The Relationship Between Different Measures of Oral Reading Fluency and Reading Comprehension in Second-Grade Students Who Evidence Different Oral Reading Fluency Difficulties
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine whether different measures of oral reading fluency relate differentially to reading comprehension performance in two samples of second-grade students: (a) students who evidenced difficulties with nonsense-word oral reading fluency, real-word oral reading fluency, and oral reading fluency of connected text (ORFD), and (b) students who evidenced difficulties only with oral reading fluency of connected text (CTD). METHOD: Participants (ORFD, n = 146 and CTD, n = 949) were second-grade students who were recruited for participation in different reading intervention studies. Data analyzed were from measures of nonsense-word oral reading fluency, real-word oral reading fluency, oral reading fluency of connected text, and reading comprehension that were collected at the pre-intervention time point. RESULTS: Correlational and path analyses indicated that real-word oral reading fluency was the strongest predictor of reading comprehension performance in both samples and across average and poor reading comprehension abilities. CONCLUSION: Results of this study indicate that real-word oral reading fluency was the strongest predictor of reading comprehension and suggest that real-word oral reading fluency may be an efficient method for identifying potential reading comprehension 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".