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NCLB – Can You Afford Not To Have A Data Warehouse
Bill FlahertyDirector of Technology Services
Hanover County Public Schoolshttp://hanover.k12.va.us/presentations/NCLB-dw
You have to be very careful if you don’t know where you are going, because you might not get there.
-Yogi Berra
How would I show SOL results in deciles by school for multiple years?
How would I provide produce a report with past SOL results by subgroup and reporting category?
How would I provide each student with a realistic prediction of what GPA and SAT is needed for admission to different colleges?
Today’s Presentation Three Questions Data Driven Decision Making MERC’s Work An Overview of Data Warehouse Technology What is Hanover’s Instructional Decision
Support System? Advantages Using IDSS to crack the NCLB nut Quality School Portfolio Program Demonstration of IDSS – Answers to the Three
Questions, NCLB Reports + More Questions
High Stakes Testing
Data-Driven Decision Making
What is Data-Driven Decision Making?
Mining the Data Analyzing the Data Communicating the Data Using the Data
Teachers Use of High-Stakes Test Score Data to Improve Instruction
Metropolitan Educational Research Consortium (MERC)
Only half the teachers received scores by reporting category
Most teachers focused on group averages and not individual students
Most teachers made instructional changes– more depth -pacing– tests taking skills -individualization – advanced cognitive processes– formative assessments– within grade collaboration
http://www.vcu.edu/eduweb/merc
Current Systems
On-line Transaction Processing System
OLTP
Building The Data Warehouse
-Bill Inmon, 1992
Data Warehouse
A subject-oriented, integrated, time variant, non-volatile collection of data in support of management’s decision-making process.
-William Inmon
Fundamental Characteristics of a Data Warehouse:Separate Decision Support System database from
OLTP systems
Storage of data only; no data is created, but it may be derived
Integrated data
Scrubbed data
Historical data
Fundamental Characteristics of a Data Warehouse:
Read only
Various levels of summarization
Subject oriented
Easily accessible
Data Warehouse
User
CommunityData
Warehouse
Data
Source
Data
Source
Data
Source
A
B
C
Hanover County Public Schools
Instructional Decision
Support System (IDSS)
Our Current System
CIMS
On-line Transaction Processing System
OLTP
IDSS
UserCommunity
(Business Objects)
Data
Warehouse
(SQL 7.0)
CIMSDatabase
TestScores
In-house Data, Surveys
www.businessobjects.com
1999-2000School Board Goals
4. Promote instructional programming in the following areas, among others.
1. Professional development2. Curriculum development
3. Implications of longitudinal assessment of student achievement
4. Graduation requirements staffing needs5. Vocational / technical / alternative education study
recommendations6. Standards of learning implementation /
implications
Support From The Top
Longitudinal Assessment Committee Chaired By The Superintendent– First Meeting May, 1999– Committee members
• Board member• Assistant Superintendent of Instruction• Assistant Superintendent of Finance & Technology• Director of Guidance & Testing• Director of Technology Services• School Principal• Supervisor Information Technology
Front Ends For The User Community Mechanism for correcting data Predefined reports – Student Information
System Business Objects
– Predefined reports– Ad hoc reporting– Technical person to assist principals in obtaining the
correct data
IDStudent
Challenges
Keeping the data in the warehouse up-to-date – nightly refresh– Information from multiple sources received in a
constant steam Keeping the data accurate New State reporting has changed the whole ball
game Keeping principals trained
Advantage of IDSS
Ease of use Speed in acquiring data Ability to create custom reports Make decisions based on information
Improve student performance
Key Elements of Success
Board and Superintendent Support– Board Goal– Personnel to support the goal
Cost effective Infrastructure in place Training Core reports
Elementary Schools % Passing vs. % Taken
– Examined by subgroups• No difference by minorities • Student with disabilities are an area of concern
School pass rate not keeping up with county pass rate over time– Used a dual line graph to illustrate to faculty
Uses bar graphs to compare scores over time Uses item analysis for improving specific
curriculum areas Work most closely with grade-level chairmen
and curriculum content specialists
Middle Schools
Data Day at SJMS– Departmental Teams (1/2 day)
– Teaching Teams (1/2 day)• How students performed in each area, including subtests.
• Do item analysis in weak areas
• How to improve performance in cross-curricular teams
– Followed Baldridge Criteria • Used quality tools, fishbone, issue bin, condense-a-gram,
etc.
– Shared information within the groups and with the faculty and administration as a whole.
Middle Schools
Request of 7th grade civics teachers Principal’s goals
High Schools
Principals share data with teachers Teachers: “Give us only what we need.” Tremendous help in identifying students
who need an IEP or a SEP Examines data by SOL Test, subgroup,
& subtest Identifies areas of concern
High Schools What do we do now that we have identified the
students?– Toughest part of the problem– Meet with individual students– Establish tutoring schedules– Establish an interdisciplinary team for 9th grade “at
risk” students We are a good school, but not a great school
because not all students are successful. “The data warehouse gives us the ability to
slice and dice the data and look at all students as individuals.” – Stan Jones, Principal, Lee-Davis High School
High Schools
Ability to identify individual students quickly after AYP information was released
Look longitudinally at courses, teachers and subgroups
Share with department chairmen– They work with individual departments to “take
the data apart.”
School and departmental goals are developed A valuable tool to help meet principals’ goals
General
Focus is now on pass rates by subcategories Principals are looking at teacher performance Ability to look longitudinally (1998 – 2004) PALS – use of this data to adjust program for
students to ensure success with SOL testing Gives teachers a look at their class over time Great tool for tracking attendance and
discipline Has become a more user friendly front end to
our Student Information System
Free Data-Driven Decision Making Tools
UCLA Quality School Portfolio Program– Used in all 50 states in more than 1,000 schools– Web-based– Collect, analyze & use data to improve student achievement
Main Functions– Disaggregate data into groups– Set goals to monitor progress– Make charts, graphs and other reports– Track student grades by classroom– Enter student work samples and see students’ progress over
the span of their school careers http://qsp.cse.ucla.edu
