Data Use Professional

Development Series201

Day 6

www.ride.ri.gov

www.wirelessgeneration.com

The contents of this slideshow were developed under a Race to the Top grant from the U.S. Department of Education. However, those contents do not necessarily represent the policy of the U.S. Department of Education, and you should not assume endorsement by the Federal Government.

Rhode Island educators have permission to reproduce and share the material herein, in whole or in part, with other Rhode Island educators for educational and non-commercial purposes.

© 2013 the Rhode Island Department of Education and Wireless Generation, Inc.

2

Welcome back!

3

Days 6, 7, and 8

4

Day 4

• Questioning Techniques for Data Conversations

• Inference Validation• Root Cause Analysis• Effort/Impact• Data Analysis

Questions• Adaptive Change and

Collaboration • Data Conversations

with Parents

Today• Welcome/Overview• Implementation

Progress• Correlation/Causation• Triangulation

BREAK• Data and Small Group

Differentiation• Assessment Literacy

LUNCH• Aggregate Data• Action Research • Implementation

PlanningBREAK

• Techniques for Data Conversations: Paraphrasing

• Data Conversations with Students

• Implementation Planning

• Wrap Up/Evaluations

Day 7: On-Site Visit

• Agenda to be determined with your coach

Day 8: Partial list of topics

• Creating Assessment Items

• Applying Action Research

• Intersection Analysis

• Revisiting Data Inventory

Data Use 201Day 6 Agenda

• Welcome/Overview• Implementation Progress• Correlation/Causation• Triangulation

BREAK• Data and Small Group

Differentiation• Assessment Literacy

LUNCH• Aggregate Data• Action Research • Implementation Planning

BREAK• Techniques for Data

Conversations: Paraphrasing• Data Conversations with

Students• Implementation Planning• Wrap Up/Evaluations 5

Objectives

By the end of Day 6, SDLTs will be able to:

• Identify impacts of their data use implementation.

• Examine potential causes for correlation/causation, and triangulate data sources.

• Evaluate assessment items for alignment to standards and Depth of Knowledge.

• Engage in Data Conversations with students.

• Articulate a plan for ongoing data use implementation.

6

Implementation Progress

7

• What impacts have you seen on teachers’ core instructional practices?

• What impacts have you seen on school culture?

• What role did our on-site visit play in advancing the work with teachers?

1 2 3 4 5 6 7 8 9 10

Integrating Initiatives

8

• How has connecting initiatives been going?

• How has using data impacted other initiatives?

• How have other initiatives impacted what you want to learn here?

9

Cycle of Inquiry

Correlation/Causation

10

Triangulation

Data set 1

• Hypothesis

Data set 2

“Triangulation” is the process of using multiple data sources to address a particular question or problem and using evidence from each source to illuminate or temper evidence from the other sources. It also can be thought of as using each data source to test and confirm evidence from the other sources in order to arrive at well-justified conclusions about students’ learning needs.

IES Practice Guide: Using Student Achievement Data

to Support Instructional Decision Making

11

TriangulationData set

1• Hypothesis

Data set 2

• Refined Hypothesis

Data set 3

• Refined Hypothesis

Data set 4

• Refined Hypothesis

12

Summary

• Impacts of the work look different at different schools.

• As educators develop more facility with data use, they will apply data analysis strategies and protocols situationally.

13

BREAK

14

Cycle of Inquiry

15

Data and Small Group

Differentiation

Types of flexible small groups:

• Short Term• Long Term• Spontaneous

16

Data and Small Group Differentiation

17

EXCEED RTI

18

Assessment Literacy

• Evaluating Assessments

• Creating Assessments

19

Assessment Literacy

Summative:

Assessment OF learning

Formative:

Assessment FOR learning

Interim:

Assessment OF or FOR learning

20

Assessment Literacy

Dimensions of Formative Assessment:

• Clearly articulated learning progressions• Identified learning goals and success criteria• Descriptive feedback• Self and peer assessment• Collaboration

21

Assessment Literacy

22

Summary

• Differentiating for small groups of students can mean flexibly adjusting core instruction for clusters of students within a Cycle of Inquiry.

• It is important for educators to plan how they will assess student learning while creating their Instructional Action Plan.

23

LUNCH

24

Evaluating Assessments

• Alignment to Standards

• Cognitive Complexity

• Data to inform instruction

25

Evaluating Assessment Items

Item Skill/concept measured DOK Part/All of standard?

What are the themes of Little Women?

Determine a theme or central idea of a text.

