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Record W3198537068 · doi:10.12775/jehs.2021.11.08.043

Education in Poland during Covid-19 pandemic

2021· article· en· W3198537068 on OpenAlexaboutno aff
Adrianna Gorecka, Dagmara Gorecka, Katarzyna Urbańska, Bartłomiej Zaremba, Paweł Oszczędłowski

Bibliographic record

VenueJournal of Education Health and Sport · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)PandemicGovernment (linguistics)Scale (ratio)PsychologyMedical educationMathematics educationMedicineGeography

Abstract

fetched live from OpenAlex

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%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.498
Teacher spread0.419 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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