The Relationships Among Writing Skills, Writing Anxiety and Metacognitive Awareness
Bibliographic record
Abstract
The purpose of this study was to investigate the relationships among students’ argumentative text writing skills, writing anxiety, and metacognitive awareness. The participants were composed of 375 8th graders in six middle schools in Sivas. Metacognitive Awareness Inventory (B Form) which was adapted in to Turkish by Karakelle & Saraç (2007) and Writing Anxiety Scale which was developed by Yaman (2010) were used to collect data. The argumentative texts that were gathered from the students were evaluated using the Argumentative Essay Rubric, which was developed within the scope of this study. Descriptive statistics were calculated, Spearman Correlation Analysis, and Kruskal-Wallis Test were applied for analyzing the data. The analyses revealed that the students’ argumentative text writing skills were inadequate. It was also determined that students with low writing anxiety were more successful in writing argumentative texts when compared with their counterparts with middle and high anxiety levels. Moreover, slight and positive relationship between argumentative text writing skills and metacognitive awareness, and a significant relationship between writing anxiety and metacognitive awareness were determined. As a result of the study, it can be stated that decreasing students’ writing anxiety and increasing their metacognitive awareness will have a positive effect on their argumentative text writing skills.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".