Data Driven Decision Making By

Document Sample
Data Driven Decision Making By Powered By Docstoc
					 Data Driven Decision Making

             By



            Bruce Reicher
            May 12, 2006
breicher@wyckoffschools.org
32 of 257 8th grade students were not proficient on the
GEPA in Math last year.

The District spends $50,000 for Terra Nova scores and
has not used data to its full potential.

Teachers don’t get the data they need to help improve
instruction in the classroom.

Classroom aides’ time is being spent compiling test data
when they can be with children for learning.
How can data driven decision
making improve instruction
in standard eight grade math?
Track standard math students from spring and fall
Terra Novas using Clarity software from McGraw
Hill.

Identify all areas of weakness and begin to look for
patterns that emerge.

Data driven decision making begins at the building
level and not the district level
It is using data collected in the normal course of
your operations to make informed decisions about
how you move toward your particular goal. You
use the analysis of the data to determine patterns
and correlations found within the data to construct
changes to assist you in achieving your goals.


Dr. Howard Woodard
Chief Information Officer
Georgia Department of Education
Assess the current and future
 needs of students
Decide what to change

Determine if goals are being met

Engage in continuous school

 improvement
Identify root causes of problems

Promote accountability
Guidelines for goals (Sparks 2000)
•Clear – Goals should be focused and clearly stated.
•Data based – The goals should be directly based on the
observed patterns seen though the data and their
connection to the evaluation criteria.
•Few – Goals should be few in number; they should be
substantive and focus on the primary purpose of
improving student achievement
•Measurable – Goals should be measurable.
They should articulate the desired outcome, not the
specific strategies.
•Sustainable – Goals should be systemic and sustainable.
The goals should lead to changes and adjustments
that can be sustained into the future.
•Community driven – Goals should be developed
with outcomes that will meet the needs of the district’s
 community.
•Developed by consensus – All team members should
 agree on all the goals.
•Attainable – The goal should be one that can be achieved.
Avoid unrealistic goals and aim for tangible, realistic goals
that cause stretching, but are attainable.
      1. Collect and organize data
      2. Examine student performance
      3. Identify strengths and weaknesses based
         on student performance data
      4. Look at equity as well as excellence
      5. Hypothesize causes
      6. Look deeper at other measures/indicators
      7. Formulate actions
      8. Set initial performance targets
      9. Set priorities.


Cromey and Hansen (2000)
“Until you have data as a
backup, you’re just
another person with an
opinion.”

-Dr. Perry Gluckman
•Rearrange the teaching of chapters so the teachers
 get to the geometry chapter before the standardized
 testing
•Have a problem of the week to practice word problems
•Begin to give more cumulative tests because the GEPA
 has a cumulative format
•Give students more practice on open ended questions
•Create our own cumulative tests, in addition to the tests
 the books provide
•Provide extra help to all students who showed a
 low performance level on the Terra Nova
As of the spring of 2006 I do no have specific results to
compare test scores because the GEPA scores are not back.

Changes in using data have occurred, but no quantitative
data is available yet.
•Put the data into teachers’ hands

•Include more data (NJASK, Terra Nova, and GEPA)

•Staff Development

•Get more computers

•Utilize current database better

•Use all digital formats for paperwork
•Make teachers stakeholders in the planning of goals

•Train all teachers to use database

•Do not have anymore development on the history
of test scores

•Utilize team meetings for generating reports

•Continue articulation between grade levels
•   PC laptop
•   Clarity software by McGraw Hill
•   GEPA review books
•   Daily open ended questions
•   Word problems of the day
•   Web pages with GEPA review
•   Test preparation books
•   Data from TerraNova test
•   Data from past GEPA test
•   Laser printers
•   Teacher laptops
•   Email
•   6th and 7th grade math aptitude tests
•8 laptop PC computers - $8,000
•8 Clarity software licenses - $1,600
•GEPA review books - $500
• Summer Hours for data review committee - $400
•Must include key stakeholders

•Was done in a collaborative setting

•Different departments worked well with each other

•Teachers need to be included in the setting of goals
 and future vision
•Summer 2006 – evaluate how the use of data to improve
instruction was used this past year.
•Summer 2006 – target the next group and subject area to
improve instruction and test scores.
•Summer 2006 – analyze GEPA scores and look for
improvement in the advanced proficient math scores.
•Summer 2006 – develop plan to put more PC computers into
the teachers’ hands.
•Summer 2006 – purchase a PC computer for each team leader
 in the school. They will be the first to have access to the
 database.
    •Fall 2006 – start training the team leaders on how the
    software works.
    •Fall 2006 – order the review GEPA books for projected
    student needs.
    •Winter 2006 – analyze TerraNova scores and identify
    areas of need for individual students.
    •Winter 2006 – send home letters to those students for
    before and after school training.
•   •Spring 2007 – Take the NJASK and GEPA tests
Data is important and it is the truth, whether we
like the results or not

Changes driven by data have a better chance to
meet the organizational goals

Decision Making is critical for any administrator.
We all are going to make decisions, so why not
use data to make more informed decisions.

Data Driven Decision Making is a key to making informed
decisions to assist you in meeting your overall goals.

Dr. Howard Woodward 2006
                               Sources
Benning, V. (2000). Va. School Overshoots Goal: Pine Spring Students Dramatically
      Improve Test Scores. Washington, DC: The Washington Post

Bernhardt, V. (2004). Data analysis for continuous school improvement (2nd ed.)
      Larchmont, NY: Eye on Education.

Cawelti, G & Protheroe, N. (2001). High Student Achivement: How Six School Districts
       Changed into High-Performance Systems. Arlington, VA. Educational Research
       Service.

Cromey, A & Hanson, M (2000). An exploratory analysis of school based student
      Assessment Systems. Naperville, IL: North Central Regional Education
      Laboratory.

Feldman, J & Tung, R. (2001). Using Data-Based Inquiry and Decision Making to
      Improve Instruction: Observations of Six Schools. Arlington, VA. ERS Spectrum.

Houston, P (2004). Using Data to Improve Schools: What’s Working. Arlington, VA.
      Association of School Administrators.

Johnson, J. (1999). Data-Driven School Improvement. Eugene, OR. Eric Clearinghouse
      On Educational Management, Number 109.

Learning Point Associates (2004) Guide to Using Data in School Efforts: A Compliation
       Of Knowledge From Data Retreats and Data Use at Learning Points Associates.
       Naperville, IL.

Rinehart, G. (1993). Quality Education: Applying the philosophy of Dr. W. Edwards
      Deming to transform educational system. Milwaukee, WI: ASQC Press.

Shewhart, W.A. (1939). Statistical method from the viewpoint of quality control.
      Washington DC: U.S. Department of Agriculture.

Sparks, D. (2000). Data should be used to select the most results oriented initiatives:
       An Interview with Mike Schmoker. Journal of Staff Development (Winter 2000).
“Truth is incontrovertible, ignorance can deride it,
panic may resent it, malice may destroy it, but in
the end there it is.”


--Winston Churchill