Error Recognition Tests as a Predictor of EFL Learners' Writing Ability
Bibliographic record
Abstract
It is not certain whether multiple-choice tests have essentially the same predictive validity for candidates in different academic disciplines, where writing requirements may vary. Still, at all levels of education and ability, there appears to be a close relationship between performance on multiple-choice and essay tests of writing ability. And yet each type of measure contributes unique information to the overall assessment. In this study the relationship between Iranian EFL students' performance on an error recognition test and their writing ability was investigated. Using appropriate statistical tests such as Pearson correlation coefficient formula and Matched t-test, the data collected from the participants who were selected randomly and voluntarily cooperated during the different phases of the study were analyzed. The results of the study showed that there is no statistically significant relationship between test takers' performance on the error recognition test and their writing ability. The finding of the study can be justified on the ground that error recognition tests gauge construct-irrelevant factors which might not be ever-present factors influencing test takers' writing ability.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".