Skip to main content

Advertisement

SpringerLink
Log in

Online learning in higher education: exploring advantages and disadvantages for engagement

  • Published:
Journal of Computing in Higher Education Aims and scope Submit manuscript

Abstract

As the popularity of online education continues to rise, many colleges and universities are interested in how to best deliver course content for online learners. This study explores the ways in which taking courses through an online medium impacts student engagement, utilizing data from the National Survey of Student Engagement. Data was analyzed using a series of ordinary least squares regression models, also controlling for relevant student and institutional characteristics. The results indicated numerous significant relationships between taking online courses and student engagement for both first-year students and seniors. Those students taking greater numbers of online courses were more likely to engage in quantitative reasoning. However, they were less likely to engage in collaborative learning, student-faculty interactions, and discussions with diverse others, compared to their more traditional classroom counterparts. The students with greater numbers of online courses also reported less exposure to effective teaching practices and lower quality of interactions. The relationship between these engagement indicators and the percentage of classes taken online suggests that an online environment might benefit certain types of engagement, but may also be somewhat of a deterrent to others. Institutions should consider these findings when designing online course content, and encourage faculty to contemplate ways of encouraging student engagement across a variety of delivery types.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Allen, E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Babson Park, MA: Babson Survey Research Group.

    Google Scholar 

  • Anaya, G. (1999). College impact on student learning: Comparing the use of self-reported gains, standardized test scores, and college grades. Research in Higher Education, 40, 499–526.

    Article  Google Scholar 

  • Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Baird, L. (2005). College environments and climates: Assessments and their theoretical assumptions. Higher Education: Handbook of Theory and Research, 10, 507–537.

    Google Scholar 

  • Braten, I., & Streomso, H. I. (2006). Epistemological beliefs, interest, and gender as predictors of Internet-based learning activities. Computers in Human Behavior, 22(6), 1027–1042.

    Article  Google Scholar 

  • Cabrera, A. F., Crissman, J. L., Bernal, E. M., Nora, A., Terenzini, P. T., & Pascarella, E. T. (2002). Collaborative learning: Its impact on college students’ development and diversity. Journal of College Student Development, 43(1), 20–34.

    Google Scholar 

  • Chen, P. D., Lambert, A. D., & Guidry, K. R. (2010). Engaging online learners: The impact of web-based learning technology on student engagement. Computers & Education, 54, 1222–1232.

    Article  Google Scholar 

  • Cohen, V. L. (2003). Distance learning instruction: A new model of assessment. Journal of Computing in Higher Education, 14(2), 98–120.

    Article  Google Scholar 

  • Dominguez, P. S., & Ridley, D. R. (2001). Assessing distance education courses and discipline differences in effectiveness. Journal of Instructional Psychology, 28(1), 15–19.

    Google Scholar 

  • Drysdale, J. S., Graham, C. R., Spring, K. J., & Halverson, L. R. (2013). An analysis of research trends in dissertations and theses studying blended learning. Internet and Higher Education, 17, 90–100. https://doi.org/10.1016/j.iheduc.2012.11.003.

    Article  Google Scholar 

  • Evans, C. (2014). Twitter for teaching: Can social media be used to enhance the process of learning? British Journal of Educational Technology, 45(5), 902–915.

    Article  Google Scholar 

  • Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage Publications.

    Google Scholar 

  • Friedman, J. (2014). Online education by discipline: A graduate student’s guide. Retrieved from http://www.usnews.com/education/online-education/articles/2014/09/17/online-education-by-discipline-a-graduate-students-guide.

  • Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers & Education, 102, 79–89. https://doi.org/10.1016/j.compedu.2016.07.003.

    Article  Google Scholar 

  • Hayek, J. C., Carini, R. M., O’Day, P. T., & Kuh, G. D. (2002). Triumph or tragedy: Comparing student engagement levels of members of Greek-letter organizations and other students. Journal of College Student Development, 43(5), 643–663.

    Google Scholar 

  • Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005.

    Article  Google Scholar 

  • Hu, S., & Kuh, G. D. (2001). Computing experience and good practices in undergraduate education: Does the degree of campus ‘‘wiredness” matter? Education Policy Analysis Archives, 9(49). http://epaa.asu.edu/epaa/v9n49.html.

