The Effects of an Extensive Reading Program on Improving English as Foreign Language Proficiency in University Level Education
Bibliographic record
Abstract
This study aimed at investigating the impact of extensive reading on improving reading proficiency. The study tried to find the effect of ER on EFL student’s reading, vocabulary and grammar. The researcher designed two instruments; a program based on the extensive reading strategy and general test. Forty-one university students who study English was purposefully chosen from several universities in the academic year 2012 and the study continued for three semesters. To establish the validity for the instruments, the method of content validity was used; the instruments were given to a jury of specialists. In addition, the reliability of the test was established. Before carrying out the experiment, a pre-test for the general reading comprehension was administrated. By the end of the experiment, the researcher administrated the general reading comprehension post-test. The researcher used the t-test to detect any significant differences between the pre-test and post-test on the reading, grammar, and vocabulary. The findings show that ER improves university EFL students’ reading, vocabulary and grammar achievement. Finally, the researcher suggested several recommendations.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".