Last week, I attended the University Professional and Continuing Education Association (UPCEA) and American Council on Education (ACE) Summit for Online Leadership and Strategy in San Antonio. Less than two years ago, I was asked to serve on the UPCEA Center for Online Leadership and Strategy Advisory Council. Part of the Center’s role was to plan the first summit that took place in San Diego last year. One of the reasons for the partnership between UPCEA and ACE was the continuing growth in online higher education, particularly among working adults seeking to complete their college education.
The 2015 Summit had a broader group of presentations than the previous year. Topics ranged from updates related to Online Education Policy to Competency Based Online Education to Marketing Online Programs to Data Analytics to Student Retention in Online Programs. One session that piqued my interest was Key Findings from Joint Study of Adult Learner Persistence and Degree Completion.
To address the lack of publicly available data on the success of adults returning to college, UPCEA partnered with ACE, Inside Track, NASPA – Student Affairs Administrators in Higher Education, and the National Student Clearinghouse to issue benchmarking data for non-first-time and first-time students. While this study did not parse out online students from face-to-face students, the study interested me because of my personal research work related to online adult student retention as well as APUS’s participation as a charter institution in the WCET/Gates Predictive Analytics Framework (PAR) project benchmarking online student retention at some of the institutions with the largest online student populations in the U.S.
Kevin Kruger (NASPA), Jim Fong (UPCEA), Deborah Seymour (ACE), and Dave Jarrat (Inside Track) presented the key findings from the yet to be published study that should be released to the public next month. The primary reason for the study was the fact that this type of data is not yet collected by IPEDS (Integrated Post Secondary Education Data System collected by the National Center for Education Statistics, part of the U.S. Department of Education) and the organizations wanted to publish benchmark data for use by other member institutions and entities to determine their relative effectiveness for non-traditional student success at their institutions.
The researchers compiled the data into two cohorts: the first, students who re-enrolled in college from 8/15/2005 to 8/14/2008. Approximately 4.5 million students meeting this re-enrollment characteristic were in the National Student Clearinghouse database. The second represented students who re-enrolled in college from 8/15/2008 through 8/14/2013. Approximately seven million students in the database met this requirement.
Research findings were organized in the following six categories: completed bachelor’s or higher; completed associates; completed certificate; completed any credential; still enrolled; and, lastly, no longer enrolled. Unsurprisingly, these categories are similar to those I selected in 2010 of graduated, active, and disenrolled students. Survey results.
I was not surprised that the researchers stated that 57 percent of non-traditional students in their dataset are first generation learners (meaning that their parents did not attend college). Examining data in previously published papers from some of the well-known student persistence researchers like Tinto, Astin, and Braxton illustrates that students who have parents who have completed college graduate at a higher rate than students who do not. For students who re-enroll in college, family and job obligations are significant obstacles to their success at completing (another well-known risk in higher education persistence identified by a number of researchers including the U.S. Department of Education).
Many of the students re-enrolling had a previous negative experience in higher education and expressed some self- doubt about their worthiness as a college student. According to the researchers, many of the re-enrolling students have used up all their financial aid before they re-enrolled in college. Finances are among the top reasons for students dropping out of college, so this finding is not a surprise either.
According to the researchers, successful institutions are those that are able to provide non-academic support (child care among the examples) to the re-enrolling students. Institutions that are able to hold proactive conversations about financial aid issues, and the risks and importance of persisting, appear to be more successful as well. Lastly, academic credit awarded for prior learning at some institutions increases the graduation rate and persistence for incoming re-enrolling students.
I am looking forward to reviewing the database and research when it is available to the public next month. If I am able to download the data and sort it, I would like to organize the cohorts into year of re-enrollment starts, that is, rather than having three-year and five-year cohorts, I would organize the cohorts into start years. I believe that this would increase the completion rate for the earlier years of the cohorts and provide guidance on how quickly (or not) students disenroll as well as how long they continue to remain enrolled. The larger cohorts may be hiding that type of data and its relevance. The researchers mentioned that prior learning was a major factor in the re-enrolled student’s success. I wonder how much of that prior learning relates to courses transferred versus specific credit offered for prior learning in non-course work?
Transfer credit at APUS is the most significant predictor of whether or not an adult student will persist. I would also like to look at the annual credit hours attempted by students in this dataset as that statistic has been found to be an important predictor at other adult-serving institutions. GPA comparisons to disenrolled students versus those still enrolled (or who have completed) would be another meaningful analysis. Lastly, I would like to break out the data between online students and students taking courses face-to-face if for no other reason than to compare the data to our data as well as the data accumulated in the WCET/Gates PAR dataset. I’m not sure that the data is identifiable by course type in the NSC dataset.
I congratulate the organizations’ cooperation in this important research project. I also want to congratulate UPCEA and ACE for improving the Summit from 2014 to 2015. Based on this year’s conference, I look forward to attending the 2016 Summit.