Contrasting Direct Instruction in Morphological Decoding and Morphological Inquiry-Analysis Interventions in Grade 3 Children With Poor Morphological Awareness
Bibliographic record
Abstract
We report on a school-based randomized control trial study comparing two morphological interventions with untaught controls: one focusing on direct instruction targeting print morphological decoding (direct decoding condition) and the other on inquiry-focused pedagogy using oral morphological analysis (inquiry-analysis condition). We identified 63 Grade 3 children with below-average morphological awareness following screening (from N = 163). This sub-sample showed average pseudoword decoding but poor language and word reading abilities. Following a 13-week supplemental intervention randomized within the 63 children, results showed a statistically significant main effect of intervention on standardized reading vocabulary measures at immediate post-test in the direct decoding condition. Pre-test morphological awareness moderated reading vocabulary effects for the untaught control group. Statistically significant moderation of growth in sentence comprehension at post- by pre-test morphological awareness was also evident in the inquiry-analysis condition. Universal screening for below-average morphological awareness followed by inquiry-based or direct instruction interventions focusing on the meaning dimensions of morphemes may be modestly efficacious for supporting reading vocabulary and sentence comprehension in such at risk learners, potentially aiding school-wide literacy improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".