The Role of Morphological Awareness in Bilingual Children's First and Second Language Vocabulary and Reading
Bibliographic record
Abstract
The present dissertation research had two main purposes. The first one was to compare the development of morphological awareness between English Language Learners (ELLs) who speak Chinese or Spanish as their first language, and between these two groups of ELLs and native English-speaking children. Participants included 78 monolingual English-speaking children, 76 Chinese-speaking ELLs, and 90 Spanish-speaking ELLs from grade four and grade seven. Two aspects of morphological awareness were measured, derivational awareness and compound awareness. The results indicated that ELLs’ morphological awareness is influenced by the characteristics of their first language. While Chinese-speaking ELLs performed more similarly to English native speakers on compound awareness than Spanish-speaking ELLs, Spanish-speaking ELLs outperformed Chinese-speaking ELLs on derivational awareness. The second purpose of this dissertation was to examine the within and across language contributions of morphological awareness to word reading, vocabulary and reading comprehension in Spanish-speaking ELLs. Morphological awareness in Spanish and in English was evaluated with two measures of derivational morphology, respectively. The results showed that Spanish morphological awareness contributed unique variance to Spanish word reading, vocabulary and reading comprehension after controlling for other reading related variables. English morphological awareness also explained unique variance in English word reading, vocabulary and reading comprehension. Cross-linguistic transfer of morphological awareness was observed from Spanish morphological awareness to English word reading and vocabulary, but not to reading comprehension. English morphological awareness did not predict performance on any of the three Spanish outcome measures. These results suggest that morphological awareness is important for word reading, vocabulary and reading comprehension in Spanish, which has a shallow orthography with a complex morphological system. They also suggest that morphological awareness developed in children’s first language is associated with word reading in English, their L2. Overall, results indicate that the ability to perform morphological analysis is important for ELLs.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".