The Changes in Teacher-Student Interaction and Communication in Higher Education Institutions Due to the Covid-19 Pandemic
Bibliographic record
Abstract
The objective of the research is to evaluate, through empirical methods, the functional relationship between educators and pupils in higher education institutions during the pandemic. The study's relevance determines the problem of two-way communication between teachers and students, negatively affecting education quality. The methodology employed in this study draws upon the valuable insights and practices of leading higher education institutions that have successfully navigated the challenges of the pandemic. Specifically, the University of Oxford serves as the focus of our investigation. To establish a reliable and effective system of communication between teachers and students, we adopt the Business Continuity Planning (BCP) method. According to the findings, distance learning models that effectively address crises and problem situations are the key focus. According to this approach, a scenario of the higher education institution's educational process is developed. It was found that the Business Continuity Planning (BCP) system of Oxford University shows the problematic situation with the organization of communications. The crux of distance education advancement centers on designing training programs using models formulated by the administration of top-tier universities. In this condition, using hybrid, virtual, personal course templates ensure the unification of teaching and learning processes. Moreover, their combination can significantly improve the interaction between students and teachers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".