Research of Enhancing Business English Writing Skills Based on the Blended Learning Model in Vocational College
Bibliographic record
Abstract
Business English writing is a compulsory skill to be mastered by Business English major students but there is little progress in acquiring writing skills due to unattractive and ineffective teaching methods. The objective of this study was to investigate the effects of using the blended learning mode of task collaborative learning in improving Business English writing skills of students in China. The sample of the study consisted of 80 second year Chinese Business English students from Guangdong Polytechnic Science and Technology; two business English lecturers also participated in this study. All the students were of the same age group with similar results in their first-year English Language examination. Both the Experimental Group and Control groups had 40 students each and they were chosen as intact-groups. The Experimental Group was taught using the blended learning mode of task collaborative learning and the Control Group was taught using the conventional method over a period of eight weeks. The quantitative data were analyzed using the SPSS Program for Windows Version 25, in which ANCOVA test were applied for the inferential statistics. The findings from the quantitative data analyses indicated that the Experimental Group outperformed the Control Group in their overall scores in writing, focusing on topic, supporting details, coherence and cohesion, grammar and vocabulary. Also, students improved their writing skills through collaborative leaning. As such, this study has crucial pedagogical implications as it suggests that the blended learning mode of task collaborative learning can be used as an alternative method in China to improve students’ Business English writing skills.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".