Second Language Vocabulary Learning from Viewing Video in an EFL Classroom
Bibliographic record
Abstract
This study examines the effects of an English vocabulary video on second language vocabulary learning by English as a foreign language (EFL) learners. The conceptual framework is underpinned by Mayer’s (2005) Cognitive Theory of Multimedia Learning. The participants were 25 undergraduate students studying at a Thai university. To collect data, five types of research instruments were utilized including a survey of English vocabulary knowledge, pre-test, post-test, the English vocabulary video, and a questionnaire. The findings of this study revealed an increase in the post-test scores after the Thai EFL learners engaged in learning second language (L2) vocabulary using an English vocabulary video. Moreover, the findings also uncovered that the EFL learners gained L2 vocabulary knowledge after viewing the video containing first language (L1) and L2 captions, images, and L2 audios which are relevant to the target words. Furthermore, the results revealed that the EFL students preferred learning L2 vocabulary via video containing both L1 and L2 captions, interesting and related images, and the proper volume of audios. Therefore, the significant findings of this study lead to theoretical and pedagogical implications regarding the significant role of multimedia learning in terms of the links between visual and auditory information.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".