Hybrid Education in the Context of the Covid-19 Pandemic: Peculiarities of Training Humanitarian Specialists
Bibliographic record
Abstract
The global Covid-19 pandemic has changed the established approaches and methods of the educational space. The education system was forced to obey the requirements and regulations implemented by the authorities to prevent spreading infectious diseases. Therefore, the evaluation of the training of humanitarian specialists requires thorough study and analysis. The study aims to consider the components of hybrid education within humanitarian education; to establish students' assessment of hybrid education. The research methodology is based on an integrated approach—the method of pedagogical experiment, statistical methods, and descriptive methods allowed to form an empirical basis. The hypothesis of the study lies in the fact that adaptation to hybrid learning involves the use of digital technologies. They include software, educational platforms, social networks, and tools for non-formal humanitarian education. However, education still requires full-time education and practical experience, which is challenging to obtain virtually. The result of the study determines the effectiveness of hybrid forms of learning using the capabilities of digital technologies for the training of a specialist in the humanities. The study involves: conducting experiments to solve the problem of training humanitarian specialists in the era of the pandemic and researching the right balance between studying at university and home. The primary purpose of such training is to maintain readiness for professional activities, reduce stress among students and teachers, and avoid professional combustion, which has become a fundamental problem of training during a pandemic. The study results made it possible to note that the majority of surveyed students have a positive attitude to the new conditions and methods of organizing the educational process. At the same time, students recognized the advantage of non-formal education. The article proved that the main problem of the implemented education systems for students was the lack of possibility of personal communication "student-student" and "student-teacher". A comparative description of forms of education (classical and hybrid) is provided. The main problem that is not solved by the introduction of hybrid education is the ineffective use of academic support, which is basic for humanitarian specialties. Based on the survey, a decreasing-increasing trend in the attendance of classes according to the mixed form of education was revealed, and the intensity of attendance increases before the final control of knowledge.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".