DIGGING DEEPER INTO THE DATA: THE ROLE OF GATEWAY COURSES IN ONLINE STUDENT RETENTION

Karen Swan, William Bloemer, Leonard Bogle, Scott Day

Abstract


Improvement in undergraduate retention and progression is a priority at many US postsecondary institutions and there seems to be a growing movement to address it by identifying gateway courses (foundational courses in which a large number of students fail or withdraw) and concentrating on “fixing” them. This paper argues that may not be the best use of limited resources.  No matter what we do, there will always be courses with high DFW rates simply because of the nature of their content and the preparation of the students who must take them.  Our research suggests that student type and academic stage affect student success and that gateway courses (courses which block student progression) can be found at all undergraduate levels.  Specifically, we have found that one can use student type, academic stage, cumulative GPA, and prior withdrawals to predict success in undergraduate courses.  Moreover, relating predictions to observed DFW rates can highlight courses exceeding expectations, and those which fall below them, to support a more nuanced understanding of where and what attention is needed.  We illustrate the utility of such approach by examining issues surrounding success in online courses at our institution.


Keywords


predictive analytics; gateway courses; student success

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References


Arendale, D. (2004). Pathways of persistence: A review of postsecondary peer cooperative learning programs. In I. Duranczyk, J. L. Higbee, & D. B. Lundell (Eds.), Best practices for access and retention in higher education. Minneapolis, MN: University of Minnesota, Center for Research on Developmental Education and Urban Literacy, 27-40.

Arnold, A. (1999, March). Retention and persistence in postsecondary education: A summation of research studies. Texas Guaranteed Student Loan Corporation.

Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2). Retrieved from: https://dx.doi.org/10.20429/ijsotl.2010.040217.

Barber, R., & Sharkey, M. (2012). Course correction: Using analytics to predict course success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 259-262. Retrieved from: http://dl.acm.org/citation.cfm?id=2330664

Bean, J. P., & Metzner, B. S. (1985). College student retention - defining student retention, a profile of successful institutions and students, theories of student departure. National Center for Educational Statistics. Retrieved from: http://education.stateuniversity.com/pages/1863/College-Student-Retention.html

Bloemer,W., Day, S. & Swan, K. (2017). Gap analysis: An innovative look at gateway courses and student retention. Online Learning, 21(3), 5-14. doi: 10.24059/olj.v21i3.1233

Bloemer, W. & Swan, K. (2014). Investigating informal blending at the University of Illinois Springfield. In A. G. Picciano, C. D. Dziuban, & C. R. Graham (Eds), Blended Learning Research Perspectives, Volume 2. New York: Routledge, 52-70.

Bloemer, B. & Swan, K. (August, 2012). Predicting student retention and success in online programs. Madison, WI: Wisconsin Distance Education Conference.

Boston, W., Diaz, S. R., Gibson, A. M., Ice, P., Richardson, J., & Swan, K. (2009). An exploration of the relationship between indicators of the community of inquiry framework and retention in online programs. Journal of Asynchronous Learning Networks, 13(3).

Cabrera, A. F., Burkum, K. R. & La Nasa, S. M. (2005). Pathways to a four-year degree: Determinants of transfer and degree completion. In Alan Seidman (Ed.). College Student Retention: A formula for success. ACE/Prager Series on Higher Education, 155-214.

Campbell, J. P., & Oblinger, D. J. (2007). Academic analytics. EDUCAUSE. Retrieved from: http://net.educause.edu/ir/library/pdf/PUB6101.pdf

Clay, M. N., Rowland, S., & Packard, A. (2008). Improving undergraduate online retention through gated advisement and redundant communication. Journal of College Student Retention, 10(1), 93-102.

