Effects of Teaching Strategies Using Multimedia on Vocabulary Retention of Grade 6 Students
Bibliographic record
Abstract
Although English language instruction has been available in Thailand since childhood, the proficiency level remains very low compared to other non-native English-speaking countries. This research aims to provide alternative instructional methods for further development of English language instructions in Thailand by focusing on vocabulary retention and how words are retained (Sayed, 2022). Vocabulary is essential in language learning since rich vocabulary enhances language proficiency (Aswad, Yassi, Pammu, Nasmilah, & Rezaei, 2022; Mahmood & Ahmed, 2023). This article reports the effects of teaching strategies using multimedia on vocabulary retention of Grade 6 students. Multimedia, such as visual aids and an online self-review platform, combined with teaching models, teaching and explaining vocabulary, blended learning, TPACK, and bite-sized learning, were used to design multimedia lessons. The research found that multimedia significantly affected the students’ short-term and long-term vocabulary retention. The short-term vocabulary retention test scores averaged 47.85, while the long-term vocabulary retention test scores averaged 39.47. The students maintained 82.5% of their vocabulary even after seven days, demonstrating the effectiveness of the multimedia teaching strategies in enhancing vocabulary retention. Students also expressed high satisfaction with the multimedia approach, with an average satisfaction score of 4.14 out of 5. Female students averaged higher satisfaction than male students across all dimensions, with the difference being significant in overall satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".