The Effect of Expansion of Vision Span on Reading Speed: A Case Study of EFL Major Students at King Khalid University
Bibliographic record
Abstract
The objective of the study is to demonstrate how and to what extent the expansion of vision span could be a decisive factor in enhancing the reading speed of EFL major students in the English Department at King Khalid University while maintaining their previous level of comprehension. The reading speed of students in the English Department at KKU is very low, which hampers their overall proficiency in the language. The sample of the study consisted of two groups (a control group and a test group), each consisting of 25 students who were in the final year of the EFL undergraduate program. The methodology includes comprehension tests to examine the sample for reading speed and comprehension at the beginning of the study. Then training in increasing the span of vision was extended to the students in the test group. At the end of the training, both the groups were tested again for reading speed and comprehension. The study illustrates that the test group increases their reading speed, while the control group shows no increase in reading speed. Comprehension remains unchanged for both the test group and the control group. The study concludes that the techniques adopted for training the study sample can be very efficacious in bringing about a significant change in the reading habits of college-going students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".