Exploring Writing Anxiety and Self-Efficacy among EFL Graduate Students in Taiwan
Bibliographic record
Abstract
This study investigates research writing anxiety and self-efficacy beliefs among English-as-a-Foreign-Language (EFL) graduate students in engineering-related fields. The relationship between the two writing affective constructs was examined and students’ perspectives on research writing anxiety were also explored. A total of 218 survey responses from engineering graduate students at Taiwanese universities were analyzed, along with qualitative data from open-ended questions and semi-structured interviews. The findings show that while master’s and doctoral students felt a similar moderate level of writing anxiety, senior doctoral students were more self-efficacious about writing research papers in English than their junior counterparts. Overall, students with higher writing self-efficacy felt less apprehensive. Additionally, among the individual variables, experience in writing for publication better predicted writing anxiety and self-efficacy than students’ self-reported English proficiency and the number of writing courses taken. The qualitative findings indicated various sources of graduate-level writing anxiety, including insufficient writing skills in English, time constraints, and fear of negative comments. Furthermore, composing different sections of a research paper provoked different levels of anxiety due to the variations in the rhetorical purposes and discourse structures of particular sections. Implications on dealing with research writing anxiety are also discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".