Transcript Data for Student Success

Data for Student Success:
Michigan’s Online Tools Supporting Local
Cultures of Quality Data and Improvement
Macul, March 19, 2009
“It is about focusing on building a culture of quality data
through professional development and web based
dynamic inquiries for school improvement.”
Introduction to
Data for Student Success
• Federal Title II Part D of the NCLB Act of
2001 Enhancing Education through
Technology Grant awarded through CEPI
• Awarded to Calhoun ISD in partnership
with Macomb ISD and Shiawassee RESD
• Beginning date: January 1, 2007
Introduction to the Grant
• Emphasis on teacher quality – at least
25% of funds must be spent on
professional development
• Focus on high-need LEA partners
• Expand the tools and professional
development activities to all ISDs across
the state
What Happens with your Data
Now?
• Local districts report to State through
SRSD, REP, etc.
• District/building seldom sees this data, so
quality/purpose/importance of data is
unclear to the district/building
• Data-based decision making to inform
school improvement, a key to increasing
student achievement, requires separate,
labor intensive effort
Goals of Data for Student
Success
• Build and bring to scale a program that helps schools develop
cultures of quality data in which there are consistent and
sustained efforts to:
– Focus on existing data that give insight into specific school
improvement questions
– Validate data provided to the State and used to support school
improvement decisions
• Identify critical questions whose answers would benefit school
districts in decision making to inform instruction
• Provide inquiries designed around the critical questions
• Provide focused professional development on data-based
decision making
• Provide a scaffold of support for the CNA and High Priority
Schools
Cycle 1 Focus
(January – September 2007)
• Local Data Initiatives
– Making connections to local data warehouses
• Local Professional Development
– Materials and approach development
– Proving ground for scaling up
• Animated Tutorials
• Prototype Dynamic Inquiries
– Putting longitudinal State data to work
Cycle 2 Focus
(October 2007 – September 2008)
• Scaling Up - Local Initiatives with the Future in Mind
– Continued robust support for Calhoun ISD, Macomb ISD and
Shiawassee RESD local data warehouses and intensive
professional development
– Intensive PD support for Branch and Barry ISDs
– Pilot of scalable PD with Dynamic Inquiries for Jackson ISD and
Gratiot-Isabella ISD
– Access to Dynamic Inquiries and “train the trainer” participation
for Eastern UP ISD
• Animated Tutorials
• Dynamic Inquiries
– Refine and extend current inquiries
– Planning for Predictive Inquiries
• Planning for Statewide rollout
Cycle 3 Focus
(June 2008 – September 2010)
• Implementing Statewide Roll Out of the
Data 4SS Professional Development
Model to many ISDs (2008-09)
– Spring/Summer 2008
• Revision of Data 4SS Professional Development
modules and website
• Wayne RESA – PD Overview
• UP Facilitators Overview
• Eastern UP Data 4SS Overview
Cycle 3 Focus
• October – December 2008
– Two Launch Events (Escanaba, Flint)
– MDE Field Service consultants presentation
– Presentation at MDE Fall School
Improvement Conference
Training Update
ISDs/ESAs that have attended a Launch:
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Barry
Bay-Arenac ISD
Branch
Calhoun
COP ESD
Copper Country ISD
Delta-Schoolcraft
Dickinson-Iron
Eastern UP ISD
Eaton ISD
Genesee ISD
Gogebic-Ontonagon
Gratiot-Isabella RESD
Jackson County ISD
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Lapeer County ISD
Lenawee ISD
Livingston ESA
Marquette-Alger
Mason-Lake/Oceana ISD
Menominee
Midland County ESA
Oakland Schools ISD
Saginaw ISD
Shiawassee
St. Clair RESA
Traverse Bay Area ISD
Washtenaw ISD
Wayne RESA
Training Update
• ISDs/ESAs that registered for Grand
Rapids Launch:
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Ingham ISD
Kalamazoo RESA
Kent ISD
Macomb
Mecosta/Ocesola
Cycle 3 Focus
• December 2008 – September 2010
– Provide support to Cohort ISDs – could be assistance
for training sessions, debriefing sessions, and future
planning
– Provide follow up/support PD
– Continuous communication with identified groups
– Provide input for Data 4SS revisions and updates
• Enhancements – MI-Access, MME, ELPA
– Launch 4
Cycle 4 – 6 Focus
• March 2009 – September 2012
– Provide train-the-trainer professional development and
support for multiple cohorts of current and remaining
ISDs/RESAs
– Enhance the online dynamic inquiries based on currently
identified data sets (MEAP, MME, ELPA, and MIACCESS)
and input from participating ISDs.
– Modify the professional development model around the
enhanced dynamic inquiries and Identify how the ‘School
Improvement Planning’ and D4SS inquiry tools could be used
to support the CNA and PA25 reporting process.
– Continue building a basis for the expansion and sustainability
of the work with local and intermediate school district staff
and among and between state agencies.
ISD/ESA Team Roles: Why?
• “Schools that explore data and take action
collaboratively provide the most fertile soil
in which a culture of improvement can take
root and flourish.”
