The Relationships among Motivation, Learning Styles and English Proficiency in EFL Music Students
Bibliographic record
Abstract
This paper reports a study on the relationships among motivation, learning styles and English proficiency in a Chinese context. 308 students who studied English as a foreign language (EFL) were sampled from seven departments in Xinghai Conservatory of Music. Quantitative data were collected through an on-line survey to address three questions: 1) Do music students have a particular learning style preference? 2) What are the relationships among motivation, learning styles and English proficiency? 3) How could EFL teachers better accommodate students’ motivation and learning styles to improve their English proficiency? Nonparametric Kruskal-Wallis tests showed that music students varied a lot in their preferences of learning styles, thus problematising the practice of using one learning style to gloss over the preferences of music students. Correlation analyses demonstrated that a) motivation and English proficiency was moderately correlated; b) none of the learning styles was correlated with English proficiency, except that active students performed slightly worse in the final exam; c) students who favoured the visual style were found to be less motivated. In light of these findings, we discuss the methods of grouping students and revamping EFL course content from English for General Purposes to English for Specific Purposes for music students.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".