Big Data Enables Student Retention: Student Success & SAP HANA

Title:  Big Data Enables Student Retention:  Student Success & SAP HANA

Presenters

  • James David Hardison, DMD, MBA, Industry Principal, Higher Education
  • Vince Kellen, Ph.D., Senior Vice Provost Academic Plan Analytics and Technologies

 

SLIDE:  Screen shots of the “Powers of Ten” film

What if you could…(I smell a sales pitch coming)

  • Manage data, have the current data, have the right answers

 

SLIDE:  30 year old data modeling is slow and inefficient, in-memory is the new hotness!  (My words, not theirs)

HANA:  High Performance ANalytic Appliance

 

SLIDE:  HANA for Higher Education through University Alliances

  • SAP donates licenses to 1,300+ univesities
  • 1.2 million students educated on HANA
  • Certifications, etc.

 

SLIDE:  Newton’s Second Law of Motion

Students at risk are not likely to change their behavior without intervention.  Used snowball rolling downhill as an example…easy to stop at the top of the hill, but irresistible at the bottom.

 

SLIDE:  Using Fast Analytics to Help Improve Student Retention

High response rates to mobile micro surveys, about 40,000 responses in 4 weeks.  Questions include things like “how much are you working this term, how stressed are you (1-5 Likert scale), do you think you’ll be successful this term, etc.

In-memory analytics requires different architecting of the data modeling; We don’t do traditional ETL (Extract Transform Load techniques).

Took an approach of making data available, rather than keeping it “close to the vest.”

We can answer the question:  do we know how many left-handed Hungarian ping-pong players we retained?  How do we break our analysis into little pieces to answer practical problems and questions.

Also collecting “technographic” information on student device types and OS, frequency of use, number of devices, etc.

Been prototyping this with lecture capture and full-text search (what he referred to as “wayfinding”), i.e. ability to find specific terms, apply metadata, ability to link out to other materials, and so on.

Graph databases are generally not used by university degree audit reports, which precludes the use of “what if” scenarios.  This is useful for day-to-day business needs.  Incidentally, graph database technology is widely used in the Social Media space, but for some reason it seems to be all new and exciting to many of the wide-eyed higher ed attendees.  Hmm…

They’re using HANA data with Tableau to make pretty graphs.  Also, showed an Enrollment Plot of Chemistry 105

 

SLIDE:  Academic Health Notifications: View in Student Mobile App

 

SLIDE:  Using this data in a personalized student profile

 

 

SLIDE:  Taxonomy?  Automatic metadata?  Automatic atomic metadata?

  • Let learners navigate an a/v stream
  • Let the system learn what the top terms are.  Let the system map terms to concepts.  Let instructional designers lightly bump the taxonomy.

Thinking of Google AdWords-style presentation of information that’s relevant to the student’s click-stream and status.

 

SLIDE:  Organizational Considerations

  • Hired Ph.D. level data scientists (there was some turnover)
  • Translated old architectures to HANA and retired old IR data warehouse
  • Opened data, many have access, personal data is protected
  • Raised skill sets in colleges and units and provide support.

 

SLIDE:  Some best practices

  • Be safe and secure
  • Be collegial
  • Help improve data quality
  • Be open-minded and inquisitive
  • Share (don’t be a taker)

 

QUESTIONS

Do you plan to include co-curricular data into your system?  Obvious items are participation in clubs and organizations, and surfacing internships and job opportunities.  Yes, it’s on the roadmap.

Are you collecting data on effectiveness of tutoring?  Yes.

 

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