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Record W4394752881 · doi:10.5430/wjel.v14n4p336

Effectiveness of Morphological Intervention on Two Measures of Vocabulary Knowledge and Listening Comprehension

2024· article· en· W4394752881 on OpenAlexvenueno aff
Shu‐Ping Chen, Nur Rasyidah Mohd Nordin

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyActive listeningIntervention (counseling)PsychologyComprehensionListening comprehensionReading comprehensionCognitive psychologyMathematics educationLinguisticsReading (process)Communication

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.334
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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