Cochran, J. D., Campbell, S. M., Baker, H. M., & Leeds, E. M. (2014). The role of student characteristics in predicting retention in online courses. Research in Higher Education, 55(1), 27–48. https://doi.org/10.1007/s11162-013-9305-8

DePaul University. (nd.). Gateway Courses Redesigned. Retrieved from: https://offices.depaul.edu/enrollment-management-marketing/student-retention/Documents/8_GatewayCourseRedesign_2012.pdf

Education Advisory Board. (2016). Bottleneck course redesign. Retrieved from: https://www.eab.com/research-and-insights/online-course-prioritization-guide/bottleneck-course

Falcone, T. M. (2011, November). Toward a new model of student persistence in higher education. Paper presented at the annual meeting of the Association for the Study of Higher Education, Charlotte, NC

Gardner Institute. (2017). Roll Out of Gateways to Completion. John N. Gardner Institute for Excellence in Undergraduate Education. Retrieved from: http://www.jngi.org/roll-out-of-gateways-to-completion

Hachey, A. C., Wladis, C. W., & Conway, K. M. (2014). Do prior online course outcomes provide more information than G.P.A. alone in predicting subsequent online course grades and retention? An observational study at an urban community college. Computers & Education, 72, 59–67. https://doi.org/10.1016/j.compedu.2013.10.012

Herzog, S. (2005). Measuring determinants of student return vs. dropout/stopout vs. transfer: A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8), 883-928.

Hosmer, David W. & Lemeshow, Stanley. (2000). Applied Logistic Regression (Second Edition). John Wiley & Sons, Inc.

Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. Journal of Higher Education, 77(5), 861-885.

Jia, P. (2014). Using predictive risk modelling to identify students at high risk of paper non-completion and programme non-retention at university. Doctoral dissertation. Retrieved on July 13, 2015.

Juhong, B. & Maloney, T. (2006). Ethnicity and academic success at university. New Zealand Economic Papers, 40(2), 181-213.

Koch, D., & Pistilli, M. (2015). Analytics and Gateway Courses: Understanding and Overcoming Roadblocks to College Completion. Retrieved from https://www.insidehighered.com/sites/default/server_files/files/Analytics%20and%20Gateway%20Courses%20PPt.pdf

Lee, Y., & Choi, J. (2011). A review of online course dropout research: implications for practice and future research. Educational Technology Research and Development, 59(5), 593–618. https://doi.org/10.1007/s11423-010-9177-y

Lewis, M., & Terry, R. (2016). Registering risk: Understanding the impact of course-taking decisions on retention. Norfolk, VA: Proceedings of the 12th Annual National Symposium on Student Retention, 364-371.

Mallette, B. L., & Cabrera, A. F. (1991). Determinants of withdrawal behavior: An exploratory study. Research in Higher Education, 32(2), 179-194.

Montmarquette, C., Mahseredjian, S., & Houle, R. (September, 2001) The determinants of university dropouts: A bivariate probability model with sample selection. Economics of Education Review 20(5): 475-484.

Moore, C. & Shulock, N. (2009, September). Degree completion: Lessons from the research literature. Sacramento, CA: Sacramento State University Institute for Higher Education Leadership & Policy.

Morris, L. V., Wu, S-S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. American Journal of Distance Education, 17(1), 23-36.

Munro, B. (June 20, 1981). Dropouts from higher education: Path analysis of a national sample. American Educational Research Journal, 18, 133-141.

Nogaj, A., & Kons, E. (2016). Beyond tutoring: Intrusive academic assistance to increase student success and retention. Norfolk, VA: Proceedings of the 12th Annual National Symposium on Student Retention, 55-66..tep: The promise of intermediate measures for meeting postsecondary completion goals. Sacramento, CA: Sacramento State University Institute for Higher Education Leadership & Policy.

Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1-16.

Shapiro, D., Dundar, A., Yuan, X., Harrell, A., & Wakhungu, P. K. (2014, November). Completing College: A National View of Student Attainment Rates – Fall 2008 Cohort (Signature Report No. 8). Herndon, VA: National Student Clearinghouse Research Center.

Tinto, V. (1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago: University of Chicago Press.

Vignare, K., Wagner, E. D., & Swan, K. (2017). The value of common definitions in student success research: Setting the stage for adoption and scale. Internet Learning Journal, 6(1), 7-23. Retrieved from: http://www.ipsonet.org/publications/open-access/internet-learning/volume-6-number-1-spring-2017-summer-2017

Wetzel, J., O’Toole, D., & Peterson, S. (1999). Factors affecting student retention probabilities: A case study. Journal of Economics and Finance, 23(1), 45-55.




DOI: http://dx.doi.org/10.24059/olj.v22i4.1515