Education in Poland during Covid-19 pandemic
Bibliographic record
Abstract
Introduction and purpose. Due to the outbreak of Covid-19 pandemic polish government in March 2020 decided to directs students to remote learning. This condition last -with minor exceptions- one and half year.Material and method. The aim of the study was an evaluation of public experience and attitude towards online learning.Results. All the respondents between March and May 2020 learned via online devices. The average note for e-learning was 2,99 in a 5-grade scale, while a score for stationary learning was 3,84. Students motivation, engagement and stress level decreased during remote-learning. 43% students claimed, that their marks improved during that time. The main disadvantages of online school were too much time spent in front of the screen and monotony of the lessons. Among the advantages was for example time for additional hobbies. Realisation of practical activities was more difficult or impossible for 74,9% of the respondents. Almost one quarter of the people did not have adequate home conditions to study online. Practical activities were often difficult or impossible to realise.Conclusions. Online learning was a necessity during the pandemic, however this type or gaining knowledge has both advantages and disadvantages. It influenced not only scientific issues, but also students’ motivation and sociopsychological aspects. To conclude, twice as many students prefer stationary than online learning – respectively 39,7% vs 21,1%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".