Insights into CEFR and Its Implementation through the Lens of Preservice English Teachers in Thailand
Bibliographic record
Abstract
The Common European Framework of Reference for language teaching, learning, and assessment (CEFR) provides a guideline for English teaching aids and curriculum development in language courses. It identifies and describes the specific skills that language learners must acquire to improve their English language proficiency for communication (CoE, 2001). In the Thai context, the Ministry of Education (MoE, 2014) has adopted the framework as a practical handbook for reforming English teaching at all levels. However, only a few studies have examined Thai stakeholders’ perceptions towards CEFR. As such, the current study investigated preservice English teachers’ perceptions of the CEFR in a Thai context. A total of 200 fourth and fifth-year preservice English teachers participated in this study. A mixed-method design was used to collect data via seven-point Likert scale questionnaires and semi-structured interviews. The quantitative findings revealed that the participants had a high level of understanding of the CEFR, specifically in the assessment domain and development of reference level descriptions. By contrast, the qualitative data revealed that Thai preservice teachers have little knowledge of the CEFR, and their understanding of the CEFR was quite limited. Nevertheless, Thai preservice teachers expressed positive views regarding the implementation of the CEFR into classroom practice. Overall, these findings indicate that the preservice English teachers had a poor understanding of the CEFR conception, which suggests that Thai stakeholders must raise awareness regarding the proper implementation of the CEFR and its alignment with the national curriculum.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".