Ukrainian Teachers’ Capacity to Teach Online Under Quarantine and Martial Law
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it