Vocabulary Knowledge and Metacognitive Awareness in L2 Listening: Testing the Core-Peripheral Hypothesis
Bibliographic record
Abstract
This study tested predictions from Hulstijn’s core-peripheral framework for second-language listening by comparing the relative contributions of vocabulary knowledge and metacognitive awareness among Chinese EFL learners. Participants were 166 third-year English majors with intermediate proficiency (TEM-4 scores 65–75). They completed comprehensive assessments of four vocabulary dimensions: written breadth, written depth, aural breadth, and aural depth. They also completed a measure of metacognitive awareness and a standardized listening test. Hierarchical regression analysis revealed that vocabulary knowledge accounted for the majority of variance in listening. Aural vocabulary measures explained substantial additional variance beyond written measures. By contrast, metacognitive awareness contributed only minimal incremental variance after vocabulary had been entered into the model. Within the vocabulary dimensions, aural depth and aural breadth were the strongest predictors of performance. The coefficients for written measures were markedly reduced once aural measures were statistically controlled. These results support a hierarchical distinction between core and peripheral components. They position vocabulary knowledge, particularly in its aural modality, as foundational to listening success, whereas metacognitive awareness plays a supplementary role that may vary across proficiency levels. The clear superiority of aural over written vocabulary challenges assessment practices that prioritize orthographic knowledge, underscoring the need for modality-specific vocabulary instruction to enhance L2 listening comprehension.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".