EFFECTIVE READ-ALOUD PRACTICES: DEVELOPING ELEMENTARY STUDENTS’ VOCABULARY KNOWLEDGE THROUGH EFFECTIVE CLASSROOM READ-ALOUD PRACTICES
Bibliographic record
Abstract
The Ontario Ministry of Education (2003a, 2003b) mandates a daily teacher-led oral reading, known as a “read-aloud”. A read-aloud is an oral delivery of a written text that involves teacher modeled or facilitated reading comprehension strategies before, during and after the reading (Ontario Ministry of Education, 2003a). Providing students with new vocabulary is a purpose for reading-aloud that is outlined in the “Early Reading Strategy: The Report of the Expert Panel in Early Reading in Ontario” (Ontario Ministry of Education, 2003b). As an integral part of literacy education, reading-aloud lacks specific instructional guidelines in key reading documents, and thus has the potential to vary in delivery and effectiveness. \n \nThe identification of vocabulary as a subcomponent skill of, and correlate to, reading comprehension (Biemiller, 2005; Oullette & Beers, 2001; Wise et al., 2007) is the justification for focusing on students’ word knowledge. A literature review on the connection between vocabulary and reading comprehension will demonstrate the research significance for using a read-aloud to develop student vocabulary. \n \nA literature review describing read-aloud practices demonstrates that specific classroom practices correlate with increased vocabulary development. These recommendations will be used to create a framework with which teachers can use their Ontario Curriculum Expectations as the foundation for their instruction, while ensuring a clear focus on the development of their student’s vocabulary knowledge.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".