2 Part

Write a brief plot summary of Little Women, explaining the themes revealed throughout the text using specific examples from the text.

Determine a theme or central idea of a text and how it is conveyed through particular details; provide a summary of the text.

3 All

RL.6.2 Key Ideas and Details: Determine a theme or central idea of a text and how it is conveyed through particular details; provide a summary of the text distinct from personal opinions or judgments.

26

Evaluating Assessment Items

Cognitive Complexity: Webb’s Depth of Knowledge

Level 1: Recallidentify, state, list, define, recognize, use, measure

Level 2: Skill/Conceptclassify, organize, estimate, compare, infer, summarize

Level 3: Strategic Thinkinggeneralize, draw a conclusion, support, hypothesize, investigate

Level 4: Extended Thinkingmake connections, synthesize, prove, analyze, design and carry out the project

27

Describe one cause of the War of 1812.

Describe the similarities between the War of 1812 and the American Civil War.

Describe the impact that the War of 1812 and the American Civil War have had on modern day America.

Evaluating Assessment Items

Cognitive Complexity

28

Cycle of Inquiry

29

Implementation Planning

30

Aggregate Data

What is it? “Student performance data reported at the largest, aggregate-group level, such as by grade level and content area for a school, district, or state.” (p. 146)

Why is it important?“It paints a broad brush picture of student achievement overall” and helps us “understand how students in their school perform in comparison to students in similar schools.”(p. 146)

Love, N., Stiles, K.E., Mundry, S., & DiRanna, K. (2008). The Data Coach’s Guide to Improving Learning for All Students

31

Overarching Questions When Examining Aggregate Data

• Do aggregated groups of students meet or exceed district or state performance levels? - Pockets of success or need within student

body

• Are we able to discern trends over time that would have implications rooted in student learning? - Analyze effectiveness of instruction,

curriculum, and resources horizontally and vertically

Love, N., Stiles, K.E., Mundry, S., & DiRanna, K. (2008). The Data Coach’s Guide to Improving Learning for All Students

Aggregate Data

32

2000 2003 2005 2007 2009 2011180

190

200

210

220

230

240

250

260

200*

210* 211*

219* 221225

232*

239* 241* 242*247 249

32*

29

Mathematics – Grade 4Average Scale Scores

White

Black

Gap

3023 26 24

NAEP Proficient: 249

Aggregate Data and Sub-Populations

33

Action Research and Expanding Cycles of Inquiry

34

Action Research

• Practitioner Researcher

• Cycle of Inquiry

• Broad area of focus

• Commitment to documenting progress

35

Rhode Island Growth Model

36

Implementation Planning

37

Summary

• Assessments should align to standards and include varying levels of Depth of Knowledge.

• It is important to be prepared for conversations about sub-groups when disaggregating large data sets.

• Engaging in Action Research is a way to examine broad areas of focus that are important to our school.

38

BREAK

39

Techniques for Data Conversations

Positive Presumptions

Paraphrasing

40

Principles of Paraphrasing

• Attend fully• Listen with the intention to understand• Capture the essence of the message• Reflect the essence of voice, tone, and gestures• Paraphrase BEFORE asking a question• Use the pronoun “you” instead of “I”

Center for Cognitive Coaching, 2010

41

Data Conversations with Students

42

Data Conversations with Students

You and Ms. Jackson both teach Antonio. The student is well behaved and is

performing well in Ms. Jackson’s class. In your class, Antonio has become disruptive and his performance on weekly quizzes has

declined over the last month.

• What kind of Data Conversation could you have with Antonio?

• What kinds of questions could you ask?

43

Finding Solutions

Is the soccer team getting in the way of your school work?

versus

GatheringInformation

Did you forget to do your homework again?

versus

Guiding Improvement

Are you going to be ready for the test Friday?

versus

Presuming Positive Intent

44

Student Data Conversations Role Plays

• Choose a role play card• Plan your Data Conversation:

– What is your goal for the conversation?– What will you say?

• Ask questions using Positive Presumptions– Open ended questions to promote thinking

and reflection

• Paraphrase as you go

45

Planning for Student Data Conversations

Think about Data Conversations that teachers currently have with students.

• How could these conversations be structured to incorporate more data?

• How could questioning techniques such as using positive presumptions and open ended questions, and paraphrasing be incorporated more often?

46

Implementation Planning

47

Day 8

Some of the topics to be covered Day 8:

• Creating Assessment Items• Applying Action Research• Intersection Analysis• Revisiting Data Inventory• Rhode Island Growth Model

continued

48

Wrap Up

49