Corpus-informed Dynamic Assessment of Content and Language in a Business English Course
Bibliographic record
Abstract
Assessing both language and content has long been recognized as a central challenge in the Content and Language Integrated Learning (CLIL). This study investigates how corpus-informed Dynamic Assessment (DA) can capture learners’ development of subject knowledge and disciplinary literacy in an undergraduate Business English course taught through CLIL. Grounded in Vygotskian sociocultural theory, DA integrates assessment with mediation, enabling assessment to reveal both actual performance and developmental potential. Thirty-two undergraduate Business English majors participated in a one-semester quasi-experimental study conducted in the course “Comprehensive Business English.” Corpus keyword analysis was applied to pre- and post-instruction writing tasks to trace changes in learners’ use of disciplinary vocabulary and collocations, while corpus-informed feedback served as mediation. After one semester of instruction, results show that learners’ increased use and greater variety of business keywords indicate deeper conceptual understanding and enhanced disciplinary literacy. Collocational and pattern analyses of the focal business keyword “price” further suggest a shift from descriptive to analytical expressions, evidencing movement within learners’ zone of proximal development (ZPD). These findings suggest that corpus-informed DA provides a useful means of assessing and fostering both content learning and language development in CLIL and English for Specific Purposes (ESP) pedagogy.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".