The Disadvantages of Distance Education Implementation, During the Covid-19 Pandemic: Greek Teachers’ Opinions
Bibliographic record
Abstract
In Greece, as in several countries all over the world, schools were closed due to the COVID-19 pandemic, in the 2019-2020 school year. The Greek Ministry of Education tried to operate online platforms so that students could have access to education. The teachers managed to respond to this challenge using mainly their own resources while most of them had not received any relevant training. A nationwide survey was designed investigating teachers' views on distance learning disadvantages. 515 teachers working in Greek primary education sector - both at kindergartens and primary schools- participated in the research. Their answers show five main disadvantages from the distance learning implementation at school education: (a) deficit in the interaction and communication among students and among the teacher and his/her students, (b) the teaching methods used were mainly teacher-centered despite the prescripts of the National Curriculum, (c) inequalities that arose for specific social groups of students, (d) schools’ deficits in infrastructure and insubstantial teachers’ in-service training regarding I.C.T. use, and (e) teachers’ concern about the protection and the maintenance of students’ personal data. Despite these disadvantages mentioned, teachers do not overlook the fact that the distance education implementation during the Covid-19 pandemic was principally an attempt to psychologically empower students 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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".