The Project-Based Flipped Learning Model in Business English Translation Course: Learning, Teaching and Assessment
Bibliographic record
Abstract
This study designs a project-based flipped learning model for Business English Translation course and tests its efficacy by an empirical study on 65 third-year English major students divided into the experimental class and control class. This study incorporates the learning, teaching and assessment activities of both the students and teachers into a project-based flipped learning model by setting translation projects and dividing the students of the experimental class into a client group and three translator groups in each business translation unit. After one 16-week semester of experiment, this study conducts a post-test, questionnaires and interviews on both the experimental class and control class to test the efficacy of this new flipped learning model. The statistics and facts collected from the above-mentioned research methods suggest that the project-based flipped learning model can significantly enhance the students’ motivation to learn out of class, stimulate their participation in class and raise their self-evaluation on translation competence. Additionally, this study finds that the traditional product-oriented summative assessment model is ineffective for Business English Translation course in a flipped-learning context. Therefore, this study tentatively proposes a process-oriented assessment model that is compatible to the flipped learning methodology so as to build integrated flipped classroom pedagogy with teaching, learning and assessment in a virtuous circle of mutual reinforcing.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".