The Relationship Between Self-Efficacy and Use of Reading Strategies: The Case of Iranian Senior High School Students
Bibliographic record
Abstract
Reading is an essential skill and probably the most important skill for second language learners to master in academic context (Grabe, 1991). Reading strategies are also inseparable part of all reading activities and tasks. Many different factors may affect one’s knowledge of reading. One of such factors is self-efficacy. The present study aims to investigate the relationship between self-efficacy and use of reading strategies among Iranian senior high school students. 45 senior high school students in Shoushtar (a city in southwest of Iran) were randomly selected to participate in this study. Instruments used in this study were two questionnaires, one of which measured students reading self-efficacy and the other one measured use of reading strategies. The questionnaires items were on a 7-point Likert-type scale. Pearson correlation coefficient was used to investigate the relationship between self-efficacy and use of reading strategies. The result of the study indicated that the participants, on averages felt confident of their capabilities to perform general reading tasks. They also demonstrated an acceptable level of using three different subcategories of reading strategies (metacognitive, cognitive and socioaffective strategies). After computations, it was revealed that reading self-efficacy and overall reading strategies use and subcategories of reading strategies were significantly positively correlated. Key words: Self- efficacy beliefs; Reading strategies; EFL context
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".