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California State University, Long Beach

(EAB) Predictive Analytics

Update: All areas are now live and trained in EAB as of May 7, 2014. EAB SSC Best Practices User Guide now available. Date: 5/8/2014


Identifying students who are at-risk in their chosen major and clearly prioritize students who need intervention as well as an informed choice of major that would be a better match.


Joining the EAB Student Success Collaborative and implementing their predictive analytics and advisor dashboard solution.

Time Frame

  • June 2013: Data analysis.
  • October 2013: Review workbooks and success markers identified for pilot groups (CHHS, COE, EOP).
  • December 2013: In person training and go-live for pilot groups (CHHS,COE, EOP).
  • February 2014: Begin to review workbooks and success markers with non-pilots.
  • April 2014: In person training and go-live for Round 1 of non-pilots (CBA, Undeclared (UCUA) and CLA)
  • May 2014: In person training and go-live for Round 2 of non-pilots (CNSM, COED and COTA) and as part of round 2 in person training in May, we will also train the "special group" advising centers that are not part of a college: SSSP/CAMP, CIE, BAC, Men's Success, Veterans, DSS, Partners, Presidents Scholars and University Honors Program.

Brief Overview

By mining our own CSULB data for comparison, (EAB) Predictive Analytics identifies at-risk students and uncovers systemic obstacles to degree completion. Once identified, the software delivers critical intelligence directly to administrators, advisors and students on a routine basis on potential areas of targeted improvement.

  • Risk Dashboard - Calculates the likelihood of graduation for every student based on their academic history compared to past students at CSULB, provides snapshot overview of students by college who have the greatest risk and may benefit from the most focus and support.
  • Prioritized Student Information for the Advisor – At-risk students are organized and prioritized for advisor follow-up.  A page for each student details his/her performance against a range of course completions, grades, GPA levels determined to be predictive of success in their program.
  • Customized Guidance on Best-Fit Course and Majors – “Major Match” interface provides advisors with guidance on a student’s likelihood to graduate in a wide range of majors around campus.  Predictions are personalized for each student. It also guides the advisor and student on course difficulty which allows the student to plan ahead for improving overall academic planning.

For more detailed information on EAB (Predictive Analytics), the Student Success Collaborative and key features, please visit the EAB website. A EAB SSC Best Practices User Guide is now available for advisors and advising centers.  Please consult this guide to help you optimize (EAB) Predictive Analytics.