then developed as a best practice based on the use of digital learning as an emergency response to the new corona virus pandemic. The findings indicate that Digital Learning, which involves and effectively uses technology, is an alternative option that is being implemented in these countries as part of the Emergency Plan in the education sector in connection with covid-19.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
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".