The Influence of English Morphological Awareness on Vocabulary Acquisition of Arab EFL Students in Israel
Bibliographic record
Abstract
This research investigates the impact of English morphological awareness on vocabulary development among Arab junior high school students. Given the distinct morphological differences between Arabic and English, the study examines how an understanding of morphemes—defined as the smallest units of meaning or grammatical function—facilitates vocabulary development in the context of learning English as a second language (ESL). Drawing on survey data from 20 English language teachers, the study explores educators’ perceptions of morphological instruction and its influence on students’ vocabulary proficiency. The findings underscore the critical role of incorporating structured morphological training into ESL curricula to enhance vocabulary acquisition and overall language competence. These findings are consistent with the results of Abdul majeed et al. (2023), they found that morphological analysis can play in important role in learning and teaching English especially in the foundation level. The findings strongly advocate for the integration of morphological awareness into EFL teaching practices, supported by targeted professional development for teachers, moreover the study concludes by recommending the strategic utilization of morphological parallels between Arabic and English to promote more effective language learning outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".