Higher Education

Analytics for Higher Education

Higher Education no longer serves the needs only of the “traditional” 4-year, full-time student living on campus. Today a full 40% of the Public university students attend part-time. Many do not live on campus, and many work part or full-time to finance their education. These dynamics increase the complexity facing the Administrators and Faculty. Predictive Analytics and Optimization techniques can be employed to help them deal with the complexity, and structure the environment to attract, retain and optimize the efficient operations of the university.

Increasing enrollment

  • Analysis to understand, identify and target prospective students with the greatest likelihood to find that university attractive
  • Analysis and scoring of prospective students who have the greatest likelihood of staying at that university, and advancing at a normal rate

Student retention and Student success

  • Identify which factors most influence student retention (e.g. courses, student background, etc.)
  • Predict the risk that an individual student may withdraw from a specific course
  • Assess intervention programs to determine which are most likely to have a positive impact relative to various student risks
  • Provide students individual access to tools where they can recognize learning patterns that indicate that they have a problem

Curriculum and Support

  • Predict which combinations of developmental, gateway and for-credit courses will most positively affect retention and advancement
  • Predict which types of instructional methods will most positively affect retention and advancement
  • Predict which type of learning support services (tutoring, skills development, etc.) most positively affect retention and advancement
  • Predict unique needs of particular student groups such as those by gender, full-time/part-time, ethnicity, on-campus/off-campus, etc.

Finance and Operations

  • Predict future Student costs, and identify cost savings
  • Predict and Optimize financial profitability, and achievement of university objectives
  • Analyze and predict ways to optimize Alumni donations
  • Optimize the application of human resources and assets
  • Predict areas of likely non-compliance in regulatory issues

For more information on CGN's Business Analytics practice, contact Syamala Srinivasan at Syamala.Srinivasan@cgn.net