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Using DataUsing Datain thein the
Delaware Delaware Performance Performance
Appraisal SystemAppraisal System
Using DataUsing Datain thein the
Delaware Delaware Performance Performance
Appraisal SystemAppraisal System
Wednesday, September 27, 2006Wednesday, September 27, 2006
2
Working Assumptions
• Everyone is thoroughly familiar with DPAS 1 and knows that DPAS 2 is being field tested.
• All administrators are familiar with the DSTP pages of the DOE website.
• All teachers and specialists have access to the DSTP pages of the DOE web site and know how to use those pages to find information about their students.
• NCLB is a data-based accountability systemand finally…
3
Working Assumptions
• Collecting and analyzing data is the best way to identify and to help focus instruction on areas of need; therefore,
4
Working Assumptions
• Collecting and analyzing data is the best way to identify and to help focus instruction on areas of need; therefore, collecting and analyzing data need to become integral parts of the school culture.
“Data Culture”
5
DPAS 1vs.
DPAS 2
6
DPAS Components
ListDPAS 11. Instructional Planning2. Organization and
Management of Classroom
3. Instructional Strategies4. Teacher/Student
Interaction5. Evaluation of Student
Performance6. Related Responsibilities
DPAS 21. Planning and
Preparation2. Classroom Environment3. Instruction4. Professional
Responsibilities5. Student Improvement
7
DPAS Components
ComparisonDPAS 11. Instructional Planning
2. Organization and Management of Classroom
3. Instructional Strategies4. Teacher/Student Inter-
action5. Evaluation of Student
Performance6. Related Responsibilities
DPAS 21. Planning and
Preparation2. Classroom Environment
3. Instruction
5. Student Improvement
4. Professional Responsibilities
8
DPAS 2 Components
Data PotentialDPAS 11. Instructional Planning
2. Organization and Management of Classroom
3. Instructional Strategies4. Teacher/Student Inter-
action5. Evaluation of Student
Performance6. Related Responsibilities
DPAS 21. Planning and
Preparation2. Classroom Environment
3. Instruction
5.5. Student Improvement Student Improvement
4. Professional Responsibilities (by encouraging certain kinds of staff development over others)
9
DPAS 11. Instructional Planning
2. Organization and Management of Classroom
3. Instructional Strategies4. Teacher/Student Inter-
action5. Evaluation of Student
Performance6. Related Responsibilities
DPAS 21. Planning and
Preparation2. Classroom Environment
3. Instruction
5.5. Student Improvement Student Improvement
4. Professional Responsibilities (by encouraging certain kinds of staff development over others)
DPAS 1 Components
Data Potential
10
So…
• How do I create a Data Culture in my school?
• How do I use DPAS 1 to encourage staff members to use data in making instructional decisions?
