Students' Attitudes to the Implementation of Vocabulary Learning Strategies in Writing Task
Bibliographic record
Abstract
Vocabulary is paramount in language learning. Learners can strategize vocabulary learning using vocabulary learning strategies (VLS), namely memory, cognitive compensation, metacognitive, social, affective, and determination. If used appropriately, VLS can help learners in writing. The study investigates the level of students' attitudes and use of VLS in writing. Also, it examines the male and female students in their attitudes and the use of VLS in writing. Finally, it investigates students' attitudes in writing according to their English grades. A quantitative research method, namely a survey, was employed as the research design in the study. It employed 71 diploma students taking engineering majors in one of the technical universities on the East Coast of Malaysia. The study found that students demonstrated a moderately high attitude in writing but moderately low VLS scores. There was also no significant difference in the attitudes and use of VLS between genders. Finally, there was insufficient evidence to show a significant difference in attitudes and English grades. Nevertheless, the study implied a need to familiarize the students with VLS to improve their writing skills. However, students were not required to use all the VLS as some VLS might be appropriate for a particular task but not the others.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".