Teaching Accounting in English in Higher Education – Does the Language Matter?
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".