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

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.

Metropolitan Nashville Public Schools 42nd largest school district in the US 88,000 students; 6,000 teachers; 4,000 support staff Students speak 100 + different languages 160 buildings

Data Systems Processes Data System People

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

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

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

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

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

Modeling the Process

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.

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

Overview of Attendance Data Journey Brad Redmond

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.

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.

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

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

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

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.

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

Exploring and Discovering

Exploring and Discovering— Calibrating Activity

Exploring and Discovering—Data Dive Observations

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

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

Wrap-Up

Debriefing Q & A

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

MNPS Collaborative Inquiry Toolkit www.mnpscollaboration.org

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

Wrap Up

Twitter: @MargieLJohnson3 Contact Information and Questions Margie L. Johnson, Ed.D. margie.johnson@mnps.org Twitter: @MargieLJohnson3 www.mnpscollaboration.org Brad Redmond bradley.redmond@mnps.org

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.