Chip and Dan Heath, authors of the best-selling books “Made to Stick” and “Switch,” once said, “Data are just summaries of thousands of stories.” Indeed, but the summaries are not the stories themselves, particularly when it comes to student retention.
The stories are more complex and nuanced. Analyzing data leads to information–a better understanding of the stories. It also can predict what’s to come in the next chapters and sequels ahead. That’s the missing puzzle piece for many higher education institutions using data to help boost retention rates and student support.
Consider this one data point: More than 40 percent of community college students neither complete college nor transfer to a different school. While that data point is a summary of countless student stories, it is just that: a summary; an ending point to more than 40 percent of higher education journeys.
But what if we looked at other data to better understand the stories that brought us to this point? What if we looked at students who dropped out and analyzed their performance over their time in college? What if the information we gleaned could tell us the moment students disconnect with their goals, their coursework, and their classes before they make the decision to leave?
What’s better is that with the knowledge found by understanding what the student data tells us, institutions and staff can intervene and provide necessary and personalized support before it’s too late.
Let’s discuss how exactly analyzing data can increase student retention and student support in higher education:
The power to predict
The role of data when it comes to increasing student retention and student support comes back to one capability: the power to predict. Predictive analytics leverages student data to not only identify students in need of assistance but also to identify the obstacles standing in the way of their success.
Predictive analytics uses student data such as test scores, engagement, and socioeconomic and demographic factors to analyze and identify each student’s risk levels and when intervention is necessary to ensure success.
Let’s say we have a student who suddenly stopped attending classes. Through a higher education equity solution, professors and staff could easily note the student’s absences, which are compiled into that person’s student risk data. Although the student has not been attending classes, the student is still turning assignments in on time. That data also can be compiled.
The absences alone as a single data point may not raise a flag if the assignments are getting done. But a wider risk analysis would reveal that some obstacle still stands in the way of the student’s ability to physically attend classes, and that hurts the student’s ability to learn and succeed to the fullest potential.
Predictive analytics and analyzing student data not only predict risk of attrition but also provide advisors and success coaches with proactive intervention opportunities and insights on what support students may need in order to continue their higher education journey successfully.
Proactive intervention improves student retention
By analyzing data, you have the ability to read between the lines of each student. This lets you see the pathway of students and where they are heading. When that pathway veers off course, you can intervene before it is too late.
Proactive intervention is a powerful factor toward increasing retention and student success. A study by Noel-Levitz found that two important contributors to student satisfaction and retention are for an advisor to demonstrate knowledge and concern.
Big data allows advisors to demonstrate knowledge and concern before students abandon their higher education journey. Colorado State University, for instance, made the decision to leverage big data for proactive student intervention. As a result, the university improved its retention rates from 82 percent to 86 percent and improved graduation rates from 62 percent to 66 percent.
It is important to note that closed-loop traceability across all campus departments and systems is crucial for gaining a full understanding of the students and their needs. Without complete insights, your efforts aren’t reaching their full potential.
Using AI-powered student success software, on the other hand, can tell you the whole story now and what’s ahead.
Pumping the brakes when students go off course
Success coaches can have automatic insights into which students need their support the most. Data solutions can place students into risk categories, and their advisors can prioritize the students in the medium- and high-risk ranges.
Jairo McMican, dean of student learning at Central Carolina Community College, compares this process to brakes on a car. The success coach acts like brakes for students on the fast-track to failure. Without the right information, success coaches lack the insight on which car is barreling toward a crash.
Improving student retention and student success is an effort consisting of several components working together. Data leads to information that leads to a better understanding of where students have been and where they are headed. Through higher education software and retention solutions, faculty and staff can see the needs of students, the challenges they are facing, and how best to help them before they quit.
More than four out of 10 community college students drop out. That result can change if higher education institutions embrace the right tools to rewrite the story.
This article was originally published in ECampus News on 4/30/2021.
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