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Rich Halverson

October 2007

“I want to call attention to smart people who are doing innovative things in their schools,” says Richard Halverson. In particular he’s watching as student services staff ramp up their expertise in using student performance data to help their schools meet performance goals.

Halverson is an education professor at the University of Wisconsin Madison, where he documents how schools’ local, home-grown solutions for student achievement help meet Adequate Yearly Progress (AYP) goals. Working with educators in several Wisconsin schools, Halverson and colleagues nurture local talent, evangelize the ideas, help schools meet Federal accountability requirements, and mentor staff as they become more savvy in using school performance data.

The No Child Left Behind (NCLB) Act has pressed school leaders to develop their own ways to translate student testing data into the kinds of information they can use to improve student learning. Halverson says educators now work in a “data-driven paradigm.”

Analyzing data and using it to improve instruction is often abstract and challenging. District-level specialists and external consultants provide some of this expertise. But Halverson sees some of this knowledge within the schools, just waiting to be scaled up. School student service staff, including school psychologists, Title I teacher, special educators, and social workers, have used achievement data for years – long before NCLB.

For example, special educators have long developed federally mandated IEPs (Individualized Education Plans) for students with special needs. (IEPs provide action plans with measurable annual achievement goals. They address both academic needs and functional needs and measure students’ progress through the general school curriculum.) Halverson found that school leaders reasoned, “Why not turn to these local experts to meet the demands of high-stakes accountability? Let’s use their practices as a model for designing student-level interventions school wide.”

Halverson’s project, Data-Driven Instructional Systems (DDIS), traces the ways local school leaders collect data, analyze it, design and align programs, and use formative feedback. As one example, Halverson points to Malcolm School. It’s a Midwestern urban K-5 school with a highly mobile population of 220 children. About 70 percent of Malcolm’s students qualify for free or reduced-price lunches. The school’s proportion of minority students is among the highest in its district. Malcolm is considered a school-wide Title I school and is eligible for state class-size reduction funding. In spite of the challenging population, Malcolm has improved its student standardized test scores to the point that they now rival those of any other school in the district. Halverson found that Malcolm leaders and teachers had developed several data-driven instructional practices to guide teaching and learning. Malcolm addressed program-level concerns and student-level concerns. The school psychologists, Title I teacher, special educators, and social workers helped the staff make sense of data from program-level and student-level interventions.

Another school in an urban district, Harrison, is a K-8 school serving a 500-student population that’s nearly 30 percent Asian, 10 percent African-American, 20 percent Hispanic and 50 percent White. Seventy percent of Harrison students qualify for free or reduced-price lunches, and 30 percent are English Language Learners.

Harrison’s transformation began with a focus on literacy and curriculum alignment. At the same time, staff developed an academic and behavioral support system that used data to help determine program-level and student-level interventions. Harrison piloted a district-wide initiative to use problem-solving as a method for providing school-wide support for struggling children. Harrison’s problem-solving team is a small group of teachers and parents who work together with student service staff to develop a data-driven plan. Their collaboration demonstrates a possible link between current practices in special education and a better future for organizing public schools.

Harrison’s version of problem-solving reflects the IEP process of referral, team staffing, and an intervention plan that includes data-based criteria for success. The transition to problem-solving at Harrison overcame the difficulties of bringing together the previously separated roles of classroom teachers, special educators and school psychologists in the problem-solving team.

In these two schools, leaders repurposed the practices of categorical specialists, and the roles of pupil support specialists, to create new forms of data-driven student support. Instead of focusing only on students designated for special education, the IEP process in both schools was adapted to serve as an intervention strategy for developing learning plans before students were assigned to special education.

Resource reallocations at Harrison and Malcolm were as much about changing professional culture as drafting a new budget. The schools reallocated and repurposed staff resources to provide a critical instructional support system for all students. That reflects a significant aspect of principal leadership expertise at both schools. The costs can be figured in terms of the human capital (the expertise) of the school leadership team to recognize which staff members would be able and willing to step into new instructional leadership roles in the school.

You can read more about the DDIS project here.

Funding: National Science Foundation. For more information see the online paper, “The Roles and Practices of Student Services Staff as Data-Driven Instructional Leaders,” in WCER’s Working Paper series.