The Benefits of Distance Education, During the Covid-19 Pandemic: Greek Teachers’ Opinions
Bibliographic record
Abstract
In Greece, the COVID-19 pandemic resulted in the schools' closure during the 2019-2020 school year. The Ministry of Education tried to develop online platforms so that the students could have access to education. The use of distance learning came to the fore. The teachers used the online teaching tools available as a response to this challenging situation. Nevertheless, most of them had not received any relevant training and had at their disposal minimal resources, mainly of their own and not public ones. A nationwide survey was designed investigating teachers' views on distance learning benefits. 515 teachers working in Greek primary education sector - both at kindergartens and primary schools- participated in the research. Their answers show three main benefits from the distance learning implementation at school education: (a), it enabled some students to access school education. Those students could not otherwise attend school education (b) it contributed to the communication among students as well as between students and teachers, and (c) it increased the ICT use by teachers. Despite these benefits mentioned, teachers stated that distance learning is not a substitute for ordinary classes and face-to-face learning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".