Metacognition and Second/Foreign Language Learning
Bibliographic record
Abstract
Metacognition appears to be a significant contributor to success in second language (SL) and foreign language (FL) learning. This study seeks to investigate empirical research on the role metacognition plays in language learning by focusing on the following research questions: first, to what extent does metacognition affect SL/FL learning? Second, what are the factors shown to influence metacognition of learners in the area of second/foreign language learning? Data from 33 studies published between 1999 and 2013 were coded based on a coding scheme adapted from previous systematic reviews (e.g., Norris & Ortega, 2001; Plonsky, 2011). The findings of the review show that the metacognitive interventions have the possibility to promote language performance, but, on the whole, mixed evidence was found for the effectiveness of the intervention in enhancing metacognitive awareness/strategy use. The results also indicate that several factors appeared to affect L2 learners’ metacognition. This review expands our understanding of the role of metacognition in language learning and will lead to pedagogical implications for SL/FL learning and teaching. Limitations of the existing studies and directions for future research are also discussed.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".