The Voice of the Successful Online Learner

Concurrent Session 3

Session Materials

Brief Abstract

Online learning presents educational access; however, online students face feelings of isolation, self-efficacy struggles and uncertain employment. Online student success is derived from both extrinsic and intrinsic factors. This presentation offers findings from a phenomenological dissertation representing the voice of successful online students and the factors attributed to their success.

Extended Abstract

While online learning has the potential to overcome limitations of space and time, the learning modality itself presents barriers. However, the online learning model continues to be recognized as appropriate for only the most motivated of students (Arbaugh, 2010; Anderson, 2008; Weber & Lennon, 2007; Yang, Cho, Mathew, & Worth, 2011).  Online students face feelings of isolation, self-efficacy struggles and uncertain employment potential (Baker, Bernard, & Dumez-Féroc, 2012; Croft, Dalton & Grant, 2011;  Ke & Carr-Chellman, 2006). In order to understand the experiences of online students we must first discover the depth of the challenges non-traditional, graduate students face and the strategies they used to overcome and succeed in their pursuit of higher education.

The future of higher education is in flux and a multitude of factors appear to indicate that the time for higher education’s disruption has begun (Selingo, 2013; Christensen & Eyring, 2011; Weise & Christensen, 2014).   There is increased public scrutiny about the institutional costs of delivering a degree simultaneously to declining federal and state fiscal support of education. (Christensen & Eyring, 2011). Current societal shifts indicate a public who is no longer satisfied with the wage premium a college degree affords over individuals without a degree (Selingo, 2013; Stone, 2015).  The factors are aligning to form what the Chronicle of Education has deemed a “perfect storm of financial, political, demographic and technical forces” (Selingo, 2014, p. 4).

Although the Ivy League universities have been late to the adoption of online learning, their implementation of Massive Open Online Courses (MOOCS) through the creation of EdX and Coursera by both MIT and Stanford University, has brought the effectiveness of online learning back to the forefront (Selingo, 2013; Christensen & Eyring, 2011; Margaryan, Bianco, & Littlejohn, 2015).  While enrollments in traditional courses have remained stagnant or declined since 2010, online education continues to experience growth and wider public acceptance (Allen & Seaman, 2016, Gray, 2014).  Despite steady enrollment forecasts and widespread adoption by higher educational institutions, online learning's effectiveness remains under debate by faculty, higher educational institutions and employers (Christensen & Erying, 2011; Macon, 2012; Swan, 2003; Weber & Lennon, 2007).

Online learning has benefited from a wealth of empirical analysis, primarily quantitative, which indicates that the learning outcomes are similar to traditional learning environments (Arbaugh, 2010; Blackmon & Major, 2012).  Russell’s WICHE (2016) website contains over 400 research reports, summaries and papers which report no significant difference between the learning outcomes of online courses and traditional courses (Swan, 2003). Even though the learning outcomes may be similar, the attrition rate of online courses versus traditional courses indicates not all students who enroll in online courses will be successful (Wojciechowski & Palmer, 2005; Boton, & Gregory, 2015; Van Doorn & Van Doorn, 2008).

Student success is of paramount concern to the institutions and the instructors who teach online.  It is an increasing concern to higher education institutions as their measure of success has changed from enrollment to completion (Stone, 2015; The White House, 2015; Massey, 2012).  In reviewing the body of literature, online student success appears to be derived from a combination of both extrinsic and intrinsic factors.   The extrinsic or external factors are the 1) the learning environment and the technology used to the delivery the class, 2)  the institutional supports such as instructional design, tutoring, orientation and mentorship program and 3) the virtual community created by the instructor and supported by both the instructor and online students within the course (Arbaugh J. , 2010; Bianco & Carr-Chellman, 2007; Glazer & Murphy, 2015; Lee & Faulkner, 2011; Palmer & Holt, 2009; Ward, Peters, & Shelley, 2010).   The intrinsic or internal factors which contribute to success of online students are their personality characteristics such as their levels of 1) self-motivation, 2) self-regulation, 3) self-efficacy and their 4) grit and sheer-will (Angelino, Williams, & Natvig, 2007; DeTure, 2004; Atchley, Wingenbach, & Akers, 2013; Bandura, 1997; Duckworth & Gross, 2014).

A review of the literature indicates a gap in the research on the lived experience of online students which represents the voice of the students.  The design of effective online courses is multifaceted, and there are conflicting findings of whether a community participation is a contributing factor to student success (Croft, Dalton, & Grant, 2011; Means, Toyama, Murphy, Bakia, & Jones, 2009; Holder, 2007; Shea, 2006).  Ke and Carr-Chellman’s (2006) study found that online students understood that learning online could be an isolating experience and yet, learning independently is the type of learning experience the students were looking for.  Tinto’s (1975; 1982; 1993; 2006) research on why students persists indicates that a sense of community is a major contributor to student persistence. 

