E-learning Challenges in the Era of Covid-19: The Georgian Case
Bibliographic record
Abstract
Digital literacy became an essential skill for learning, living and working. Therefore, the Internet remains a main part of modern life, constantly evolving and facilitating people’s lives. The global Covid-19 pandemic has made the issue of effective use of information and communication tools and proper possession of digital skills and its importance even more urgent. Higher education around the world has largely shifted to a distance/online-learning format. Covid-19 pandemic has affected many countries on a large scale, and Georgia's higher education system was no exception. Due to the wide-scale spread of the virus and in order to reduce the disastrous consequences, the state decided to continue education throughout Georgia remotely through online learning platforms, which posed a number of challenges for representatives of higher education institutions, as well as academic staff and students. The transition to online teaching has also created problems in the process of fully implementing student support activities. As the university mandates the creation of a student-friendly environment, to offer relevant services, to inform and support students with low social status and students with disabilities, it became a great challenge for Georgian universities to provide socially disadvantaged students with the resources needed for E-learning. The study is about Georgian case, how effective Georgian government managed learning prosses in the period of Covid-19 pandemic; What kind of challenges students had in this period and what are their vision regarding the online learning and its perspective for future development of teaching/studying process.
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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 | 0.001 | 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 | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".