"The Collaborative Advantage." Educational Leadership Dec/Jan (2009)
ISD/ESA Team Roles: Why?
• What is the technology leader's role in helping to create
a culture of collaboration? Summary of actual responses
from district technology leaders:
– Support efforts toward collaboration by attendance and
participation.
– Be a part of that culture. The trust factor is critical for the tech
leader to be an effective resource. The tech director needs to be
seen and trusted as an educator.
– The technology leader's role is to act as an active member of the
school's leadership team that models collaboration and creates
an environment that supports collaboration for all stakeholders.
– Model and promote means to improve collaboration,
communication, data access, analysis, and reporting.
PD Modules
Using Data to Improve Student Achievement is
a series of four modules designed to support
principals and school teams in leading school
improvement efforts through data-driven
instructional decisions. The modules intend to
enhance the skills of school leaders to analyze and
use their state assessment, school and classroom
data to improve student achievement.
Using Data to Improvement Student
Achievement Modules
• Using State Data to Identify School
Improvement Goals
• Using School Data to Clarify and Address
the Problem
• Examining Student Work to Inform
Instruction
• Using Classroom Data to Monitor Student
Progress
For more detailed information please go to www.data4ss.org
Data for Student Success
PD Tools
• Each professional development module
will utilize the following tools:
– In depth focus questions to help determine
outcomes
– Agenda for participants
– PowerPoint presentations to guide the
workshop
– Worksheets for participants
– Animated tutorials
Let’s take a tour of the
Data4SS Web site
www.data4ss.org
Let’s investigate the Dynamic
Inquiry Tool…
• All data mining efforts must be based on inquiry
– asking the right questions, and then asking
more questions of the answers in order to make
informed decisions.
• “Data-driven decision making does not simply
require good data; it also requires good
decisions.”
"The New Stupid." Educational Leadership Dec/Jan (2009)
• “The essential-questions approach provides the
fuel that drives collaborative analysis.”
“Answering the Questions that Count." Educational Leadership Dec/Jan (2009)
Dynamic Inquiry Tool
• Interactive inquiries that allow a user to drill
down into their student data
• Six inquiries based on essential questions
aligned with the school improvement process:
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MEAP Proficiency
Students Near Proficiency
Comparative Item Analysis
Cohort Proficiency
Student History
Admin Review
Access the Demo Site
• Go do www.data4ss.org
• Click on Dynamic Inquiries page and
access the Data Inquiry Tool
• Username: demo_test1
• Password: fall_01
• Examples of the Questions and Inquiries
used follow
MEAP Proficiency Inquiry
“How did students perform on MEAP tests
by content area, strand, and GLCE?”
MEAP Proficiency - All
Students
WHY?
• Why compare school to district?
• Why compare school to ISD?
• Why compare school to state?
• AYP Targets – Cautions
– State average does not mean proficient
MEAP Proficiency - Statistical
Information
MEAP Proficiency - Student
Drill Down
MEAP Proficiency
AYP Subgroups
MEAP Proficiency
Other Subgroups
Sub Group Statistical
Information
Students Near Proficiency
Inquiry
“What are the demographic characteristics
of students who are close to being proficient
on a specified test?”
“How well did those students perform by
strand, GLCE, and comment codes?”
Students Near Proficiency Graph
Students Near Proficiency Drilldown
Cohort Proficiency Inquiry
“What is the evidence of one year’s growth
for one year of instruction?”
Cohort Proficiency - Graph
Cohort Proficiency Statistical Information
Cohort Proficiency - Drilldown
Student History
“What is the complete academic history of
an individual or group of students?”
Student History
• Provide student level data from SRSD and
MEAP
• Student data in 4 areas
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Student Identification
Student Attendance
Program Participation
Achievement History
• Useful when students transfer to a new school
because of the ability for district key staff to view
or download the data.
Student History –
Student Record
Student History - Attendance
Student History - Participation
Student History Achievement
Comparative Item Analysis
Inquiry
“How did student performance within a
district or building or ISD compare to the
State?”
• The comparative item analysis inquiry also
answers the following question:
“How did we do in comparison to the state
on items/GLCE in a strand?”
• Will help to identify curriculum and
teaching areas that may need adjustment
Comparative Item Analysis Chart
Comparative Item Analysis:
Numbers and Operations Detail Graph
Comparative Item Analysis
Numbers and Operations Tabular Results
Comparative Item Analysis –
Released Items – Student Data
FERPA Issues
• Legally and ethically responsible
• Data security – physical and confidential
– Paper, electronic, conversations
• What do our LEAs know?
– How secure/protected is the data?
– Do we have confidentiality issues?
– Are the annual requirements and notifications
in place?
• What is the ISD’s role?
