Roles of Vocabulary Knowledge for Success in English‐Medium Instruction: Self‐Perceptions and Academic Outcomes of Japanese Undergraduates
Bibliographic record
Abstract
This study investigated the relationship between vocabulary knowledge (written and aural receptive vocabulary size and self‐rating of vocabulary knowledge) and self‐perceptions of four language skills (reading, listening, writing, speaking) targeting undergraduate students in English‐medium instruction (EMI) courses in Japan. The students’ academic performance (course grades and quiz scores) was also compared to their vocabulary knowledge. Results showed that learners with larger aural vocabulary sizes were more confident in spoken language use, and those who self‐rated higher on their vocabulary knowledge were more likely to perceive themselves as proficient in productive language skills. Interestingly, learners with larger written vocabulary sizes tended to perceive themselves as less proficient in performing EMI tasks. Results also showed that none of the vocabulary measures were significantly associated with academic outcomes. Interview data suggest that EMI students’ performance could be affected by the complex interplay of various factors, though not necessarily a large vocabulary size alone. Based on these findings, implications are discussed in terms of teaching and assessing vocabulary knowledge in EMI courses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".