Construct Validity of MSRT Reading Comprehension Module in Iranian Context
Bibliographic record
Abstract
Many researchers were interested in validity of the language proficiency tests in the previous decades. The present study aims to study the construct validity of the Ministry of Science, Research, and Technology Reading Comprehension module (MSRT) in the Iranian context. After administering a standard language proficiency test (OPT) 65 intermediate EFL learners were selected. The participants passed some 72 hours learning MSRT techniques and the strategies needed to be adopted in the MSRT test sessions. After all these hours, MSRT test was administered to those learners in some institutes. Later EFL questionnaire on reading was run among them and they were asked to check the skills they have applied during MSRT reading comprehension test. Both qualitative and quantitative approaches helped this study to collect and analysis the data. By qualitative approaches, this study collected expert’s and test takers’ judgments and by quantitative approaches, collected factor analysis. The results revealed that there were a significant consent among the judgments of the experts and an agreement among the test takers on the skills being measured by MSRT reading module. Finally, explanatory factor analysis did not reveal similar findings as those in the judgmental phase of the study. The items in the MSRT reading comprehension tests didn’t confirm that MSRT reading parts assess the reading skills in the Iranian context. This study highlighted the importance of designing and using more reliable and valid tests, for researchers and designers, and those who are interested in the use of such tests.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".