FL Vocabulary Learning of Undergraduate English Majors in Western China: Perspective, Strategy Use and Vocabulary Size
Bibliographic record
Abstract
The present study, by use of questionnaire and vocabulary tests, has investigated the foreign language vocabulary learning situation of 481 undergraduates in terms of their perspective of vocabulary learning, strategy use and vocabulary size. Based on the questionnaire investigation and vocabulary level tests, the characteristics of the subjects’ foreign language vocabulary learning have been outlined: (1) Objects have the belief that vocabulary should be learned by using them; the most frequently used strategies include dictionary use, guessing the meaning and note-taking; objects average out to a small vocabulary size. (2) Significant differences exist in the use of some strategies between different graders and different majors. (3) Four strategies (cognitive & meta-cognitive) significantly correlate with vocabulary size. In order to facilitate learners’ foreign language vocabulary development, four tentative solutions have been offered to solve the relevant problems: (1) Raising learners’ awareness of FL vocabulary development and try to enlarge their vocabulary size; (2) Developing learners’ productive vocabulary by using the target words in authentic contexts; (3) Fostering learners’ awareness of using appropriate vocabulary learning strategies; (4) Broadening learners’ vocabulary learning approaches by utilizing both the intentional (direct) learning methods and the incidental (indirect) ones. The research results may probably offer empirical reference for the foreign language vocabulary teaching and learning in China.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".