TEACHING AND LEARNING DURING COVID19 CRISIS : CHALLENGES, OPPORTUNITIES AND THE WAY FORWARD
Bibliographic record
Abstract
The COVID-19 pandemic as a big crisis in quarter one of 21century has created unprecedented challenges to the all sectors of life. The COVID-19 pandemic has exposed gaps in the digital infrastructure and network performance as internet usage rises. In the education sector, the \nclosure of many educational institutions had resulted in the disruption of face-to-face learning which significantly impacted the majority of students. While it is obvious that online learning provides many advantages like easy access, unlimited access to resources, flexibility in learning and easy collaboration, lack of infrastructure had caused a serious setback to the core principles of traditional pedagogy such as learner interaction, access to study materials, attentional skills, regularity, time management, and assessment. Although online learning faces many issues and challenges among stakeholders, especially students and educators, it also serves as a great alternative for the spread of knowledge and represents a significant benefit \nwhen distance is no longer a barrier. COVID-19 pandemic in particular has brought online learning to a new level where its adoption within the education world was not an optional but mandatory for the survival of our new generation. Therefore, it is crucial in the post-pandemic era to integrate the traditional pedagogy approach and blend it with online learning in order to establish a resilient education system capable of fulfilling the demand of all stakeholders.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".