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Computer Data

Predictive Modeling as a Tool for Collaboration, Introspection, and Student Success

Student Success Orientation, Transition and Retention
April 13, 2020   |  1:00 PM - 2:00 PM Kimberlyn Brooks Sima Sharghi

Please note: All times are displayed in Eastern (ET) Time. Please adjust as necessary based upon your location.
Sponsored by the Orientation, Transition, and Retention Knowledge Community

Presenters from Bowling Green State University will share a synopsis of how they have used predictive models and data to gain a better understanding of first-year cohorts, create data driven strategies, and discover gaps between student expectations and student experiences.

Course Length
60 minutes
Course Type
Live Briefing, Short Course

Take This Course

About

BGSU began working with predictive modeling tools for first-year student retention 4 years ago. It began with a whim, a few data points, and a couple of intrepid data geeks. The project has mushroomed into a system that incorporates data from at least 15 sources and is shared with most of the University offices that work with student retention and success – which is almost all of them! One of the most gratifying parts of this experience has been the number of campus partners that have asked to be part of the process, have had their data included into the modeling, and have the students they work with monitored for retention.

While the modeling has become an important tool to stimulate conversations and develop new retention strategies, it also made us aware of areas that had the potential to be good data sources if only the data collection methods were improved. This led to the creation of new systems, not just for gathering data, but also for sharing data, both of which had their own unintended positive effects. All of this data modeling, processing, and sharing has given BGSU a way to “democratize” the data, allowing more units to share in the creation of new strategies and discover methods for assessing them.

Learning Outcomes

As a result of attending this session, participants will:

  • Identify processes necessary for predictive modeling using in-house resources
  • Discover tools BGSU uses and evaluate applicability at other institutions
  • Examine BGSU data models and processes to determine which parts, if any, would be useful at their institution

 

Knowledge Community Pricing

To ensure that you receive the appropriate pricing for the course, you must be a member of NASPA, and also a member of the sponsoring Knowledge Community. You will see the correct price in your cart once you complete the registration process and before you check out.

If the price does not come up as free for you, please take the following steps:

  • DO NOT complete your registration. We will not issue refunds once you have completed the purchase of the course.
  • Login to naspa.org by clicking the green "Login" button at the top of the screen.
  • After you login, the green login button will become a drop down with menu options - click "Edit My Profile."
  • From the left hand menu, click "My NASPA Engagement"
  • You will see the KCs that you are currently a member of; if your membership to the sponsoring KC is not listed, please add it.
  • Click "Save" at the bottom of the page.
  • At the top of the left hand menu, click "My Personal Snapshot."
  • You will see a link to renew your membership if yours is not currently active. You must be a member to recieve the discount.