Online Learning: An Opportunity for Minority Serving Institutions?
It is regularly touted that online education has the potential to radically change the landscape of higher education by expanding academic access on an exponential scale. Although not new, online education in recent years has seen an increase in visibility, adoption, and implementation. With the convergence of increased broadband access, social networking, acceptance of online education, and other related trends, 2008 saw the rise of Massive Open Online Courses and the coining of the term “MOOC.” In addition to content delivery, MOOC providers often offer students interactive resources such as peer-to-peer social learning, virtual reality environments, and access to online tutors and tutorials. But actually understanding how these new and existing delivery systems will impact educational communities requires careful attention to assessment data and forceful advocacy for effective educational outcomes for underprepared and underrepresented students who may not understand the opportunities and challenges of online delivery and may not be able to fully take advantage of these new approaches.
A Promise of Expanded Access…Not Yet Realized
As reported by 2,820 degree-granting institutions in the U.S. during 2011, the percentage of students at these institutions taking at least one online course (as a percent of total enrollment) reached an all-time high of 32.0 percent. In the fall of 2012, 86.5 percent of these higher education institutions had some sort of online offering, with 62.4 percent of those institutions providing complete online programs (1). Interestingly, the demographic profile for online students enrolled in a fully online undergraduate or graduate degree, certificate, or licensure program reveals that the “typical” online student is a “Caucasian female about 33 years of age who [is] not the first in their family to attend college and who typically [has] a total family income of about $66,500” (2). Moreover, a recent working paper highlighted in Nature confirms that nearly 80 percent of worldwide participants enrolling in MOOCs are well educated (have a bachelor’s degree or higher) and male (3). Other studies examining the demographic data of MOOCs (4-8) also reiterate that, as with for-credit online courses, 70 to 75 percent of students enrolled in MOOCs already have a degree. These emerging sources of information suggest that, although there is a tremendous promise for online educational platforms for the most part, those currently using the opportunity are already well educated and that, currently, the most promising role for online education is perhaps that of professional development, rather than institutional replacement.
An Emerging Understanding
Despite the numerous and well-advertised online initiatives, there are notable gaps in our understanding of the efficacy of online courses (including MOOCs), particularly within Minority-Serving Institutions (MSIs). According to a 2011 Pew Report (9), 91 percent of two-year colleges and four-year universities offer online courses, including many MSIs who offer entire online degrees. Yet, in the MOOC landscape, we find that MSIs are conspicuously absent from the roster of partner institutions represented within the “big three”—Coursera, Udacity, and edX. What we do see are efforts to bring MOOC content to MSIs in blended formats, with mixed reviews (10). A notable example of this strategy was the San Jose State University Plus pilot, an effort to study outcomes of MOOCs at MSIs that was funded by the National Science Foundation (11).
In addition, most studies of college students’ online course performance have focused on efforts at elite universities (12), and have rarely disaggregated their data to examine impacts on first-generation, low-income, or ethnic minority student groups; for example, Christensen et al. (3). One exception is a study (13) of university students in an economics course. This work disclosed that students who were randomly assigned to take an online section performed only about 1 point worse (on a 100-point scale) on exams in comparison to students who took the face-to-face section of the course. When the researchers examined results by ethnicity, they discovered that, on average, Hispanic students performed a worrisome 11 points worse in the online section compared to the face-to-face section. Moreover, over half of low-income, first-generation, and ethnic minority students are attending two-year institutions where the infrastructure, culture, and resources are vastly different from those at more traditional four-year schools. Several recent studies of community college students suggest that they performed worse online than face-to-face and that this gap is particularly wide for Hispanic and Black students (14-15). Early indications suggest that many of the characteristics that prevent students (in general) and underrepresented students (specifically) from succeeding in online courses are a lack of digital literacy and a lack of a complete understanding of how to effectively engage with peers and the subject matter (16). Other factors include, in some cases, a lack of mastery of the English language (17) and a sense of being isolated from one’s peers and instructor (15, 18). The latter factor may be particularly important for first-generation students given that a sense of social connection, encouragement, and personal and cultural validation have been shown to be important for this population’s success in college (19-20).
