Guided Pathways to Success at SIUC

1. Guided Pathways to Success at SIUC Combining streamlined degree paths with early-warning intervention  • Scaling Up: Effective Practices in Higher Education October 31st , 2013

2. Presentation Outline • Some background and definitions • A look at the Process Flowchart (Logic Model) • Essential Components • General Education Credit Hour Reduction • Remove course redundancies across programs • Where we are at SIUC • Predictive capability of “Early intervention metrics” • Flexible Components • Lessons Learned and Potential Pitfalls

3. Purpose and Outcomes • Purpose • Removing barriers to and providing support for successful degree completion • Provide data-driven support structures for students to remain on degree paths • Outcomes • Increased retention rates • Increased graduation rates • Easily calculated metrics

4. Process Flowchart

5. Essential Components • Top-level Institutional Commitment • Broad-based General Education Faculty Committee • Significant institutional buy-in to curricular technology platforms for student data collection (For Early Warning system)

6. Step 2

7. General Education Credit Hour Reduction • General Education requirements represent the first programmatic step in the process of streamlining degrees • The requirements cut across degree programs, so streamlining has broad impact • Institutionally, it is (almost) mandatory to have a well-respected faculty committee that oversees this work • The work at SIU is done by the “Core Curriculum Executive Council”

8. Core Curriculum Reduction at SIU Creativity and flexibility required for “double dipping” across degree paths • STEM Fields • Students in use advanced courses in science and math to satisfy general education requirements in these categories. • Microeconomics or Macroeconomics satisfies a general education requirement in Social Science. • Art and Design • Students take courses in Art History to satisfy their general education Humanities requirement

9. Step 3

10. Remove course redundancies across programs • Much harder work, since it involves program changes at department level • Again, look for changes with broad impact in areas that cut across Colleges and Departments • Work can often help to reconsider the design of important courses • Example: Statistics

11. Statistics Redesign at SIUC • Why the proliferation of Statistics courses? • Those (**%#*%!!) Mathematicians just want to teach Math! • “Our” program is special. It requires both a statistics course and an experimental design course. (Because “our” experiments are really special!) • Cooperation from the Chairs of Math and Educational Psychology • Core Statistics and Research Design taught in Large Lecture Sections with Hybrid/Flipped classrooms (1 lecture a week) • Twice a week students meet in small departmental level section in computer labs for discipline specific material • Win-win for departments by optimizing faculty resources.

12. Where are we in the process?

13. OK-Now we’ve got the degree path. How do we keep them on it? • Keeping students on the optimally designed degree path is at least as important as designing the degree path. • Computer-aided instruction (CAI) platforms permit real-time data retrieval. • CAI allows integration across programs and courses.

14. Early Intervention Metric in Math at SIU • Week 3 • For all students in Math 107-108 (Intermediate Algebra/College Algebra) • 25%*(In-class pretest) + 25%*(HW) + 50%*(Test 1) • 25% (preparation) + 25%(motivation) + 50% (achievement) • RED: 0% to 55% • ORANGE: 56% to 65% • YELLOW: 65% to 75% • GREEN: 76% to 100% • Students also receive an intervention score in week 7 and week 11 that is simply their course grade at that time. • All of this data is given to the students and to colleges and advising staff.

15. Does the Early Intervention Metric Correctly Identify Students? • Intermediate Algebra : Success rate of Week 3 metric (C or better) • Red/Orange 22/86 (25.6%) • Yellow 5/12 (41.6%) • Green 54/64 (84.4%) • College Algebra : Success rate of Week 3 metric (C or better) • Red/Orange 31/155 (20%) • Yellow 18/40 (45%) • Green 142/193 (73.6%)

16. Early-intervention platforms for student data collection • The success of the Early Intervention System in Math has prompted the SIUC Provost to put together a Task-force on Early Intervention Technology. • Coordinate data collection and metrics across critical gateway courses • Coordinate software platforms for seamless integration of data

17. What are the Flexible Components for your campus? • Mechanism for General Education streamlining • Campus curricular integration methods. • Campus course articulation and course equivalencies • Early intervention metric structure

18. Lessons Learned and Potential Pitfalls • Strong leadership an essential requirement • Departments will try to protect credit hour generation and core courses. • Try to create "win-win" situations when eliminating course redundancies.