Data-Driven Practices: A Balance Between Compliance Data and Data to Inform Teaching and Learning Ellen B. Mandinach CNA September, 2008

2 Why is Data-Driven Decision Making Important NCLB and other mandates. The context of accountability and compliance. The realities of classrooms and schools.

3 Information is the key to holding schools accountable for improved performance every year among every student group. Data is our best management tool. I often say that what gets measured, gets done. Once we know the contours of the problem, and who is affected, we can put forward a solution. Teachers can adjust lesson plans. Administrators can evaluate curricula. Data can inform decision making. Thanks to No Child Left Behind, we’re no longer flying blind. Secretary Margaret Spellings (2005)

4 Old and New What is not new: Data-driven decision making. Educators have been using data for a long time. What is new: The coupling of data and accountability for compliance. Yet there is a further distinction…..

5 Accountability from Berliner (2007) and Petrides (2006) Assessments of learning. –Provides data on what and how much students have learned. Assessments for learning. –Provides data to help improve teaching and student learning. And we all know the kind of accountability depicted here…..

6 The Emerging Curriculum

7 The Disconnect: State Assessments from Stecher and Hamilton (2006) Features of state assessments used for accountability limit their utility for data-driven decision making. They present a conflict of data use for accountability as opposed to inform instructional practice. As depicted in yet another classic cartoon…..

8 The Disconnect

9 Why Now?: The Good the Bad, and the Ugly Technological Advances. –State-wide longitudinal data systems. –Data warehouses and SIS. –Accountability systems such as Grow and NYStart. –Handhelds. The Proliferation of Data. Human Capacity or Lack Thereof.

10 Cultural Differences The Need for Data Cultures. Accountability Cultures vs. Organizational Learning Cultures (Firestone & Gonzalez, 2007). –Short-term focus on test scores vs. long-term emphasis on student learning. –Compliance to mandates vs. identifying problems to improve instruction.

11 To Understand How States, Districts, and Schools Use Data and Technology Solutions to Create Data Cultures

12 Components of a Conceptual Framework for Data-Driven Decision Making

13 Data Continuum Data - exist in a raw state without meaning. Information - data given meaning in context. Knowledge - collection of information deemed useful to guide action.

14 The Cognitive Skills Data - Collect, Organize Information - Analyze, Summarize Knowledge - Synthesize, Prioritize

15 Levels of the System: Hierarchical Nesting (State) District Building Classroom

16 Decision Making Feedback Loop Make a Decision. Implement the Action. Determine the Action’s Impact. Feedback for Additional Decision Making Actions. Here’s what the framework looks like…..

17 Conceptual Framework for DDDM Click to edit Master text styles –Second level  Third level  Fourth level »Fifth level

18 The CHOPS: Challenges to and Opportunities for DDDM To answer pressing questions. To address programmatic questions, of “Does it work?”. To respond to ROI questions. To provide data according to state and federal mandates. To provide compliance and accountability data. To increase awareness and gain a clear snapshot of what is happening. To improve teaching and learning.

19 Challenges and Barriers to Implementation Access. Disconnect between the right data and practice. The data that reside on the SLDS and their intended uses. Technical Expertise - data literacy, assessment literacy, pedagogical data literacy. (Confrey) Training and Professional Development. Schools Routinely Don’t Think about the Link Between Instructional Practices and Student Outcomes. Overcoming Typical, Deeply Engrained Practices. Motivation. Leadership. Time and Resources. Misalignment among vision, data, and needed use.

20 The Goal for DDDM How do we move teachers, schools, districts, and states from being data-rich but information poor to using data and transforming them into usable knowledge? –Adequate and targeted professional development. –Technological infrastructure and tools. –Determining what are the right data elements and collecting them BEFORE someone asks, Does it work? –Having the right data. –Explicit vision. –Explicit need. –SUPPORT.