The Effectiveness of Using Mobile on EFL Learners’ Reading Practices in Najran University
Bibliographic record
Abstract
This paper investigates the efficiency of using mobile technology in English as a Foreign Language (EFL) reading classroom of 30 male students at Preparatory Year, Najran University. Specifically, the study aims to explore the role of this new integrated method in enhancing the EFL learners’ reading practices. Integrating Freebody and Luke’s (1990) four resources model of reading practices within Mobile Assisted Language Learning (MALL), a mix-method research design was used in this study. The reading class was allowed and encouraged to implement specific mobile features and applications. A pretest was employed to construct the baseline data. During the treatment, WhatsApp group, self-reflection journals, posttest, and semi-structured interviews were used. The findings revealed that using mobile WhatsApp, online and offline dictionaries, mobile camera, online resources, and memos remarkably improved the participants’ code breaking practices and text participation practices; text using and text analyzing practices were slightly improved. Participants used the aforementioned tools and features to share images, photos of summaries and mind maps and to look up for new vocabulary, pronunciations and parts of speech. The study recommends further investigation on the effect of WhatsApp on writing practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".