Improving Communication Processes between Teachers and Students in Higher Education Institutions During the Pandemic
Bibliographic record
Abstract
The article is devoted to determining the features of the scientific and communicative communication between students and teachers in the context of distance learning during the Covid-19 pandemic. The article aims at determining the benefits of using online educational platforms for scientific and communicative interaction between students and teachers. The main scientific research method is a survey conducted within the Kyiv National University of Culture and Arts. During the survey, which was conducted during 2020-2021, the effectiveness of the educational platform "Mentimeter" was investigated. Study results. The online platform "Mentimeter" motivates students to acquire new knowledge and carry out communication processes within this platform. The platform provides students with the educational materials necessary to obtain professional knowledge within the specialty. Teachers use Mentimeter to organize the educational process and ensure two-way communication between students and teachers. During the online survey, information was obtained on the communication effectiveness and the possibility of their application during distance lectures. Students noted that such a platform establishes effective communication between teachers and students under quarantine restrictions. This practice can significantly improve learning efficiency and lead to the potential for improving teaching methodology in the future.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".