Morphological awareness predicts reading comprehension in first grade students
Bibliographic record
Abstract
Research examining a role for morphological awareness in first grade students’ reading comprehension is scarce, although it is a well-established predictor for students in mid-to-late elementary school. One question that remains is whether morphological awareness explains unique variance in these young readers’ comprehension after accounting for other oral language skills. In this longitudinal study, we assessed Grade 1 students’ inflectional morphological awareness as a predictor of their concurrent (n = 58) and Grade 2 (n = 55) reading comprehension. When controlling for decoding and vocabulary, Grade 1 morphological awareness explained unique variance in concurrent and subsequent reading comprehension (4% and 5%, respectively). In novel analyses that controlled for decoding, vocabulary, and syntactic awareness, morphological awareness explained unique variance in Grade 2 reading comprehension (5%), but not in concurrent reading comprehension. This unique contribution only in second grade may be because decoding skills accounted for less of the overall variance in second than in first grade comprehension or due to the expectation that polymorphemic words are more frequent in second grade texts. Overall, morphological awareness emerged as the strongest oral language predictor in all models. These results support morphological awareness’ relevance to reading comprehension from early in children’s reading development and highlight the need for research to further explore the effects of targeting English morphological awareness with young students.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".