NSI 2014: Data-Driven Decision Making
description
Transcript of NSI 2014: Data-Driven Decision Making
NAVIANCE SUMMER INSTITUTE 2014 PALM SPRINGS, CALIFORNIA
NAVIANCE SUMMER INSTITUTE 2014 | PALM SPRINGS, CALIFORNIA
Data Driven Decision Making
Amy McDonald, Consultant
Wendy Webster, Consultant
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Agenda
• What is DDDM? • Measuring Success • Group Activity/Brainstorming • Review of Outcomes/KPIs • Staff Involvement • Q/A and Review of Resources
NAVIANCE SUMMER INSTITUTE 2014 | PALM SPRINGS, CALIFORNIA
Overview of DDDM
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Data Driven Decision Making is…
• The collection and analysis of data to make decisions that improve student success.
• Continual evaluation accompanied by incremental changes.
• Translation of data into knowledge and actionable strategies.
• Collaboration and communication throughout the school, district and community.
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Use data to make decisions
Data Decisions
Data
Decisions
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What we want to happen
Helpful Data
I need to…
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What happens in reality
Teacher Evaluations
Partner
Assessments
SAT
GPA
ACT
Attendance
Activities
1600
4.0
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1.7
365
???
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Focus on outcomes
Outcome
Variable
Variable
Variable
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How do you measure success?
Staff and students have completed all of their assigned tasks.
Students are career and college ready.
Productivity Outcome
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Outcomes/Key Performance Indicators
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Focusing your analysis
• Outcomes are the ultimate goal. • Variables are the many data points for each
student. They include everything that affects a student’s outcomes.
• Key performance indicators are measurements to determine if you are on track to attain a particular outcome.
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Example
Outcome: Increase college-going rate of student population Naviance Controlled Variables: • SuperMatch (11th grade) • Colleges in Colleges I’m Thinking About (11th grade) • Colleges I’m Applying to (12th grade) • Colleges Accepted/Attending (12th grade) KPIs: • % of Students Completing College SuperMatch • Average # of Colleges Added per Student (Thinking About
and Applying) • % Accepted
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Activity
• 5 Groups
• Come up with 3 Outcomes (topic will be provided)
• Come up with Variables within each outcome
• Come up with KPIs for each Variable
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Student Growth & Proficiency
• Grade Point Average • Test score averages
• PLAN
• PSAT
• SAT
• ACT • State assessment(s)
• International Baccalaureate scores
• % of students who used PrepMe at least once
• % of students who complete the learning style assessment
• % of students who complete Do What You Are assessment
• % of students who complete Career Key assessment
• % of students who complete a Course Plan
• Course Plan Rigor distribution
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College Planning
• College Power Score distribution
• Alignment of Course Demand Forecast with college readiness curriculum determined by school/district
• Student interest in specific courses that school/district indicate align with college readiness goals
• Number of applications for individual colleges
• Number of applications for individual colleges
• % of students who submit one or
more college applications
• % of students admitted to one or more colleges
• % of students who intend to attend college after graduation
• Meaningful and up-to-date scholarship database available for student use
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Career Planning
• % of students who identify careers and career clusters of interest
• % of students interested in professional careers
• % of students interested in technical careers
• % of students interested in careers with specific characteristics, such as STEM, that are determined by the school/district
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Student Engagement
• % of students who report they understand the knowledge and skills necessary for success in their careers of interest
• % of students who set goals
• % of students who met goal
• % of students who completed tasks that align with college and career readiness as determined by the school/district(e.g. FAFSA completion, internship/ mentorship requirement)
• % of students who report understanding their learning styles
• % of students who report they have explored colleges and careers based on learning style assessment
• % of students who report they understand the links between careers, preparation needed, college major and projected income
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Alumni Performance
• % of students who enrolled in college
• % of students who completed college degrees
• % of students who completed college degrees within a specified timeframe
• % of students with positive perceptions of college and career readiness
• % of students satisfied with teaching or other specified aspects of their K-12 experience
• % of students who are satisfied with their post high school plans
• % of students who enrolled in remedial college mathematics, English or other courses
• % of students who completed remedial college math, English or other courses
NAVIANCE SUMMER INSTITUTE 2014 | PALM SPRINGS, CALIFORNIA
Workshops with Staff
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Staff Workshops
• Involve multiple staff members from various roles in the development of data processes.
