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(And a few that do)

22 Variables that Don’t Affect Retention of

Online or Dev Ed Courses Anywhere

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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 recordsOver half a million studentsFocused on online and dev-ed through 2010Just the Proof of Concept

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The Variables

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CHAID Analyses

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Challenges/Benefits

Coincident variablesConsistent definitions and meanings

among multiple institutionsImpossible to summarize!

Can tease out fringe populations with high predictive power

“Create the e-Harmony of Higher-Ed” – Dr. Phil Ice, APUS

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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.

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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.

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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).

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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.

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Others!

Other factors that haven’t shown much effect

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Some things that do influence success

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Frank Sinatra

Students who listen to Frank Sinatra do 500%* better than those who don’t.

*Conclusion pending verification

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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.

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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!

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Age

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

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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.

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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...

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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%

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Certificate Program

People taking certificates do better than those in associates degree programs. What an odd result!

AA AS Bach. Cert. Und

PAR 62% 59% 79% 66% 72%CCCS 52% 58% N/A 68% 67%

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Beyond PAR

12 more institutions More variables More analyses

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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 active CAPP inactive Not reportedPass % 72.98% 23.79% 49.70%

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Thank you for comingJonathan Sherrill

Data Analyst ProfessionalCCCOnline

(720) 858-2789Jonathan.Sherrill@cccs.edu