digging into student data oct. 24, 2006. plan for today 4:10-4:25 introduction to digging deeper...
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Plan for Today4:10-4:25 Introduction to Digging Deeper
4:25-4:55 Looking at Student Work
4:55-5:45 Single Problem Assessment Protocol
5:45-5:55 Break
5:55-6:15 A Sample Data Overview
6:15-7:00 School Team Work
What are we digging for?
GoalTo identify a
learner-centeredproblem
that we want to work together
to solve
Steps of the Process1. Share a Data Overview with faculty
2. Begin to think about how to frame a learner-centered problem
3. Start a conversation about what else we need to know to define the problem more precisely
4. Collect the data and state the problem
Two Approaches to “Digging”
1. Look closely at a single data source
2. Explore multiple data sources
1. Look carefully at a single data source Choose a source
that will help you really understand student thinking
Allow for challenging of assumptions
Why start with a single data source? Help move past “stuck points”
Slow down, avoid leaping to solutions
Make the data analysis more engaging
Move beyond generalizations
0
1
2
3
4
Average Score
4 11 26 27
Question Number
Grade 4 MCAS Math 2006: Our Open Response Scores vs. State
Our School State
2. Dig into multiple data sources “Triangulate” on
the problem
Get a more complete picture of student performance
Why dig into multiple sources? Develop a shared understanding of the
knowledge and skills students need
Develop a common language
Avoid making inappropriate inferences from test results
Questions for Reflection Do you have a solid understanding of why
students are performing as they are?
Can you state the learner-centered problem in a way that focuses on the knowledge and skills you want students to have?
Is your understanding of the problem supported by multiple sources of data?
If you solve this problem, will it help you meet your larger goals for students?
A306Data Wise: Step-by-Step Guide for Using Assessment Results to Improve Teaching
& Learning
Harvard Graduate School of EducationOctober 24, 2006
Looking at Student
WorkSteve Seidel
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
Thinking of student work as “raw” data…
What to do with that data?
How to make sense of it?
How to use those insights to improve instruction?
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
Questions for a Pair/Share: How have you encountered “looking at
student work?”
What worked and what didn’t work about your experiences in structured conversations about student work?
What questions did those experiences leave you with?
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
Five things you need to use that data well:
Collaboration
Clarity of purpose
A structure or method for analyzing it
A way to integrate that analysis into a larger view of what is going on in your classroom, school, or district
Follow-up and follow-through
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
A Brief History of Looking at Student WorkBefore 1985…
The Prospect Center and Descriptive Review Processes Literacy “Digs” Teacher’s Seminars on Children’s Thinking
1985 to 1990… Portfolios, Process-folios…but where’s the assessment?
1990 to now… The Massachusetts School Reform Act of 1993 Coaches, Coaching, and Looking at Student Work:
Required, then rejected
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
Some lessons learned over the years from looking at student work:
You can gain insights in many realms – about learners, the learning environment, the nature of work on a particular task.
You can’t do it alone. (Or, at least, you are much better off doing it with other people.)
You need a structure that serves your purpose.
Look first (and last) at work of students who you feel are not having satisfactory learning experiences in your classroom or school.
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
How to find the one in the many and the many in the one?
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
9th Grade Student’s Self Assessment
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
The Steps of the Protocol (approximately 35 minutes)1. Read the problem and work out your own answer to it. Keep some notes
on your thinking process. (5 minutes)
2. Share observations about the problem and your own problem-solving process. Specifically, what was striking to you about the problem and your efforts to solve it? (5-7 minutes)
3. Look at the student work (read silently). (3 minutes)
4. Describe what this student did and how she/he thought about the problem. (5 minutes)
5. Identify and share questions that have come up for you about this student’s grasp of the mathematical content of this question, based on your examination of this work so far. (5 minutes)
6. Share ideas about the implications for teaching and learning that you draw from your examination of this work. (10 minutes)
The Single Problem Assessment ProtocolDeveloped for Data Wise □ Steve Seidel
Harvard Graduate School of Education □ ©Steve Seidel □ October 2006
Looking at student work in the context of the Data Wise process
Purpose of today’s meeting
To start a conversation about math performance at Franklin by looking at
how our 10th graders performed on the state
test last spring
Agenda
3:00-3:25 Overview of presentation/discussion
3:25-3:45 Breakout to brainstorm questions
3:45-4:00 Reconvene to discuss next steps
How has performance changed over time?
