Predictive Modeling for Identification and Retention of At-Risk Students

The Problem:  

To increase retention, organizations that provide post secondary education need a way to be proactive about stemming attrition and increasing retention. There is not a single issue that explains attrition for all, or even a majority, of students.  Worse still, the issues are only known after the fact, if ever. 

The Solution:

The root cause of attrition is complex but it is not random.  Demystifying attrition starts with getting to know your students better than they know themselves. 

Attrition is driven by both internal and external factors.  Some can be easily controlled; others are more difficult to control.  Mapping the factors into these dimensions and understanding which factors impact which groups of students is critical to building accurate, predictive models. 

Overview of Building a Predictive Model:

• Assess the outcomes to be studied (attrition)
• Define segments of the population that might be impacted differently from others
• Develop a list of factors suspected to impact the outcome
• Identify the data required to establish the correlations
• Collect data from internal systems
• Where required, gather additional data through research, interviews or surveys
• Statistically validate the impact of each factor on each segment
• Based on validated findings, build the predictive model

The payoff is significant for organizations that make the effort to discover the underlying causes of attrition.  Student satisfaction levels increase.   Retention levels rise.  Attrition is reduced.

Contact Kiran today about working together to build a predictive model for your organization.

Operational Assessment   |    Process Improvement    |   Student Satisfaction Survey    |  Predictive Modeling