11
Creating a Data Culture
• Open access to all data, for all staff– DOE website – The Honeycomb– Display data all around the school
• Hallways• Copy room!• School publications
• Model using data– If you don’t use it, why should your staff?– Setting school goals– Ask questions in terms of data
• in meetings• in both formal and informal conversations with staff
12
Set the stage
• Make your expectations known up front– In writing– To all certified staff– Early in the observation cycle
• i.e., before you begin any observations
– School-Wide Expectations• summer letter• “First day” packet
– Reinforce frequently• every meeting: staff, departments/teams, SIP or
other leadership teams
13
EXAMPLE: School-Wide Expectations
Areas of emphasis for next year: 1) classroom assessments and 2) focusing on individual low performers – There can be no doubt that we must continue our ongoing work on integrating reading, writing and math instruction into all content areas and that we must continue our work on identifying and meeting the needs of students in on our most problematic NCLB cells. As Chester and I observed classes, analyzed data and took stock of our status and progress, however, two things have become apparent. First, we must focus on classroom assessments as well as on state assessments. To be successful in reaching ever higher performance targets, we must be ever more clear as to what our instructional goals are, and we must plan all instruction to contribute directly to reaching those goals. In theory, the process is simple: we become thoroughly familiar with the state standards, we develop classroom assessments that will show that our students have met the state standards, and then we create lessons to teach students to do well on those assessments. This is exactly what many of you do, and in your cases Chester and I will be documenting your work and, I hope, working together with you to take your assessments to that proverbial “next level.” Where we need to work with others of you on the fundamentals, we will do that as well. Second, to meet ever higher performance targets, we must aim our instruction more and more directly toward meeting the needs of individual students. We have done well at teaching to the majority of learners, and we have done significant work at reaching out to bring in other less traditional learners. Literature circles, All Kinds of Minds analyses, and the CMP practice of expecting that in any class students will derive multiple methods of arriving at the correct answer to a problem are good examples of how we are doing this kind of work, for they are based on a belief that instructional goals should be the same for all students while instructional materials and methods should vary depending on individual needs. One destination, many paths. To meet ever higher performance targets we must continue providing what we know works best for the majority of mainstream learners, while focusing our professional growth efforts on finding ways to reach the exceptional cases. Our future success depends on us reaching the outliers. As you do your summer planning, then, do so with the expectation that Chester and I will be asking for copies of all assessments that relate in any way to the lessons we observe (from the vocab quiz coming up in a day or two to the final major assessment for the current unit) and that we will be asking what specific steps you are taking to reach your students with different needs and styles.
14
Areas of emphasis for next year: 1) classroom assessments and 2) focusing on individual low performers – There can be no doubt that we must continue our ongoing work on integrating reading, writing and math instruction into all content areas and that we must continue our work on identifying and meeting the needs of students in on our most problematic NCLB cells. As Chester and I observed classes, analyzed data and took stock of our status and progress, however, two things have become apparent. First, we must focus on classroom assessments as well as on state assessments. To be successful in reaching ever higher performance targets, we must be ever more clear as to what our instructional goals are, and we must plan all instruction to contribute directly to reaching those goals. In theory, the process is simple: we become thoroughly familiar with the state standards, we develop classroom assessments that will show that our students have met the state standards, and then we create lessons to teach students to do well on those assessments. This is exactly what many of you do, and in your cases Chester and I will be documenting your work and, I hope, working together with you to take your assessments to that proverbial “next level.” Where we need to work with others of you on the fundamentals, we will do that as well. Second, to meet ever higher performance targets, we must aim our instruction more and more directly toward meeting the needs of individual students. We have done well at teaching to the majority of learners, and we have done significant work at reaching out to bring in other less traditional learners. Literature circles, All Kinds of Minds analyses, and the CMP practice of expecting that in any class students will derive multiple methods of arriving at the correct answer to a problem are good examples of how we are doing this kind of work, for they are based on a belief that instructional goals should be the same for all students while instructional materials and methods should vary depending on individual needs. One destination, many paths. To meet ever higher performance targets we must continue providing what we know works best for the majority of mainstream learners, while focusing our professional growth efforts on finding ways to reach the exceptional cases. Our future success depends on us reaching the outliers. As you do your summer planning, then, do so with the expectation that Chester and I will be asking for copies of all assessments that relate in any way to the lessons we observe (from the vocab quiz coming up in a day or two to the final major assessment for the current unit) and that we will be asking what specific steps you are taking to reach your students with different needs and styles.
EXAMPLE: School-Wide Expectations
Let’s look at some Let’s look at some sample data.sample data.
Let’s look at some Let’s look at some sample data.sample data.
16
Trend Data
PerformanceOverTime
17
Note:
All data on the following Christina School District graphs was taken from the public pages of the DOE website. It is freely available to anyone with access to the internet, and the graphs can be created by anyone who
can use Excel.
