Efficiency of Using a Web-Based Approach to Teach Reading Strategies to Iranian EFL Learners
Bibliographic record
Abstract
Applying new technologies with their effective potentials have changed education and, consequently, the L2 teacher role. Coping with online materials imposes the necessity of employing Web-based approaches in L2 instruction. The ability to use reading strategies in a Web-based condition needs sufficient skill which will be fulfilled if it is integrated in pedagogy. The main focus of the present study was to investigate the efficiency of teaching reading strategies in a Web-based condition to Iranian EFL learners. Thirty L2 learners at upper-intermediate level from an English institute in Isfahan were selected as the experimental group. The group received a 20-hr reading strategy training in Web-based condition. Participants were taught 4 general reading strategies which were practiced through 3 stages of cognitive theory: cognitive, associative, and autonomous proposed by Anderson (1993). The instruments included a pre/posttest of hypertext reading comprehension, a pre/posttraining questionnaire for assessing the participants’ familiarity with general reading strategies, a pre/posttest of reading comprehension, and an attitude questionnaire towards Web-based reading strategy instruction. Findings indicated that after reading strategy program, the participants’ frequent usage of each reading strategy increased significantly and, consequently, they performed better at the reading tasks in Web-based and conventional conditions. Furthermore, the group had positive attitudes towards Web-based reading strategy instruction.
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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 | 0.001 |
| 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".