Upload
karin-benson
View
214
Download
0
Tags:
Embed Size (px)
Citation preview
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