A Comparative Study of Test Takers’ Performance on Computer-Based Test and Paper-Based Test Across Different CEFR Levels
Bibliographic record
Abstract
Computer-based test (CBT) and paper-based test (PBT) are two test modes to the test takers that have been widely adopted in the field of language testing or assessment over the last few decades. Due to the rapid development of science and technology, it is a trend for universities and educational institutions striving rather hard to deliver the test on a computer. Therefore, research on the comparison between these two test modes has attracted much attention to investigate whether the PBT could be completely replaced. At the same time, task difficulty is always a key element to reflect test takers’ performances. Numerous studies have laid a solid foundation and guidance about the comparative study of test takers’ performance on CBT and PBT, but there still remains a scarcity from the perspective of task difficulties with different Common European Framework of Reference for Languages (CEFR) task levels in particular. This study, therefore, compared the test takers’ performance on both CBT and PBT across tasks with different CEFR levels. A total of 289 principal recommended high school test takers from Macau took the pilot Test of Academic English (TAE) at a local university. The results indicated that there was a difference between test takers’ performance on different test modes across different CEFR levels, but only CEFR A2 level showed a statistically difference between CBT and PBT. And since science and technology are continuously developing, it is essential for the university to consider switching the test mode from PBT to CBT.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".