The Effects of Test Facets on the Construct Validity of the Tests in Iranian EFL Students
Bibliographic record
Abstract
Language testing as a main device in assessing the learners` knowledge and language abilities plays a key role in training programs. Generally, the goal of language testing is to assure the extent to which learners have achieved the instructional goals during a course. The main objective of many studies in language testing has been to investigate whether test facets affect construct validity of the test or not. Therefore, in this study, we investigated whether the EFL Iranian participants` performances were different with respect to the different test facets and if these performances had some effects on the construct validity of the tests. In this investigation, the students were selected of 50 Iranian EFL students aged between 21 to 30 years, from two branches of Islamic Azad University, Dezful and Andimeshk, Iran. The 17 participants, placed at the low level in the Nelson proficiency test, received a test. The test facets included the integrative forms such as cloze-test, c-test, and discrete test items such as multiple choice and true/false. By statistics analyses, the significant differences were assessed in the test facets. Our results revealed that significant differences existed in the test facets among the performances of Iranian EFL students. Because of the integrity of the several abilities and mental strategies, the cloze-test was the most difficult form of testing. Keywords : Test facets; Construct validity; Integrative test items; Discrete test items
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".