    Article  Google Scholar 

  • Jacob, S., & Radhai, S. (2016). Trends in ICT e-learning: Challenges and expectations. International Journal of Innovative Research & Development, 5(2), 196–201.

    Google Scholar 

  • Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58(1), 162–171.

    Article  Google Scholar 

  • Junco, R., Elavsky, C. M., & Heiberger, G. (2013). Putting Twitter to the test: Assessing outcomes for student collaboration, engagement, and success. British Journal of Educational Technology, 44(2), 273–287.

    Article  Google Scholar 

  • Junco, R., Heiberger, G., & Loken, E. (2010). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted learning. https://doi.org/10.1111/j.1365-2729.2010.00387.x.

    Article  Google Scholar 

  • Kent, C., Laslo, E., & Rafaeli, S. (2016). Interactivity in online discussions and learning outcomes. Computers & Education, 97, 116–128. https://doi.org/10.1016/j.compedu.2016.03.002.

    Article  Google Scholar 

  • Kim, K. J., & Bonk, C. J. (2006). The future of online teaching and learning in higher education: The survey says…. Educause Quarterly, 4, 22–30.

    Google Scholar 

  • Kuh, G. D. (2001). The National Survey of Student Engagement: Conceptual framework and overview of psychometric properties. Bloomington, IN: Indiana University, Center for Postsecondary Research.

    Google Scholar 

  • Kuh, G. D., & Hu, S. (2001a). The effects of student-faculty interaction in the 1990s. Review of Higher Education, 24(3), 309–332.

    Article  Google Scholar 

  • Kuh, G. D., & Hu, S. (2001b). The relationships between computer and information technology use, student learning, and other college experiences. Journal of College Student Development, 42, 217–232.

    Google Scholar 

  • McMillan, J. H., & Schumacher, S. (2001). Research in education: A conceptual introduction. New York: Longman.

    Google Scholar 

  • Miller, A. L. (2012). Investigating social desirability bias in student self-report surveys. Educational Research Quarterly, 36(1), 30–47.

    Google Scholar 

  • Miller, A. L., Sarraf, S. A., Dumford, A. D., & Rocconi, L. M. (2016). Construct validity of NSSE engagement indicators (NSSE psychometric portfolio report). Bloomington, IN: Center for Postsecondary Research, Indiana University, School of Education. http://nsse.indiana.edu/pdf/psychometric_portfolio/Validity_ConstructValidity_FactorAnalysis_2013.pdf.

  • Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Belmont, CA: Wadsworth.

    Google Scholar 

  • Murayama, K., & Elliot, A. J. (2009). The joint influence of personal achievement goals and classroom goal structures on achievement-relevant outcomes. Journal of Educational Psychology, 101(2), 432–447. https://doi.org/10.1037/a0014221.

    Article  Google Scholar 

  • National Survey of Student Engagement. (2015). NSSE 2015 overview. Bloomington, IN: Indiana University, Center for Postsecondary Research.

    Google Scholar 

  • Nelson Laird, T. F., Shoup, R., & Kuh, G. D. (2005). Measuring deep approaches to learning using the National Survey of Student Engagement. Paper presented at the annual meeting of the Association for Institutional Research, Chicago, IL. http://nsse.iub.edu/pdf/conference_presentations/2006/AIR2006DeepLearningFINAL.pdf.

  • Ormrod, J. E. (2011). Human learning (6th ed.). Upper Saddle River, NJ: Pearson.

    Google Scholar 

  • Pace, C. R. (1980). Measuring the quality of student effort. Current issues in Higher Education, 2, 10–16.

    Google Scholar 

  • Parsad, B., & Lewis, L. (2008). Distance education at degree-granting Postsecondary Institutions: 2006–2007 (NCES 2009–044). National Center for Education Statistics, Institute of Education Sciences. Washington, DC: US Department of Education. http://nces.ed.gov/pubs2009/2009044.pdf.

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Pike, G. R. (1995). The relationship between self-reports of college experiences and achievement test scores. Research in Higher Education, 36(1), 1–22.

    Article  Google Scholar 

  • Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x.

    Article  Google Scholar 

  • Pollack, P. H., & Wilson, B. M. (2002). Evaluating the impact of internet teaching: Preliminary evidence from American national government classes. PS. Political Science and Politics, 35(3), 561–566.

