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DataRobot > Case Studies > How Florida International University Predicts the Future to Help At-Risk Students
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How Florida International University Predicts the Future to Help At-Risk Students

Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Education
Applicable Functions
  • Human Resources
Use Cases
  • Predictive Quality Analytics
  • Predictive Replenishment
Services
  • Data Science Services
The Challenge
Florida International University (FIU), one of the largest universities in Florida, was facing a challenge in identifying and assisting at-risk students. Many of their students come from low-income areas, are the first in their family to go to college, or are the first of their family to enter the country. These factors often present obstacles that make it difficult for these students to progress. The university's analysis was more reactive than proactive, identifying students who had already faced academic or financial obstacles. The university wanted to be more proactive with data to better serve their students.
About The Customer
Florida International University (FIU) is one of the largest universities in Florida and the country, with two major campuses in Miami-Dade County. It is officially designated as a minority-serving and Hispanic-serving institution, providing a world-class education to the local communities in the city of Miami. Many of the students come from situations that present obstacles, such as coming from low-income areas, being the first in their family to go to college, or being the first of their family to enter the country.
The Solution
FIU adopted automated machine learning as a means of powerful insights, analysis, and decision-making. They started working with DataRobot, which had an immediate impact and produced significant results. With dozens of models accurately predicting and identifying students in real-time, the team shared these reports with various departments across the university, including administrators, academic advisors, and financial advisors. To bridge the gap between the analytics and the actions that could help save at-risk students, they paired DataRobot with Tableau dashboards and filters. This combination put the power of machine learning in many more hands, without exposing them to any of the complexities of machine learning.
Operational Impact
  • The university was able to identify at-risk students in real-time.
  • Predictive models were made widely available to administrators and departments around the university.
  • The democratization of data science put the power of machine learning in many more hands.

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