The Effects of Multimodal Teaching on English Vocabulary Knowledge of Thai Primary School Students
Bibliographic record
Abstract
It is increasingly prevalent in digital learning and teaching strategies for discerning a global perspective on creating the student learning experience. Multimodality is an emergent phenomenon that may influence how digital learning is designed, especially during the COVID-19 pandemic in which immersive learning environments, such as a virtual learning platform, were employed. This immersive platform may assist learners in engaging in, paying attention to, and reflecting on their learning. This quasi-experimental study examined the effects of multimodal teaching on primary school learners’ English vocabulary and their attitude toward the learning environment. The participants were 59 primary school students in the northeastern part of Thailand. They were divided into two groups: experimental and control groups. The former consisted of 33 students, while the latter comprised 26. Following Nation’s (2013) word knowledge framework, two tests were developed to measure participants’ receptive and productive knowledge of the words. L2 vocabulary scholars validated the tests, and the reliability of the tests was checked using Cronbach’s alpha coefficient. The questionnaire was also developed to explore the participants’ attitudes toward using multimodal teaching methods to improve their vocabulary knowledge. The results showed that although both groups increased their vocabulary knowledge, the statistical analysis revealed that the multimodal teaching technique significantly enhanced participants’ receptive and productive vocabulary knowledge. The results also indicated that primary school participants had a positive attitude toward using multimodal teaching methods to improve their vocabulary knowledge. The current study suggests that the multimodal teaching method effectively improves Thai primary school learners’ receptive and productive word knowledge and helps them learn new vocabulary.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".