Effects of reading Strategies and Depth of Vocabulary Knowledge on Turkish EFL Learners’ Text Inferencing Skills
Bibliographic record
Abstract
Within the framework of foreign language teaching and learning, reading strategies, depth of vocabulary knowledge and text inferencing skills have not been researched extensively. This study tries to fill this gap by analyzing the effects of reading strategies used by Turkish EFL learners and their depth of vocabulary knowledge on their text inferencing skills. Three different measures were used in the study: Word Association Test (WAT), Metacognitive Awareness of Reading Strategies Inventory (MARSI), and inferencing questions used in a standardized national test. The association test and reading strategies scores were regressed on inferencing scores of the participants. The results revealed that depth of vocabulary knowledge was a better predictor of inferencing skills compared to reading strategies. However, the model created by using these two predictors accounted for only 15% of the variance, and the major implication of this result is that there are other more significant factors which affect text inferencing skills of EFL learners than reading strategies and depth of vocabulary knowledge.
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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 | 0.000 | 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 | 0.000 |
| 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".