Developing a Scenario-Based English Language Assessment in an Asian University
Bibliographic record
Abstract
A new computer-assisted test of academic English for use at an Asian University was commissioned by administrators. The test was designed to serve both placement and diagnostic purposes. The authors and their team conceptualized, developed, and administered a scenario-based assessment with an online delivery with independent and integrated language skills tasks. The project provided many advantages: (1) the test would be locally developed by university faculty and students who would have a good understanding of the test takers and the needs of the university, (2) the test would use topics, texts, and materials and technology that are socially and culturally appropriate and sensitive to the local context, and (3) the sustainability of the test would be higher as it were cost-effective in the long run in comparison to purchasing and renewing a license for an international test. This article documents the key considerations and processes in the development of this new scenario-based test of academic English that was conceptualized and designed by faculty and students collaboratively. It also discusses the challenges involved in the implementation of such a test, including resistance from local assessment culture and high workload of language teachers.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".