Here’s a hypothetical: Let’s say Jane, a student I’ve never met, comes into my office, wanting to return to college after many years. When I pull up her transcripts, I’m obligated to tell her that many of her classes (Dance Aerobics, for instance) will not count towards her desired teaching degree, and that her overall low grades could prevent her from obtaining a teaching license. She says her advisor never told her what to take, or maybe that her advisor recommended the wrong courses to take, or that no advisor was made available to offer academic resources. Whatever the case, all I’m able to do is trust Jane’s explanation, apologize, and frankly say that this degree will take her longer than she anticipated.
Enter analytics. Thanks to technological enhancements of cross-departmental communication, that conversation has completely changed. With analytic data and software, I’m able to discover that Jane did, in fact, meet with an advisor who discouraged her from taking Dance. I can also see that she was referred to tutoring for her History course, but never made it. I can deduce that her F in History is demonstrative of her eventual attrition, and as a first-generation student, she was already at a greater risk of dropping out. Had Jane responded to the outreach offered, she could have increased her probability of staying and graduating.
With all of this insight, my advisement has become immensely more accurate and effective. Now, instead of attempting to decipher the student’s own relaying of past circumstances, I can advise her with the facts—if she wants to return, she needs to take responsibility for her previous academic decisions. Because in the past she did not attend tutoring, or heed her advisor’s to-dos, her graduation timeline now extends much further. Most importantly, however, this data offers me the ability to develop for the student an individualized “tool kit” of support, one that has been proven successful through research, trial and implementation.
Just like many universities, Middle Tennessee State University (MTSU) continues to see a growth in its student population. However, this means that advisor caseloads are increasing as well. Such an influx of students has also caused a change in the nature of advising. The role of the advisor is becoming more involved, and intricate. Students and their parents alike have increasing demands and higher expectations of their university, and no longer see advisors as a simple registration guide. Advisors help them navigate resources, develop academic structure, and achieve successes. With added customer service-like skills to the role, it is imperative that advisors be on the front end of all relevant information. Data is therefore essential to keeping up with the quickened pace, while also cutting from such inflated information any potential misinformation.
With the analytic database eCampus, a part of Education Advisory Board’s (EAB) Student Success Collaborative, MTSU advisors are now provided with a powerful view of each student. We can monitor real-time student progress, identify potential risk factors (e.g., low midterm grades or excessive absences), and enhance our outreach campaigns so that those at immediate risk receive interventions. There is less concern for error, confusion and miscommunication, as well as less time spent shuffling students around to different departments. With real-time data, advisors can swiftly identify a need or weak point in student success initiatives, and fortify our decisions moving forward.
I’ve given you the hypothetical example. Now, here is a more tangible one. In an effort to bolster retention rates, MTSU hired several new advisors who will increase individualized student interaction and decrease current advisor caseloads. The university has trained our increased staff of professional advisors with EAB’s eCampus monitoring and communication program, so that we can readily obtain student data. Initially, most of MTSU’s attrition-prevention programs were aimed at freshmen; however, analytics determined that, in fact, most students who leave school do so during or after their second year. This required a deeper look into our students at risk of leaving without a degree.
According to the SSC’s National Data Set, nearly two-fifths of students who finish their first year with a GPA between 2.0 and 3.0 do not graduate. This means about 13 percent of freshmen students won’t graduate. For MTSU, that is close to 400 students a year at risk of attrition. With the help of EAB’s analytics, MTSU was able to identify a student population that had previously gone undetected—“the murky middle.” Such information shifted MTSU’s focus from credit hours towards GPA. This crucial insight helped to identify a large group of students not previously considered at-risk but who, in actuality, should be treated as such. With this in mind, advisors began to create more directed campaigns. We modified our advising techniques for these students by increasing interactions and taking in many cases a more prescriptive approach in order to make sure these students didn’t get overwhelmed or feel left behind. We are now focused on quickly identifying the “murky middle.” Now that we are informed with the data, advisors can do the following to increase the chances of retaining these students:
- catch students before their academic performance worsens and they fall into probation,
- teach them how to use campus resources to keep them academically motivated,
- and encourage participation in campus organizations/activities to keep them socially involved.
Without the analytic data provided to us, MTSU may not have identified this at-risk student population, and would have continued to miss a key retention opportunity. Such information helped us to redirect our efforts with more precision and less guesswork.
Data-driven technology aids the streamlining of information distribution, enhances cross-departmental collaborative experiences, and sheds light on significant trends in student success. From an advisor’s perspective, I feel more informed and capable of predicting challenges, which makes my work more proactive and less reactive. Analytics unifies advisors, especially on larger campuses, because real-time data increases advisor interaction with proficiency, accuracy, and efficacy. Whether it is behind the desk or in the classroom, such unity can only strengthen a university’s chances of student success.
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