Data Use Professional Development Series 301

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. © 2012 the Rhode Island Department of Education and Wireless Generation, Inc. 2

Welcome back! 3

Days 4, 5, and 6 4 Today Welcome Implementation Progress Data Conversations Inference Validation BREAK Data Analysis Questions Correlation/Causation LUNCH Triangulation/Intersection Analysis Implementation Planning Adaptive Work and Collaborative Structures BREAK Revisit Data Conversations: Conversations with parents Implementation Planning Wrap Up/Evaluations Day 5: On-Site Visit Possible activities for the Data Analysis Coach are: Collaboration time with the SDLT and/or school and district leaders. Observing Communities in Practice or Data Team meetings. Model/review Turn Key Activities. Analyze classroom data with classroom teachers. Model low stakes data conversation. Access NARS (NECAP Analysis and Reporting System) and other RIDE resources online. Day 6: Partial list of topics Action Research and Expanding Circles of Data Use Data Conversations with Students Aggregate Data and Sub- Populations Intersection Analysis

Data Use 301 Day 4 Agenda Welcome Implementation Progress Data Conversations Inference Validation BREAK Data Analysis Questions Correlation/Causation LUNCH Triangulation/Intersection Analysis Implementation Planning Adaptive Work and Collaborative Structures BREAK Revisit Data Conversations: Conversations with parents Implementation Planning Wrap Up/Evaluations

Objectives By the end of Day 4, SDLTs will be able to:  Identify challenges and successes of their data use implementation.  Engage in Data Conversations with colleagues and parents using Positive Presumptions.  Employ various data analysis techniques such as root cause analysis, triangulation, and assessing correlation, as applicable; and consider effort/impact on student learning when prioritizing action.  Articulate questions appropriate to various data sources and types.  Articulate a plan for ongoing data use implementation. 6

Implementation Progress 7

8 How many educators have you implemented with? What has surprised you the most about implementing this work at your school? What has been the biggest challenge?

9

Data Conversations Three types of Data Conversations: Gathering Information Guiding Improvement Finding Solutions 10

Data Conversations Which of the three types of Data Conversations did you have most frequently? What challenges did you encounter? Gathering Information Guiding Improvement Finding Solutions 11

12

Inference Validation 13

Effort/Impact 14

Summary Implementation of the work looks different at different schools. As educators develop more facility with data use, they will apply strategies and protocols situationally. It is important to have educators think through factors like effort and impact on student learning, when prioritizing where to take action.

Data Analysis Questions

Correlation/Causation

LUNCH

Triangulation Data set 1 Hypothesis Refined Hypothesis “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

Triangulation Data set 1 Hypothesis Data set 2 Refined Hypothesis Data set 3 Refined Hypothesis Data set 4 Refined Hypothesis

Implementation Planning 21

Adaptive Change and Collaborative Structures In general, what is the meeting topic? What questions are being asked? How can we help structure questions so that more data can be brought in to answer them?

Data Sources and Types What are the general questions we should be asking of all data sets? What are the questions unique to specific data sets or data types?

Summary The purpose of Triangulation is to use additional data to illuminate or temper evidence from another data source. Understanding the best questions to ask of various data sources can help facilitate productive data meetings and Data Conversations.

Data Conversations with Parents 25

Involve thinking through what you really want to know, and what assumptions you are making before you ask a question. Presume a positive result has already taken place; so you ask a question with this assumption already in mind. Presuming positive intent is not the same as “being positive.” Positive Presumptions 26

27 Implementation Planning

On-Site Visit 28 Day 5: On-Site Visit Possible activities for the Data Analysis Coach are: Collaboration time with the SDLT and/or school and district leaders. Observing Communities in Practice or Data Team meetings. Model/review Turn Key Activities. Analyze classroom data with classroom teachers. Model low stakes data conversation. Access NARS (NECAP Analysis and Reporting System) and other RIDE resources online.

Day 6 29 Some of the topics to be covered Day 6: Action Research and Expanding Circles of Data Use Data Conversations with Students Aggregate Data and Sub-Populations Intersection Analysis

Wrap Up 30