The Community of Inquiry is a theoretical model which represents the external factors the students will encounter in an learning environment as represented by a three part Venn diagram; one for teaching presence, one for social presence and one for cognitive presence (Arbaugh, et al., 2008; Lee & Faulkner, 2011; Garrison & Arbaugh, 2007; Garrison & Cleveland-Innes, 2005; Boston, et al., 2014). However, this model’s ability to represent intrinsic factors of student success, such as the student’s self-regulation skills, has been an area for new COI research for the past five years.

Recent research has called for an adjustment in the COI theory, however its form and function is still under debate.  In addition to the meta-cognitive construct proposed by Garrison and Akyol (2011, 2013, 2015) or Shea’s, et al., (2012, 2012, 2014) learning presence, other contenders include emotional presence (Cleveland-Innes & Campbell, 2012; Rienties & Rivers, 2014),  and autonomy presence promoted by Lam (2015) which favors internal behaviors including an individual intrinsic drive.  One of the original COI author’s Terry Anderson, has suggested in his blog (2016) the addition of agency presence which builds upon Bandura’s social cognitive theory. While the name and definition of the missing fourth presence are unclear, what is clear is the need to conduct further research to identify this element in order to have a theory which represents the holistic nature of online learning.

 Phenomenology was the method used and in-depth interviews of over 15 co-participants was the data collection process. The participants are selected as a convenience and purposive sample as part of what is referred to a “backyard research” (Englander, 2012). The population represented in this sample is typical of what Hillman (2014, 2016) described as an “educational desert”.  Hillman has taken the phrase “food desert”, which is access to healthy food is limited due to geographic constraints and socio-economic indicators, and applied it to the lack of access to education (Sadler, 2016). A majority of the co-researchers lived over 1.5 hours from the closest university which could offer an appropriate educational experience while also being employed fulfilling familial responsibilities during the time of their graduate program.

This presentation will include a discussion of the insights and findings from a completed qualitative doctoral study. The goal was to study the lived experience of successful students who have graduated from for-profit and nonprofit online programs using a non-homogeneous sample of students. and include support of an expanded Community of Inquiry model.  These findings include strategies used by successful online students  to overcome what can be deemed as an isolating experience.   The presentation of research  will include a discussion on the critical extrinsic and intrinsic factors of online students success as well as identification of barriers to that success as well as strategies online students used to be successful.  The findings also include identification of the formal and informal social supports used successful online students use as well as support of an expanded Community of Inquiry model. The intended audience for this presentation are representatives from the field of online learning in both K-12 as well as Higher Educational institutions in both the for-profit and nonprofit arenas.


Allen, E., & Seaman, J. (2016). Online report card: Tracking online education in the United States. Retrieved March, 23, 2016.

Angelino, L. M., Williams, F. K., & Natvig, D. (2007). Strategies to engage online students and reduce attrition rates. Journal of Educators Online.

Anderson, T. (2008). The theory and practice of online learning. Athabasca University Press.

Anderson, T. (2016, January 4). A fourth presence for the Community of Inquiry Model? Retrieved March 17, 2016, from Virtual Canuck: Teaching and learning in a net-centric world:

Arbaugh, J. B. (2010). Do undergraduates and MBAs differ online?: Initial conclusions from the literature. Journal of Leadership and Organization Studies, 17(2), 129-142.

Atchley, W., Wingenbach, G., & Akers, C. (2013, September). Comparison of Course Completion and Student Performance in Online and Traditional Courses. The International Review of Research in Open and Distributed Learnng, 14(4).

Akyol, Z., & Garrison, D. R. (2011). Assessing metacognition in an online community of inquiry. The Internet and Higher Education, 14(3), 183-190.

Baker, M., Bernard, F. X., & Dumez‐Féroc, I. (2012). Integrating computer‐supported collaborative learning into the classroom: The anatomy of a failure. Journal of Computer Assisted Learning, 28(2), 161-176.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freedman.

Bianco, M., & Carr-Chellman, A. (2007). Exploring qualitative methodologies in online learning environments. Online learning communities, 299-318.

Blackmon, S., & Major, E. (2012). Student experiences in online courses: A qualitative research synthesis. The Quarterly Review of Distance Education, 13(2).

Boton,, E., & Gregory, S. (2015). Minimizing Attrition in Online Degree Courses. Journal Of Educators Online, 12(1).

Cleveland-Innes, M., & Campbell, P. (2012). Emotional presence, learning, and the online learning environment. The International Review of Research in Open and Distributed Learning, 13(4), 269-292.

Croft, N., Dalton, A., & Grant, M. (2011). Overcoming isolation in distance learning: Building a learning community through time and space. (CBE, Ed.) Working Paper: Department of Planning and Architecture, 18(7).

DeTure, M. (2004). Cognitive style and self-efficacy: Predicting student success in online distance education. American Journal of Distance Education. American Journal of Distance Education, 18(1), 21-38.

Duckworth, A., & Gross, J. (2014, October). Self-control and grit: Related but separable determinants of success. Current Directions in Psychological Science, 23, 319-325.

Englander, M. (2012). The Interview: Data Collection in Descriptive Phenomenological Human Scientific Research. Journal of Phenomenological Psychology, 43, 13-35.

