Psychological Aspects of Effective Communication between Teachers and Students in Online Learning
Bibliographic record
Abstract
The study focuses on the psychological aspects of effective communication between teachers and students in online learning. The relevance lies in the need to understand the challenges that arise in the process of virtual communication and identify ways to overcome them. The purpose of the study is to analyse the main psychological aspects of effective communication between teachers and students in the context of using an online learning environment. The research methodology involves the use of statistical methods, content analysis, theoretical and comparative analysis. The study used surveys, small group interviews, and observation. A systematic selection procedure was used to select the respondents, aimed at forming a representative and informative sample. The participants of this study include 121 teachers and 70 students from higher education institutions in Ukraine. The results demonstrate the main learning platforms and messengers used for communication. The level of efficiency of their use is also determined. The key factors affecting the psychological comfort of participants were also identified and recommendations for improving communication effectiveness were developed. The results emphasise the importance of psychological readiness for digital change, the use of active engagement strategies and a digital diet to ensure psychological resilience. The study highlights the problems of lack of non-verbal cues and virtual communication fatigue, recommending the use of art therapy and isotherapy to alleviate psychological difficulties. The general conclusions emphasise the importance of conscious use of digital technologies to ensure successful online communication and psychological well-being of all participants in the learning process.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".