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


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


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


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?


Make Change

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Now What?

I have all of this data, now what?

•  ANALYZE!

•  Update and adjust goals/plans


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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nsi 2014: data-driven decision making

Education

students outcomes

college applications

college degrees

college major

college ready

college readiness curriculum

college supermatch average

use data