Navigating Motivation: Freshmen’s Quest for English Proficiency in Taiwanese University Contexts
Bibliographic record
Abstract
English language proficiency is crucial in today’s globalized world, prompting Taiwanese universities to mandate one to four years of English courses. With the implementation of a national bilingual education program for college students, universities aim to enhance English proficiency through Freshman English courses, facilitating a smooth transition to English-mediated instruction in subsequent years, particularly in specialized subjects. Despite the official emphasis on English’s importance for future success, the effectiveness of language instruction hinges on understanding students’ motivations. This study investigates English learning motivations among Taiwanese undergraduates, utilizing a Likert-scale questionnaire to discern general trends. The sample comprises 360 undergraduates from a northern Taiwanese university, with factor analysis revealing five motivational factors: intrinsic appreciation, instrumental motivation, external expectations motivation, exam-driven motivation, and interpersonal influence. These findings offer valuable insights into the multifaceted nature of English language learning motivations among Taiwanese freshmen, contributing to existing literature by highlighting nuanced motivational factors. The identified motivational factors hold implications for both theoretical understanding and pedagogical approaches, providing educators with insights to tailor instruction to students’ diverse motivations. This study aims to enrich the discourse on language learning motivation and serve as a foundation for future research in similar contexts.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".