Statewide Analysis

II. Student achievement adjusted for "value-added":
Measuring how well schools support the student/teacher relationship

Value-added charts in PDF format
High || Middle || Elementary

Leveling the Playing Field

If you took the raw achievement scores by district and sorted them high to low, you would probably find that you had also roughly sorted by the median family income of each district. Without the strong intervention of schools, students tend to achieve according to their socio-economic backgrounds. This pattern is by no means peculiar to RI.

Schools with high concentrations of low-income or special needs children have always complained about being unfairly compared to schools whose less challenged children perform at high levels on standardized tests. The public tends to compare high-performing schools with low-performing schools without considering differences in student characteristics. In fact, poverty is the strongest predictor of student achievement, except for that student's prior achievement. (Without a Universal Student Identifier system in place, RI is not able to factor prior achievement into its research.)

Statistical Modeling

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. 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
• Special learning needs, and
• 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.

Statistical models make it possible to establish an achievement benchmark that acknowledges the challenges that can affect children's readiness to learn. The models adjust for certain student characteristics to look at the same achievement data through a lens that filters out some of the students' challenges. This lens provides a different, but newly uniform and, in some ways, more practical benchmark against which to measure actual achievement.

Critical note: Rhode Island's 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 begining of a journey towards full proficiency. Over time, as the schools improve, the computer-generated ranges wil themselves rise.

RI's Value-Added Charts

Click here to download and/or print this graph in PDF format.

The dark red number indicates that the school's students performed above their counterparts statewide and the bold black number indicates that the school's students performed below similar students statewide. ‘Regular type' indicates the school's students performed the same as similar students statewide.

Colored and black dots with years indicate those schools who in prior years performed consistently above the statistical projection acoss all subtests and those schools who performed consistently below on all modeled subtests. Those at either extremes of the list are indicated by a dot with the year this performance took place.

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 school's 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. (Next year, RIDE will further tighten required participation rates since a set of alternative tests for special needs children will be fully operational, making it possible to account for virtually every child within the state's assessment system.)

Which schools appear to add value?

In these charts, all the numbers you see are expressed as percentages of each school's children who met or exceeded the standard on each subtest. Again, 100% proficiency is the standard for all children, but these charts give us an idea as to which schools are helping more of their students – including their challenged students – to progress toward the standard. Each subtest was modeled separately, showing how comparable students statewide would be expected to perform on the subtest. The performance of these comparable students (virtual students, if you will) is then compared with the school's actual achievement. Schools whose students perform better than the comparable students (especially over multiple years) are considered to be adding more value to their students' education.

Thus, in the "value-added" lists, you are looking at all RI schools separated by level, in reverse chronological order – high, middle and elementary – sorted according to the results of the modeling. Each school's individual modeling is represented on the second field on the first page of each Information Works! school report.

The Rhode Island Model

For the specific elements involved in this year's statistical model, please consult the Information Works! Users Guide. A technical brief about the design and building of the modeling is available on the Information Works! home page infoworks.ride.uri.edu.  You can also obtain a hard copy through the Office of Information Services and Research at the RI Department of Education.

Some schools are more helpful than others when dealing with similar populations

The value-added charts tell us that certain schools have probably assembled a variety of strategies that are more successful at helping their children learn than others. The red dots indicate what schools have been high-achieving over time. Spending a day in a relatively successful school, observing teaching, learning, and communications strategies can provide demonstrations of techniques that struggling schools might want to emulate. Educators who have been part of SALT visiting teams will tell you that observing a school for a week was arguably the best professional development they have ever had.

Similarly, all schools' SALT data is on-line, accessible through the Information Works! home page. Those data will illuminate what practices might be helping successful schools. Do teachers share more decision-making? Do students feel they have someone to talk to? Do students have the opportunity to revise work that has not yet met standard? Do teachers communicate high expectations? Are parents involved? Even without a visit, much information about these schools is available.

While some schools might be having excellent luck with a math program or a reading initiative, a good general statement about the higher-functioning schools is that the teachers and students are more accessible to one another and more content passes between them in a way that is retained. The conditions are such that teachers and students, together, are more productive. Presumably, these schools are more supportive of the student/teacher relationship than others.

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