Enhancing Student Success: Problem-Solving Driver System

1. These materials were produced with Title I, Part A funds and are in the public domain.

2. The MI Excel Statewide Field Teamat Calhoun Intermediate School District proudly recognizes our partners in this work: Eastern UP Intermediate School District Gogebic Ontonagon Intermediate School District Muskegon Area Intermediate School District We are grateful for their willingness to share their expertise with us and the entire state. Thank you!

3. Making Use of the Problem-Solving Driver System LeeAnn Moore, MI Excel Statewide Field Team

4. Dramatic Improvement in Student, Teacher, and Leader Performance in a short amount of Time. The Blueprint: Systemic Reconfiguration

5. Session Description How do you ensure your district’s Problem-Solving Driver System is being utilized in all areas of the organization? How are teachers and principals using this driver system to make decisions that lead to increased student, teacher, and leader performance? This session will focus on using the Problem-Solving Driver System and high-quality instruction data to make both building and classroom-level decisions that will lead to increased student, teacher, and leader performance.

6. Session Outcomes Participants will: • Review the Problem-Solving Driver System; • Use the Problem-Solving Driver System to make building-level professional learning decisions using high-quality instruction data; • Use the Problem-Solving Driver System to make classroom level instructional decisions using high-quality instruction data.

7. Classroom-Level Decisions Building-Level Decisions Connector – With Your Table Group . . . High-Quality Instruction Data

8. Problem-Solving Driver SystemPurpose This system provides a specific process for facilitating effective data conversations that establishes a spirit of inquiry and positions the analysis of multiple measures of data at the heart of the exploration and action plan development.

9. Problem-Solving Driver SystemPurpose • Ensure that there is a common tool/protocol for data conversations • Ensure that the protocol drives the installation of district systems and routines • Guide the work of the District and Building Networks • Provide a consistent and convenient structure for data conversations within every system in the Blueprint to ensure effective problem identification, causation, and action. • Develop practical structures for using data to focus a group’s attention and energy, and • Utilize a repertoire of tools for developing productive group learning, planning, and problem-solving using data

10. Problem-Solving Driver SystemEvidence of Practice • Establishes high-performing groups for data collection, analysis, and action planning. • Uses data conversations within every system in the Blueprint to ensure effective problem identification and problem solution. • Uses all three phases of the data conversation/ problem-solving protocol. Those phases are, 1. Activate and Engage, 2. Explore and Discover, 3. Organize and Integrate. • Uses multiple measures of data (demographic, achievement, process, and perception) where appropriate to effectively and accurately guide decision making.

11. Problem-Solving Driver SystemEvidence of Practice • Establishes meaningful action plans based on data conversations to successfully and positively impact student achievement and to drive the Blueprint installation process at the district, building, and classroom levels. • Uses the data conversation process to identify system-, building-, and classroom-level issues and selects the appropriate triangulated data sets to investigate the identified issue or question. • Invites appropriate participation from stakeholders in data conversations.

12. Problem-Solving Driver SystemEvidence of Practice Data Literacy is . . . Data Use is . . . the ability to collect, analyze, communicate, and use multiple measures of data to continuously improve all aspects of the learning organization, especially teaching and learning. the ability to transform data into information and then into action to improve all aspects of the learning organization. Data use will not happen on its own. An organizational shift away from a singular focus on compliance, toward a true commitment to improvement through a shared vision is required” Bernhardt, V.L., (2013). Data analysis for continuous school improvement, 3d edition. Larchmont, NY: Eye on Education.

13. Collaborative Learning Cycle Laura Lipton and Bruce Wellman Got data? Now what?

14. Collaborative Learning Cycle Three Stages/Phases Phase 1 – Activate and Engage . . . What do we expect to see in the data? Phase 2 – Explore and Discover . . . What do we actually see in the data? Phase 3 – Organize and Integrate . . . What are the possible causes for this problem AND what are we going to do about these causes?

