Debt without degrees: That’s the reality facing more than half of all young people entering four-year colleges — they seemingly vanish from university rolls into the vapor of attrition. Instead of tossing mortarboards into the air come June, these
Not only is the higher education retention struggle a crisis for the country’s future professional workforce, but it’s also an immediate concern for public and private educational institutions.
And like any major problem, this mass exodus of dropouts must be addressed sooner rather than later through innovative interventions. Sweeping the issue under the rug makes no sense; hitting it head-on with technology does. Enter the use of big data and predictive analytics.
Big Data Hits the Quad
While many universities have begun to dabble in the realm of big data and predictive analytics, they often limit their use of these tools to evaluating academic performance as a major predictor of attrition. Even online schools, where data can be more available due to vibrant learning management systems, are guilty of merely scratching the surface of data capture and analysis.
What’s being missed? The “soft” predictors, those subtle hints that are precursors to student dropout. These clues exist not just in classroom performance or student transcripts, but also on social media sites and in other virtual landscapes. To be sure, not all university personnel are eschewing social platforms’ value; Kaplan’s study in 2015 suggested that 40 percent of surveyed institutions based potential freshman acceptance rates partially on candidates’ web social interactions. Still, universities could be using social cues more effectively.
Seem a bit like “1984” coming to life? It’s less invasive than it sounds.
Let’s face it: Big data already exists in droves — and it’s publicly available for campuses that take the time to look. From the friends and followers potential students tout on their social feeds, to the number of times incoming freshmen buy supplies or food on campus using a swiped ID card, the data is ripe for the picking.
In fact, the sheer breadth of student-based data is absolutely staggering. Without a doubt, every touchpoint has the potential to play a key role in the predictive analyses of student retention. The goal? Capture the data, then understand how to use it accurately to lower attrition rates, which affect institutions’ pocketbooks anywhere from 9 to 33 percent annually. Ironically, an attrition improvement of just 1 percent could cascade to a billion-dollar influx in the community a given institution serves.
Slaying the Attrition Monster With Data
Physics 101 tells us a body in motion tends to stay in motion, while a body at rest remains at rest — in other words, the sooner you dive into big data, the easier it will be to keep going. Here’s how to get started:
1. Focus on geotargeted data.
The university yield rate, or the ratio of accepted applicants who attend versus those who do not, is one of the key performance indicators (KPIs) most institutions home in on to better predict enrollment and retention. Geographic-based historical data can prove useful in improving yield rates across the board.
For instance, if you know you tend to get a higher yield rate in certain areas, you can increase the likelihood of enrollment while whittling away at marketing costs by simply focusing on those markets. Over time, an increase in yield rates can improve metrics used by U.S. News and World Report’s lists of best colleges for rankings, making your college or university more competitive in the eyes of future applicants. You’ll end up working smarter, not harder, at the process of getting students in the door.
2. Evaluate social signals.
In an era when undergraduates are deeply engaged in social media, indicators of flagging institutional interest are at your fingertips. You just have to find and evaluate them appropriately.
Social media engagement data analysis can assist in targeting potential dropouts before they disappear from your rosters. Everything from real-time insights on friends, followers, and likes, to peer interactions and engagement with the university’s own social channels can give clues as to a student’s relationship to the campus. Over time, predictive analyses will help you understand when and how to intervene.
3. Develop early academic warning systems.
Is a student tanking in a course that’s a prerequisite for his or her major? This cue should be used to warn advisors immediately of a retention problem. Rather than waiting until trimester’s or semester’s end to count Cs and Ds, university leaders can make contact with students during the course. Setting a plan for action earlier can make all the difference.
Interestingly, students find this type of partnership approach fulfilling and empowering when done with compassion and understanding. Consider giving them the upper hand by allowing them to use online appointment scheduling software to make advisor appointments using the web. Not only will this allow them to feel more in charge, but you’ll also be able to capture their data in the process. At the Texas-based community college Lone Star College network, online appointment scheduling, as well as online advising, allowed advisors to more efficiently expand service hours and effectively retain students before they joined the ranks of dropouts.
Higher education institutions are in a clearly competitive arena; without predictive analytics tools driven by big data, they risk losing student populations and the funding that goes along with them. Rather than bemoaning attrition, higher ed pros owe it to both their students and their academic missions to utilize technology in support of struggling learners. Why play Monday morning quarterback when you can intervene before the final score?
Bob La Loggia
Bob La Loggia is the founder and CEO of AppointmentPlus, a fast-growing SaaS business based in Scottsdale, Arizona. His company has won a number of awards, including CareerBuilder’s Best Places to Work award. Bob is a serial entrepreneur who’s passionate about his business and helping Arizona develop a world-class startup ecosystem.
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