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

Questions

http://hanover.k12.va.us/presentations/NCLB-dw

bill@hcps.us