The Effect of Jigsaw Task on Reading Ability of Iranian Intermediate High School EFL Learners
Bibliographic record
Abstract
Reading comprehension is a major component of language learning and finding an appropriate and effective way for teaching it, was considered by many researchers. However, one effective way for achieving this goal can be using different activities. The purpose of the researcher is to show the effectiveness of cooperative learning, more specifically jigsaw task on reading ability of Iranian intermediate high school EFL learners. In order to check general language proficiency of the students, 120 female students were randomly selected and Nelson Proficiency Test was given to these students. Sixty students were selected from among 120 female students and they were assigned in two different classes. After selecting 60 students, they were divided in two groups, one of them as the experimental group ( G1) , and the other as the control group ( G2). Each group consisted of 30 students. Their ages ranged from fifteen to nineteen. First session both groups received pretest. After that the experimental group received treatment in jigsaw task during six sessions and control group received traditional method. Last session both groups received posttest in order to check reading ability. Statistical results revealed that the experimental group did much better than control group and consequently jigsaw task was effective on reading ability of Iranian intermediate high school EFL learners.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".