18
Examining Trend Data
CSD: Math Percentile Trends - 5th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
19
Examining Trend Data
CSD: Math Percentile Trends - 5th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
20
Examining Trend Data
CSD: Math Percentile Trends - 3rd Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 5th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
5
3
21
Examining Trend Data
CSD: Math Percentile Trends - 3rd Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 5th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 8th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
5
3
8
22
Examining Trend Data
CSD: Math Percentile Trends - 3rd Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 5th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 8th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 10th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
10
5
3
8
23
Examining Trend Data
CSD: Math Percentile Trends - 10th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 8th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 5th Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
CSD: Math Percentile Trends - 3rd Grade
0
10
20
30
40
50
60
70
80
90
100
PL5 PL4 PL3 PL2 PL1
Performance Level
Per
cecn
t at
Lev
el
1998
1999
2000
2001
2002
2003
2004
2005
2006
10
8
5
3
24
Examining Performance Against NCLB Targets
CSD- Performance Against Targets: 3rd Gr. MATH
0
10
20
30
40
50
60
70
80
90
100
Testing Years
Per
cen
t M
eeti
ng
Sta
nd
ard
Target 33.00 33.00 41.375 41.375 49.75049.750 58.12566.500 74.87583.250 91.625100.00
Actual 53.520 60.41073.60073.23072.88075.420 77.180 82.32 79.790 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
25
Examining Performance Against NCLB Targets
CSD- Performance Against Targets: 3rd Gr. MATH
0
10
20
30
40
50
60
70
80
90
100
Testing Years
Per
cen
t M
eeti
ng
Sta
nd
ard
Target 33.00 33.00 41.375 41.375 49.75049.750 58.12566.500 74.87583.250 91.625100.00
Actual 53.520 60.41073.60073.23072.88075.420 77.180 82.32 79.790 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
CSD - Performance Against Targets: 5th Gr. MATH
0
10
20
30
40
50
60
70
80
90
100
Testing Years
Per
cen
t M
eeti
ng
Sta
nd
ard
Target 33.00 33.00 41.375 41.375 49.75049.750 58.12566.500 74.87583.250 91.625100.00
Actual 41.320 46.76054.790 56.80057.890 67.730 69.99 74.22071.910 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
CSD - Performance Against Targets: 8th Gr. MATH
0
10
20
30
40
50
60
70
80
90
100
Testing Years
Per
cen
t M
eeti
ng
Sta
nd
ard
Target 33.00 33.00 41.375 41.375 49.75049.750 58.12566.500 74.87583.250 91.625100.00
Actual 33.27031.62028.700 29.90 40.48 30.78025.30038.14050.930 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
CSD - Performance Against Targets: 10th Gr. MATH
0
10
20
30
40
50
60
70
80
90
100
Testing Years
Per
cen
t M
eeti
ng
Sta
nd
ard
Target 33.00 33.00 41.375 41.375 49.75049.750 58.12566.500 74.87583.250 91.625100.00
Actual 32.170 25.68027.030 23.88 28.780 40.71041.890 42.160 43.170 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
10
8
5
3
26
Disaggregations
Getting to theschool,
teacher, andclassroom
levels.
