Ukrainian Teachers’ Capacity to Teach Online Under Quarantine and Martial Law
Bibliographic record
Abstract
The article analyzes the capacity of Ukrainian pedagogical university faculty and students to teach remotely under unstable conditions like quarantine and martial law. Issues associated with their self-directed preparation to teach online under these conditions are also discussed. The study involved 594 students at Vinnytsia State Pedagogical University (420 bachelor program students and 174 master program students), 387 faculty members (206 social sciences and liberal arts teachers, 181 natural sciences teachers), and forty-five experts (twenty-five university leaders and twenty regional stakeholders). To determine the level of skills and abilities of the pedagogical university students, the authors monitored their educational progress in fundamental, professional, and didactical disciplines beginning in June 2020 when the first wave of the COVID-19 pandemic started, until June 2023 when there was a partial adaptation of teachers and students to online teaching in emergency situations. Included in this period was the point at which Russia’s invasion of Ukraine peaked in intensity, June 2022. The authors propose organizational and methodological activities to help improve the skills that pedagogical university teachers and students need for online teaching under quarantine and martial law. The effectiveness of the applied experimental methods was determined by analytical reports of all faculties regarding the quality of the acquired knowledge. Statistical analysis was used to determine the results of expert evaluation. Keywords: COVID-19 pandemic, distance learning, martial law, online learning, quarantine, self-directed 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".