Investigation of the Writing Anxiety of Syrian Refugees Learning Turkish as a Foreign Language in Turkey
Bibliographic record
Abstract
The aim of this study was to determine and investigate the writing anxiety of Syrian refugees learning Turkish as a foreign language in Turkey according to different variables. The study group of the research, in which survey model was used, consisted of 214 Syrian refugees chosen via purposive sampling method and who learn Turkish as a foreign language in Turkey. “Writing Anxiety Scale for Learners of Turkish as a Foreign Language” was used as data gathering tool in the research. In the analysis process, firstly normality test was conducted and it was determined that it indicated normal distribution. Accordingly, t-test was used in pair independent samples, and one-way ANOVA test was used in multi-group comparisons. As a result of the research, it was determined that writing anxiety level of the Syrian refugees learning Turkish as a foreign language (L2) in Turkey, was at medium level and also their writing anxiety was caused by environmental/social factors rather than internal/mental factors. In addition, when the situation was evaluated according to their language levels, it was determined that students at B2 level had the highest level of anxiety; and that using Turkish in social media reduces the action-oriented anxiety of the students. When the participants’ writing anxiety was investigated according to gender, age, and time spent in Turkey it was found that a significant difference was not indicated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".