Application of Case-Task Based Approach in Business English Teaching—A Case Study of the Marketing Course in SEIB of GDUFS
Bibliographic record
Abstract
Business English Teaching aims at cultivating students’ ability to analyze and solve problems, improving students’ comprehensive language competence and honing their business practical skills. Adhering to the principle of learning by doing and learning by teaching others, Case-Task Based Approach emphasizes students’ ability of language use in authentic situation as well as their competence of taking part in social practices, which, to a large extent, corresponds to the objectives of Business English Teaching. Based on a diachronic combing of research ideas of Case-Task Based Approach, this writing analyzes the marketing course offered in School of English for International Business of Guangdong University of Foreign Studies through Case-Task Based Approach, expounds the implementation process of this approach and investigates into its strengths and weaknesses. Finally this writing will have a tentative exploration on how to improve the approach in practice so as to enhance the understanding of its application in Business English Teaching.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".