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Record W2363435664

Bilingual Education in College Specialist Courses: An SLA Perspective

2005· article· en· W2363435664 on OpenAlexaboutno aff
Ding Zhan-ping

Bibliographic record

VenueJournal of Zhejiang University(Humanities and Social Sciences) · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Relevance (law)CurriculumPedagogyPerspective (graphical)Foreign languageBilingual educationInternationalizationSociologyMathematics educationPsychologyComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Bilingual education in China's college specialist c ourses is an important step toward internationalization of the country's higher education. In practice, bilingual education in this context is realized by using a foreign language, mostly English, to teach specialist courses specified in th e curriculum. English in the Chinese context is in the extending circle and is l earned as a foreign language mainly through school education, which is often cha racterized by poor environmental support, lack of relevance to learner needs and weak learner motivation. Bilingual education in specialist courses, however, he lps to create a meaningful environment for learners to acquire English. It also provides relevance and a context for learners to examine and internalize how Eng lish is used by a specialist discourse community to achieve its communicative pu rpose. From the perspective of second language acquisition (SLA), using English to teac h specialist courses is a valuable approach to improve learners' comprehensive a bility to use English. A specialist field as a discourse community has a number of specialist genres in its furtherance of academic and professional goals, such as journal articles, coursebooks and newsletters. Use of language in these spec ialist genre types is governed and constrained by the rules and conventions of r elevant specialist discourse communities, which provides an ideal ″comprehensib le input″for learners. Learning English in this manner also provides relevance to learners' learning needs and future work needs, improving learners' motivati on to learn English as a result. Bilingual education in specialist courses as is advocated in Chinese colleges di ffers greatly from foreign language education, but it also differs in nature fro m the three models practiced mainly in the United States and Canada-immersion program, maintenance bilingual education and transitional bilingual education. The main differences lie in the three aspects of teaching purposes, the target l anguage to be learned and the language acquisition environment. To investigate the validity of bilingual education in specialist courses at Zhej iang University, P. R. China, a study was carried out using questionnaire survey s, classroom observations and interviews. The results show that the attitudes of both teachers and students towards these bilingual education programs are posit ive. Based on the theoretical discussion and the research data from this empirical st udy, we propose an English-Chinese interdependencemodel of bilingual educat ion in current college specialist courses. This model should be based on the fol lowing three principles: (1) Bilingual courses should use original English textb ooks. These textbooks should be selected to reflect the latest developments of t he field concerned and should be used with guidance of the course syllabus. (2) Bilingual teachers should mainly use English in class, and they should utilize C hinese as valuable resources when need arises. (3) Bilingual teachers and Colle ge English teachers need to collaborate on bilingual programs. A link should be established between general college English courses and bilingual specialist cou rses. This model mainly argues that while using English to teach specialist cour ses the supportive role of Chinese should not be neglected. This is beneficial t o the learning of specialist knowledge and the acquisition of English as well.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.278
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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