STUDENT PERFORMANCE -- MATHEMATICSGrade 10 State Comprehensive Assessment
Franklin High School, 2003-2006
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2003 (n=430) 2004 (n=422) 2005 (n=425) 2006 (n=418)
Year
Perc
en
tag
e o
f S
tud
en
ts
Advanced
Proficient
Needs Improvement
Failing
How did 10th graders perform in 2006? DISTRIBUTION OF STUDENT
PROFICIENCY-- MATHEMATICS Grade 10 State Comprehensive AssessmentFranklin High School, Spring 2006 (n=418)
47%
39%
10%
4%
0%
10%
20%
30%
40%
50%
60%
Failing Needs Improvement Proficient Advanced
Proficiency Level
Pe
rce
nta
ge
of
Stu
de
nts
Compared to state? DISTRIBUTION OF SCHOOL AND STATE
STUDENT PROFICIENCY-- MATHEMATICSGrade 10 State Comprehensive AssessmentFranklin High School, Spring 2006 (n=418)
47%
39%
10%
4%
34%
27%
15%
24%
0%
10%
20%
30%
40%
50%
60%
Failing Needs Improvement Proficient Advanced
Proficiency Level
Pe
rce
nta
ge
of
Stu
de
nts
School State
Which students are failing? By program… Number of Students in Each Proficiency Groupwith Failing Students by Instructional Program
Grade 10 State Comprehensive AssessmentFranklin High School, Spring 2006 (n=418)
Advanced4%
Proficient10%
Needs Improvement
38%
Failing47%
English Language Learners (n=79)
11%
Students with Disabilities (n=82)
10%
Students in Regular Education
(n=257) 27%
Which students are failing? By race/ethnicity…Breakdown of Failing Students, Race/Ethnicity
Grade 10 State Comprehensive AssessmentFranklin High School, Spring 2006 (n=418)
Advanced4%
Proficient9%
Needs Improvement
40%
Hispanic (n=50) 12%
Asian(n=3) 1%
Other48%
African-American (n=118) 29%
White(n=22) 5%
Mixed/Other (n=2) 0%
Native American(n=1) 0%
How does performance differ by strand?
PERCENTAGE OF STUDENTS ANSWERING EACH MULTIPLE-CHOICE ITEM CORRECTLYGrade 10 State Comprehensive Assessment - Mathematics
Franklin High School, Spring 2006 (n=418)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
27 29 40 38 25 24 9 12 7 6 11 8 4 1 26 10 34 13 5 39 3 33 2 35 37 36 28 30 23 14 22
Item Number
Perc
en
tag
e o
f S
tud
en
ts
Geometry and Measurement
Number Sense Patterns, Relations, and Functions
Statistics and Probability
Compared to state?PERCENTAGE OF STUDENTS ANSWERING EACH MULTIPLE-CHOICE ITEM CORRECTLY
Grade 10 State Comprehensive Assessment - Mathematics Franklin High School, Spring 2006 (n=418)
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
29 40 27 38 25 24 9 12 4 7 11 8 1 6 26 10 13 34 3 5 33 39 2 35 37 36 14 23 28 30 22
Item Number
Dif
fere
nce
in P
erce
nta
ge
of
Stu
den
ts
Geometry and Measurement
Number SensePatterns,
Relations, and Functions
Statistics and Probability
What questions does this overview raise? Brainstorm in groups:
Group 1 Group 2 Group 3 Group 4
Roger Mallory Sasha Adelina
Eddie Will Karyn Dottie
LaShawn Miguel David K. Ervin
David S. Sondra Jamal
For Nov 3: Data Overview
Comprehensive picture of your school’s data
Intended to be shared with your faculty
Opportunity to address adaptive challenges
Opportunity to develop technical skills