The Perceptions of Students Learning Turkish as a Foreign Language Towards "Writing in Turkish"
Bibliographic record
Abstract
This study aimed to determine the perceptions of students who learn Turkish as a foreign language towards "writing in Turkish." The study was conducted using the phenomenology pattern, one of the qualitative research methods. The study sample consisted of one hundred seventy-five (175) students who were from two state universities in Turkey and learned Turkish as a foreign language in the 2019-2020 academic year. The study data were collected using an online form, and the participant students were asked to complete the statement in the form as follows: "Writing in Turkish is like ……, because ……………". As a result of the research, the students generated one hundred and eleven (111) valid metaphors about "writing in Turkish." Ninety (90) of them were positive, and 21 were negative. The categories with the highest number of positive responses were as follows: "Writing in Turkish: an Enjoyable Task" (n: 20), "Writing in Turkish: an Improving Task" (n: 17), "Writing in Turkish: a Similar Task" (n: 13) and "Writing in Turkish: an Achievable Task" (n: 12). The category with the most negative responses was the "Writing in Turkish: a Difficult Task" (n: 12).
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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 | 0.003 | 0.010 |
| 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 | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".