Overview

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Estimated Time to Complete: 2 hours

Ohio's collaborative leadership teams are doing pioneering work with data (Data Quality Campaign, 2014, p. 4). This module explains the why, what, and how of wise data use in leadership teams: with the emphasis on how.

The following topics are discussed in the module text, linked resources, and embedded videos from Ohio schools:

  • Why use data?
  • Misconceptions about using data.
  • Collective efficacy and leadership teams.
  • Owning the data (instead of punishment by data).
  • Owning the data about equity.
  • Leadership and inquiry.
  • The importance of context.
  • Simple tools for data analysis.
  • Cautions about data use.
  • And key issues for TBTs, BLTs, and DLTs.

This module is most closely related to the OLAC modules on Assessment and the Ohio Improvement Process. It addresses Ohio's Leadership Development Framework in the following areas:

  • Area 1 Data and the Decision-Making Process
  • Area 2 Focused Goal Setting Process
  • Area 3 Instruction and the Learning Process
  • Area 4 Resource Management Process

Module Content

You can download a document that includes all content from the module, with the exception of videos. This resource can support your learning while completing the module or be saved for future reference.

Additional Documents

Choosing the Wrong Drivers - Fullan (External Link)

Data Walls: Cautions, Use, and Alternatives

Individual and Collective: Efficacy Instruments for Use by DLTs, BLTs, and TBTs

Peer Observation Forms for Teacher-based Teams: Resources from Around the World

Spirit of Collaborative Inquiry Assessment Tool

Tools for Implementing MTSS

Discussion Questions

  1. Data analysis seems to be a big deal when experts mention "data-driven decision-making." What do team members (TBT, BLT, or DLT) in your district bring to the table in terms of the capacity for analyzing data?

  2. Have any team members in your TBT or BLT ever felt "punished by data?" What circumstances led to that feeling? What were the consequences of feeling "punished by data?" How can your current team (TBT or BLT) act in order to avert such punishment?

  3. For everyone in education, the challenge of genuine collaboration turns on trust. Is there enough trust on the team to support collaborative work? What evidence do you have about the level of trust among members of your TBT or your school's BLT? If there isn't enough trust for effective collaboration, what can the team do to build trust?

  4. How might the BLT at your school identify the various kinds of capacities for data use that are already present among the teachers, intervention specialists, administrators, and other personnel whom the school employs? How might the BLT create a distributed network that allows personnel to share their capacity for data use with others at the school?

  5. What strategic indicators (including equity indicators) does your district's DLT currently track? What other strategic (and equity) indicators do you think the DLT should track in order to help the district achieve its goals?

Activities

Introductory Activities

  1. Data-driven decision-making

    "Data-driven decision-making" used to mean looking primarily at test scores, and among test scores primarily at annual achievement-test or graduation-test scores. In your TBT or BLT assess where the team is now on this practice and where members think it should go next. Here's a checklist that might help:

    We mostly examine...

    • annual achievement and/or graduation test scores (aka accountability testing).
    • benchmark data.
    • data from formative assessments.
    • data from progress monitoring.
    • student products.
    • teacher implementation data.
    • teacher products.
    • student opinion data.
    • parent opinion data.
    • data on collective efficacy.
    • team process implementation data.

    Note that the module offers a strong caution about the first option only. In fact, the module stresses everything besides accountability testing.

    After the TBT or BLT determines the types of data it typically examines, discuss the benefits and challenges associated with a decision to examine one of the types of data that it currently does not examine. Why might reviewing this additional type of data be helpful? What would be the drawbacks of starting to review this additional type of data?

  2. The Spirit of Inquiry

    The spirit of inquiry represents an approach to collaborative work with data on leadership teams. Use the module's spirit-of-inquiry tool in your team (TBT, BLT, or DLT), with all members completing the tool. Completion can be anonymous if the team prefers. Have one member summarize the data. Discuss the implications for future team development. (Note: This activity may require teams to devote a portion of two or more meetings to its completion.)

Advanced Activities

  1. Owning Data

    The best way for a team to own data is to create some: with a survey, with interviews, with document analysis. Given the team's current concerns, decide on a data-gathering activity to address those concerns with data from a survey, a series of short interviews, or a review of student products. For a deeper understanding of data use, put the ideas you discuss into practice by developing and using a survey instrument, interview protocol, or product-review rubric.

    Discuss the data you generate, first by summarizing it using one of the data analysis methods that the module presents and then by devoting one meeting to a discussion of what the data mean and how the team might respond to what they have learned by collecting and analyzing the data.

  2. School Climate

    (This is an activity for a BLT or a DLT rather than a TBT).

    School climate is a kind of omnibus measure of the cohesiveness of a school community, its norms of inclusiveness, its safety and security, and its focus on learning. Knowledge about existing climate instruments adds to a team's research, evaluation, and data capacity.

    Find and discuss four school climate instruments. What are their merits and what are their limitations? Select the one you think is best and use it to measure climate in at least one school in the district. For this activity, two BLTs might collaborate, or a DLT might collaborate with one or more BLTs.

  3. Displaying Data

    In your TBT, experiment with a few different ways to display data, using some of the ideas presented in the resources to which the module links. After trying out each different display, discuss what it allows you to see and what it doesn't allow you to see (or even what it keeps you from seeing). Evaluate various data displays by considering which displays fit best with the TBT's current needs, which are easiest to produce, and which provide the most useful information.

You can earn credit and contact hours for modules, webinars, and podcasts completed on the OLAC site.

For more information, visit the Credit Corner. If you’re seeking credit for the Gifted Education Professional Development Course or the Culturally Responsive Practices Program courses, you can find that information on the course overview pages.

Clearview Local Schools: An Interactive DLT Process for Developing Consistency and Commonality Across the District

Clearview Local Schools: Dr. Paul Kish Describes the District's Interactive DLT Process

Clearview Local Schools: Implementing A Redesigned BLT Process to Improve Teaching and Learning

Clearview Local Schools: Lynne Stark Describes the District's Redesigned BLT Process

Clearview Local Schools: Using Data in Leadership Teams

Doug Reeves: Do Your Own Research

Introduction to Teams Using Data Wisely

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