The Opinions of Turkish Learning Foreign Students about the Educational System in Turkey and Their Respective Countries, Turkish Language and the Language Areas
Bibliographic record
Abstract
Today, it is not enough for individuals to only speak a native language. Individuals feel the need to learn a second or more language. In the developing world, it is seen that by the help of the technology people can reach the world from home and can learn one or more languages and may even grow in a multicultural family and become bilingual. In this research, it is aimed to investigate the views of Turkish learning international students on the education system in their countries, the education system in Turkey, Turkish and the language areas they study. The purposive sampling method was used in this study. In order to collect qualified data, B2 level students who can easily express themselves in Turkish were selected and interviewed. The research survey was applied to 30 students who were volunteers. To obtain in-depth information, the semi-structured interview method was decided to be used. In this study, it is seen that Turkmen students are not satisfied with the education system in their country while Azerbaijanis are satisfied and both groups liked the education system in Turkey. It is determined that the learners have positive and beautiful thoughts about Turkish. In terms of linguistic areas they do not find listening necessary, they do not mind much about reading, and on the contrary, they care a lot about speaking, and they give importance to writing and grammar, though they have difficulties in both.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".