Cross-Mode Comparability of Computer-Based Testing (CBT) Versus Paper-Pencil Based Testing (PPT): An Investigation of Testing Administration Mode among Iranian Intermediate EFL Learners
Bibliographic record
Abstract
Advent of technology has caused growing interest in using computers to convert conventional paper and pencil-based testing (Henceforth PPT) into Computer-based testing (Henceforth CBT) in the field of education during last decades. This constant promulgation of computers to reshape the conventional tests into computerized format permeated the language assessment field in recent years. But, enjoying advantages of computers in language assessment raise the concerns of the effects that computerized mode of testing may have on CBT performance. Thus, this study investigated the score comparability of Vocabulary in Use test taken by 30 Iranian undergraduate students studying at a state university located in Chabahar region of Iran (CMU) to see whether scores from two administrations of testing mode were different. Therefore, two similar tests were administered to the male and female participants on two testing mode occasions with four weeks interval. Employing One-Way ANOVA statistical test to compare the mean scores and Pearson Correlation test to find the relationship between mode preference and performance revealed that two sets of scores were not different and gender difference was not also considered a variable that might affect performance on CBT. Based on the results, computerized version of the test can be considered a favorable alternative for the state undergraduate students in Iran.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".