Garrison, D. R., & Akyol, Z. (2013). Toward the development of a metacognition construct for communities of inquiry. The Internet and Higher Education, 17, 84-89.

Garrison, D. R., & Akyol, Z. (2015). Toward the development of a metacognition construct for communities of inquiry. The Internet and Higher Education, 24, 66-71.

Garrison, D., & Arbaugh, J. (2007). Researching the Community of Inquiry framework: Review, issues and future directions. The Internet and Higher Education, 10(3), 157-172.

Garrison, D., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. The American Journal of Distance Education, 19(3), 133-148.

Glazer, H., & Murphy, J. (2015). Optimizing success: A model for persistence in online education. American Journal of Distance Education, 29(2), 135-144.

Gray, D. (2014). Barriers to online postsecondary education crumble: Enrollment in traditional face-to-face courses declines as enrollment in online courses increases. Contemporary Issues in Education Research (CIER), 6(3), 345-348.

Hillman, N. (2014). Differential impacts of college ratings: the case of education deserts. Paper presented at the 2014 Civil Rights Project Research and Policy Briefing at the U.S. Capitol building. Washington, D.C.: Civil Rights Project

Hillman, N., & Weichman, T. (2016). Education deserts: The continued significance of "place" in the twenty-first century. Viewpoints: Voices from the Field. Washington, D.C.: American Council on Education.

Holder, B. (2007). An investigation of hope, academics, environment, and motivation as predictors of persistence in higher education online programs. The Internet and Higher Education(10), 245-260.

Ke, F., & Carr-Chellman, A. (2006). Solitary Learner in online collaborative learning: A disappointing experience? Quarterly Review Of Distance Education, 249-265.

Lam, J. (2015). Autonomy presence in the extended community of inquiry. International Journal of Continuing Education and Lifelong Learning, 81(1), 39-61.

Lee, R., & Faulkner, M. (2011). The roles of extrinsic factors in a Community of Inquiry Model of E-Learning. E-Learning and Digital Media, 8(1).

Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCS). Computers & Education, 80, 77-83.

Macon, S. (2012). Is my online degree worth anything? The lived experience of learners choosing online education despite opposition. (Vol. Dissertation & Theses). Capella University.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington: US Department of Education.

Palmer, S., & Holt, D. (2009). Examining student satisfaction with wholly online. Journal of computer assisted learning, 25(2), 101-113.

Rienties, B., & Rivers, B. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Community Exchange.

Sadler, R. (2016). Strengthening the core, improving access: Bringing healthy food downtown via a farmers' market move. Applied Geography, 119-128.

Selingo, J. (2013). College unbound. New Harvest.

Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of communities of inquiry in online and blended learning environments. Computers & Education, 4, 1721-1731.

Shea, P., & Bidjerano, T. (2012). Learning presence as a moderator in the community of inquiry model. Computers & Education, 59(2), 316-326.

Shea, P., & Bidjerano, T. (2014). Does online learning impede degree completion? A national study of community college students. Computers & Education, 75, 103-111.

Shea, P., Hayes, S., Smith, S. U., Vickers, J., Bidjerano, T., Gozza-Cohen, M., . . . Tseng, C. (2013). Online learner self-regulation: Learning presence viewed through quantitative content and social network analysis. The International Review of Research in Open and Distributed Learning, 14(3).

Swan, K. (2003). Learning effectiveness online: What the research tells us. Elements of quality online education, practice and direction, 4, 13-47.

Stone, A. (2015, September 12). Weekly Address: A New College Scorecard. White House.

Van Doorn, J. R., & Van Doorn, J. D. (n.d.). Van Doorn, J. R., & Van Doorn, J. D. (2014). The quest for knowledge transfer efficacy: blended teaching, online and in-class, with consideration of learning typologies for non-traditional and traditional students. Frontiers in Psychology, 5(324).

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125.

Tinto, V. (1982). Limits of theory and practice in student attrition. Journal of Higher Education, 53(6), 687-700.

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student retention. Chicago: The University of Chicago Press.

Tinto, V. (2006). Research and practices of student retention: What's next? Journal of College Student Retention: Research, Theory & Practice, 8(1), 1-19.

Ward, M., Peters, G., & Shelley, K. (2010). Student and faculty perceptions of the quality of online learning experiences. The International Review Of Research In Open And Distributed Learning, 11(3), 57-77.

Weber, J., & Lennon, R. (2007). Multi-course comparison of traditional versus web-based course delivery systems. Journal Of Educators Online, 4(2).

Weise, M., & Christensen, C. (2015). Hire Education: Mastery, Modularization, and the Workforce Revolution. Clayton Christensen Institute.

Wojciechowski, A., & Palmer, L. (2005). Individual student characteristics: Can any be predictors of success in online classes? Individual student characteristics: Can any be predictors of success in online classes?, 8(2).

Yang, Y., Mathew, S., & Worth, S. (2011). College student effort expenditure in online versus face-to-face courses:The role of tender, team learning orientation, and sense of classroom community. Journal of Advanced Academics, 22(4), 619-638.