FERPA Quiz Group Results
• Sample of the results from the Ann Arbor
Data4SS Launch event…
1: Data 4SS Ann Arbor Launch FERPA Quiz: Schools must provide a parent with an
opportunity to inspect and review his or her child's education records within 60
days of receipt of a request. FALSE
3: Data 4SS Ann Arbor Launch FERPA Quiz: When a student turns 18 years old and
the rights under FERPA transfer from the parent to the student, the school must
obtain consent from the student in order to disclose grades and other education
records to the parents. FALSE
4: Data 4SS Ann Arbor Launch FERPA Quiz: In a legal separation or divorce
situation, both parents have the right to gain access to the student’s education
records. TRUE
5: Data 4SS Ann Arbor Launch FERPA Quiz: A school may designate and disclose
any information on a student as "directory information," as long as the school
notifies parents and provides them with an opportunity to opt out. FALSE
7: Data 4SS Ann Arbor Launch FERPA Quiz: To be considered an "education
record," information must be maintained in the student's cumulative or permanent
folder. FALSE
8: Data 4SS Ann Arbor Launch FERPA Quiz: When a student transfers to a new
school, the former school is required to send the student's education records to the
new school. FALSE
Legal Consequences
• Federal Law
• Local Board of Education policy
– Release of information
– Board of Education determines directory
information
– Need for a “go to” person at district level
– Access log
• Paper trail
Go to…
www.data4ss.org
For your FERPA resources
How do Data4SS and local data
warehousing tools work together?
• Together they provide the ability to triangulate data from
multiple sources
– Both provide non-negotiable state data
• Data4SS is based on enrollment at time of MEAP
• Local warehouse is based on live/current enrollment
– Local warehouse provides analysis of district required
assessments
– Local warehouse provides analysis of classroom
performance data
– Local warehouse provides frequent systematic
monitoring for growth to avoid unexpected results
How do Data4SS and local data
warehousing tools work together?
• How does your data warehouse
complement the Data 4SS Inquiries?
– Frequently monitor student achievement
using local assessment data
– Monitor groups of students to identify
trends based on state and local
assessments as well as other data such
as involvement in various programs
Data Inquiry Tools at a Glance
Data for Student Success
Inquiry Tool
 Historical data:
◦ State, District, School and
student level
 Inquiry tools:
◦ MEAP
◦ MEAP Cohort Comparison
◦ MEAP Strand, GLCE, Item
Analysis
◦ Students Near MEAP
Proficiency
◦ Student History
◦ Admin Review - 2009
◦ MiACCESS – 2009
◦ ELPA – 2009
◦ MME – 2009
Local Data Warehouse
 Current data:
◦ Consortium, district, school,
grade, teacher and student
level
 Inquiry tools:
◦ MEAP
◦ Cohort (Pivot) Comparison
for MEAP, grades, test series
◦ MEAP Strand, GLCE
Analysis
◦ MEAP and MME Percent
Proficient
◦ Student Profile
◦ DIBELS
◦ Local Assessments
◦ Administer exams (bubble
sheets and online)
◦ More
Example:
Data Mining with Data4SS
• Local district 2006 MEAP writing
– 35% 3rd grade students proficient
– 37% 4th grade students proficient
• Local district 2007 MEAP writing
– 72% 3rd grade students proficient
– 55% 4th grade students proficient
• Increase in proficiency
– 37 percentage points 3rd grade writing
– 18 percentage points 4th grade writing
Example:
Local Data Warehouse
Classroom Assessments
• Used to determine
if students are on
track with
expectations
• Used as pre and
post-tests
• Adjust teaching
based on data
Local
Warehouse
Example:
8th Grade Math
MEAP
compared to 9th
Grade Algebra
Grade
Next Question:
What area of 8th
grade math
curriculum
needs to be
reviewed?
Questions Generated by
Superintendents
(yes – engage Superintendents!)
• Is this a curriculum alignment issue?
• How does Algebra 1 correlate to 8th grade
math MEAP?
• Is this due to transition issues? The culture
of 8th grade to 9th grade – could they need
some nurturing to transition the culture
change?
• Grading – are teachers giving zeros for no
homework?
Our Role – Data 4SS
• Helping ISDs focus their LEAs on building
a culture of quality data
• Asking the hard questions
• Giving ISDs tools for accountability
• Providing scaffolding
– Gradual release of responsibility
– Provides a structure for improvement
• Giving ISDs and LEAs ownership of their
data
Data 4SS Model
• Designed around the “right work” and
common sense
• Organized instructional improvement
around a process with specific,
manageable steps
• Supports LEAs to build confidence and
skill in using data
• Professional Development and
collaboration are essential
Getting Involved with
Data4SS
• Through participating ISDs
– ISDs attend ‘Launch’ events
– ISDs provide professional development and
access to the Inquiry Tool
• www.data4ss.org
• [email protected]
Data for Student Success
Key Contact Information
• General
– www.data4ss.org
– [email protected]
• Andrew Henry – Data 4SS Project Director
– [email protected]
• Stephen Brodeur – Data 4SS Project Coordinator
– [email protected]
• Mary Gehrig, Assistant Superintendent, Calhoun ISD
– [email protected]
• Mike Oswalt, Assistant Superintendent, Calhoun ISD
– [email protected]
• Becky Rocho, Assistant Superintendent, Calhoun ISD
– [email protected]
• Maureen Slamer – Data 4SS PD Director, Calhoun ISD
– [email protected]