Higher Education Institutions Management in a Pandemic
Bibliographic record
Abstract
The article studies higher education institutions' management systems during the pandemic. The research field was determined by two modern higher educational institutions - Kyiv University named after Taras Shevchenko and Lviv Polytechnic National University. Their activities were evaluated on the basis of developed strategies, development plans and internal quality management documentation. The conducted research revealed a complex of interrelated problems. Technical problems are associated with the involvement and maintenance of relevant software complexes. Educational and methodological problems consist in the improvement and adaptation of methodological complexes and the system of evaluating the results of student learning in the aspect of control. Management problems focused on solving operational control over the educational process and its quality content. The research proved that the management of higher education institutions solves the identified problems independently through the formation of auxiliary departments of academic mobility, internal control and audit, targeted training, and international cooperation. However, it was noted that the problem related to communication - "student-teacher" remains unsolved, despite personal-oriented training in combination with traditional ones based on many years of pedagogical experience. In general, the necessity of applying flexible teaching methods to higher educational institutions to adapt to the long-term pandemic was noted.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".