The Effect of Four-Square Writing Method on Writing Anxiety of Learners of Turkish as a Foreign Language: A Mixed Method Study
Bibliographic record
Abstract
The study aims to examine the effect of the Four Square Writing Method (FSWM) on the writing anxiety of the learners of Turkish as a foreign language at the B2 level. The mixed-methods sequential explanatory design was applied, and 50 students at the B2 proficiency level who studied at the Turkish Teaching Practice and Research Center (BAİBU TÖMER) at Bolu Abant İzzet Baysal University in the 2020-2021 academic year were selected through the purposeful sampling. The quantitative data were obtained with a weak experimental design consisting of a pre-test and a post-test, while the qualitative data consisted of interviews with the participants. As data collection instruments, the Writing Anxiety Scale (WAS) developed by Şen and Boylu (2017) and the Semi-Structured Interview Form designed by the researchers were used. Quantitative data were analyzed by hypothesis testing, whereas qualitative data were scrutinized using content analysis. The quantitative data showed that the FSWM had a statistically significant effect on the writing anxiety of the learners of Turkish as a foreign language. On the other hand, the qualitative findings were categorized into two main themes: “writing skills” and “affectivity.” The writing skills category included 13 codes and 72 views; “general, transition, and linking expressions, developing strategies, developing thinking skills, learning vocabulary, enhancing writing style, planning in writing, graphic organizer, drafting, collaborative writing, organization, writing quality, and generating ideas. The affectivity category consisted of 5 codes and 31 views, including relaxation, enjoyment, motivation, increased self-confidence, and dislike. The findings determined that the FSWM played a role in positively affecting writing anxiety, contributing to the development of writing skills, promoting participants’ psychological comfort, allowing them to have a fun time, gaining self-confidence, and motivating them to write. Lastly, the study detailed the aspects where the qualitative findings supported the quantitative results contributing to the interpretation of the data.
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.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".