Using Miscue Analysis to Assess Comprehension in Deaf College Readers
Bibliographic record
Abstract
For over 30 years, teachers have used miscue analysis as a tool to assess and evaluate the reading abilities of hearing students in elementary and middle schools and to design effective literacy programs. More recently, teachers of deaf and hard-of-hearing students have also reported its usefulness for diagnosing word- and phrase-level reading difficulties and for planning instruction. To our knowledge, miscue analysis has not been used with older, college-age deaf students who might also be having difficulty decoding and understanding text at the word level. The goal of this study was to determine whether such an analysis would be helpful in identifying the source of college students' reading comprehension difficulties. After analyzing the miscues of 10 college-age readers and the results of other comprehension-related tasks, we concluded that comprehension of basic grade school-level passages depended on the ability to recognize and comprehend key words and phrases in these texts. We also concluded that these diagnostic procedures provided useful information about the reading abilities and strategies of each reader that had implications for designing more effective interventions.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".