In the clamor for increasing graduation and persistence rates, are we ignoring the student at risk factors and student characteristics?
In the early days of online education, a commonly discussed phenomenon was the low completion rates of students. Some chose to explain the departure of students using characteristics such as lack of social integration and academic integration for students matriculating in online programs as identified by Vincent Tinto and others. As technologies utilized in the classroom improved and subsequently, online teaching techniques, student persistence improved as well, but not close to the levels sustained by some of the best face-to-face programs.
In research that I conducted initially for my doctoral dissertation and then later in a paper with my colleagues Phil Ice and Angela Gibson, I identified several factors as significant variables leading to student disenrollment from an online program. These variables include no transfer credit received, student’s last grade of F, student’s last grade of W, and low number of courses completed by the student in a 12-month period.
Over the past year and a half, my colleagues and I have continued to examine the student disenrollment patterns at the American Public University System (APUS) and have discussed those patterns with colleagues at a number of other institutions offering online programs. More and more, I have come to believe that the persistence of students who complete three or more undergraduate courses at APUS and the tendency of students who complete fewer than three courses at APUS to eventually disenroll are much more correlated to adult student behaviors previously identified by researchers using data from traditional institutions.
During the past decade, a major increase in enrollments has occurred with the number of adults attending online programs versus face-to-face programs. The reasons are obvious: working adults are able to attend online programs from any location at any time. Those with jobs that frequently take them out of town no longer have to juggle schedules to meet the requirement of taking a face-to-face class, but can log in from another city or country; the only requirement is a computer and an internet connection. Additionally, adult students with a family can come home from work and log in to their classroom after dinner and after the children go to bed. Those adults whose jobs require them to work non-traditional evening or night shifts can log in during times that suit them and not worry about losing sleep to attend face-to-face courses at a local college or university.
One of the earlier studies regarding persistence rates of adult students was published by the U.S. Department of Education’s National Center for Education Statistics (NCES). In this study, researchers Laura Horn and Mark Premo identified seven risk factors that were associated with the likelihood that a student would not graduate from college. These risk factors were: being independent, attending college part-time, working full-time while enrolled, having dependents, being a single parent, delaying entry to college, and not having a traditional high school diploma. Working adults attempting to complete an associates’ or bachelor’s degree are likely to have at least three of these risk factors and those with children may have five or six.
Other studies of persistence relating to adult students provide explanations and characteristics of transfer students. Noting that my own research indicated that students who had transferred credits were more likely to graduate than those who did not transfer credit, I reviewed some of the literature about transfer students and student attendance patterns. Wright and de los Santos wrote about this phenomenon in “Maricopa’s Swirling Students: Earning One-Third of Arizona State’s Bachelor’s Degrees” in 1990. Later, director of Indiana University’s National Survey of Student Engagement, Alex McCormick, wrote a research article titled “Swirling and Double-Dipping: New Patterns of Student Attendance and Their Implications for Higher Education,” outlining the various patterns of student attendance and their implications. In his article, McCormick attributes the likelihood of students to attend multiple institutions to the standardization of credits and the ability to transfer credits from one institution to another rather easily. McCormick outlines eight different patterns of attendance for swirling students. These are:
• Trial enrollment – Students experimenting with another institution before formally transferring
• Special program enrollment – Students completing most of their coursework at their home institution but completing a special program (e.g., semester abroad) elsewhere
• Supplemental enrollment – Students enrolling at another institution for one or more terms to supplement or accelerate their program (examples include summer programs or taking a course at another institution because it’s unavailable at the home institution)
• Rebounding enrollment – Students alternating enrollment at two or more institutions
• Concurrent enrollment – Students taking courses at two institutions simultaneously
• Consolidated enrollment – Students who satisfy their home institution’s residency requirements but a substantial number of their credits come from at least two other institutions
• Serial transfer – Students who make one or more intermediate transfers sequentially in order to complete a degree
• Independent enrollment – Students pursue work unrelated to their degree program and no credits are transferred
McCormick notes that several longitudinal studies exist and while they provide descriptions of attendance patterns, they fail to provide explanations for those patterns. He cites Clifford Adelman’s 1999 study that examined the longitudinal data of the 1982 high school graduates’ cohort and identified that students who attended multiple institutions accounted for approximately 60 percent of all students who began at four year institutions. Approximately 37 percent of all students from the 1982 cohort attended two institutions and 22 percent attended three institutions. Confirming some of the classifications outlined by McCormick was Adelman’s finding that three in five of the 1982 graduates who attended two institutions returned to their first college, as did half of the 1982 graduates who attended three colleges. It’s important to note that Adelman’s longitudinal study over 16 years examined college attendance data over a much longer period than the NCES data required to be reported by institutions participating in the Federal Student Aid program.
The studies mentioned by McCormick and Adelman are excellent starting points for discussions about why more research is necessary to determine the explanations of student swirling. Is it possible that adult students might attend even more institutions today because the availability and popularity of online courses and programs has increased substantially over the past decade? The Sloan Consortium’s most recent publication, Going the Distance: Online Education in the United States 2011, has identified that nearly one third of all college students completed at least one online course in 2010-2011. At the recent Council of College and Military Educators (CCME) Conference, the Department of Defense reported that 75 percent of all military tuition assistance payments were for students attending online programs. While the military may be more mobile than most working professionals, the data otherwise supports the increasing trend of adult students enrolled in online courses/programs. As noted earlier, the risk factors for college completion are compounded for working adult students and may lead to explanations of some institutions’ student transfers.
More and more, I am convinced that the United States needs a national database that tracks the progress of college students as they attend multiple institutions. The Predictive Analytics Reporting (PAR) Framework funded by the Bill and Melinda Gates Foundation and managed by the Western Interstate Commission for Higher Education (WICHE) Cooperative for Education Technologies has a database with 640,000 student records from students attending online programs at six different institutions. That’s approximately 10 percent of the students who took online courses last year according to the Sloan survey mentioned earlier. The National Student Clearinghouse provides data as it is contributed by participating institutions, but institutions are not required to belong to the consortium and only recently has the Clearinghouse been able to accept student data from institutions with non-traditional financial aid enrollment periods. In addition, that data does not aggregate by institutional profile in a more granular format (adult serving, commuter college, online, etc.).
During the last reauthorization of the Higher Education Opportunity Act in 2008, Congress inserted a provision prohibiting the Department of Education from creating such a database. To quote McCormick, “if educators, policymakers, and researchers are to assess institutional impact, they will need better information about [an] institution’s attendance profile, if not detailed information about the source of credits at the student level.” As Adelman and others have identified, there are multiple types of institutional profiles as well as profiles of students who attend those institutions. Before a policymaker or pundit makes a disparaging remark about student persistence rates at a particular institution or group of institutions, they need to have data far beyond that related to first-time, full-time students that has been the baseline for institutional reporting to the Integrated Post-secondary Education Data System (IPEDS). Institutions and researchers should collaborate to provide more insights to explanations for adult student drop out beyond “life happens.” More research on adult student swirling needs to be conducted, particularly for those students attending online programs or institutions.