Effectiveness of Multimodal Glossing Reading Program on English Vocabulary Acquisition
Bibliographic record
Abstract
Vocabulary is an essential factor in English language learning. The competency in vocabulary acquisition enables learners to develop their language skills, especially, reading skill. Presently, with the advent of technology, teaching media with visual aid is used worldwide for media-assisted language learning. The study aimed to develop, implement Multimodal Glossing Reading Program (MMGR), used for enhancing English vocabulary acquisition, and compare the program with Textual Glossing Reading Program (TGR) and a control group. One control group and two experimental groups were performed by 72 university learners of English as a Foreign Language (EFL). An experimental research with randomized pretest-posttest control group was used. Pre-and post-tests of meaning and form recognition were administered. The scores learners obtained from the pre-test and post-test within groups and between groups were analyzed by MANOVA. The findings revealed that MMGR was effective than TGR and the control group. It is suggested that teaching English vocabulary through MMGR program not only helps learners have the ability in vocabulary acquisition, but also enables the instructors to use the program as a potentially supplemental material or alternative method in teaching vocabulary as well.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".