Corpus-aided Business English Collocation Pedagogy: An Empirical Study in Chinese EFL Learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study reports an empirical study of an explicit instruction of corpus-aided Business English collocations and verifies its effectiveness in improving learners’ collocation awareness and learner autonomy, as a result of which is significant improvement of learners’ collocation competence. An eight-week instruction in keywords’ collocations, with the help of AntConc and self-constructed Business English Pedagogical Corpus combined with COCA general corpus and Wikipedia corpus, was imparted to 23 undergraduate learners majoring in Business English in Guangdong University of Foreign Studies. They took the collocation competence pre-test and post-test before and after the teaching experiment which was phased into two themes and submitted learning reflective journals at the end of each theme instruction and answered a questionnaire at the final end. The data from the tests, reflective journals and questionnaire collaboratively suggest that given appropriate guidance EFL Business English learners can take a more active role in raising their collocation awareness and developing learner autonomy and thus improve their collocation competence significantly. The results from the test analysis also indicate that the corpus-aided Business English collocation pedagogy is proved to be more effective for intermediate and advanced level learners rather than lower level ones. The findings have pedagogical implications for EFL Business English instructors and learners.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it