Cohort-Based Advising and Student Success Platform


Develop a secure, highly scalable, and usable platform that integrates analytical insights with relationship and planning tools for advisors of large cohorts and the students they support.


How do academic advising staff assess how students are performing academically before the semester becomes more challenging than necessary for those who might need additional support?

A cohort-based advising and student success platform starts to answer this question by providing advisors with analytical tools that help them discern early signs of academic trouble and respond as needed. Over the course of the project, powerful analytics capabilities will be enhanced and integrated with other advisor-facing tools that allow for tracking of appointment notes and cases, communication with individual students and assigned cohorts, and the creation of academic plans. While UC Berkeley’s excellent retention and graduation rates reflect the academic preparedness of our undergraduates, in absolute terms, there are still many students on campus who could benefit significantly from the professional guidance and support that our advising staff offer. The new platform will help advisors efficiently identify and connect with those students who are most in need of their help, while bringing the sheer volume of routine tasks involved in serving very large cohorts under much better control.

The work to develop a platform for academic advisors represents, in reality, a few projects in one. RTL’s Programming and Design Group (PDG) team has met a significant challenge by developing underlying infrastructure to store and combine relevant data from multiple sources, including bCourses, the campus Learning Management System (LMS) and the SIS. This technology, which is built on Amazon Web Services (AWS), is highly secure and robust enough to support the complex mixing and matching of data required by this advisor application. Granting advising staff access to the LMS directly is not a manageable solution because advising student cohorts span multiple courses and departments. Each student has a unique set of activities and enrollments. To complement existing and rich institutional data and tools, the application provides dynamic indicators on students who may be struggling in the moment. One challenge, for example, is to aggregate and summarize student cohort data in a unified visualization or view.

PDG has to date partnered with 3 advising populations on campus to develop the current application. It is being designed to meet the specific needs of these groups with a keen eye towards eventual adoption by the wider campus advising community. Crucially, we bring our campus partners into our agile process to develop the algorithms, dashboards and other features such as notes in an iterative fashion, meeting their needs through constant improvement. The success of the project is largely predicated on the involvement and feedback of the campus advising community.


Oliver Heyer, Associate Director:

Vanessa Kaskiris, Manager:


  • College Advisors, College of Letters and Science  
  • Bob Jacobsen, Dean of Undergraduate Studies in the College of Letters and Science
  • Philip Kaminsky, Executive Associate Dean and Professor, College of Engineering
  • Derek Van Rheenen, Executive Director, Athletic Study Center



  • Jenn Stringer, Chief Academic Technology Officer & Assistant VC Teaching and Learning 
  • Catherine Koshland, Vice Chancellor for Undergraduate Education 
  • Bob Jacobsen, Dean of Undergraduate Studies in the College of Letters and Science

Project Timeline

Timeline (draft)

Opens in Google Docs


Intro Slide Deck

Slide Deck

Opens in Google Docs


  • UC Berkeley Learning Analytics Conference 2017, “Earlier and Better Feedback for Student Success: An Analytics Pilot,” presented by Vanessa Kaskiris and Flint Hahn, UC Berkeley, October 6, 2017.
  • Learning Impact Leadership Institute 2018, “Do the Hard Stuff First: Cohort-Based Analytics,” 30 minute presentation, presented by John Crossman and Sandeep Jayaprakash, IMS Conference, Baltimore, Maryland, May 23, 2018.


Demo (no audio)