Yabancı Dil Eğitiminde Kullanılan Dil Yeterlik Çerçeveleri Üzerine Bir İnceleme
Bibliographic record
Abstract
Language proficiency frameworks serve as systematic guidelines that guide language teaching, learning, and assessment processes. This study examines three widely used language proficiency frameworks in foreign language education: the American Council on the Teaching of Foreign Languages Proficiency Guidelines (ACTFL, 2024), the Canadian Language Benchmarks (CLB, 2012), and the Common European Framework of Reference for Languages (CEFR, 2020). These frameworks have been evaluated in terms of how they define and structure language skills, and their development processes and current applications have been identified. The study also explores their impact on language education policies, as well as their integration into curriculum design and assessment practices. Their contributions to determining language proficiency levels, establishing educational standards, and developing international language tests have been examined. Additionally, empirical studies have been reviewed to present key findings regarding their validity, reliability, and pedagogical applicability. This study provides a detailed analysis of the opportunities and limitations these frameworks offer in language teaching, assessing their implementation in various contexts. The influence of these frameworks on language learners, teachers, and educational institutions have been explored, and insights into their applicability in different educational settings have been discussed. Furthermore, the advantages and challenges associated with each framework have been examined, along with their potential for integration into diverse educational systems. Finally, recommendations have been proposed for enhancing the effective use of these frameworks in foreign language education. This study aims to serve as a comprehensive resource for policymakers, educators, and researchers in the field of foreign language education.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".