The Impact of iPad-Based Translation Apps on English Language Proficiency: The Mediating Role of Learning Engagement among Saudi Learners
Bibliographic record
Abstract
This study examines the impact of iPad-based translation apps on English language proficiency among Saudi learners, focusing on factors such as attitudes toward the use of translation apps (ATU), perceived ease of use (PEU), perceived helpfulness (PH), and learning engagement (LE). The study employs a mixed-methods approach integrating quantitative and quasi-experimental design with a pre-test and post-test. The sample comprises a purposively selected group of first-year students enrolled in an English proficiency (EP) course among iPad-using students at four public universities in Saudi Arabia. The study employs Partial Least Squares Structural Equation Modelling (PLS-SEM) for analysing the direct and mediating relationships between these factors and EP. The results indicate that ATU, PEU, and PH influence learning engagement, enhancing English proficiency. The mediation analysis confirms LE is crucial in linking translation apps to EP. The analysis revealed statistical significances in mediating effects for all constructs: PEU (β = 0.125, p < 0.001), PH (β = 0.215, p < 0.001), and ATU (β = 0.121, p < 0.001). These effects were evident in reading proficiency gains (Z = -5.221, p < 0.001). The results of the pre-tests and post-tests reveal a significant improvement in language proficiency. Reading and vocabulary achieved the highest positive ranks (Mean Rank = 18.00, N = 35, Sum of Ranks = 630.00), while grammar followed (Mean Rank = 17.50, N = 34, Sum of Ranks = 595.00). All Z-values were highly significant (p = 0.000). The study highlights the effectiveness of iPad translation apps in improving language learning and underscores the importance of engagement in mobile-assisted language learning. This study offers valuable insights and recommendations to assist English language practitioners and learners, particularly within the growing domain of iPad usage in language learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".