A Study on EFL Vocabulary Teaching for Non-English Major College Students in China Based on Multimodal Theory
Bibliographic record
Abstract
In the era of global diversification and technological advancement, the vocabulary teaching of teaching English as a Foreign Language (EFL) faces new challenges and opportunities. Multimodal theory-based teaching has gained significant attention in academic research. This study addresses the challenges in EFL vocabulary acquisition among non-English major students by employing the Explanatory Sequential Design (ESD). Initially, this study conducted a quantitative semi-structured questionnaire involving 78 non-English major college students in China. It identified key vocabulary learning issues, including monotonous vocabulary presentation content, limited vocabulary teaching methods and tedious vocabulary tasks. To address these challenges, this research implemented a research practice at Wenzhou Business College (WZBC) for one semester with 20 non-English major students, focusing on pre-class, in-class, and post-class stages. This study collected data to evaluate the effectiveness of the intervention and conducted a follow-up interview with 5 students to gain deeper insights. This study shows that applying multimodal theory in EFL vocabulary teaching enhances vocabulary acquisition by engaging students with varied content, interactive methods, and customised tasks that build confidence, thereby boosting interest and participation in vocabulary learning. These findings offer valuable insights for advancing EFL educational practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".