Data Literacy Power Point (6/13)

Data Literacy & Collaborative Inquiry

Date: June 13, 2013 Presenter: Diana Nunnaley

Leaders need preparation to use data

Most people who currently work in public schools weren’t hired to do this work, nor have they been adequately prepared to do it either by their professional education or by their prior experience in schools. —Elmore, 2002, p. 5

Using Data ©TERC 2013

Let’s Agree On What We Mean  Data Driven Culture?

 What should the result be?  What is effective data use?

 Turn to someone near you and discuss the above questions.

Using Data ©TERC 2013

Key Characteristics – High Performing Schools  Thoughtful public discussion of important and central issues

 Collaboration –co-constructing the new, not just cooperation  Shared norms and values  Focus on student learning Using Data ©TERC 2013

Karen Seashore Louis, 2008 http://workingconditions.net/?ca t=8 NSDC St. Louis, 2009

Synthesis – Research on Data Use • Establish a clear vision for school wide data use. • Develop and maintain a district wide data system. • Make data part of an ongoing cycle of instructional improvement.

• Provide supports that foster a data-driven culture within the school.

• Teach students to examine their own data and set learning goals. Using Data ©TERC 2013

The Data Divide

Data

Results

Professional Development Needed

Data

Leadership & Capacity

Structured Collaboration

Frequent Data Use

Results Instructional Improvement

Grade 4 Data Team

Using Data ©TERC 2013

The Data Team Data teams are school- or district-based teams comprising administrators, teacher-leaders, and data and other specialists who engage in ongoing dialogue informed by data and use the process of collaborative inquiry to improve teaching and learning. Using Data ©TERC 2013

Data Coach Data coaches are school and/or district leaders or service providers who work directly with a Data Team to lead them through the process of data analysis using collaborative inquiry. Using Data ©TERC 2013

An Underlying Assumption Data have no meaning. Meaning is imposed through interpretation. Frames of reference, the way we see the world, influence the meaning we derive from data. Effective data users become aware of and critically examine their frames of reference and assumptions. Conversely, data can be a catalyst to rethinking our assumptions. Based on Bruce Wellman and Laura Lipton, Data-Driven Dialogue, 2004

Data-Driven Dialogue Phase 1

Phase 2

Phase 3

Phase 4

Predict

Go Visual

Observe

Infer/Question

Surfacing experiences, possibilities, expectations

What are some predictions that we might make? • What assumptions might be underlying our predictions? • What are some questions we are asking that this data might help us answer? •

•

What might we be able to learn from this data?

Communicating ideas about data using visual images and representations •

What data do we want to convey?

• What visual would communicate this data most clearly, accurately and completely? • What information and labels will we need to provide to the visual to ensure that the data represented is clear?

Analyzing and making observations about data

• What important points seem to “pop out”? • What are some patterns or trends that are emerging? • What seems to be surprising or unexpected? • What are some things we have not explored?

Generating possible explanations for the observations of the data

• What inferences and explanations can we draw? • What questions are we asking? • What additional data might we explore to verify our explanations? • What tentative conclusions might we draw

Adapted from Wellman, B., & Lipton, L., 2004. Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: MiraVia LLC. Used with permission.

The Using Data Initiative, TERC© 2007. All rights reserved.

DCK Chart & Handout

Multiple Measures Sharpen Our

Using Data ©TERC 2013

Data Drill Down Triangulate Student Learning Data

Task 5: Build Data Literacy

Source 2: Benchmark Assessments

Task 6: Aggregate Data Task 7: Disaggregated Data Task 8: Content Strand Task 9: Item Data Task 10: Student Work Task 11: Common Assessments & other

Task 12: Develop a Student-Learning Problem and Goal Using Data ©TERC 2013

What’s My Line? - Do the Work! The line segment below has been divided into three equal parts and labeled with letters as shown. The value of point A is 0, point B is 3/4, and the value of point D is 2.25 A