27
Disaggregating by NCLB CellsCSD - Disaggregated DSTP Trend Data - 10th Grade MATH
0
10
20
30
40
50
60
70
80
90
100
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Test Year
Per
cen
t M
eeti
ng
Sta
nd
ard
Targets
All
28
Disaggregating by NCLB CellsCSD - Disaggregated DSTP Trend Data - 10th Grade MATH
0
10
20
30
40
50
60
70
80
90
100
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Test Year
Per
cen
t M
eeti
ng
Sta
nd
ard
Targets
All
White
Asian
29
Disaggregating by NCLB CellsCSD - Disaggregated DSTP Trend Data - 10th Grade MATH
0
10
20
30
40
50
60
70
80
90
100
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Test Year
Per
cen
t M
eeti
ng
Sta
nd
ard
Targets
All
Af-Am
Hisp
White
Asian
Low Inc
LEP
30
Disaggregating by NCLB CellsCSD - Disaggregated DSTP Trend Data - 10th Grade MATH
0
10
20
30
40
50
60
70
80
90
100
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Test Year
Per
cen
t M
eeti
ng
Sta
nd
ard Targets
All
SpEd
Af-Am
Hisp
White
Asian
Low Inc
LEP
31
Disaggregating by NCLB CellsCSD - Disaggregated DSTP Trend Data - 10th Grade MATH
0
10
20
30
40
50
60
70
80
90
100
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Test Year
Per
cen
t M
eeti
ng
Sta
nd
ard Targets
All
SpEd
Af-Am
Hisp
White
Asian
Low Inc
LEP
32
Disaggregating at the
TeacherLevel- - - - -
ChartingCohort
Progress
Cohort Progress: FMS 2006 -- 8th Grade Special Ed -- MATH
300
325
350
375
400
425
450
475
500
525
550
575
3rd 4th 5th 6th 7th 8th
Grade Level
Sca
le S
core
Bergold, Heather
Blades, Michael
Blaw n, David
Boyer, Matthew
Bracy, Mica
Brow n, Shelbi
Buckley, Brett
Bumbrey, Fatima
Cannon, Shilar
Coffee, Harold
Dixon, Azariah
How ard, Tiffany
Johnson, Erica
Kepner, Michelle
Krambeck, Erica
Manley, Jovon
Mannings, Robert
Michalski, Brice
Miller, Katie
Myers, James
O'Neal, Steven
Parrish, Dillion
Roos, Cody
Sanchez, Jessica
Sargent, Zachery
Spencer, Robert
Stevens, Ty'Ree
Stew art, Mary
Sylvain, Sharon
White, Crystal
Wilkerson, Dennis
Wintjen, Courtney
PL 2
PL 3
Median (middle)
Old PL 3
33
Incorporating
Datainto
DPAS
34
The Link
• The purpose of DPAS is to document how well (or whether or not) a staff member is doing his/her job.
• If you have framed that job in terms of using data– to set instructional goals and– to make decisions as to how to achieve those
goals, then
data and DPAS are a natural fit.
35
DPAS Components
DPAS 11. Instructional Planning
2. Organization and Management of Classroom
3. Instructional Strategies4. Teacher/Student Inter-
action5. Evaluation of Student
Performance6. Related Responsibilities
DPAS 21. Planning and
Preparation2. Classroom Environment
3. Instruction
5.5. Student Improvement Student Improvement
4. Professional Responsibilities (by encouraging certain kinds of staff development over others)
36
Data in DPAS 1 ComponentsINSTRUCTIONAL PLANNING:
• provides appropriate instructional objectives• provides methods and materials which maximize learning• includes provisions for evaluating objectives• provides scope and sequence for lesson
ORGANIZATION AND MANAGEMENT OF CLASSROOM:• arranges classroom for instructional effectiveness• uses instructional time efficiently• establishes, communicates and maintains standards for students• maintains high engagement rate• maintains a positive classroom environment• monitors the learning activities of students
37
Data in DPAS 1 ComponentsINSTRUCTIONAL STRATEGIES:
• uses and organizes appropriate methods and activities in their proper sequence and time frame, i.e., reviews, modeling, guided and independent practice, and closure• demonstrates sufficient knowledge of subject matter being taught• uses available instructional media and materials effectively• establishes a mind set for learning• focuses lesson on teaching objective• uses level of instruction that is appropriate• maintains pace of learning• provides opportunities for student differences• checks for student understanding• conveys appropriately high expectations for students
38
Data in DPAS 1 ComponentsTEACHER/STUDENT INTERACTION:
• promotes high rate of student interest• provides prompt and specific feedback in a constructive manner• provides opportunities for active participation• uses questioning techniques effectively• demonstrates fairness and consistency in dealing with students• speaks and writes clearly, correctly and at an appropriate level for student understanding
EVALUATION OF STUDENT PERFORMANCE:• uses appropriate formative and summative tools and techniques• makes effective use of norm- and/or criterion-referenced test data• provides prompt feedback and constructive comments on tests, homework and other assignments• maintains accurate records documenting student performance
39
Data in DPAS 1 ComponentsRELATED RESPONSIBILITIES:
• complies with policies, regulations and procedures of school district/building• engages in professional development• communicates effectively with parents• works cooperatively with staff• performs non-instructional responsibilities as assigned
40
Data in DPAS 1 Components
1. Instructional Planning• “What data did you use in deciding to teach
this lesson?”• “What data-identified needs are you
addressing with this lesson?”– How does this lesson address trends shown in
the graphs I presented in my opening day presentation (or Sept. staff meeting, etc.)?
