Cognitive Styles and Influences on Academic Writing: An Empirical Investigation among English Language Learners
Bibliographic record
Abstract
This study explores how different cognitive styles influence writing performance among English language learners, focusing on a group of 220 second- and third-year students at Dai Nam University. The research aims to understand whether the way students think and process information, whether they are Field Dependent or Independent, Analytic or Holistic, Visual or Verbal, Reflective or Impulsive, affects their ability to excel in writing tasks. To assess this, we used a well-established cognitive style inventory and evaluated writing skills through the IELTS Writing Task 2, a standardized test known for its rigor in measuring academic writing proficiency. Our analysis reveals some interesting patterns. Students with a Field Independent or Analytic cognitive style tended to score lower on writing tasks compared to those who were Field Dependent or Holistic thinkers. This suggests that students who prefer to rely on their own internal judgment and focus on details might struggle more with writing tasks that require broader thinking and external guidance. On the other hand, whether a student was more visual or verbal, reflective or impulsive, didn’t seem to make a big difference in their writing performance. These findings highlight the importance of recognizing and adapting to the diverse cognitive styles in the classroom. By understanding how students think, educators can better support their learning and help them develop stronger writing skills. This study offers valuable insights for teachers and curriculum designers aiming to improve writing instruction and outcomes for English language learners.
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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 | 0.009 |
| 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 | 0.000 | 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".