Students’ Perceptions of the Effectiveness of Using Smartphone Applications in Enhancing Vocabulary Acquisition
Bibliographic record
Abstract
The normalization of mobile technology has given rise to mobile devices that are increasingly becoming effective learning platforms. This study explores Saudi learners’ perceptions about the application and effectiveness of smartphone applications (apps) in enhancing their vocabulary acquisition. It also examines the factors that might affect their perceptions of smartphone apps' potential role in vocabulary building. An online questionnaire and the researcher’s observation were employed to elicit the data from 270 English majoring students at the University of Bisha, Saudi Arabia. SPSS and NVivo software programs were used to analyze the data. The overall findings revealed that respondents have positive perceptions of the effectiveness of smartphone apps in advancing vocabulary acquisition as they have a transformational role in providing them exposure to sufficient vocabulary input. It was also found that the two factors of familiarity with the apps' use and age affected respondents’ perceptions of the apps effective role in vocabulary acquisition. Other factors of gender, possessing smartphones, college, educational level, frequency of using the smartphone apps, and the hours spent surfing the apps did not affect respondents’ perceptions of the apps effective role in vocabulary acquisition. Hence, it is recommended that teachers and educational policymakers should encourage students to use their free time in accessing smartphone apps to enhance their vocabulary. Program designers should also consider users’ learning needs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".