Need for a New & Inclusive Models of Assessment
The science of assessing and characterizing the efficacy of the actual learning at these larger scales is in its infancy (e.g., the MOOC Research Hub recently funded by the Bill & Melinda Gates Foundation). As our student populations transform into “digital natives” (individuals professing and actualizing a better understanding and comfort with online information and tools), it will be incumbent on us as a community of scientists to determine what characteristics of the emerging digital environments actually promote learning. Given that most underrepresented students, including many Hispanics and Native Americans, do not have access to, or sufficient preparation with, many of the online learning experiences and practices; we, as a community, need to make reasonable decisions about the challenges and opportunities of online courses and how they specifically impact learning at MSIs. In addition, basic factors, such as access to high bandwidth networks and appropriate data caps that includes monthly limits on devices as set by a provider (21), must be considered when students from disadvantaged groups are involved. Relatedly, we must question and understand what proportion of the potential STEM MSI population cannot participate in high-quality STEM programs due to geography. In other words, how many geographically isolated students have the necessary infrastructure to participate in a high-quality, highly interactive online course? Do we need to concentrate on making fully online learning significantly higher quality than it is right now, or are fully online programs not necessary for access? If the latter, should we concentrate on and promote hybrid or flipped models, which may bolster the quality of the currently existing STEM programs and benefit some of our students?
Moving Forward Equipped with Data to Advance Learning Opportunities
Answering these and other related questions will help us move forward and better understand whether fully online learning can or will improve minority STEM access and competency. Additionally, our current understanding of successful online courses suggests that strong instructor guidance and presence are important for student persistence and learning. This current thinking would argue that, for a fully online course to be successful, it would need to effectively maximize the students’ interactions with the instructor and among peers. Providing instructors from underrepresented groups who can serve as role models and provide opportunities to interact with peers and mentors also can contribute to self-efficacy and provide opportunities to contextualize the course (22-25); for example, focusing on achievements by underrepresented students and professionals in the given STEM field.
While we are focused on underrepresented minorities (URMs), the discussion can be extended to underprepared students. Evidence is beginning to demonstrate that socioeconomic challenges are as important (if not more so) than ethnic representation. How we approach the challenges with underprepared students in general, and URMs specifically, will affect our efforts to successfully inject additional STEM majors into the U.S. economy, as recommended by the President’s Council of Advisors on Science and Technology (26).
Collectively, we urge the community to examine new and promising educational initiatives in the online space and engage in rigorous iterative evaluation with clear and vetted metrics to determine which approaches and characteristics, if any, benefit student learning and are particularly useful for underrepresented and underprepared students. When using online approaches that service multiple thousands, we often lose sight of what works with our individual student. There may be, as it turns out, great ways to instruct the masses, but these efforts should not lose sight that, at its core, the unit of education is an individual (not an institution or group). Effectively making decisions regarding online efforts should account for the needs of individuals or localized populations of students, and assess and customize what works in a more place-based context.
1. Elaine Allen and Jeff Seaman. Changing Course: Ten Years of Tracking Online Education in the United States. Sloan Consortium. PO Box 1238, Newburyport, MA 01950, 2013.
2. Carol B. Aslanian and David L. Clinefelter. Online college students 2012: Comprehensive data on demands and preferences (Learning House, Incorporated, 2004), 27.
3. Gayle Christensen, Andrew Steinmetz, Brandon Alcorn, Amy Bennett, Deirdre Woods, and Ezekiel J. Emanuel, “The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why?” Accessed November 15, 2013. Available at Social Science Research Network: http://ssrn.com/abstract=2350964. Cited by Nature, 503:342 (21 November, 2013).