• Collaborate to make the best possible decisions.
• Use data for decisions and information, not just compliance.
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Staff Workshop: Report Review
Purpose: Review the reports in Naviance and identify needs. Activities:
• Review reports in Naviance. • Identify helpful reports. • For each report, determine:
» Audience: Who should receive this report? » Parameters: Which students/tasks/variables should be included? » Frequency: When and how often should this report be run?
Next Steps: • Determine data needed to populate report.
» Ensure data is collected during activities throughout the year. • Customize and schedule reports in Naviance.
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Staff Workshop: KPIs & Outcomes
Purpose: Define the key performance indicators and outcomes that are important. Activities:
• Brainstorm student outcomes. What does it mean for students to be successful?
• For each outcome, determine associated KPIs. » Addendum: Key Performance Indicators
Next Steps: • Document and communicate KPIs and outcomes. • Map KPIs and outcomes to Naviance activities and
reports.
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Staff Workshop: Identify Variables
Purpose: Identify variables that should be tracked to link to outcomes and KPIs. Activities:
• Review identified outcomes and KPIs. • Brainstorm variables that could impact outcomes. • Determine how variables are tracked and stored.
» SIS » Naviance Activities » Naviance Surveys » Other
Next Steps: • Incorporate into Naviance activities and data collection.
» Addendum: Data Collection in Naviance • Develop maintenance plan.
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Staff Workshop: Survey Development
Purpose: Create surveys to collect data and inform decisions. Activities:
• Review previously identified needs. » Direct data collection. » Indirect collection through reflection and and feedback.
• Brainstorm and organize questions.
Next Steps: • Setup surveys in Naviance. • Incorporate survey(s) into activities throughout the
year.
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Staff Workshop: Scope & Sequence
Purpose: Define a plan for the activities that need to occur throughout the year. Activities:
• Review available activities in Naviance. • Review previously identified data needs. • Review suggested activities in Naviance
Implementation Guide and Naviance Network. • Develop a plan for the activities to be completed by
students and staff throughout the year. Next Steps:
• Document and communicate scope and sequence. • Map to tasks in Success Planner and assign to
students.
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Staff Workshops
What else have you done at your school or
district?
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Make Change
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Now What?
I have all of this data, now what?
• ANALYZE!
• Update and adjust goals/plans
NAVIANCE SUMMER INSTITUTE 2014 | PALM SPRINGS, CALIFORNIA
Resources
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Naviance Resources
• Naviance Network Community Forums: http://community.naviance.com/t5/Community-Forums/ct-p/succeed • Naviance Network Help Library, Reporting Section: http://community.naviance.com/t5/Reporting/tkb-p/Reporting%40tkb
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Workshop Resources
• ATLAS – Looking at Data: http://www.nsrfharmony.org/protocol/doc/atlas_looking_data.pdf * • Data.gov in the Classroom, Education Materials: http://www.data.gov/education/page/datagov-classroom
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MS Office Resources
• Office Support: http://office.microsoft.com/en-us/support/ • VLOOKUP (joining data in Excel): http://office.microsoft.com/en-us/excel-help/vlookup-HP005209335.aspx • Excel Review, Duke University: https://faculty.fuqua.duke.edu/~pecklund/ExcelReview/ExcelReview.htm
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Misc Stats and Analysis Resources
• Data Mining: The Tool of the Information Age Revolution, Rajan Patel, Stanford (recorded webinar): http://myvideos.stanford.edu/player/slplayer.aspx?coll=2e431434-84e4-4de0-81c9-76035c36a18f&co=12138da9-eab8-405b-a06f-cc11f12e5871&w=true
• Introduction to Statistics and Data Analysis, University of Michigan (open course materials): http://open.umich.edu/education/lsa/statistics250/spring2013
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