Incorporating Vocabulary Instruction in Individual Reading Fluency Interventions With English Language Learners
Bibliographic record
Abstract
The purpose of this preliminary study was to determine whether incorporating vocabulary instruction in individual reading fluency interventions for English Language Learners (ELLs) would improve reading comprehension. Two vocabulary instructional procedures were contrasted with a fluency-building only condition in an alternating-treatments design with four ELL students in Grades 3 and 5. Results indicated that the two vocabulary instructional procedures, on average, did not affect reading comprehension. Despite no consistent overall effects, one student had better comprehension of passages used in fluency-building activities when definitions of key target words were taught, and two students demonstrated better comprehension of untaught passages following vocabulary instruction that included processing questions; however, all effects were of small magnitude. Reducing instructional time spent on fluency-building activities to incorporate the vocabulary activities did not attenuate intervention effects on reading fluency. Practical recommendations and future directions for incorporating vocabulary instruction in individual reading interventions are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".