The Analysis of the Problems in Business English Teaching Assessment System and Suggestions for Improvements
Bibliographic record
Abstract
As an undergraduate program, Business English is still in the initial stage of development in China. It is a new inter-disciplinary and applied discipline, the teaching of which is practical and diverse. Teaching assessment is an important part of the curriculum teaching because it is beneficial for the teacher to obtain feedback, improve teaching quality and maintain the teaching foundation. It is an effective measure for students to find the most suitable learning methods, correct learning habits and enhance learning efficiency. Teaching assessment plays a macro-control role in the implementation of teaching activities and can ensure the realization of teaching effects. Most of the current assessments of Business English teaching is in line with the language test mode of college English. Their assessment of the students’ Business English ability is conducted from the perspectives of using vocabulary, syntax and text. They only detect one of the students’ comprehensive abilities, namely, language ability while ignoring the assessment of application ability and professional literacy as well as other capabilities. This paper conducts a questionnaire survey on sophomores, juniors, seniors, and students who have graduated majoring in Business English at Guangdong University of Foreign Studies and Hubei University, aiming at finding out problems in the current assessment system for Business English teaching. Based on the analysis of the problems, suggestions for establishing a new Business English teaching assessment system are proposed.
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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 | 0.044 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".