4. Tucker Balch. January 27, 2013, “MOOC Student Demographics.” Accessed November 15, 2013, http://augmentedtrader.wordpress.com/2013/01/27/mooc-student-demographics.
5. Clare Huhn, “UW‐Madison Massive Open Online Courses (MOOCs): Preliminary Participant Demographics.” Accessed November 15, 2013, http://apir.wisc.edu/cssimages/UW-Madison_MOOC_Demographics_August_2013.pdf.
6. Charles Severance, “Internet History, Technology, and Security (IHTS) internet lecture.” Accessed November 15, 2013, http://www.slideshare.net/fullscreen/csev/internet-history-technology-an....
7. Yvonne Belanger and Jessica Thornton, “Bioelectricity: A Quantitative Approach, Duke University’s First MOOC.” Accessed November 15, 2013, http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/6216/Duke_Bi....
8. MOOCs and Edinburgh 2013 Report #1. May 10, 2013, accessed November 15, 2013, https://www.era.lib.ed.ac.uk/bitstream/1842/6683/1/Edinburgh%20MOOCs%20R....
9. Kim Parker, Amanda Lenhart and Kathleen Moore. “The Digital Revolution and Higher Education.” (Pew Internet and American Life Project, 2011). Accessed November 15, 2013, http://www.pewinternet.org/~/media//Files/Reports/2011/PIP-Online-Learni....
10. California Faculty Association. “SJSU data on MOOC ‘experiments’ found dubious.” Accessed November 15, 2013, http://www.calfac.org/headline/sjsu-data-mooc-experiments-found-dubious.
11. Pat L. Harris. September 11, 2013, “SJSU Plus: Fall 2013 Update.” Accessed November 15, 2013, http://blogs.sjsu.edu/today/2013/sjsu-plus-fall-2013-update-2.
12. Shanna S. Jaggars. “Online learning: does it help low-income and underprepared students?” Columbia University, Teachers College, Community College Research Center, Working Paper No.26 (2011).
13. David N. Figlio, Mark Rush, and Lu Yin. “Is it live or is it Internet? Experimental estimates of the effects of online instruction on student learning.” National Bureau of Economic Research, Working Paper No. 16089 (2010).
14. Ray Kaupp. “Online penalty: The impact of online instruction on the Latino-White achievement gap.” Journal of Applied Research in Community Colleges 12, No. 2 (2012): 1-9.
15. Di Xu and Shanna S. Jaggars (in press). “Performance gaps between online and face-to-face courses: Differences across types of students and academic subject areas.” Journal of Higher Education.
16. Colin Milligan, Allison Littlejohn, and Anoush Margaryan. “Patterns of engagement in connectivist MOOCs.” Journal of Online Learning and Teaching 9, No. 2 (2013): 149-159.
17. Antonio Fini. “The technological dimension of a massive open online course: The case of the CCK08 course tools.” The International Review of Research in Open and Distance Learning 10, No. 5 (2009).
18. Cynthia S. Bambara, Clifford P. Harbour, Timothy Gray Davies, and Susan Athey. “Delicate Engagement: The Lived Experience of Community College Students Enrolled in High-Risk Online Courses.” Community College Review. 36, no. 3 (2009): 219-238.
19. Elisabeth A. Barnett. “Validation experiences and persistence among community college students.” The Review of Higher Education 34, no. 2 (2011): 193-230.
20. Laura I. Rendón-Linares and Susana M. Muñoz. “Revisiting Validation Theory: Theoretical Foundations, Applications, and Extensions.” Enrollment Management Journal 5, No. 2 (2011): 12-33.
21. Benjamin Lennett and Danielle Kehl. “Data Caps Could Dim Online Learning’s Bright Future,” The Chronicle, March 4, 2013. Accessed November 16, 2013, http://chronicle.com/article/Caps-on-Data-Use-Dim-Online/137653.
