Views of Physical Education Teachers on Distance Education During the Covid-19 Pandemic Period: A Qualitative Study
Bibliographic record
Abstract
This study was carried out in order to examine the opinions of physical education teachers about the remote (online) education of schools due to the Covid-19 epidemic in our country and in the world in recent years, and the remote teaching of physical education lessons in this process. Within the scope of this study, it was tried to determine the opinions of physical education teachers about the distance education process, the problems they encountered, the efficiency and adequacy of the lessons, the advantages and disadvantages of the process. The study was designed using a qualitative research method, and the study group was determined using the stratified sampling method. Study data were collected by the researcher by interview method. The analysis of the data was carried out using the content analysis method. According to the findings of the research, in the evaluations of the participants; It has been observed that the majority of them define distance education as virtual education, the majority of them express that it is important and necessary to teach physical education lessons in distance education, the biggest advantage in the process of teaching the lessons is the lack of time and space limit, and the disadvantage is that distance education cannot provide the same equality of opportunity for every student. At the end of the study, suggestions were given to make physical education lessons more efficient in the distance education 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".