A Technology-based Intervention to Increase Reading Comprehension of Morphosyntax Structures
Bibliographic record
Abstract
The purpose of this study was to examine the effectiveness of a technology-based intervention (LanguageLinks: Syntax Assessment and Intervention®; Laureate Learning Systems, Inc., 2013) to improve reading comprehension for d/Deaf and hard of hearing (DHH) elementary students. The intervention was a self-paced, interactive program designed to scaffold learning of morphosyntax structures. Participants included 37 DHH students with moderate to profound hearing levels, 7-12 years of age, in Grades 2-6. Assessment data were collected pre- and post- an 8-week intervention using a randomized control trial methodology. Findings indicate the intervention did not appear to be effective in improving performance, and 17 out of 36 morphosyntax structures were found difficult to comprehend for participants in the treatment group. These difficult structures included aspects of pronominalization, the verbal system, and number in nouns. Results are compared to previous research, with recommendations for future areas of research related to increasing knowledge of morphosyntax for learners who are DHH.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".