A Review of Studies on Cognitive and Metacognitive Reading Strategies in Teaching Reading Comprehension for ESL/EFL Learners
Bibliographic record
Abstract
Being able to read well is important for English language learners. Through the process of reading, the learner becomes an active participant in producing an interaction with the writer of the text through predicting, analyzing, summarizing and using other types of reading strategies. However, building such a connection between the reader and the written information of the text is complex and for English as a second language (ESL) and English as a foreign language (EFL) students, it can be quite difficult for them to apply different types of reading strategies. This article provides a review of literature on 27 studies on the teaching of reading strategies (particularly cognitive and metacognitive reading strategies) for ESL/EFL learners, which reveals that ESL/EFL teachers need to keep updating their teaching methods to meet the ESL/EFL students’ needs, particularly in the use of correct reading strategies. The authors also highlight some of the main issues that prevent ESL/EFL students from improving and developing their reading comprehension. Furthermore, the authors discuss and conclude the article by suggesting to ESL/EFL teachers some teaching strategies to be applied in the reading lesson to improve the ESL/EFL students’ use of reading strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".