The Effect of Explicit Teaching of Lexical Inferencing Strategies on the Vocabulary Learning Among Iranian Field-Dependent and Independent EFL Learners
Bibliographic record
Abstract
It is claimed that lack of vocabulary knowledge is one of the most challenging issues for foreign language learners. Moreover, both language learners and teachers are after the most viable method of vocabulary learning and teaching. Along the same lines, this study focused on the effect of explicit teaching of lexical inferencing strategies on the vocabulary learning of Iranian foreign language learners with different cognitive styles. To this end, three groups of learners, namely, field dependent, field independent, and a control group of English language learners were formed. Field dependent and field independent language learners received explicit teaching of lexical inferencing strategies while the control group just received the conventional method of vocabulary teaching. The performances of the three groups of the study on a vocabulary posttest were analyzed using one way ANOVA. The results of statistical analysis indicated that both field dependent and field independent language learners outperformed the control group in terms of vocabulary learning. However, no significant difference was found between field dependent and field independent learners in terms of vocabulary learning when they received explicit instruction of inferencing strategies. This led to the conclusion that explicit teaching of lexical inferencing strategies has a positive effect on Iranian foreign language learners with different cognitive orientations.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".