A Bibliometric Review on Latent Topics and Trends of Language Learning Strategy (1990-2022)
Bibliographic record
Abstract
Despite the fact that language learning strategy (LLS) has been playing an important role in language learning and teaching over the past few decades, scant research has systematically tracked and synthesized the development of LLS. This study, therefore, aims to conduct a comprehensive review of LLS within a time span of 1990 to 2022. A total of 927 articles related to LLS were analyzed via bibliometric analysis and structural topic modeling. Performance analysis and science mapping, which included the annual production, the most influential journals, countries, institutions, authors and their collaborative networks, were figured out by bibliometric analysis. 24 important topics were identified by structural topic modeling, which showed that six topics were related to skill-based strategies, three topics were concerned with the subjects of LLS, three topics were about multilingualism issues and the rest concerned different types of LLS and the factors to influence LLS effect. This study provides a panoramic review of LLS in applied linguistics, pointing out potential future directions in this field.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.080 | 0.113 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".