Is Field Dependence or Independence a Predictor of EFL Reading Performance?
Bibliographic record
Abstract
In this study it was hypothesized that field dependence or independence would introduce systematic variance into Iranian EFL learners' overall and task-specific performance on task-based reading comprehension tests. One thousand, seven hundred, forty-three freshman, sophomore, junior, and senior students, all majoring in English at various Iranian universities and colleges, took the Group Embedded Figures Test (GEFT). The resulting 582 field-independent (FI) and 707 field-dependent (FD) students then took the 1990 version of the IELTS. Using SPSS commands for collapsing continuous variables into groups and participants’ IELTS scores (based on the 25th, 50th, and 75th percentiles), four proficiency groups were identified for each cognitive style. From each proficiency group, 36 FD and 36 FI individuals were selected through a matching process. The resulting sample of 288 participants took the Task-Based Reading Test (TBRT) designed for the study. Data analysis revealed that individuals’ cognitive styles resulted in a significant difference in their overall test performance in the proficient, semiproficient, and fairly proficient groups, but not in the low-proficient group. The findings also indicated that cognitive style resulted in a significant difference in participants' performance on true-false, sentence completion, outlining, scanning, and elicitation tasks in all proficiency groups.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".