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50 Shadesof Green:Continuous Quality Improvement for Tier I with SWIS
Kelsey MorrisUniversity of Missouri
Leverage Your Learning
What to you know?
What do you want to learn?
What did you
learn?
What will you do
with your learning?
Tier I:Universal Systems
OSEP, 2008
Equitable Outcomes
Systemsfor Staff
Practicesfor Students
Datafor Decisions
How is your organization making decisions
at Tier I?
0
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4
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10
12
Aug Sept Oct Nov Dec Jan Feb Mar Apr May Jun
0-1 2-5 6+
Cumulative Mean ODRs Per Month for 325+ Elementary Schools 08-09
McIntosh, K., Frank, J. L., & Spaulding, S. A. (2010). Establishing research-based trajectories of office discipline referrals for individual students. School Psychology Review, 39(3), 380.
Cum
ulativ
e M
ean
ODR
s
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0
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4
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10
12
Aug Sept Oct Nov Dec Jan Feb Mar Apr May Jun
0-1 2-5 6+
Potential Trajectories with Early Identification
McIntosh, K., Frank, J. L., & Spaulding, S. A. (2010). Establishing research-based trajectories of office discipline referrals for individual students. School Psychology Review, 39(3), 380.
Cum
ulativ
e M
ean
ODR
s
Performance Gap
Valued Outcomes
Current Reality
Current Reality
Valued Outcomes
Gilbert, T.F. (1978). Human competence: Engineering worthy performance. New York: McGraw-Hill.
Causal Analysis
InformationResources Resources
Incentives Incentives Incentives Incentives
Current Reality
Valued Outcomes
• What are the expectations? What is the system? What data do we have?
• What materials/tools do we have? Is there time allocated? What are our processes?
• How are we encouraging good work?
Gilbert, T.F. (1978). Human competence: Engineering worthy performance. New York: McGraw-Hill.
Causal Analysis
InformationResources Resources
Incentives Incentives Incentives Incentives
Individual Motives
Personal Capacities
Knowledge & Skills
Current Reality
Valued Outcomes
Is there a desire (motive) to work
and excel?
Is there an ability (capacity) to
learn and do?
Is there the requisite knowledge and skill
base?Gilbert, T.F. (1978). Human competence: Engineering worthy performance. New York: McGraw-Hill.
Where does your organization spend most
of its time and effort?
Causal Analysis
Information
Current Reality
Valued Outcomes
Cycle of Continuous Quality
Improvement
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Continuous Quality Improvement
EvaluateIdentify problems with precision
Establish goal(s)
Develop solutionsImplement plan with integrity and fidelity
Monitor outcomes and compare to goals
Reassess and revise solutions as needed
Problem Solving with Precision
Primary
Precise
Who?What?
When?Where?
Why?How often?
Problem Solving with Precision
• Primary• Too many referrals• Gang behavior is
increasing.• The cafeteria is out of
control.• Students are way too
disrespectful.
• Precise• There are more
referrals for disruption in the hallway during the morning than last month. These involve a large number of students across grade levels, and it seems to be related to getting access to peer attention.
Problem Solving with Precision
• Precise• There are more
referrals for disruption in the hallway during the morning than last month. These involve a large number of students across grade levels, and it seems to be related to getting access to peer attention.
Who?What?When?Where?Why?How often?
What is the value in problem solving with precision?
How are problems solved in your organization?
Keep Your Finger on the Pulse
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Checking the Vital Signs
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
How often are we issuing referrals?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
Where are we issuing referrals?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
What behaviors are we referring?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
When are we issuing referrals?
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May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
When are we issuing referrals?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
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Who is receiving referrals?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
Who is receiving referrals?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
Checking the Vital Signs
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
Are referral rates equitable?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
Are racial/ethnic groups referred at the same rate?
Out of all the referrals issued, do subgroups receive referrals
proportionate to their size in the population?
Are referral rates equitable?
May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., Sugai, G., & Sprague, J. (2017) School-wide Information System 5.8.0 b10. University of Oregon, Eugene, Oregon.
Of the students with referrals, do subgroups receive referrals
proportionate to their size in the population?
How much more/less likely is a subgroup to receive referrals in
comparison to others?
Drill Down for Deeper Analysis
What problem behaviors were
referred between
10:30a and 1:30p in March?
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Drill Down for Deeper Analysis
Where were referrals issued
between 10:30a and
1:30p in March?
Drill Down for Deeper Analysis
Were referral rates
equitable for the hallway between
10:30a and 1:30p in March?
Drill Down for Deeper Analysis
Why do we perceive students
engaged in referral
behaviors in the hallway between
10:30a and 1:30p in March?
What data can your organization access to
problem solve with precision?
Meeting Foundations
System for detailed meeting minutes
Formal roles with substitutes:• Facilitator• Data Analyst• Minutes Manager
Expectations for before, during, and after the meeting
Accessible/Usable data
Live display of minutes and data
Procedure for monitoring meeting fidelity
System for detailed meeting minutes
Todd, A. W., Newton, J. S., Algozzine, K., Horner, R. H., Algozzine, B., Cusumano, D. L., & Preston, A. I. (2013). The Team Initiated Problem Solving (TIPS II) Training Manual. Eugene, OR: University of Oregon, Educational and Community Supports.
Solution Components
What are the Action Steps?
Who is Responsible? By When? How will Fidelity
be Measured? Notes/Updates
Prevention
Teaching
Recognition
Extinction
Corrective Consequences
What data?Who is responsible for gathering the
data?
When/How often will data be gathered?
Where will data be shared?
Who will see the data?
Data Collection
Precise Problem Statement: Goal:
Solution Development
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Sustainability
• Enablers• Administrative support• Staff buy-in• Fidelity• Data
• Barriers• Resources• Turn-over• Staff buy-in• Fidelity
McIntosh, K., Predy, L. K., Upreti, G., Hume, A. E., Turri, M. G., & Mathews, S. (2014). Perceptions of contextual features related to implementation and sustainability of school-wide positive behaviour support. Journal of Positive Behavior Interventions, 16, 29-41.
McIntosh, K., Mercer, S. H., Hume, A. E., Frank, J. L., Turri, M. G., & Mathews, S. (2013). Factors related to sustained implementation of School-wide Positive Behavior Support. Exceptional Children, 79, 293-311.
Schools benefit from PBIS teams with efficient systems and regular use of data.
Data Storytelling
Enga
ge
Data
Data
Data
Narrative Narrative
Narrative
Visuals
Visuals
Visuals
Dykes, B. (2016, March 31). Data Storytelling: The Essential Data Science Skill Everyone Needs. Retrieved from https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/#11ed323a52ad
How is your organization making decisions
at Tier I?
Leverage Your Learning
What to you know?
What do you want to learn?
What did you
learn?
What will you do
with your learning?
50 Shadesof Green:Continuous Quality Improvement for Tier I with SWIS
Kelsey MorrisUniversity of Missouri
Data Integration
https://pbisapps.wistia.com/medias/92yddhh6ql