The Use of Oxford’s Memory Strategies to Improve Vocabulary Learning Among Eleventh Grade EFL Students in Oman
Bibliographic record
Abstract
Omani students must acquire English vocabulary to achieve educational qualifications and effectively communicate with English-speaking individuals, as vocabulary plays a crucial part in studying a foreign language and achieving academic success. Omani eleventh-grade students require assistance in employing effective ways for acquiring and retaining English vocabulary. They struggle with word retrieval in both spoken and written language. This study investigates the memory strategies utilised, preferred strategies, and influencing factors among eleventh grade EFL students in Oman. This study utilises an explanatory sequential mixed methods methodology. Convenience sampling was used to involve 126 students in this study. The researcher gathered data through a questionnaire, a semi-structured interview, and an observation checklist. The questionnaire data is analysed by descriptive statistical analysis, which includes calculating the mean and standard deviation. Thematic analysis is utilised for analysing the semi-structured interview data, while observation analysis is carried out to assess the classroom observation checklist. The findings showed that the most utilised category of methods is reviewing well (x=3.99), followed by employing actions (x=3.07). Interviews reveal that students favour utilising movies, visual aids, gaming, or competitive activities while interacting with their peers and friends. Additionally, CML tactics are predominantly utilised by teachers in their classrooms. The study results would assist educators in developing their vocabulary teaching strategies more efficiently, resulting in increased advantages for students. The curriculum developers could create tasks that enhance language acquisition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".