1. PAR (And a few that do) 22 Variables that Don’t Affect Retention of Online or Dev Ed Courses Anywhere

2. PAR What is PAR  A Gates funded grant bringing together 6 institutions in matched pairs  2 Community Colleges  2 Universities  2 For-Profit Institutions  Almost three million enrollment records  Over half a million students  Focused on online and dev-ed through 2010  Just the Proof of Concept

3. PAR  The Variables

4. PAR

5. CHAID Analyses

6. PAR Challenges/Benefits  Coincident variables  Consistent definitions and meanings among multiple institutions  Impossible to summarize!  Can tease out fringe populations with high predictive power  “Create the e-Harmony of Higher-Ed” – Dr. Phil Ice, APUS

7. PAR Some results  The PAR POC is still analyzing the data.  Here are some preliminary results on Course Success guided by the initial findings.  All credit goes to the PAR group. All mistakes I reserve for myself.

8. PAR Gender  Females outnumber males almost 2 to 1 in higher ed  Less than 1% of variation explained.  Across all 6 institutions, Males and Females were approximately equal.  However, individual organizations can very. At CCCS, 59.5% of females pass, compared to 51.5% of males.

9. PAR Veterans Predictive  Veterans are only marginally more likely to pass than non-veterans.  At CCCS, veterans have precisely the same chance of success as non-veterans.  Military students have a 7% higher pass rate compared to non-military students (4% at CCCO).

10. PAR Course Length  At CCCS, Course Length affects pass rate by less than 1%  But individual lengths outside the usual 10 or 15 can vary wildly – due to only small distinct populations being that length.  Phantom correlations show here due to different institutions having different average course lengths.

11. PAR Others!  Other factors that haven’t shown much effect

12. PAR Some things that do influence success

13. PAR Frank Sinatra  Students who listen to Frank Sinatra do 500%* better than those who don’t. *Conclusion pending verification

14. PAR Concurrent Courses  Concurrent credit bearing courses have a significant negative effect on course success.  The correlation is strongest within the first six or seven courses, reaching as high as half a grade level per additional course.  As students gain experience, the correlation drops dramatically.

15. PAR Priorterm GPA  What average grade did a student receive last term?  This was a valid predicting factor. Students who averaged C or better on their prior term had a pass rate 10% higher in aggregate.  It just was not as big of factor as we thought it would be.  Huge differences from institution to institution, but not correlated with average term length!

16. PAR Age  Students older than 25 have a pass rate 6- 16% better (avg 10%) than students 25 and under.

17. PAR Ethnicity  Individual institutions can have dramatic (30%+) differences in pass rates among different ethnic categories, both directly and also when combined with other variables (military, etc). These most likely collectively indicate average socioeconomic status of populations in the region around the school.

18. PAR Amount of Dev Ed  Dev ed classes do matter.  At CCCO, students who have had to take a single dev-ed course have an 8% lower pass rate in regular courses. The more dev-ed needed, the less well they do.  On the other hand, having a few dev-ed courses under their belt actually increases a student’s chance of success in dev-ed classes.  This could just be a somewhat Darwinian process...

19. PAR Transfer Credits  Students with transfer credits are more likely to pass a class (76% overall average) than those who do not (62.1% average).  For CCCS, that is 68.5% vs 54.3%

20. PAR Certificate Program  People taking certificates do better than those in associates degree programs. What an odd result! AAASBach.Cert.Und PAR62%59%79%66%72% CCCS 52%58%N/A68%67%

21. PAR Beyond PAR  12 more institutions  More variables  More analyses

22. PAR For example!  Does the order you take Dev-Ed matter? Early in degree? Late in degree? Eng before Mat?  Can we predict student involvement or satisfaction?  What LMS data can we use? Simple example: at CCCOnline, we require instructors to have two graded items due by Census. Huge predictive power for pass rate CAPP activeCAPP inactiveNot reported Pass %72.98%23.79%49.70%

23. PAR Thank you for coming Jonathan Sherrill Data Analyst Professional CCCOnline (720) 858-2789 Jonathan.Sherrill@cccs.edu