Morphological awareness and reading achievement in university students
Bibliographic record
Abstract
Abstract We examined morphological awareness and reading achievement in university students in two ways. First, students with and without a self-reported history of reading difficulties were compared on word reading and text reading achievement, and on the reading-related skills of morphological awareness, orthographic processing, and phonological processing. Second, the unique contribution of morphological awareness to reading achievement was examined for a larger sample of first-year university students. Students with a self-reported history of reading difficulties ( n = 54) showed moderate to large gaps in each area of reading achievement, and timed reading comprehension appeared more severely impaired than word-reading efficiency. These students had a deficit in morphological awareness that persisted even when (a) phonological awareness and orthographic processing skills, or (b) word-reading accuracy were statistically controlled. In the larger first-year sample ( N = 211), morphological awareness contributed to variance in word reading beyond that accounted for by phonological awareness and orthographic processing. Furthermore, of the reading-related skills, only morphological awareness made a unique contribution to reading comprehension beyond variance accounted for by word reading. Taken together, these results demonstrate that morphological awareness makes unique contributions to university students’ reading achievement and is an additional difficulty for students with a self-reported history of reading difficulties.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".