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Rapid Insight Retention Modeling Cohort

Sessions are ongoing for cohort participants.

There are no more upcoming sessions for this event.

Are you interested in learning how to use data to predict student retention in Rapid Insight? If so, you are a great candidate to join the Rapid Insight Retention Modeling Cohort. This three-part cohort series will provide practical guidance and time to build and apply models for student retention. Since each session builds upon the previous one, cohort participants are expected to attend all three working sessions to fully understand and utilize Predict. Read more about the sessions and dates below.

By participating, you‘ll receive guided recommendations and interactive worksheets to help prepare your dataset, time to explore it for unexpected patterns, and instructions on how to build a model. You’ll also be able to score your students and follow up with them!

This series is open to all construct users involved with student success, academic affairs, and retention. We will provide trial copies of Predict for those who don’t have their own license. Please contact [email protected] with any questions.


Use the drop-down to register for a session.

Session One: Assembling Data and Building the First Model

In the first convening of the Retention Modeling Cohort, attendees will begin with raw data and leave with a predictive model. The focus of our analysis will be on application characteristics which will have two-fold benefits. First, this means that the data requirements are modest. You can bring more, but you don’t need to worry if you can’t wrangle a large dataset. Secondly, it ensures that your model will be applicable to students from day one! Attendees will receive a heavily supported data preparation process and a user-friendly walkthrough of predictive modeling.

Due to the ambitious goal of leaving the session with a predictive model, there will be required pre-work to gather data before the first meeting. We will provide a worksheet detailing which student characteristics are most valuable when predicting the outcome of retention and participants will be asked to gather as much data in the fields as they can. While participants may feel intimidated by this request, please know that even the most basic data about student demographics can teach us about student outcomes.

  • Wednesday, July 12, 2023 | 1:00 p.m. – 2:30 p.m. Eastern Time

Session Two: Scoring Your Incoming Students and Building the Second Model

In our second session, participants will score incoming students and build another model. Getting an early source of insight on which students might be at the highest risk of attrition means that your support staff has the opportunity to truly improve student outcomes. Believe it or not, scoring students will be easier than you might imagine so we won’t stop there! In this session, we’ll also discuss a few successful strategies for using the scores and collectively brainstorm a few more. This session will also require some pre-work to ensure that we have historic “first semester” outcomes available. A predictive model founded on incoming characteristics will be impressive but incorporating institutional experiences like extracurriculars and academic outcomes will enhance your model even further.

Attendees are encouraged to bring colleagues from the student success domain for the first half of this session. Everyone can expect to see their individually scored students, learn about outreach strategies, and get excited as we collectively build a foundation for even more insight in our third session.

  •  Tuesday, September 26, 2023 | 1:00 p.m. – 2:00 p.m. Eastern Time

Session Three: Scoring Your Returning Students and Looking Forward

The third session is the capstone for the cohort, but hopefully the beginning of your independent work in predictive modeling for student success. Like the second session, participants will begin by applying the model they built off of “first semester” data and refine their understanding of students’ challenges. Then, we’ll explore the variety of alternate student success outcomes you can predict using these same methods in the future. As we collectively complete our work in this Retention Modeling Cohort, Strategic Leaders will take the opportunity to establish individual check-ins with attendees.

Attendees can expect to see a second round of scores for their students and explore new ways of predicting student success, leaving the series with an improved grasp of predictive modeling and a network of support for all their future analyses.

  • Wednesday, January 10, 2024 | 1:00 p.m. – 2:00 p.m. Eastern Time

Experts associated with this event

EAB Experts

Lily Brennan

Lily Brennan

Strategic Leader, Data and Analytics

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James Cousins

James Cousins

Senior Strategic Leader, Data and Analytics

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