Digital Literacy in Saudi Tertiary EFL Context: Perspectives of Students and Potentials for Listening Skills
Bibliographic record
Abstract
Technology is regarded as a vital aspect in modern society, revolutionising various fields. Consequently, evolutionary knowledge and skills raise in this paradigm, referred to as digital literacy. The necessity to reveal the influence in EFL, English as a foreign language, context is fundamental. The exploration seeks to examine digital literacy perspectives of 117 Saudi undergraduate EFL learners enrolled in English Language and Translation bachelor’s programme at a Saudi university. It also scrutinises the prospects for improving English listening skills. To achieve the exploration objectives, mixed-method approach is integrated via adopted questionnaires from scholarly work (Mudra, 2020; Najmi & Navaee Lavasani, 2021; Ng; 2012a; Ng; 2012b; Yukselir & Yuvayapan, 2019) with minor adjustments. Findings demonstrate EFL learners’ confidence to their digital literacy skills. They reveal high confidence to solving technical difficulties; conversely, the least positive positioning denotes towards literacy to web relevant activities. Findings advocate for the benefits of listening skills, especially for pronunciation and vocabulary improvement, authentic materials for learning, language learning motivation, and access to entertaining educational materials. The exploration serves endeavour to subsequent academic and pedagogical implementations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".