Сhallenges for scientific and pedagogical staff of universities after pandemia 2019
Bibliographic record
Abstract
Nowadays situation at the universities in Ukraine and all other countries round the world are faceted with different challenges almost every new quarter of the year. The very vivid example of it was the pandemic period, which determined transforming of forms of education and different approaches to education. Moreover, the Pandemia provoke the huge wave of challenges for scientific and pedagogical staff of universities. A great number of changes took place at the system of educational process organization: pandemic has exacerbated the need for digital, technology-enabled education experiences, new types of online-classes appared and new ways to scale them etc. But after pandemic period and all these transmitions in universities their appared the need for analyses of challenges which we are going to face with, when we come back to ordinary style of teaching? what problems are we going to solve? To answer these questions, we created a Questionnaire for teaching staff and students of different Universities in Ukraine in different regions. We asked them to give their feedbacks, opinions and feelings of the quarantine restrictions of COVID-19, what difficulties they had durining the next few months. The investigation helped to distinguish challenges to scientific and pedagogical staff of higher educational institutions, the nature of which is the peculiarities of the professional activities of teachers and challenges to the scientific and pedagogical staff of higher educational institutions, the nature of which are the features of the educational activities of students.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".