2013 Pathways to the Prize - School Winners | Page 32

Pathways to the Prize
Lessons from the 2011 SCORE Prize School Winners

Pathways to the Prize
Lessons from the 2011 SCORE Prize School Winners

Data analysis team. The school features a highly effective Data Analysis Team that reviews multiple forms of

data, including writing assessments, PLAN tests, ACT tests, and EOC tests. The team also compares results on
these assessments with the state and county. Building matrices show the achievement and progress of students over
time. Teachers give a common benchmark exam every four and a half weeks to monitor student learning of specific
state standards and prepare for the EOC at the end of the semester.
Root cause analysis. About once a year, the Data Analysis Team undertakes a root cause analysis. In Decem-

ber 2010, for example, the team examined school value-added reports, investigating mean predicted scores, mean
student scores, and school effects. The team asked, “Did students make above, below, or expected gains?” and
answered the question for every grade and every content area. The team then examined the TVAAS school search
reports and conducted a subject-by-subject comparison to other schools. The team then drilled down further.
Completing the “Focus on My Building Matrix,” they reviewed the progress quintile (gain) that matches the gain
quintile on the school search report and the achievement quintile (mean) that matches the mean on the school
search report. They determined:
•

Building weaknesses (lowest levels of progress and achievement)

•

High levels of progress but low achievement

•

High levels of achievement but low levels of progress

•

Greatest aggregate level strengths

•

Such collaboration has stimulated many teachers to review their instructional strategies and make their
classroom instruction more engaging. They traded ideas for making even “mundane” topics more relevant.
“One way we will be sharing best practices is through a ‘group activity recipe book,’” Ms. Angle said.
“Each teacher shared one small-group activity with the details for carrying it out (how many students,
what items are needed, how long it will take).” These teacher-generated ideas will be compiled in a
booklet for distribution at the beginning of the next scho ol year. “This type of sharing has really
helped us,” Ms. Angle said. “We are becoming more creative and trying new ways to instruct the
students so they will become and stay engaged. It’s starting to become who we are.”

Building strengths (highest levels of progress and achievement)

•

came to a consensus about department and subject-area strengths and needs,” Ms. Angle said. “The information
from the data team’s analysis was presented to the faculty in a PowerPoint. Specific pages from the team’s report
were also displayed on the faculty bulletin board.” Ms. Angle said they were careful not to present the information
as “the history department says you should do this…each department must determine what works for it and its
subject. Ideas from the history department were broad; other departments adapted their ideas as they saw fit.”

Most aggregate-level challenges

From the school diagnostic reports, the Data Analysis Team looked at EOC/AYP patterns and determined the
greatest diagnostic strengths and challenges.
The team then made a priority list of those strengths and challenges, ranking strengths based on the degree to
which patterns were reflected across large segments of the student population and the magnitude of a particular
strength relative to other strengths. They identified the top three strengths and then conducted a parallel analysis
to determine the top three challenges. Next, they conducted a root cause analysis, examining curriculum, quality of
instruction, leadership, and structures in relation to strengths and challenges.
The school-wide data team meets frequently and systematically, in contrast to other schools that might convene
such meetings once or twice a semester. In Mt. Juliet’s first comprehensive school-wide data investigation, the team
met twice for the initial analysis of summative data, and then leaders combined all of the information. Subject-specific teams met once every four and a half weeks to analyze the latest results of benchmark tests to determine areas
of need. Teachers in curricular areas showing strong results shared their instructional ideas with teachers in the same
areas who had weaker results.
In 2010-11, the school-wide data team identified Mt. Juliet’s greatest strength in the End of Course U.S. History
results. The group asked the U.S. History teachers what they had done and why they believed they were so successful. The team determined that success was based on factors such as the nature of the content (much of it “factbased” and teachable through memorization) and the use of hands-on activities and other strategies to make the
information lively and relevant to students’ lives.
“Mt. Juliet
Data Team Meeting”
tnscore.org/MtJuliet-Data-Team.pdf

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Replicating best practices. Taking advantage of data that might prove

valuable in other departments, the principal met with the history teachers to
further discuss what they considered to be their best practices. “The group

2011 SCORE Prize High School Winner: Mt. Juliet High School

2011 SCORE Prize High School Winner: Mt. Juliet High School

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