1. Collaborative Inquiry
November 16, 2017
Margie Johnson, Ed.D.
Brad Redmond

2. Today’s Outcomes
Provide an overview of MNPS’ Data-Informed Decision Making Ecosystem.
Model the MNPS collaborative inquiry process as an approach for developing a data-informed decision making culture.

3. Metropolitan Nashville Public Schools
42nd largest school district in the US
88,000 students; 6,000 teachers; 4,000 support staff
Students speak different languages
160 buildings

4. Data Systems
Processes
Data System
People

5. Conducting a Needs Assessment
“Data” professional development documentation
Data warehouse utilization report
Data use research

6. Data-Informed Decision Making Ecosystem
Culture of Collaborative Inquiry
Common Language
Data Literacy & Analysis
Data Access
Johnson, 2016; Johnson, in press

7. Data have no meaning. Meaning is imposed through interpretation
(Wellman & Lipton, 2004, pp. ix-xi).

8. How do we bridge the gap between data and results?
Collaborative
Inquiry
Data
Results
Love, 2009

9. MNPS Collaborative Inquiry
Collaborative Inquiry is a data-based team process that consciously uses the collaborative learning cycle (activating and engaging, exploring and discovering, and organizing and integrating) and the qualities of effective groups (fostering a culture of trust, maintaining a clear focus, taking collective responsibility and data-informed decision-making).
MNPS Collaborative Inquiry Community of Practice

10. Modeling the Process

11. Purpose and Outcomes
Our purpose is to foster a culture of collaboration to support student success.
Our outcomes today are to model the collaborative inquiry process for analyzing MNPS attendance data and making recommendations for improvement.

12. Activating and Engaging--Grounding
TOPIC: ATTENDANCE
My name is …
My relationship to the topic is …
My expectations for today are …

13. Overview of Attendance Data Journey
Brad Redmond

14. Common Contributing Factors to Poor Student Attendance
Fear of a bully or of being teased Dislike/disinterest in school/lack of direction
Problems at home Medical issues
Peer pressure Drug use/abuse
Transportation issues Health Problems
Emotional or mental health problems Academic frustration and failure
Perceived unfair treatment by school staff The idea that they have better things to do
The mission of Support Services is to support the whole child by improving the conditions of learning while helping ALL students and their families overcome life’s challenges.

15. Unpacking Attendance Terms
Understanding the Progression of Truancy Intervention
Unpacking Attendance Terms
Average Daily Attendance
The percentage of enrolled students who attend school each day. It is often used for allocating funding.
Truancy
Refers only to unexcused absences. In Tennessee, a student is considered truant at 5 unexcused absences. It signals the potential need for legal intervention under state compulsory education laws.
Chronic Absence
Missing 10% or more of school for any reason – excused, unexcused, suspension, etc. It is an indication that a student is academically at risk due to missing too much school.

16. High Percentages of ADA Can Mask Chronic Absence
93% and even 95% ≠ A
We often focus on ADA because it is tied to funding, but it is not a good measure to determine if a school has good attendance. A school with a 95% may have a chronic absence problem.
98% ADA = little chronic absence
95% ADA = don’t know
93% ADA = significant chronic absence

17. 2012-2017 MNPS Student Attendance Data
The Scope of the Problem
MNPS Student Attendance Data

18. 2016-2017 MNPS Chronic Absence by Grade Level
The Scope of the Problem
MNPS Chronic Absence by Grade Level

19. Percent Students Scoring Proficient or Advanced Based on Attendance
The Scope of the Problem
Percent Students Scoring Proficient or Advanced Based on Attendance
*Achievement Gap >20% in both subject areas across all tiers*
Students can’t learn if they aren’t in school.

20. 3 Tier Approach to Attendance Intervention
Understanding the Progression of Truancy Intervention
3 Tier Approach to Attendance Intervention
Recovery
Programs
Legal Intervention
A small fraction of student body
Students with severe chronic absence
and/or truancy
Students with chronic absence or
truancy over multiple years
Intervention
Programs
Early Intervention: Reducing barriers to attendance
Some students
Students at risk, or in the early
stages, of chronic absence and/or truancy
Prevention:
Establishing expectations and positive school climate
All students
School-wide strategy
to promote & encourage
regular daily attendance

21. Exploring and Discovering

22. Exploring and Discovering— Calibrating Activity

23. Exploring and Discovering—Data Dive Observations

24. Collaborative Learning Cycle
Activating and Engaging
Exploring and Discovering
Managing
Modeling
Mediating
Monitoring
Organizing and Integrating
--Lipton, L. & Wellman, B. (2012)

25. Organizing and Integrating--Recommendations
Given the data observations, what might be some recommendations you have for improving attendance for MNPS students?

26. Wrap-Up

27. Debriefing Q & A

28. MNPS Collaborative Inquiry
Collaborative Inquiry is a data-based team process that consciously uses the collaborative learning cycle (activating and engaging, exploring and discovering, and organizing and integrating) and the qualities of effective groups (fostering a culture of trust, maintaining a clear focus, taking collective responsibility and data-informed decision-making).
MNPS Collaborative Inquiry Community of Practice

29. MNPS Collaborative Inquiry Toolkit

30. Reflection
What might be some ideas you take from this session to implement in your organization and/or share with others?

31. Wrap Up

32. Twitter: @MargieLJohnson3
Contact Information and Questions
Margie L. Johnson, Ed.D.
Brad Redmond

33. References
Johnson, M. (in press). Empowering educators to make data-informed decisions: A district’s journey of effective data use. In E. Mense and M. Crain-Dorough (Eds.), Data leadership for K-12 schools in a time of accountability. Hershey, PA: IGI Global. Johnson, M. (2016). Experience from the field. In J. Rankin, How to make data work: A guide for educational leaders (pp. 171). New York City, NY: Routledge Love, N., Stiles, K.E., Mundy, S., & DiRanna, K. (2009). The data coach’s guide to improving learning for all students: Unleashing the power of collaborative inquiry. Thousand Oaks, CA: Corwin.