Effectiveness of Morphological Intervention on Two Measures of Vocabulary Knowledge and Listening Comprehension
Bibliographic record
Abstract
The morphological intervention has been long advocated to enhance students’ vocabulary knowledge and language outcomes. Nevertheless, little is known about how the intervention affects listening comprehension through the mediation of vocabulary knowledge. To address this unanswered gap, this study used a quasi-experimental design to examine the direct effects of the morphological intervention on two measures of vocabulary knowledge (vocabulary depth and breadth) and listening comprehension and whether increased vocabulary depth and breadth mediate the effects of the intervention on listening comprehension. Two groups of Chinese university students (experimental n=32; control n=32) participated in this study. Results of MANOVA measurements indicated direct, significant effects of the intervention on vocabulary depth and breadth. Additionally, analysis from a PLS-SEM modeling found effects of morphological intervention on listening comprehension were totally indirect and the indirect effects were significantly mediated through both vocabulary depth and breadth. Notably, vocabulary depth showed a stronger mediating effect than vocabulary breadth. The findings of this research expanded the existing understanding of how morphological intervention improves EFL university students’ vocabulary knowledge and 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".