THE EFFECTS OF EARLY IDENTIFICATION AND INTERVENTION ON READING SCORES AT THE KINDERGARTEN LEVEL
Bibliographic record
Abstract
Research indicates that reading fluently is the key to success: academically, economically, socially, as well as to a healthier lifestyle (Forrest, 2018; Wanzek et al., 2018). While research has shown that Response to Intervention (RTI) is a positive instructional program that will increase primary students’ academic abilities at grades 1 and 2 (Richards et al., 2007), there is a need for more research regarding RTI with kindergarten students. This quasi-experimental quantitative research study examined if early identification and intensive intervention through the addition of Response to Intervention (RTI) at the kindergarten level will lead to increased reading scores and better grades. Two kindergarten classrooms from a small elementary school in Western Kentucky provided 14 students for the sample. They became the experimental and comparison groups because their September STAR Early Literacy Assessment scores and their first quarter Reading Foundational Skills grades revealed they were struggling to learn how to read. Findings revealed using RTI with both groups of students was statistically significant for their STAR Early Literacy Assessment Scores and Reading Foundational Skills grades. Discussion includes the study’s relation to P-20 goals and suggestions for future research.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".