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NCLB

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www.businessobjects.com. 1999-2000. School Board Goals ... gives us the ability to slice and dice the data and look at all students as individuals. ... – PowerPoint PPT presentation

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Title: NCLB


1
NCLB Can You Afford Not To Have A Data
Warehouse
  • Bill Flaherty
  • Director of Technology Services
  • Hanover County Public Schools
  • http//hanover.k12.va.us/presentations/NCLB-dw

2
  • You have to be very careful if you dont know
    where you are going, because you might not get
    there.
  • -Yogi Berra

3
  • How would I show SOL results in deciles by school
    for multiple years?

4
  • How would I provide produce a report with past
    SOL results by subgroup and reporting category?

5
  • How would I provide each student with a realistic
    prediction of what GPA and SAT is needed for
    admission to different colleges?

6
Todays Presentation
  • Three Questions
  • Data Driven Decision Making
  • MERCs Work
  • An Overview of Data Warehouse Technology
  • What is Hanovers 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

7
High Stakes Testing
8
What is Data-Driven Decision Making?
  • Mining the Data
  • Analyzing the Data
  • Communicating the Data
  • Using the Data

9
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
10
Current Systems
  • On-line Transaction Processing System
  • OLTP

11
(No Transcript)
12
Building The Data Warehouse -Bill Inmon,
1992
13
Data Warehouse
  • A subject-oriented, integrated, time variant,
    non-volatile collection of data in support of
    managements decision-making process.
  • -William Inmon

14
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

15
Fundamental Characteristics of a Data Warehouse
  • Read only
  • Various levels of summarization
  • Subject oriented
  • Easily accessible

16
Data Warehouse
Data Source
Data Source
Data Source
17
Hanover County Public Schools
  • Instructional Decision
  • Support System (IDSS)

18
Our Current SystemCIMS
  • On-line Transaction Processing System
  • OLTP

19
IDSS
CIMS Database
User Community (Business Objects)
Data Warehouse (SQL 7.0)
Test Scores
www.businessobjects.com
In-house Data, Surveys
20
1999-2000School Board Goals
  • Promote instructional programming in the
    following areas, among others.
  • Professional development
  • Curriculum development
  • Implications of longitudinal assessment of
    student achievement
  • Graduation requirements staffing needs
  • Vocational / technical / alternative education
    study recommendations
  • Standards of learning implementation /
    implications

21
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

22
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

23
Student
24
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

25
Advantage of IDSS
  • Ease of use
  • Speed in acquiring data
  • Ability to create custom reports
  • Make decisions based on information
  • Improve student performance

26
Key Elements of Success
  • Board and Superintendent Support
  • Board Goal
  • Personnel to support the goal
  • Cost effective
  • Infrastructure in place
  • Training
  • Core reports

27
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

28
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.

29
Middle Schools
  • Request of 7th grade civics teachers
  • Principals goals

30
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

31
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

32
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

33
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

34
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

35
Questions
http//hanover.k12.va.us/presentations/NCLB-dw bil
l_at_hcps.us
36
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