E-Learning After the Pandemic from the Perspective of Digital Skills Teachers
Bibliographic record
Abstract
The coronavirus disease (COVID-19) crisis and the coincidental shift in public education to distance education was a starting point from which all educational institutions, especially those in developing countries, must benefit in terms of e-learning development. The need to effectively explore the use of e-learning after returning to school (face-to-face) has been increasing. Thus, we investigated the use of e-learning after the COVID-19 pandemic by examining the case of Saudi Arabia. The findings of this case study can be applicable and beneficial to other countries. By applying a qualitative research design, 15 Saudi digital skills teachers were invited to voluntarily participate in this study, and an open-question survey was conducted electronically using the snowball technique and a focus group. The teachers’ reflections on this experience have implications for their professional development and are an opening for using educational technologies and utilizing students’ digital skills in education. The study shows that the teachers continued using e-learning in their daily teaching after the pandemic. The three most important theme determinants in this study were the teachers’ use of e-learning for blended learning, communication, and professional development, in this order. Moreover, the teachers believed that they and their students had acquired and developed many skills as a result of using online learning. Finally, the study implications and suggestions are highlighted in this article.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".