Response to Intervention as a Structuring Benchmark for Organizing Services for Students at Risk and With Learning Difficulties in Reading: A Multiple Case Study in Three Elementary Schools
Bibliographic record
Abstract
Although Response to Intervention (RTI) is viewed as a promising model for preventing learning difficulties in reading, several authors have highlighted the challenges associated with its implementation in educational settings (Barrio et al., 2015; Fuchs & Vaughn, 2012; Mitchell et al., 2012). After a decade of implementing this model in the United States, researchers insist on the need for more studies on the practical issues related to the implementation of this model (Barrio et al., 2015; Simonsen et al., 2010). The purpose of this article is to present the results of a multi-case study on the roles of teachers and remedial teachers in the context of the implementation of the RTI in reading in three elementary schools. It focuses more specifically on the organization of services based on the different evaluation and intervention procedures associated with the RTI. The results indicate the presence or emergence of a data culture in the three sites studied. It was possible to observe that RTI evaluation and intervention procedures are viewed as structuring benchmarks for organizing and planning services throughout the school year for first and second Grade students at-risk and those already struggling with reading 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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".