How do Concept-Maps Function for Reading Comprehension Improvement of Iranian Advanced EFL Learners of Both Genders?
Bibliographic record
Abstract
This study was an attempt to examine the effect of concept mapping on reading comprehension of Iranian EFL learners. Pretest-posttest design was employed to scrutinize the possible improvement of the study’s participants who were male and female learners whose ages ranged from 19 to 40 and had taken general English courses at Islamic Azad University of Kharg. Two groups (one experimental and one control group) were exposed to the same reading comprehension test as the pre-and post-test, however, only the experimental group received the special treatment of concept mapping techniques for different texts during the treatment. After the application of the study’s treatment, data analysis procedure initiated which indicated that experimental group who received explicit concept mapping instruction had a better performance than the control group on the final reading comprehension test. In addition, the findings shows that students who used this strategy during the seven-week treatment had better results on comprehension reading test and could understand the reading passages better. The findings imply that concept maps can positively affect the reading comprehension ability of Iranian advanced EFL learners. It was also revealed that female learners were superior at employing concept-maps in comparison with their male peers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".