What Are the Effective Tests for Measuring English Proficiency in a Japanese Project-Based English Program? A Comparison of the Test of English for International Communication, Global Test of English Communication, and CNN Global English Test Service
Bibliographic record
Abstract
Despite significant efforts to improve English education, challenges such as declining motivation and slow progress persist. In this study we examine the relationships among three English proficiency tests—Test of English for International Communication (TOEIC), Global Test of English Communication (GTEC), and CNN Global English Test Service (CNN GLENTS)—in the context of university-level English education in Japan. We aim to explore the effectiveness of these tests by comparing scores and improvements across various sections. The analysis reveals notable differences in the constructs measured by each test. CNN GLENTS exhibits weak or negative correlations with GTEC and TOEIC, suggesting divergent skill assessments, particularly in the international studies section. We observe high correlations between the listening and reading sections of GTEC and TOEIC in the second round, possibly due to increased test familiarity, whereas correlations involving speaking and writing are weaker. These findings indicate that CNN GLENTS emphasizes different proficiencies, such as background knowledge and contextual understanding, which GTEC and TOEIC do not directly capture. The study underscores the need for a multifaceted approach to language assessment, given no single test fully captures the complexity of English proficiency. Future researchers should explore the underlying factors that influence these correlations to improve the utility and interpretation of these assessment tools in educational settings.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".