15. Building-Level Professional Learning Decisions Using High-Quality Instruction Data

16. Building Network Purpose The Building Network is defined as the building principal, other building administrators (if any), and teacher leaders who often comprise the building leadership team. This team recognizes and reinforces the district’s sense of urgency to realize dramatic improvement in student, teacher, and leader performance in a short amount of time and strives to sustain that level of urgency throughout the building that is anchored in a culture of collective responsibility that is collegial, collaborative, and professional.

17. Performance Management Purpose This driver system enables the district to understand and respond to the quality of the Blueprint’s installation on two distinct levels: (1) the extent to which the district systems and drivers have been installed at scale to support dramatic improvement in student, teacher, and leader performance in a short amount of time; (2) the extent to which each building’s analysis of multiple measures of data indicates the degree to which the building is on track to meet or exceed its annual performance goals.

20. Collaborative Learning Cycle Phase 1 Activate and Engage . . . What do we expect to see in the data? Generate predictions and surface assumptions

22. Collaborative Learning Cycle Phase 2 Explore and Discover . . . What do we actually see in the data? Analyze data and develop narrative statements

24. Collaborative Learning Cycle Phase 3 Organize and Integrate . . . What are the possible causes for this problem AND what are we going to do about these causes? Generate (and confirm) causal theory and explore solutions (action planning)

25. Curriculum: Design and implementation Instruction: Methods, materials, and resources Infrastructure: Schedules, programming, and resources Causal Theories Teachers:Knowledge, skills, and dispositions Leadership: Systemic planning and implementation, knowledge, skills, dispositions Students: Knowledge, skills, and dispositions

27. Reflection With your same shoe partner discuss . . . • What causation and action steps did your table group determine? • How might your students benefit from the results of these actions? • What AH-HAs or takeaways did you have from this process?

28. Classroom-Level Instructional Decisions Using High-Quality Instruction Data

29. Teacher Collaborative Routines Purpose: These routines are designed to position classroom teachers in the collaborative role of guiding each other in the ongoing quest of instructional improvement at scale in the building. Installed organically rather than as events, these routines daily support the ongoing mission of increasing student, teacher, and leader performance in a short amount of time. EoP - #8 - Teachers meet to discuss various approaches to teaching subject-specific ideas that are aligned to the district’s visions for high-quality subject-specific instruction and curricular documents.

30. Communications Driver System Purpose: This system provides clear internal and external communication to present the district’s approach to systemic reconfiguration, to illustrate how the district guides and supports improvements in teaching and learning, and to clarify the distinct but interconnected roles of both the district and the school in the Blueprint installation process.

33. Collaborative Learning Cycle Phase 1 Activate and Engage . . . What do we expect to see in the data? Generate predictions and surface assumptions

34. Collaborative Learning Cycle Phase 1

35. Collaborative Learning Cycle Phase 2 Explore and Discover . . . What do we actually see in the data? Analyze data and develop narrative statements

36. Collaborative Learning Cycle Phase 2

37. Collaborative Learning Cycle Phase 3 Organize and Integrate . . . What are the possible causes for this problem AND what are we going to do about these causes? Generate (and confirm) causal theory and explore solutions (action planning)

38. Curriculum: Design and implementation Instruction: Methods, materials, and resources Infrastructure: Schedules, programming, and resources Causal Theories Teachers:Knowledge, skills, and dispositions Leadership: Systemic planning and implementation, knowledge, skills, dispositions Students: Knowledge, skills, and dispositions

40. Building-Level Decisions Classroom- Level Decisions Reflection – With Your Table Group . . . High-Quality Instruction Data

41. References Bernhardt, V.L., (2013). Data analysis for continuous school improvement, 3d edition. Larchmont, NY: Eye on Education. Lipton, L. & Wellman, B, (2012). Got data? Now What? Creating and leading cultures of inquiry. Bloomington, IN: Solution Tree Press.

42. Follow the MI Excel Statewide Field Team on Social Media Twitter - @Blueprint_SWFT Facebook - MI Excel Blueprint Professional Learning Instagram - @blueprint_swft

43. These materials were produced with Title I, Part A funds and are in the public domain.