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 don’t 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. 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

7. High Stakes Testing Data-Driven Decision Making

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

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 management’s decision- making process. -William Inmon

14. Fundamental Characteristics of a Data Warehouse:  Separate D ecision S upport S ystem 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 User Community Data Warehouse Data Source Data Source Data Source A B C

17. Hanover County Public Schools Instructional Decision Support System (IDSS)

18. Our Current System CIMS On-line Transaction Processing System OLTP

19. IDSS User Community (Business Objects) Data Warehouse (SQL 7.0) CIMS Database Test Scores In-house Data, Surveys www.businessobjects.com

20. 1999-2000 School Board Goals 4. Promote instructional programming in the following areas, among others. 1.Professional development 2.Curriculum development 3.Implications of longitudinal assessment of student achievement 4.Graduation requirements staffing needs 5.Vocational / technical / alternative education study recommendations 6.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. ID 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 7 th grade civics teachers  Principal’s 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 9 th 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 http://qsp.cse.ucla.edu

35. Questions http://hanover.k12.va.us/presentations/NCLB- dw bill@hcps.us