1. Data Discussion: What do you want to know? Exploring Data at Mid.
September 19, 2016

2. Exploring Data at Mid. What do you want to know?
Do you have questions about:
Help us shape the future Data Discussions.
What kind of data do we have now?
Enrollment
Foundation
Budget
Nation-wide trends
Facilities
Value of Associate’s Degree
Financial Aid
Instruction/Curriculum

3. Former Data Discussions
Profile of MMCC Students
Finances: Ratios, projections, and zero-based budgeting
External perspectives on Higher Education, Community Colleges, and MMCC
Economic and Workforce Development: Trends and talent
Enrollment Trends and Projections
Satisfaction Matters: Results of the PACE and Noel Levitz Surveys
The Shape of Curriculum – Courses and programs
Living in a Virtual World: Technology Demographics of our students
The Budget – Allocations and projections
Life Beyond the Classroom – The co-curricular experience
Big Data: What it is and how to use it
Instructional Innovation: Guided pathways, experiential education, and more
Key Performance Indicators
Student Success – Transfer and Employment
Title III Wrap Up
And we have infographics . . .
Employee demographics and trends

4. Student Demographics

5. Course Information

6. Educational Goals

7. Financial Aid

8. Misc. Information and Graphics

9. Schedule for Data Discussions
October 17, 2016 – Student Enrollment: Show me the numbers.
November 14, 2016 – Official Term Data: Consistent data year after year
December 12, 2016 – AQIP Category One: Helping Students Learn
January 9, 2017 – AQIP Category Two: Meeting Student and Other Key Stakeholder Needs
February 20, 2017 – AQIP Category Three: Valuing Employees
March 20, 2017 – AQIP Category Four: Planning and Leading
April 17, 2017 – AQIP Category Five: Knowledge Management and Resource Stewardship
May 15, 2017 – AQIP Category Six: A Focus on Continuous Quality Improvement

10. Next Steps What do you want to learn? Presentations posted on Intranet
Matt Miller at
Presentations posted on Intranet
Finish up each Data Discussion with:
What have we learned?
What did you see in the data?
What are the next steps?
What more do you want to know?