User's Guide: School Report Page 1
2003 Value-Added Indicators
Percentage of students who met or exceeded the standard
compared to similar students statewide

What you are looking at
This chart shows the relationship between the
actual performance of students in this school expressed as
the percentage of students who met the standard on the state tests
and the statistically generated performance range of similar
students statewide. This chart uses only the 2003 assessment data.
What you are looking for
You are hoping to see the schools students
performing at or above the performance range of similar students
statewide. This computer-generated model is not a standard, and
performing as well or even better than similar students across the
state is only the beginning of a journey towards 100% proficiency
of all students. Over time, as the schools improve, the computer-generated
ranges will themselves rise. This model helps us understand that
schools do not start on a level playing field, and some will need
more time, specialists, resources or any number of supports to help
all of their children reach proficiency.
Statistically generated performance models
level the playing field
Schools with high concentrations of students
living in poverty or students with disabilities have always complained
about being unfairly compared with schools whose less challenged
children perform at higher levels on standardized tests. So, for
example, the achievement demonstrated by schools on this years
Performance Progress charts roughly reflects their average socioeconomic
background. The schools designated as high performing tend to have
children who come from more affluent backgrounds; the reverse is
also true.
In general, the public tends to compare schools
without considering differences in student characteristics. In fact,
poverty is the strongest predictor of student achievement, except
for that students prior achievement. (Without a Universal
Student Identifier system in place that would enable RIDE to know
students grade-point averages, RI is not able to factor prior
achievement into its research.)
The rationale
Increasingly, education researchers are using these models,
often called value-added, to calculate what results
schools are likely to achieve when taking into consideration the
characteristics of their student body. Value-added allows
us to determine whether an individual school adds more value, or
improves the childs skill set more effectively, than other
schools. For more than 40 years, researchers have known that the
achievement results of different sets of students, such as those
from different schools, vary in association with several specific
key factors, including:
1. Poverty (by far the strongest predictor of
student achievement, with the exception of prior achievement)
2. Non-English speaking background
3. Educational background of the parents
4. Having special learning needs, and
5. Having a minority racial-group identity
Though individuals with one or more of these characteristics
can and do perform well on state assessments, the majority tend
to perform less well than children who do not have these characteristics.
The many reasons for these historic patterns of lower achievement
include such things as school expectations, the availability of
flexible grouping and different types of instruction, inadequate
funding and support to the schools these children attend, individual
and family health, and the quality of social services offered to
students.
Statistical models allow the public and those
evaluating school performance to look at the achievement data through
a lens that factors in some of the students challenges. This
value-added perspective helps us to see to what extent the challenges
facing each school influence performance.
These models predict only for groups of students
with similar characteristics; they can not predict any individual
students performance. As always, the unit of accountability
in RIs school reform agenda is the school and not the individual
student.
The Rhode Island model
Rhode Island researchers created a model that considers the
five characteristics mentioned above. Because Rhode Island is such
a small state, the entire body of 159,205
students enrolled in public schools serves as the context
from which the test and grade-specific ranges were derived. Thus,
students within a school are compared with similar groups of students
statewide; schools themselves are not sorted for comparisons. The
computer-generated ranges will change depending on the test because,
for example, a writing assessment is more strongly affected by language-minority
status than a mathematics test. The model uses only the 2003 assessment
data.
For more detail, go to the Technical
Brief on the Statistical Model.
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