    Article  Google Scholar 

  • Pukkaew, C. (2013). Assessment of the effectiveness of internet-based distance learning through the VClass e-Education platform. International Review of Research in Open and Distance Learning, 14(4), 255–276.

    Article  Google Scholar 

  • Restauri, S. L., King, F. L., & Nelson, J. G. (2001). Assessment of students’ ratings for two methodologies of teaching via distance learning: An evaluative approach based on accreditation. ERIC document 460-148, reports-research (143).

  • Richardson, J. T. E., Morgan, A., & Woodley, A. (1999). Approaches to studying distance education. Higher Education, 37, 23–55.

    Article  Google Scholar 

  • Robinson, C. C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101–108.

    Article  Google Scholar 

  • Serwatka, J. A. (2002). Improving student performance in distance learning courses. Technological Horizons in Education THE Journal, 29(9), 48–51.

    Google Scholar 

  • Shuey, S. (2002). Assessing online learning in higher education. Journal of Instruction Delivery Systems, 16, 13–18.

    Google Scholar 

  • Stallings, D. (2002). Measuring success in the virtual university. The Journal of Academic Librarianship, 28, 47–53.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.

    Google Scholar 

  • Tess, P. A. (2013). The role of social media in higher education classes (real and virtual)—A literature review. Computers in Human Behavior, 29(3), A60–A68.

    Article  Google Scholar 

  • Thurmond, V., & Wambach, K. (2004). Understanding interactions in distance education: A review of the literature. International Journal of Instructional Technology & Distance Learning, 1, 9–33. http://www.itdl.org/journal/Jan_04/article02.htm.

  • Tomei, L. A. (2006). The impact of online teaching on faculty load: Computing the ideal class size for online courses. Journal of Technology and Teacher Education, 14, 531–541.

    Google Scholar 

  • Umbach, P. D. (2007). How effective are they? Exploring the impact of contingent faculty on undergraduate education. Review of Higher Education, 30(2), 91–123. https://doi.org/10.1353/rhe.2006.0080.

    Article  Google Scholar 

  • U.S. Department of Education, National Center for Education Statistics. (2016). Digest of education statistics, 2014 (NCES 2016-006), Table 311.15. Retrieved from https://nces.ed.gov/fastfacts/display.asp?id=80.

  • Whitley, B. E. (2002). Principles of research in behavioral science (2nd ed.). New York, NY: Routlegde.

    Google Scholar 

  • Wijekumar, K., Ferguson, L., & Wagoner, D. (2006). Problems with assessment validity and reliability in wed-based distance learning environments and solutions. Journal of Educational Multimedia and Hypermedia, 15(2), 199–215.

    Google Scholar 

  • Wojciechowski, A., & Palmer, L. B. (2005). Individual student characteristics: Can any be predictors of success in online classes? Online Journal of Distance Learning Administration, 8(2), 13.

    Google Scholar 

  • Wu, W., Wu, Y. J., Chen, C., Kao, H., & Lin, C. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59, 817–827. https://doi.org/10.1016/j.compedu.2012.03.016.

    Article  Google Scholar 

  • Xu, D., & Smith Jaggars, S. (2013). Adaptability to online learning: Differences across types of students and academic subject areas (CCRC Working Paper). New York, NY: Teachers College, Columbia University. Retrieved from http://ccrc.tc.columbia.edu/publications/adaptability-to-online-learning.html.

  • Zhou, L., & Zhang, D. (2008). Web 2.0 impact on student learning process. In K. McFerrin et al. (Eds.), Proceedings of society for information technology and teacher education international conference (pp. 2880–2882). Chesapeake, VA: AACE.

Download references

Author information

Authors and Affiliations

  1. University of South Florida, 4202 E Fowler Ave, EDU 105, Tampa, FL, 33620, USA

    Amber D. Dumford

  2. Indiana University Bloomington, 1900 E. 10th St, Suite 419, Bloomington, IN, 47406, USA

    Angie L. Miller

Authors
  1. Amber D. Dumford
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angie L. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Angie L. Miller.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dumford, A.D., Miller, A.L. Online learning in higher education: exploring advantages and disadvantages for engagement. J Comput High Educ 30, 452–465 (2018). https://doi.org/10.1007/s12528-018-9179-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12528-018-9179-z

Keywords

Search

Navigation