An Examination of the Relationship between Students' Use of the Fast ForWord Reading Program and Their Performance on Standardized Assessments in Elementary Schools.
Bibliographic record
Abstract
The purpose of this study was to compare the academic achievement of students through the use of standardized testing to examine the relationship of participation in a computer-based phonics instructional system called Fast ForWord®. The sample included students enrolled in the fifth and sixth grades at four elementary schools in the Grainger County, Tennessee, school system. The comparison group consisted of same-grade peers at the four elementary schools in Grainger County who were not enrolled in the Fast ForWord® program. Students' scores were compared using the 2003 Terra Nova standardized assessment test and using their 2001 and 2002 test scores as a control. Comparisons were made using the reading, language, math, science, and social studies subsections of the Terra Nova. Differences between students who received Fast ForWord® and students who did not receive Fast ForWord® were analyzed. The study examined the variables of gender, school enrollment, socioeconomic status, time of intervention, and ability grouping. These variables were examined with analysis of covariance to determine differences. When differences did exist between groups, posthoc tests were used to determine specific differences between groups. The findings indicated that there were measurable differences in the performance of students who received Fast ForWord® compared to students who did not receive Fast ForWord®. Significant differences were found in reading and language subsections of the Terra Nova test for students who had participated in the Fast ForWord® reading program. The findings from the examination of other variables indicated that gender as well as gender x the intervention (Fast ForWord®) interaction were the same for females and males in their performance on the Terra Nova. The findings from the variable socioeconomic status were determined using system data for free/reduced or paid meals. The study determined that socioeconomic status did not significantly affect scores of students including the socioeconomic status x the intervention interaction. The study did determine differences in students' performance among schools attended. The study found some differences for intervention administration times and among ability groups. Posthoc tests were performed to determine which groups were different.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".