The Awareness of Morphemic Knowledge for Iraqi High School Learners’ Vocabulary Acquisition in the EFL Context
Bibliographic record
Abstract
The study attempts to assess the awareness of morphemic knowledge among Iraqi high schoollearners in the domain of English Foreign Language (EFL) context. Two tests were employed in this study namely, “Morphological Relatedness Test (MRT)” and “Morphological Structure Test (MST)” adopted and adapted from Curinga (2014). These two tests are essential and crucial instruments employed to measure the students’ morphemic knowledge for this research. The students’ ability was measured by the two tests to reflect and manipulate morphologically complex derived words in English. Twenty Iraqi high schoolstudents were involved to achieve the purpose of the study. The study analysis disclosed that the participants accomplished poorly in both tests of MRT and MST. The findings also revealed that there was no significant difference between the students’ performance on MRT and MST. They were unable to reflect and manipulate efficiently. However, the students’ performance on reflective aspect was a little higher than manipulative aspect. It is true that Iraqi students are suffering from the phenomenon of the morphemic knowledge. They indeed need to be aware of the importance of the morphemic knowledge because this knowledge can drive to construct new words and deconstruct the complex words in addition to the reading comprehension.
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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 | 0.004 |
| 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 | 0.000 | 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".