The Effects of Using Summarization Strategies on Iranian EFL Learners' Reading Comprehension
Bibliographic record
Abstract
It is already known that for being effective readers we need explicit strategy training and it is generally agreed that well-developed reading comprehension ability is the key to students’ academic success .This comprehension ability is not a passive state which one possesses, but it is an active mental process which needs to be nurtured and improved. The study aims to explore the effectiveness of using summarization strategies makes any significant difference in EFL learners' level of comprehending English texts. It also aims to examine whether using summarization strategies at undergraduate level affect significantly the performance of male and female students' comprehension of texts. The data for this study were collected through two comprehension tests and a personal questionnaire from 40 English students who study at one of Payam Noor University branches in Isfahan. The data were analyzed descriptively and also inferentially. The overall findings of the study which enjoys pretest-posttest design indicated that after receiving summarization strategies training participants outperformed in posttest and there was not a significant difference between performance of female and male participants. The findings of the present study would help teachers and teacher trainers to construct and implement summarization strategies in EFL classes more effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".