Navigating Vocabulary Learning in Mobile-Assisted Language Learning: Mapping Benefits and Addressing Challenges
Bibliographic record
Abstract
The proliferation of mobile technologies has fueled the growth of mobile-assisted language learning (MALL), providing learners with opportunities to customize their language learning experiences. Numerous studies have investigated the impact of MALL on enhancing language skills such as listening, speaking, reading, and writing, with a particular focus on vocabulary development. However, in addition to the benefits, the use of MALL is accompanied by specific challenges. This study systematically synthesizes existing literature to map the benefits and challenges associated with vocabulary learning within MALL contexts. An analysis of 76 scholarly articles retrieved from the Web of Science Core Collection reveals that MALL substantially facilitates vocabulary acquisition (43%), promotes learner motivation (24%), improves retention of vocabulary items (15%), and offers authentic contexts for vocabulary application (6%). Identified challenges encompass technological constraints of mobile devices and platforms, potential negative impacts associated with smartphone usage, user skepticism regarding the effectiveness of MALL, and external environmental pressures. To fully realize the potential of MALL, further empirical research is necessary to deepen understanding of its advantages and to develop effective strategies for mitigating identified challenges.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".