22. Ann Q. Gates, Sarah Hug, Heather Thiry, Richard Aló, Mohsen Beheshti, John Fernandez, Nestor Rodriguez, and Malek Adjouadi. “The Computing Alliance of Hispanic-Serving Institutions: Supporting Hispanics at Critical Transition Points.” ACM Transactions on Computing Education (TOCE) 11, no. 3 (2011): 16.
23. David R. Arendale. “Pathways of Persistence: A review of Postsecondary Peer Cooperative Learning Programs.” In Best Practices for Access and Retention in Higher Education, edited by Irene M. Duranczyk, Jeanne L. Higbee, and Dana Britt Lundell, 27-40. Minneapolis, MN: Center for Research on Developmental Education and Urban Literacy, University of Minnesota General College Press. 2004.
24. Keith J. Topping. “The effectiveness of peer tutoring in higher education: A typology and review of the literature.” In Mentoring and Tutoring by Students edited by Sinclair Goodlad, 49-69. Sterling, VA: Stylus Publishing Inc. 1998.
25. Nancy E. Betz and Gail Hackett. “The relationship of mathematics self-efficacy expectations to the selection of science-based college majors.” Journal of Vocational Behavior 23, no. 3 (1983): 329-345.
26. Steve Olson and Donna G. Riordan. “Engage to Excel: Producing One Million Additional College Graduates with Degrees in Science, Technology, Engineering, and Mathematics. Report to the President.” Executive Office of the President (2012).
About the Authors (in Alphabetical Order)
Dr. Jose Herrera is the dean of the College of Arts and Sciences at Western New Mexico University and lifetime member of SACNAS. Dr. Herrera previously was a NSF rotating program officer within the Division of Undergraduate Education where he oversaw a portfolio of educational and scientific programs, many of which focused on increasing retention and matriculation by underrepresented students.
Dr. Dorothy Jones-Davis, is an AAAS Science and Technology Policy Fellow - Division of Engineering Education and Centers (EEC), Directorate for Engineering, National Science Foundation. Dr. Jones-Davis is interested in developing strategies to broaden participation in the STEM workforce, particularly in the engineering and technology fields. Her primary area of interest at NSF is examining the ways in which educational disruption (e.g., MOOCs, inverted classrooms, educational technologies, digital games, and “maker” culture) can be leveraged to improve access to engineering education for traditionally underrepresented populations.
Dr. Ann Quiroz Gates is the chair of the Computer Science Department at the University of Texas at El Paso and past associate vice president of Research and Sponsored Projects. Her research areas are software property elicitation and specification, and semantic-enabled technologies. Gates directs the NSF-funded Cyber-ShARE Center that focuses on the development of cyber-infrastructure to support interdisciplinary teams. Gates leads the Computing Alliance for Hispanic-Serving Institutions (CAHSI), an NSF-funded consortium to advance Hispanics in computing, and is a founding member of the National Center for Women in Information Technology (NCWIT), a national network to advance participation of women in IT.
Dr. Shanna Smith Jaggars is the assistant director, Community College Research Center, Teachers College, Columbia University. Dr. Jaggars’ research focuses on low-income students’ access to and progression through college, as well as these students’ long-term education and labor-market outcomes. Her particular areas of focus include developmental education, student self-advising, and online coursework. Together with Tom Bailey and Davis Jenkins, she is writing a book on redesigning open-access colleges for student success, to be published by Harvard University Press in 2014.
Dr. Marilyn Suiter is a program director within the Education and Human Resources Directorate (EHR) at the National Science Foundation (NSF). Her responsibilities are in geoscience education and diversity issues as they are implemented in K-12, undergraduate, and graduate education. Dr. Suiter’s career has included positions as director of Education and Human Resources at the American Geological Institute, and educator positions at American University and in the Philadelphia Public Schools. Dr. Suiter is also a fellow of the American Association for the Advancement of Science.
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