Enhancing Vocabulary Knowledge Through Multimodal Learning in Thai Primary Classrooms
Bibliographic record
Abstract
Vocabulary acquisition is a core part of English as a Foreign Language (EFL) learning, especially for young learners who are building their language infrastructure. Many conventional textbook-driven methods focus on rote learning, which can lead to disengaged students and less authentic, less effective learning. It explores the use of multimodal strategies of learning for vocabulary learning of Thai primary school students. Multimodal training combines visual, auditory and kinesthetic aspects; this contributes to reaching different types of learners and favors more efficient ways of processing and retaining information. The study utilized a quasi-experimental research design with 14 first and second graders learning EFL in a Thai public primary school. The intervention consisted of 16 hours of multimodal instruction, which included images, gestures, pronunciation practice, and interactive activities. Data were obtained from pre- and post-intervention tests of vocabulary (receptive and productive vocabulary knowledge), while focus groups were used to probe students’ experiences of the learning process. Quantitative results showed that participants’ receptive and productive vocabulary knowledge increased significantly, and the multimodal instruction approach had a strong influence on them. From the qualitative results, students perceived multimodal learning as enjoyable, motivating, and low in anxiety and thus developed more positive attitudes toward learning English vocabulary. Such results align with the integration of multisensory approaches in primary EFL, confirming their ability to enhance motivation and language achievement across diverse learner characteristics. Practical implications for EFL teachers who aim to integrate student-centered tasks and active learning are provided in this research. In this sense, incorporating multimodal activities into vocabulary teaching can contribute to more inclusive and effective learning environments that help develop young learners’ language skills.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".