Creating or Improving the Effectiveness of Data Teams

Creating or Improving the
Effectiveness of Data Teams
Dan Hyson
HVED Data Management Coordinator
Review agenda and objectives
Questions to add?
What should the data teaming process look like?
What data or information should data teams be
looking at?
Who should be involved in data teams?
How can we make time for data teams to meet?
Next steps for creating/refining data teams
1. Use summative and benchmark screening overall and
strand data to identify whether most of your students
are meeting standards and which students may be in
need of supplemental instruction
2. Review technology tools available to view data to
accomplish Objective 1 (e.g., AIMSweb, Cognos)
3. Introduce one model for creating or improving the
effectiveness of data teams in your school or district
4. Plan for followup face-to-face or virtual coaching
sessions to sustain effectiveness of data teams
4. What should the data teaming
process look like?
• Many models, but all some form of Problem
Solving Model, at the systems level
• Kovaleski offers a useful script with guiding
questions for walking through that model
during planning meetings at the beginning of
the year and other meetings throughout the
1. Problem Identification
What is the discrepancy between what is
expected and what is occurring?
5. Plan Evaluation
2. Problem Analysis
Is the intervention
plan effective?
Why is the problem
4. Plan Implementation
How will implementation
integrity be ensured?
3. Plan Development
What is the goal?
What is the intervention plan to address this goal?
How will progress be monitored
5. What data or information should
data teams be looking at?
• Should be comprehensive - multiple measures, serving
multiple functions
– Review primary functions of assessment
– Share Bernhardt multiple measures diagram
• Some possible sources of data/information
– Summative and some diagnostic – Data Book Power Point
– Benchmark screening
• AIMSweb – Tier Transition report
• Cognos – AIMSweb/MAP Proficiency Change Across Norm Periods
– Progress monitoring
• AIMSweb
• Can use other technology tools like Microsoft Excel and Chart Dog
to progress monitor on skills not measured by AIMSweb
Primary functions of assessment
• Summative
– Did students meet standards? (e.g., MCA)
• Formative
– Benchmark screening
• Are all students meeting standards or growing at a rate that will make them more
likely to meet standards in the future? If not, which students are not meeting
standards or not growing at the necessary rate? (e.g., AIMSweb, NWEA MAP,
classroom formative assessments)
– Progress monitoring
• Are those identified students responding to additional intervention we’re
providing? (e.g., AIMSweb, Chart Dog, Microsoft Excel)
– Diagnostic
• If students are not responding, what specific areas of weakness are getting in the
way? (e.g., MCA and NWEA MAP sub-skill strands, classroom formative
6. Who should be involved in data
• Multiple possible levels of teams – e.g.,
district, building, grade/content area
• Could be already created team(s), BUT
important to clearly refocus team on goals of
data teaming
• Onsite data coaches critical to making data
teaming part of system
– Love et al. handout on characteristics and job
description of data coaches
7. How can we make time for data
teams to meet?
• Should at least meet at beginning of year or end of
previous year to plan and after each benchmark
– Preferably more often to examine progress monitoring
data to see how at-risk students responding to
supplemental instruction
• See Resources slide at end for links to 2 documents
with ideas for making time
• BUT highly recommend that you form a staff team in
your building to brainstorm options that will work in
YOUR school, present them to the rest of the staff
8. Questions?
9. Next steps for creating/refining data
• What do you think you need to do next to
create or refine the use of data teams within
YOUR building?
• What can I do or what can we do as a group to
Kovaleski, J.F., Roble, M., & Agne, M. (2008). The RTI Data
Analysis Teaming Process. Retrieved February 15, 2012
Love, N., Stiles, K.E., Mundry, S., & DiRanna, K. (2008). The
data coach’s guide to improving learning for all students.
Thousand Oaks, CA: Corwin Press.
Making time for collaboration handout from –
MDE secondary level scheduling Power Point

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