Case Study I:
School administrators, teachers, and other school staff participate in an end-of-year meeting at North Creek High School, a large (approximately 1,500 students), low performing high school in a high-poverty, urban district. These school personnel are gathered to reflect on challenges experienced throughout the school year and to discuss potential strategies to address these issues for the upcoming school year. Principal Anita Kline opens the meeting with a discussion of student test scores. She comments that North Creek students are still demonstrating low performance, with many students failing to meet the minimum basic standards in math and English. She moves the discussion forward to consider how to better support student learning and school achievement.
Over the summer, an interdisciplinary data team is formed, including teachers from each academic department, the school counselor, and the two assistant principals. Mrs. Kline decides to make some adjustments to the school budget so that she can hire a part-time data coach.
During the summer, Mrs. Kline meets with the data coach, Ms. Lang, and the data team to discuss data use goals for the upcoming school year. First, the data coach helps the principal and data team create a list of student data currently available. This includes student performance data (e.g., class grades, standardized test performance), demographic information (e.g., age, economic status, English Language Learner (ELL) status, race) and other variables related to student performance (e.g., attendance records, discipline records). After much discussion about student performance, the group decides the focus for the year will be: 1) to better understand the relationship between student grades and performance on state-mandated tests; 2) to explore differences in academic performance based on demographics or other individual factors, to better target groups needing more support; and 3) to determine whether school disciplinary records from 8th grade predict students’ academic performance in high school. They decide that while there are other areas they could explore, this list addresses many of the issues that have been troubling the teachers for a while and is a good start.
(n.d.). Retrieved June 27, 2015, from http://datause.cse.ucla.edu/readmore.php?page=scenarios&level=3&step=0
School administrators, teachers, and other school staff participate in an end-of-year meeting at North Creek High School, a large (approximately 1,500 students), low performing high school in a high-poverty, urban district. These school personnel are gathered to reflect on challenges experienced throughout the school year and to discuss potential strategies to address these issues for the upcoming school year. Principal Anita Kline opens the meeting with a discussion of student test scores. She comments that North Creek students are still demonstrating low performance, with many students failing to meet the minimum basic standards in math and English. She moves the discussion forward to consider how to better support student learning and school achievement.
Over the summer, an interdisciplinary data team is formed, including teachers from each academic department, the school counselor, and the two assistant principals. Mrs. Kline decides to make some adjustments to the school budget so that she can hire a part-time data coach.
During the summer, Mrs. Kline meets with the data coach, Ms. Lang, and the data team to discuss data use goals for the upcoming school year. First, the data coach helps the principal and data team create a list of student data currently available. This includes student performance data (e.g., class grades, standardized test performance), demographic information (e.g., age, economic status, English Language Learner (ELL) status, race) and other variables related to student performance (e.g., attendance records, discipline records). After much discussion about student performance, the group decides the focus for the year will be: 1) to better understand the relationship between student grades and performance on state-mandated tests; 2) to explore differences in academic performance based on demographics or other individual factors, to better target groups needing more support; and 3) to determine whether school disciplinary records from 8th grade predict students’ academic performance in high school. They decide that while there are other areas they could explore, this list addresses many of the issues that have been troubling the teachers for a while and is a good start.
(n.d.). Retrieved June 27, 2015, from http://datause.cse.ucla.edu/readmore.php?page=scenarios&level=3&step=0