The Impact of Mobile Language Learning (WhatsApp) on EFL Context: Outcomes and Perceptions
Bibliographic record
Abstract
Seeking to identify the impact of mobile language learning (WhatsApp) on the achievements of EFL learners, a quasi-experimental design study was applied at Al-Baha University in Saudi Arabia. This study examines the impact of mobile language learning in enhancing EFL students’ English skills ability when learning English as a foreign language context. Particularly, the study intends to investigate the impact of mobile language learning (WhatsApp) in comparison to traditional learning in learning English skills on the achievement of EFL learners. Thus, the participants in this study included 48 male learners, aged 18–22 years, preparatory year at Al-Baha University. The results highlighted that there are significant differences between the mean scores of the EFL learners who were taught English in the Mobile language learning (WhatsApp), and those who were taught English by using the traditional learning (the control group) in the post-test. This difference was in favour of the experimental group. However, the findings revealed that are not statistically significant differences between the EFL learners in the experimental classes and the students in the control groups in their English achievement test score at the pre-test. Furthermore, the results of this research revealed that most EFL learners claimed that they were enthusiastic to join English lessons through WhatsApp groups and expressed the belief that working in a WhatsApp group can boost their motivation and their academic results. Also, most EFL learners highlighted that using mobile language learning method (WhatsApp) enabled them to increase their social skills, confidence, while helping them to create positive relationships with their colleagues and the teacher. However, there were some obstacles and barriers to join WhatsApp learning groups, such as lack of access to the internet and lack of tendency to share and participate in the WhatsApp group.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".