Why do students drop out of college?
It’s a deceptively simple question, and a critical one for universities to answer. Students who complete a college degree enjoy more earning power over their lifetimes as well as greater opportunities overall. Universities also have a vested interest in improving graduation rates since they play a key factor in how schools are ranked.
If graduation rates are meant to create win-win scenarios, why do they pose such a challenge? Every student is different, and the factors that motivate them to drop out can range from financial concerns to family obligations to burn out. It can be tough to know where to begin searching for answers.
That’s what makes the case of the John Jay College of Criminal Justice at the City University of New York (CUNY) so fascinating. It’s a great study in how one school harnessed the power of data analytics to understand and improve graduation rates, with some very important lessons for the rest of us.
How Data Analytics Identifies Hidden Student Trends
While every student has a unique story, a collective analysis reveals some general trends. These trends are important to consider because they may run counter to the conventional wisdom or assumptions universities might hold.
This was the case for CUNY when they uncovered the fact that a significant portion of college dropouts were juniors and seniors. While common sense might suggest that freshmen were most at risk, data analysis revealed the opposite. By spotting these trends, CUNY could then make plans to intervene with at-risk students who previously had been overlooked.
This is the power of data analytics. It shows us the errors in our current thinking while pointing us back toward the truth. You can’t fix a problem you don’t see. Data analytics brings those hidden trends to light.
How Data Analytics Helps Underserved Students
Another key finding in CUNY’s review of student data regarded tuition assistance. Because these financing programs were structured to disburse payments over the course of up to eight semesters, students who took longer to finish would lose financial aid during their junior and senior years. Students who depended on this assistance were leaving school before graduating in order to work full-time.
By identifying the core problem, CUNY was able to recognize the unintended consequences of their financial aid programs. It gave them the necessary knowledge to intervene sooner with students at risk. But none of this would have been possible without a careful analysis of their student experience data.
When students are struggling just to get by, they may not have the time or the resources to ask their university for help. Students most in need are therefore most likely to slip through the cracks. By using data analytics to identify these problems quickly, the university can be more proactive.
How Data Analytics Enables Targeted Student Assistance
Another promising outcome of CUNY’s use of data analytics was the ability to create small, targeted interventions. By studying the data closely and identifying trends, risks, and critical moments in the student experience, CUNY had a unique opportunity to try out new strategies more efficiently.
Think of the possibilities: Instead of rolling out new, university-wide policies, data analytics empowers universities to segment their students and identify who needs what kind of help. These students can then be reached directly and given the specific resources that best suit their individual needs, whether that’s financial assistance, professional guidance, or mental health counseling.
Universities that embrace data analytics can be more empathetic to students. They are better equipped to understand the students they serve and connect those individuals with the resources to thrive through graduation and beyond. They can prepare rather than react.
Data analytics is key to maintaining graduation rates, keeping them as win-win scenarios for student and university. What is your student experience data waiting to tell you?
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