The Effect of Comprehension Strategy Instruction on EFL Learners’ Reading Comprehension
Bibliographic record
Abstract
The purpose of this study is to investigate the effect of Collaborative Strategic Reading (CSR) on Taiwanese university students’ reading comprehension with reference to specific types of reading comprehension questions. The participants were 110 students from two intact classes who had low-intermediate to intermediate level of English. This study adopted a pre-test and post-test design with a control group. The data mainly came from statistical results of One-Way ANOVA, but would be triangulated by multiple data sets including the questionnaire responses and transcripts of group discussions during CSR. The findings indicated that CSR had a positive effect on the Taiwanese university learners’ reading comprehension particularly in relation to the comprehension questions on getting the main idea and finding the supporting details. However, the statistical analysis did not show that CSR significantly promoted the EFL learners’ strategic reading competence in regard to predicting, making inferences and dealing with vocabulary problems. The findings of the study suggest that implementing comprehension strategy instruction for one semester may help learners adopt some degree of strategic reading behaviours, but it takes long-term efforts and practices for EFL learners to fully develop their strategic reading abilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".