On the Impact of Teacher-Made Vocabulary Tests vs. Standardized Vocabulary Tests on Reading Comprehension Performance of Iraqi Intermediate EFL Learners
Bibliographic record
Abstract
This study Investigates the impact of Teacher-Made Vocabulary Tests (TMVT) vs. Standardized Vocabulary Tests (STVT) on Reading Comprehension Performance of Iraqi Intermediate EFL Learners. After analyzing collected data out of treatment, assessment, and instrumentation, the researcher evaluated and interpreted the score obtained by 66 young Iraqi female students in two language schools in Iraq. The interpretation shows how the participants reacted to Teacher-made vocabulary tests as compared to their reactions to Standardized Vocabulary test. The crux of the matter was the degree to which such vocabulary tests affect the EFL learners’ performance in Reading Comprehension tests. The researcher reported that the use of Standardized vocabulary tests DOES play a significantly positive role in promoting the reading comprehension skills of Iraqi EFL college students. She also found that Teacher-made vocabulary tests DO play a significantly positive role in promoting the reading comprehension skills of Iraqi EFL college students. Finally, the researcher found that the t test value of 2.635 indicated a significant difference between the STVT and TMVT. This means that the use of TMVT is more effective than the implementation of STVT in encouraging Iraqi EFL learners to read more successfully.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".