Ideal Classroom Setting for English Language Teaching Through the Views of English Language Teachers (A Sample from Turkey)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
English is the most common foreign language given as a class in Turkey. Although English language education has been given for many years, it is seen that there is not a desired result yet. There are many factors that affect this situation such as language, program, method, language education policies, teacher, and student. One of the factors affecting language education is the pysical classroom setting. Within this context, it is searched for ideal classroom setting in language education at high schools. 22 English language teachers from 9 different high schools participated in the study. Views of teachers were reported to Word and analyzed through content analysis. In the context of the current research, it is stated that there are some technological problems, the areas where foreign language materials are exhibited in the classroom environment are limited, and the classrooms do not allow different seating arrangements. According to English language teachers, it was stated that there should be technological equipment and hardware in an ideal language learning setting, there should be sufficient areas for displaying visual materials, furniture should be flexible and classroom population should be at an ideal level.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it