A Bibliometric Study of Mobile-Assisted Language Learning from 2013 to 2023: Research Themes and Trends
Bibliographic record
Abstract
The widespread use of mobile-assisted language learning (MALL) in learning and teaching foreign languages has attracted significant interest. Following the substantial impact of COVID-19 on language education, the focus on MALL among researchers has increased to a new level. This paper reviewed the MALL-related Social Science Citation Index (SSCI) articles from 2013 to 2023, sourced from the Web of Science (WOS). Out of 535 identified articles, 241 were analyzed systematically to generate knowledge maps using CiteSpace. Based on the bibliometric study, this research identified and discussed the popular themes and research trends in MALL. Three major themes emerged through keyword co-occurrence analysis: 1) a primary focus on the English language; 2) technology integration in language learning; and 3) a learner-centered focus. The results also revealed four research trends: 1) development and application of mobile learning technologies; 2) in-depth study of language acquisition and skills development; 3) innovative research on learner characteristics and teaching methods; and 4) a shift in research methods. This research contributes to the existing literature by consolidating current knowledge and offering guidance for future investigations or developments in MALL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.126 | 0.248 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".