Prioritizing information over grammar: a behavioral investigation of information density and rhetorical discourse effects on EFL listening comprehension
Bibliographic record
Abstract
This study investigated the impact of information density on English as a Foreign Language (EFL) listening comprehension, testing the hypothesis that listeners prioritize message understanding in information-rich discourse over grammar-focused analysis in rhetorical discourse. A quasi-experimental design was employed with 26 EFL college students, who listened to two audio passages: one information-rich and the other rhetorical. Behavioral measures, including listening comprehension scores and response times, revealed that participants demonstrated significantly higher comprehension accuracy (96% vs 44.3% accuracy) and faster processing times (37 min versus 41 min) when listening to the information-rich audio compared to the rhetorical audio (p < .005). Survey data further indicated that participants prioritized semantic content extraction over grammatical analysis, especially when engaging with the rhetorical passage. These findings support the hypothesis that listeners strategically adjust cognitive processing based on discourse information density, favoring meaning extraction in more informative contexts, with a decrease in grammatical parsing routines. The results highlight the role of information density in L2 listening comprehension and suggest that language learning materials should prioritize informative discourse to facilitate more efficient and effective processing.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".