Teachers’ Experiences and Views Regarding Distance Education Courses for Foreign Language Teaching at Secondary Education Level
Bibliographic record
Abstract
The Covid-19 pandemic is a significant event for the whole world and our country. It is thought that this epidemic, which started in 2020 and whose effects continue to be felt, has negatively affected all areas of life and will continue to affect them for a long time. Countries have taken a series of measures to prevent the spread of the epidemic, and within the framework of these measures, every level and sector of education has had to switch from the face-to-face education model to distance education practices. In this context, the aim of our study is to examine the experiences and views of teachers regarding distance education courses in foreign language teaching at secondary education level and to offer suggestions for the future. The study group of the research, which was prepared within the scope of qualitative research, consists of 20 foreign language teachers, who were determined with a holistic multiple case design, one of the purposive sampling methods. A questionnaire consisting of open-ended questions was sent to the participants via WhatsApp due to the ongoing epidemic conditions, and the data obtained were subjected to content analysis. Participants stated that distance education courses were not spent productively for students, but that they could be adapted to the new order with a number of measures to be taken.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".