A Systematic Review of CEFR-Related Research of English Education in South Korea
Bibliographic record
Abstract
This study aims to analyze the trend of CEFR(Common European Framework of Reference) related research in South Korea using the method of a systematic review and to discuss the research fields required in relation to CEFR. The Council of Europe released the Common European Framework of Reference for Languages (CEFR) in 2001. It acts as a standard for curriculum, teaching, learning, and evaluation. With this, thirty kinds of literature from the years 2000 to 2020 that satisfied the selection criteria were chosen from a search of CEFR-related research on English education. After the 2015 revised national curriculum was implemented, studies related to CEFR increased by 70% from 2018 in terms of publication year, and 60% of those studies used quantitative methodologies. After organizing the subjects of the studies by the Korean academic levels and CEFR levels, the data showed a focus on research for elementary and university while a wide range of CEFR levels from Basic User to Proficient User was represented. Since CEFR builds vocabulary, grammar, and language competence based on corpus data, 80% of the studies were performed in relation to the curriculum and evaluation using the corpus. However, in order to successfully apply CEFR to Korean English education, research on more detailed level settings and the linkage between each level needs to be actively conducted. More studies are necessary to adapt CEFR to the EFL context in Korea since CEFR describes communication skills that L2 learners should have, including pluricultural competence. This means a wide range of studies on CEFR are needed to expand the quality and quantity of English education in Korea.
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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 | 0.021 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.035 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".