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Record W2788632345 · doi:10.5539/elt.v11n3p50

Teaching Accounting in English in Higher Education – Does the Language Matter?

2018· article· en· W2788632345 on OpenAlexvenueno aff
Huan Cai, Meining Wang, Yingmei Yang

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyActive listeningMathematics educationBusiness EnglishAccountingEnglish languageReading (process)Language proficiencyPedagogyLinguistics

Abstract

fetched live from OpenAlex

Learning business related courses, especially accounting, in English is a challenge for many Chinese students. The purpose of this study is to provide some insights into the role of the language in accounting learning. We investigate this issue in the program of Teaching Business Related Courses in English for undergraduate students at Guangdong University of Foreign Studies. Accounting courses in English at GDUFS are taught to two different groups: English majors with higher English proficiency who are required to receive 2 years of intensive training in listening, speaking, reading and writing before taking the accounting course in English and non-English majors who do not receive the same level of English training as English majors do. We find that there is no direct significant relationship between accounting learning and students’ English proficiency but we do find a strong correlation between students’ analytical ability and their accounting learning instructed in English. We also find that motivation, specifically students’ clear career path in the accounting field, plays an important role in determining their performance in accounting learning. The findings in this paper have meaningful implications for the feasibility of teaching non-English majors accounting in English and for designing a good learning environment in English educational settings.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.248
Teacher spread0.241 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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