Enhancing Reading Skills for Saudi Secondary School Students through Mobile Assisted Language Learning (MALL): An Experimental Study
Bibliographic record
Abstract
This study addresses the issue of integrating mobile-assisted language learning (MALL) systems into L2 reading instruction in the Saudi secondary schools in order to improve the reading comprehension skills of struggling EFL students. The focus is to find out whether students’ language performance is accelerated by using MALL together with teacher instruction versus conventional instruction alone. In order to assess the effectiveness of MALL systems and activities in improving reading comprehension skills in EFL contexts, an experimental study was carried out where 120 participants of grade ten students in four public secondary school of Riyadh District in Saudi Arabia were randomly divided into two groups: experiment and control. Reading skills of the participants’ were measured by pre-test and post-test by a panel of three national experts. The comparison between the experimental group and the control group pinpoint that MALL materials and systems improve reading comprehension skill among EFL students. The findings indicate clearly that there was a significant difference between MALL users and nonusers in favour of the experimental group (p < .05). It can be then generalized that MALL systems and applications in general provide a motivating learning environment for teaching reading which has its positive implications on improving the reading skills of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".