Field 2: Statistically Generated Performance Range/Actual Performance
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 performance range of similar students statewide. (See below for an explanation of this research tool.)
The question mark: Participation in the assessments is expected to be 100% unless a child is specifically exempted by an Individual Education Plan (IEP) or another valid, clearly defined reason such as prolonged illness. When a schools participation rate falls below 80% of the eligible students, a question mark appears on the bar indicating that the data might be unreliable because of the large number of eligible students who did not take the test.What You Are Looking For You are hoping to see the schools students performing at or above the performance range of similar students statewide. CRITICAL NOTE: Rhode Islands goal is for all students to become proficient in all subjects. 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 full proficiency. Over time, as the schools themselves 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 things to help all of their children reach proficiency. Schools which are under-performing according to the model over multiple years are signaling the need for intervention of some kind. Statistically Generated Performance Models In recent years educational researchers have begun building statistically generated models which can calculate what results schools are likely to achieve when taking into consideration the characteristics of their student body. The point of these models is to establish an achievement benchmark that acknowledges the challenges that can affect childrens readiness to learn. The public tends to compare high performing schools with low performing schools without considering differences in student characteristics. In fact, student composition impacts heavily on the performance of the school itself. These statistical models provide uniform and practical benchmarks against which to measure actual achievement. For over 30 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:
Poverty (by far the strongest predictor of student achievement, with the exception of prior achievement)
Non-English speaking background
Educational background of the parents
Having special learning needs, and
Having a minority/racial group identity
While 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. There are many reasons for these historic patterns of achievement. They 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, and the quality of social services offered to students.The Rhode Island Model
Rhode Island researchers have created a model which considers the above characteristics. Because RI is such a small state, the entire student body of over 153,000 students served as a context from which the test and grade specific ranges were derived. Thus, groups of students within a school were 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.
This years model uses two years worth of assessment data for those tests that have been given for two years in order to double the number of students in the sample. Therefore, this years statistically generated performance band is smaller than last years because as the number of students in the study increases, the errors in measurement decrease.
Over time, the model will continue to evolve to become more precise, but the above characteristics will always be its foundation since, for example, poverty alone accounts for at least one third of the variation in student achievement scores across groups of students. These models predict only for groups of students with similar characteristics; they can not predict any individual students achievement.
NOTE: A technical description of this model is available at the RI Statistically Generated Model or upon request from the RIDE Public Information Office.Special to the District and State Templates The district and state pages include tables that show the total number of schools whose students met or exceeded the standard compared with similar students statewide on selected subscales of the New Standards tests.