Improving Reading Comprehension through Oral Language Intervention in EFL Grade 3 Students
Bibliographic record
Abstract
This study examines the impact of an oral intervention program on the reading comprehension of third-year EFL (English as a Foreign Language) students with limited oral language since kindergarten. It investigates the effectiveness of providing rich, systematic instruction in story retelling through interactive read-alouds to improve reading comprehension and highlights the crucial role that oral language plays in the reading comprehension of EFL students. For this purpose, a total of 371 EFL third-grade students were chosen from four private schools in Lebanon. The experimental group, consisting of 184 participants, received a 10-week oral program of high-quality read-aloud stories over two consecutive semesters. The remaining 187 students were assigned to the control group but continued with the main reading curriculum. Two retelling assessments and two reading comprehension tests were performed for both groups. The data were analyzed utilizing a 3-step comparative statistical approach (MANOVA, one-way ANOVA, and LSD). The results showed a statistically significant difference between the control and the experimental groups for all narrative element variables (characters, setting, main events, and solution) in the second retelling assessment; however, no significant difference was found in the first retelling. Furthermore, comparisons of reading comprehension assessments showed a small to medium effect size in favor of the experimental group. Understanding the relationship between oral and reading comprehension calls for the importance of enhancing oral language in the early years of EFL classroom.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".