Exploring Identity Perception and Bilingual Education Dynamics in Taiwanese University Settings
Bibliographic record
Abstract
In an increasingly interconnected world, proficiency in English is becoming indispensable, prompting Taiwanese universities to implement English language requirements ranging from one to four years. This initiative aligns with a national bilingual education program aimed at bolstering the English proficiency of college students to enhance their international competitiveness. Consequently, English-medium instruction has become prevalent in various university courses, facilitated by the Freshman English course serving as a transition to English-mediated teaching. While linguistic development is emphasized, the dynamics of identity perception among students cannot be overlooked, as language identity profoundly impacts their learning experiences and growth. This study delves into the identity perception of college freshmen in Taiwan, where bilingual education is heavily emphasized by the government. The purpose of the study is to investigate how Taiwanese college freshmen perceive their identity as they participate in English language learning. In addition, this study aims to examine the influence of gender and college major on the individual differences in identity perception among college freshmen engaged in English language learning. Employing Gao et al.’s (2005) Likert-scale questionnaire on self-identity change, the research surveyed 360 freshmen from a university in northern Taiwan. Data analysis performed with SPSS includes two stages. At the first stage, descriptive statistics revealed that participants exhibited agreement on self-identity changes in four categories: self-confidence, zero, productive and additive. At the second stage, a multivariate analysis of variance demonstrated significant main effects of gender and major on identity changes. Female students exhibited higher self-confidence, additive and productive changes compared to male students. Furthermore, liberal arts majors experienced more pronounced self-confidence, additive and productive changes than their counterparts in business, science, and engineering majors. A Post Hoc test unveiled significant differences, with business majors scoring higher than science majors in subtractive and split changes, while science majors differed significantly from liberal arts majors in zero change. The study’s implications extend beyond theoretical understanding, informing pedagogical practices to enhance language learning experiences.
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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 | 0.002 | 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".