The Relationship Between EFL Students’ Word-Knowledge in a Text and Their Reading Comprehension as Demonstrated by Saudi University Students
Bibliographic record
Abstract
This study reports the outcomes of research investigating the relationship between two variables; percentage of word knowledge in an academic English text and reading comprehension scores the participants got for the same text. The study was conducted at Prince Sattam Bin Abdulaziz University, Preparatory Year Deanship, during the first semester of 2017-2018. Seventy-one Saudi male students participated in the study. They came from four groups of students selected from the Preparatory Year population to represent four various levels of English (Groups one, seven, fourteen and seventeen) based on their achievement in the placement test. Data was collected using two instruments: two word—meaning association test-lists and a reading comprehension test. The results showed that there is a statistically significant correlation (r=.702) between percentage of known words and reading comprehension in general i.e., for all the participants. The effect of word knowledge on reading comprehension was very high R square =.49. However, results for the groups separately showed that correlation was positive but not statistically significant for the higher levels i.e., groups one and seven. Also, there was a low effect of word knowledge on comprehension. For the lower levels i.e., groups 14 and 17, the correlation was positive and statistically significant. Results also showed a very high effect of vocabulary knowledge on reading comprehension.Based on the results and analysis of the study, the researchers provided recommendations that will help improve reading curriculum selection and English teaching practices, and suggestions for deeper research into the topic that will include female students and other related concepts.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".