Effectiveness of Distance Learning in Higher Education Institutions under the Martial Law
Bibliographic record
Abstract
The aim of the research was an empirical study of the effectiveness of distance learning in pedagogical HEIs of Ukraine under the martial law. The research involved surveying the subjects of the educational process of Ukrainian HEIs. The survey respondents were the students of pedagogical educational institutions, teachers studying at the advanced training courses, academic staff. The criteria for assessing the effectiveness of distance learning in times of war were the possibility and quality of feedback, access to educational/didactic and methodological content, the possibility and frequency of monitoring performance, consultations, meeting deadlines, interactivity, technical capabilities of Internet access. The survey evidenced the negative impact of the martial law on online learning, the limited interactivity of classes, the unsystematic consultations and monitoring, and poor-quality communication between the subjects of the online educational process. At the same time, the positive impact of the varied approaches of teachers to presenting educational materials to students on the effectiveness of distance learning was confirmed. As a result, students with different technical capabilities were able to access the educational content provided by the curricula. The results of the study can be used by other HEIs of Ukraine in the context of sharing progressive pedagogical practices of distance learning organization in times of war. Further studies involve expanding the research, generalizing the unique experience of higher education of Ukraine in providing distance learning in crisis conditions, and sharing it with the world scientific communities.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".