Comparing Types of Student Placement and the Effect on Achievement for Students with Disabilities
Bibliographic record
Abstract
Since implementing No Child Left Behind, schools have improved student achievement while also preparing students for the 21st century. Schools continue to strive for 100% proficiency in all subgroups by 2014, but achievement gap exists for students with disabilities. This study used a causal comparative research design to test the concept of co-teaching by comparing types of placement for students with disabilities and the effect on student achievement. For this study, placement was considered where the student received instruction in either a general education class or a co-taught class during 9th through 11th grade. Student achievement was based on the 11th grade Pennsylvania System of School Assessment results for reading and math. This study examined the results from southwestern Pennsylvania high schools for the 2011-2012 school year. The independent variable was student placement in co-taught classes or general education classes. The dependent variable was student achievement based upon the state test results. A causal-comparative design matched 67 math and 68 English co-taught participants with non-co-taught participants based upon gender, disability, and full scale IQ. Analysis of variance was used to determine if the different placements had differential effects on the students' state math and reading scores. Math and English pairs were analyzed separately. The results of this study found that there was a significant difference in mean scores for class placement in both math and English classes. The students with disabilities placed into a general education class scored significantly higher on the respective 2012 PSSA tests.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".