Exploring Primary School Students’ Morphological Awareness in Thailand
Bibliographic record
Abstract
Understanding how words are formed is a crucial component of learning new words. A child’s ability to manipulate the morphological elements of words is related to their subsequent vocabulary development. Morphological awareness can also enhance learning new syntactic and semantic properties of morphologically complex words to meet the demands of language production. However, there is a dearth of research on how receptive-productive morphological awareness is acquired, especially in an EFL context. This study used a quantitative design to explore the nature of morphological awareness in 104 Thai primary school students and to investigate the relationships between receptive-productive morphological awareness and vocabulary knowledge. All participants were given six measures of morphological awareness and two vocabulary knowledge tasks. The results revealed the close relationship between the students’ morphological awareness and vocabulary knowledge, both receptively and productively. The results also indicated that Thai primary school students’ morphological awareness grows gradually along the receptive and productive continuum and that morphological knowledge is learned at varying rates and improves with learners’ increased education levels. Indeed, all aspects of morphological awareness contributed to receptive and productive vocabulary knowledge. Overall, the current study highlighted the importance of the word family construct for teaching and learning morphologically complex words. It was also shown that morphological awareness is a crucial mechanism for vocabulary acquisition and growth and a facilitative scaffold for forming morphologically complex words.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".