A Study on English Vocabulary Learning Strategies for Non-English Majors in Independent College
Bibliographic record
Abstract
This paper has investigated the pattern of English vocabulary learning strategies used by the non-English major students in Chinese Independent Colleges: their attitudes to vocabulary learning; the strategies they usually use; the problems of vocabulary learning in English study; the most effective vocabulary learning strategies they assume; differences among the students with different grades, genders, English proficiency and so on. The survey has been done on the non-English majors from grade 1 to 3 in Beihai College of Beihang University. The aim of the paper is to help English learners to improve their ability of vocabulary learning and develop their English proficiency by providing some practical suggestions to both teachers and learners.Key words: Vocabulary learning strategies; Non-English majors; Suggestions Resume: Cet article a etudie les modeles des strategies de l'apprentissage de vocabulaire anglais utilises par les etudiants qui ne sont pas dans la specialite de langue anglaise dans les universites independantes chinoises: leurs attitudes a l'apprentissage du vocabulaire, les strategies qu'ils utilisent habituellement, les problemes de l'apprentissage du vocabulaire dans l'etude de l'anglais, les strategies de l'apprentissage du vocabulaire les plus efficaces qu'ils assument, les differences chez les etudiants ayant des qualites differentes, des sexes differents, des niveaux de la maitrise de l'anglais differents et ainsi de suite. Le sondage a ete effectue sur les etudiants qui ne sont pas dans la specialite de langue anglaise de la 1ere annee a la 3eme annee a Beihai College de l'universite de Beihang. L'objectif de ce document est d'aider les apprenants en anglais aameliorer leur capacite d'apprentissage du vocabulaire et de developper leurs competences en anglais en fournissant des suggestions pratiques aux enseignants et aux apprenants.Mots-cles: strategies d'apprentissage du vocabulaire; specialite non-anglophones; suggestions
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".