Non-English Majors’ Writing Self-Efficacy and Attribution in Learning English as a Foreign Language
Bibliographic record
Abstract
This study examines the relationship between writing self-efficacy, attribution, and writing proficiency of college students in learning English as a foreign language (EFL) context. The scales of writing self-efficacy and attribution were administered to 142 Chinese first-grade non-English majors. Research findings showed that these EFL learners maintained a medium level of writing self-efficacy and tended to attribute their writing outcomes to internal causes. Independent sample t-test indicated that gender exerted no significant influence on EFL writing self-efficacy, and only the attribution cause luck significantly differed between boys and girls. Besides, high-achievers reported stronger writing self-efficacy and skill self-efficacy, while no significant difference in task self-efficacy was found between high-achievers and low-achievers. One-way ANOVA results revealed that regardless of writing level, students tended to attribute their writing success or failure to internal factors such as ability and effort, while low-achievers were also inclined to attribute externally. Pedagogical implications were also discussed.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".