• “How do you plan to meet the needs of students in our target NCLB cells?”
41
Data in DPAS 1 Components
3. Instructional Strategies• “What data is there to show that the
instructional strategies you have chosen are effective for meeting your instructional goals?”
– general data from professional development– specific data generated by the teacher for this
group of students
• Timed scans of the classroom– time on task, incidences of specific behaviors
42
Data in DPAS 1 Components
5. Evaluation of Student Performance• “How have you determined that your chosen
assessment strategies will accurately reflect student learning?”
• “How closely does data you have collected from your classroom assessments mirror data from the DSTP?”
• If there are discrepancies, why?• Are students learning?• Are the assessments not good enough?• Are changes in instruction indicated?
43
Data in DPAS 1 Components
6. Related Responsibilities• “What data analysis work have you undertaken
on your own?”– Review of your particular students’ DSTP scores?
Instructional needs comments?– Data generated from classroom assessments?
• How have you applied what you have gained from professional development opportunities (course work, district workshops, individual reading) to improve your understanding of your students’ performance and instructional needs?
44
Objections & Responses
“I taught it, but they didn’t learn it.”
NCLB & the DSTP don’t care what you did. They only care about your results.
“My job is to teach. It’s the kids’ responsibility to learn.”
No, your job is to bring about student learning and to improve student performance.
There may once have been a time when your job was simply to “put it out there,” but that time is long gone.
45
Objections & Responses
Like it or not, districts, schools and teachers are no longer being judged on what they do. What matters now is what their students do.
Results matter above all, and process (teaching) is valued only to the degree that it produces the desired outcome (student performance at or above targets).
46
“Teaching the Curriculum”
vs.Teaching to needs identified by data
• Should be a false dichotomy• Teachers are expected to teach the curriculum,
however…• that should never be an excuse for not meeting
students’ needs.• If there are problems, it is important to identify where
they lie.
Objections & Responses
47
Curriculum Alignment
Instruction(implemented by the teacher)
Curriculum(set by the district)
Standards(set by the state)
X
48
Curriculum Alignment
the
TAUGHTcurriculum
Xthe
ASSESSEDcurriculum
the
WRITTENcurriculum
49
Tools & Strategies
• Differentiated Instruction• Authentic & embedded assessments• Understanding By Design (& similar systems)• Rubrics• Staff cooperative work sessions:
– analysis of school/grade data– planning of common lessons– scoring of common assessments
• Gates-McGinitie, Dibels, unit tests that come with texts
50
Other Data
• Attendance
• Discipline
• TAG / Special Ed referral test results
• Schools Attuned / A Mind at a Time info
• Learning Styles inventories
• Teachers’ grade distributions
• Comparisons of grade distributions with DSTP scores of the same students
51
Still not enough?
Ask for more!
Sample form:
Performance Appraisal Request for Additional Information
(see handout)
52
Keep in mind:Analyzing school data can point out
both strengths and weaknesses…but the best thing that data does is to
raise questions.
Your school’s ability to find those questions and then to answer them will determine your future success.
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