Flip This School by Eller John F.;Eller Sheila A.;

Author:Eller, John F.;Eller, Sheila A.;
Language: eng
Format: epub
Publisher: Solution Tree
Published: 2019-08-15T00:00:00+00:00

Figure 5.4: Template and sample completed team agenda.

Visit www.SolutionTree.com/leadership for a free reproducible version of this figure.

Now that the data-analysis team is formed and trained for their work, it’s time for the team to begin its work gathering data.

Starting the Data-Gathering Process

Schools have extensive data at their disposal. The focus of the data-analysis team will depend on the challenges the school is facing. Here are some common sources of data from which school teams may gather.

Policies and procedures that may impact students, such as the attendance policy, tardy policy, the suspension policy, and others

Course and program offerings

The time allotted for each academic content area or course

Standardized testing scores

Local assessment results

Grading practices and trends in student grades

Formative and summative assessment results

Districtwide benchmarks

The number of students receiving interventions

Parent-teacher conference attendance rates

Unpacking the academic standards

Attendance records

Office referral rates

These types of data are primarily quantitative or numerical. With quantitative data, teams look for averages, trends, and frequencies. They provide measurable information about core elements of school performance and student learning.

Quantitative Data

The data-analysis team’s first focus will likely be on gathering quantitative data. Barr and Yates (2010) provide a map to help teams identify key data sets identified by the Maryland State Department of Education (2011) and where they may be located. This map appears in figure 5.5 (pages 100–103).

A majority of the data in Barr and Yates’s (2010) map is quantitative or numerical. While quantitative data are important and provide a good foundation for the development of a school-improvement plan, there are other data that school leaders and data teams will find helpful.

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