The Effects of Attitude & Motivation on the Use of Cognitive & Metacognitive Strategies among Iranian EFL Undergraduate Readers
Bibliographic record
Abstract
Studies in reading strategies bring together the assumption that individual characteristics may influence reading performance; different readers may process the same text in different ways, depending on their purposes, motivation, attitudes, interests and background knowledge. The research aims to study the effect of motivation and attitude on the use of cognitive and metacognitive reading strategies among EFL undergraduate students who passed all reading comprehension modules. For this purpose, University of Ahvaz of Iran was chosen as a case study. 72 students have had this feature. Among these students, 51 homogenous students, based on their performance on Michigan Test of English Language Proficiency (2010) were selected to take part in this study to fill two questionnaires and took a reading. After checking the reliability and validity of the instruments, a normality parametric test was used to ensure normality distribution of data using SPSS 20 software. To analyze the data, t-test and Pearson correlation test were performed. The findings of the research pointed to the impact of EFL learners’ level of motivation and attitude, on their reading comprehension ability indicating a relatively high direct correlation (0.67). The results also revealed that the highly motivated students were in favor of using cognitive and metacognitive strategies more than less motivated ones. Overall, the finding suggests that learners' individual differences in terms of their motivation and attitude levels should be taken into account in their development of reading comprehension skills and reading strategy use.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".