Virtual Education in English Literature Courses during Pandemic COVID-19: Merits and Demerits, and Needs
Bibliographic record
Abstract
Distance education and e-learning became widespread and necessary with the miracle of the internet and its increasing influence among the individuals much within the days of a pandemic outbreak of COVID-19. Many universities, institutions, and learners encourage using e-learning and begin growing in this field. This paper examines the merits and demerits of distance and online education teaching for English literature educators and students. Technology and increasing demand for education, traditional strategies do not meet the growing requirements of academic communities and virtual education and e-learning with all their benefits and drawbacks attempt to meet these needs. This paper investigates e-teaching and e-learning infrastructure, needs, benefits, and limitations, in addition to opportunities and challenges of online education within the days of the coronavirus occurrence. The research method of the current study was conducted through a library study along with empirical study and descriptive analysis. New challenges of English literature educators and students in pandemic COVID-19 were identified and new approaches to remove the constraints are suggested. The results confirm online education is a constant educational need not limited to pandemic period and have to be compelled to develop productivity and creativity in learning with the appearance of recent technologies such as computers, the web, and social networks. These skills development can assist educators to find solutions for these difficulties in various areas of educational, cultural, and social issues. Blended learning can contribute to post-pandemic English literature classes and sustainable higher education.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".