Finding the factors that influence students

trevor.kenney — Fri, 08 Feb 2013 19:04:50 +0000

The Recruitment and Retention Project (RRP) Team has identified a need to better understand the risk factors that impact new high school students and their persistence (retention) rate from the first to the second year of post-secondary studies. Further, the RRP Team is looking to develop strategies to enhance recruitment and improve student retention as a result.

The Predictive Modelling Subject Matter Team (SMT) was created to identify and review risk factors that impact persistence of new high school students. Through Institutional Analysis, the SMT evaluated data with Noel-Levitz, a higher education consulting firm, to identify significant factors that influence U of L learners' persistence rates. The result was a model that can now be used to predict new high school student persistence.

The Predictive Modelling SMT subsequently identified the need to develop an applicant-to-enrolment model to effectively target recruitment efforts. Noel-Levitz assisted in the development of this second model.

While related, the created models are very distinct, since risks and potential solutions for the applicant to enrolment phase are unique from the risk and solutions to the first- to second-year phase of a student's time at university.

"These two models will improve our ability to target recruitment and retention initiatives much more effectively," says Mandy Moser, team leader of the Predictive Modelling SMT and manager, Institutional Analysis.

"Identification of risk factors and the knowledge that is generated by the application of predictive models is critical to a student-centred approach that will permit the delivery of more timely and effective services for our learners," adds Heather Mirau, director of Integrated Planning.

The goals of the models and the resulting strategic initiatives are to further increase learner engagement and satisfaction, to enhance recruitment and to improve retention. As an added benefit of the consulting process, the U of L now has an improved understanding of how to effectively employ institutional data that, in turn, allows for future in-house development of predictive modelling to inform and facilitate recruitment and retention strategies.

The Predictive Modelling SMT is now entering the next stage of the process – sharing the information with the ��Ѹ��Դ��߿�Ƭ as a whole.

Later this month (Feb. 14-15), a Noel-Levitz representative will present the two models and facilitate sessions with ��Ѹ��Դ��߿�Ƭ employees to develop potential recruitment and retention strategies. Any inquiries regarding these sessions can be forwarded to either Mandy Moser (mandy.moser@uleth.ca) or Heather Mirau (mirau@uleth.ca).

The following factors significantly impact an applicant's likelihood of enrolling

Residence
Geographic region
Program
Application date
Entering average
Campus

The following factors significantly impact a first-year student's likelihood of persisting

Residence
Application date
Entering average
Loans
Scholarships

This story first appeared in the February 2013 edition of the Legend. For a look at the full issue in a flipbook format, follow this link.

UNews - Noel-Levitz

Finding the factors that influence students

The following factors significantly impact an applicant's likelihood of enrolling Residence Geographic region Program Application date Entering average Campus

The following factors significantly impact a first-year student's likelihood of persisting Residence Application date Entering average Loans Scholarships

Related Topics

The following factors significantly impact an applicant's likelihood of enrolling

Residence
Geographic region
Program
Application date
Entering average
Campus

The following factors significantly impact a first-year student's likelihood of persisting

Residence
Application date
Entering average
Loans
Scholarships