Identifying effective reading intervention strategies for Grade 2 and 3 students
Bibliographic record
Abstract
This mixed methods inquiry examined the effectiveness of reading intervention strategies on students who are at-risk for reading failure. The targeted, intensive, and effective reading instruction in which students participated in helped to shed light on this study's central research question: Which reading strategies are effective for a small group of Grade 2 and 3 students with reading difficulties in a large urban school in Whitehorse, Yukon? This project contains a thorough literature review drawing upon relevant research with respect to programming, strategies, and intervention models. Pre-test assessments using two Level B standardized assessments were conducted on four Grade 2 and 3 students in January 2014. Students received 10-weeks of intense reading instruction within the five components of reading. Upon completion of the study post-test assessments employing the same two Level B standardized assessments were conducted on the students in March 2014. The quantitative data results indicated that the implemented intensive reading intervention strategies were significantly effective for all four students. The qualitative data collected from my both field notes and reflective journal indicated that the intensive reading strategies were successful in increasing students' reading performance skills. Data gathered from student records and assessments added further information and helped to reveal possible reasons why students are at-risk for reading failure. --Leaf ii.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".