The Role of Persian Elaboration on Incidental Vocabulary Learning from Reading by EFL Learners
Bibliographic record
Abstract
This study attempted to tap into the potential of reading for incidental vocabulary learning by exposing EFL learnersto elaborated texts. This study was also concerned with investigating learners’ attitudes toward using elaboratedpassages in their reading classes. To this end, 38 students were selected as the participants of this research anddivided into two groups. Students attending the experimental group (N=20) read Persian elaborated texts in whichthe Persian meanings of the specified target words were provided in apposition to them. On the other hand, studentsof the control group (N=18) were required to read the non-elaborated version of the aforementioned texts. Generally,the results of the post-test pointed to the effectiveness of this approach in incidental vocabulary learning, and theparticipants of the experimental group were found to gain a significant vocabulary improvement in comparison to thecontrol group. Furthermore, the interview suggested that students held positive attitudes to reading elaborated textsand regarded them as effective in their vocabulary learning experience. The findings of this study have implicationsfor material developers who need to reconsider the role of modified materials.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".