Building Leadership Teams - Aug. 2

Reconnecting with your Building Leadership Teammates!
Doris Ray & Sue Card

Victoria L. Bernhart - Data Analysis for Continuous School Improvement
http://eff.suchico.edu


1. What types and levels of data can BLTs look at to make school-level improvement decisions?

Systematic -> A logical, structured process of collecting, analyzing to increase understanding of your school's system.

Systemic -> Considers all the processes and procedures that contribute to student learning.

Continuous Improvement -> Done on an ongoing basis - Proactive

From Random Acts of School Improvement to Focused School Improvement

Think-Pair-Share

1. As a BLT, what is your purpose for collecting data at your school?

attendance, to see strengths and weaknesses of students,

2. What TYPES of data do you already collect?

No Child Left Behind, make AYP, AIMSweb, D-Step, DACS, looking at individual

Types of Data

Demographics - Enrollment, Attendance, Drop Out Rate, Ethnicity, Gender, Grade Level - Over time, indicates changes in the context of the school

Perceptions - perceptions of learning environment, values, beliefs, and attitudes, observations - Over time, can tell us about environmental improvements

• What people think and feel when the enter your school
• Family walk through
• Student interviews
• culture surveys
• staff surveys
• grapevine data
• feedback forms

Student learning - State, district, school and teacher developed assessment, teacher observations - over time, gives us information about student performance on different measures

School Processes - Descriptions of school programs and processes - over time, tells us how classrooms change

• what went well
• what didn't go well
• where it work
• where it didn't work
• what you need to do to improve
• discipline, how? who? where do the most infractions occur
• Marzano strategies - who? where?

As a BLT...
1. Start a list - an inventory - of the DATA TYPES you already collect.

• Demographics - attendance, enrollment, mobility of students, gender (number of boys and girls) in each class
• Perceptions - take students as is, parent walk throughs, district culture surveys, district staff surveys
• Student Learning - AIMS Web, D-Step, DACS, anecdotal notes, WIDA
• School Processes - not really

2. What do you notice?

• Need to work on perceptions and school processes

LEVELS of Data Analysis

1. Snapshots - ex. enrollment for 09-10 = 2083
2. Snapshots over time - ex. 2001 to 2010 (10 years) Average number of students enrolled = 2442
3. Two or more variables within the same type of data - ex. 9th graders 2005-06 school year - average days enrolled: 129/168 and average days absent 30/168
4. Two or more variables within same type of data over time - ex. 2001-2010 - TCSD: average days enrolled 132/163; average days absent 16/163
5. Intersection of two types of data - ex. Class of 2009; enrolled as seniors as of May 2009 - 77; Seniors had earned enough credits to graduate = 44
6. Intersection of two types of data over time - ex. 2001-2010; average number grade 9 = 259; average number of graduates = 65 students
7. Intersection of three types of data
8. Intersection of three types of data over time
9. Intersection of all four types of data
10. Intersection of all four types of data over time

Levels used at HEDOG

Level 1 - snapshots
Level 5 - intersection of two types of data - test scores and demographics

Data Driven Dialogue
Phase 1: Predict and Ground
Phase 2: Observe
Phase 3. Infer/Question