Author's Views
While I respect the issues raised by Gerber, particularly that the implementation of RtI represents some very significant challenges, I support Reschly's position that current L.D. identification practices are problematic, their effectiveness and accuracy are unsubstantiated by research, and therefore significant changes in identification practice are needed. I do find it hard to fathom Reschly's contention that notion of change driven by outcome criteria is "controversial." From a public policy perspective, the relationship between and among screening, diagnosis, classification, intervention, and identifiable results should be without question.
One concern of many researchers about the current model of L.D. identification is the lack of exit criteria for those students identified as L.D. and referred to special education. Figure 1 provides data regarding the percentage of students in New York state "declassified from special education" after being referred during their school-age years. While the data is not broken down by disability category, since the L.D. classification covers more than half of the students in special education, the data is clear that there is almost no exiting of these students from special education once referred.
Figure 1: Percentage of school-age students with disabilities declassified
Other New York data also gives us more urgency for moving towards a new model of L.D. identification. Figure 2 shows the significant discrepancy among classification rates when comparing districts in New York by wealth categories. High need indicates districts that are low wealth while low need indicates wealthy districts. It is the poorer districts that have the highest classification rates by far.
Figure 2: Classification rates are generally higher in the high need/resource N/RC category of school districts
Contrary to popular perception, New York City, which because of its size is treated as a separate category, does not have as high a classification rate as other urban areas. Figure 3 shows that poor districts segregate their special education students into special classes and separate buildings at a dramatically higher rate than average wealth and wealthy districts.
Figure 3: High need school districts use the "special class" model for greater percentages of students with disabilities
Furthermore, Figure 4 shows that minority students, when referred to special education, are far more likely to be placed in separate classes and schools than are their white counterparts.
Figure 4: Compared to white students with disabilities, a greater percentage of minority students are placed in more restrictive settings
Figure 5 shows that students receiving special education services in poorer districts score significantly lower on state assessments than students receiving special education in wealthy districts, even though the wealthy districts identify dramatically fewer students who are likely to be more severely disabled.
Figure 5: Students with disabilities in "low need" distrives achieve at Level 3 at a far greater rate than students with disabilities in poor districts
In Figure 5, levels 3 and 4 represent students meeting or exceeding state standards.
When this data is woven together, it creates a rather disturbing picture.
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