B

0

3/4

C

D 2.25

1a. What is the value of point C? 1b. Plot the value of point C on the number line. Show all of your work. Explain how you found the point value of C. Source:STAR Program,Center for Educational Services, Auburn,Maine (permission pending)

Using Data ©TERC 2013

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Using Data ©TERC 2013

PM p. 121

PM, pp. 125 & 126

Student Work: Deconstruct Task

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Task Deconstruction Example

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Sample Student Learning Problem Statement Seventh-grade students at Lincoln School are below grade level in mathematics. Weak areas are number sense and algebraic

reasoning as evidenced by these data: • 64% of students are below proficiency on the 2013 Nebraska State Assessment

• 52% of students are below basic on the 2013 district benchmark assessment • 37% of students scored a 1 on the 2013 school common assessment These performance gaps were noted: •

49% of Special Needs students are below proficiency, while 33% of all other students are below proficiency as evidenced by the 2013 Nebraska State Assessment

Using Data ©TERC 2013

“Too often in education, we start with answers before we have understood the problem we’re trying to solve.” Tony Wagner, Co-Director of Change Leadership Group, Harvard University Graduate School. Education Week, August 15, 2007.

Fishbone Cause-and-Effect Analysis

Also known as the Ishikawa Diagram

Fishbone w/Spend a Buck Equity All students not taught at grade level.

Critical Supports

School culture not collaborative

Teachers have low expectations for some students

6th-grade unit on motion not aligned with new standards. K-5 curriculum not developing motion concepts

Curriculum

Teacher Preparation

Teachers not comfortable with physical science content

Fragmented PD

Inconsistent implementation of curriculum units

Instruction

Unit assessments not aligned with national assessments

Assessment

Problem 6th-grade students are below proficiency in physical science; achievement gap between Special Education students and all other students

Verify Causes: Research Wall Priority Causes

Questions

Sources

Findings

Cause 1

Cause 2

Cause 3 Using Data ©TERC 2013

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Logic Model Chain Student Learning Problem & Cause

IF WE DO… Strategy

Strategy

Strategy

A

Student Learning Goal B

Outcome

Outcome

THEN… Using Data ©TERC 2013

Outcome

Monitoring 1 To what degree are we implementing our strategy?

2 Is the strategy being implemented achieving the desired outcome?

How will we know?

How will we know?

What evidence will need to collect and summarize?

What evidence will need to collect and analyze?

Using Data ©TERC 2013

Classsroom Focused: Action Plan

Classroom Focused Action Plan School: Memorial Elementary

Content Area: Mathematics

Data Team: Grade 4

Contact Person: Patti Wright

Student Learning Problem: Students are not performing well when computing fractions, decimals and percentages. Targeted Cause(s)/Hypothesis: If we provide more opportunities for students for problem solving, particularly at level 2 DOK, they will have the knowledge and skills they need to improve results. Strategy to be Implemented What will you do? We will increase the use of academic language at DOK level 2 in classroom questioning. (See attached.)

Beginning When? Immediately

We will develop or identify and use at least 6 tasks requiring students to explain the connection between their visualization and the procedures to solve the problem or represent how to solve the problem if they don’t yet have the procedure.

Tasks identified and refined – within one week

©TERC 2013 Using DataRoom Next meeting: 201, Sept. 19.

Use of tasks – in one week

Completed by?

Resources needed?

Oct. 1

Additional information re: structuring more rigorous lessons

Sept 19

Open-ended tasks at DOK 2 (Patti Wright will share with group via email.)

Data collected to monitor implementation? Results? · Peer observations · Teacher reflection · Lesson plans · Data Team meeting notes

· Assessment items · Student results on assessment items · Progress monitoring results · Interim benchmarks results in November

Data Analysis Leads to School Improvement Plans

• Grade and course teams report findings to whole faculty.

• Faculty analyzes the results for trends and patterns.

• Results of the faculty analysis inform the development of the School Improvement Plan. Using Data ©TERC 2013

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