Analysis of Distance Learner Value Assessment of Distance Education in Engineering
Publication: Journal of Professional Issues in Engineering Education and Practice
Volume 140, Issue 1
Abstract
Numerous research studies have evaluated whether distance learning is a viable alternative to traditional learning methods. These studies have generally made use of cross-sectional surveys for collecting data, comparing distance to traditional learners with intent to validate the former as a viable educational tool. Inherent fundamental differences between traditional and distance learning pedagogies, however, reduce the reliability of these comparative studies and constrain the validity of analyses resulting from this analytical approach. This article presents the results of a research project undertaken to analyze expectations and experiences of distance learners with their degree programs. Students were given surveys designed to examine factors expected to affect their overall value assessment of their distance learning program. Multivariate statistical analyses were used to analyze the correlations among variables of interest to support hypothesized relationships among them. Focusing on distance learners overcomes some of the limitations with assessments that compare off- and on-campus student experiences. Evaluation and modeling of distance learner responses on perceived value for money of the distance education they received indicate that the two most important influences are course communication requirements, which had a negative effect, and course logistical simplicity, which revealed a positive effect. Combined, these two factors accounted for approximately 47% of the variability in perceived value for money of the educational program of sampled students. A detailed focus on comparing expectations with outcomes of distance learners complements the existing literature dominated by comparative studies of distance and nondistance learners.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors would like to thank Dr. Corey Cook of Skidmore College for his support.
References
Allen, I. E., and Seaman, J. (2011). “Going the distance: Online education in the United States.” 〈http://sloanconsortium.org/publications/survey/going_distance_2011〉 (Dec. 13, 2012).
Connolly, T. M., MacArthur, E., Stansfield, M., and McLellan, E. (2007). “A quasi-experimental study of three online learning courses in computing.” Comput. Educ., 49(2), 345–359.
Duffy, T., Gilbert, I., Kennedy, D., and Kwong, P. W. (2002). “Comparing distance education and conventional education: Observations from a comparative study of post-registration nurses.” ALT-J, 10(1), 70–82.
Field, A. (2009). Discovering statistics using SPSS, SAGE Publications, Thousand Oaks, CA.
Galusha, J. (1998). “Barriers to learning in distance education.”, Univ. of Southern Mississippi, Hattiesburg, MS.
Grimm, L. G., and Yarnold, P. R. (1995). Reading and understanding multivariate statistics, American Psychological Association, Washington, DC.
Hooper, D., Coughlan, J., and Mullen, M. (2008). “Structural equation modelling: Guidelines for determining model fit.” Elec. J. Bus. Res. Methods, 6(1), 53–60.
Hu, L., and Bentler, P. M. (1999). “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives.” Struct. Equat. Model., 6(1), 1–55.
Kenny, D. A. (2012). “Measuring model fit.” 〈http://davidakenny.net/cm/fit.htm〉 (Apr. 8, 2012).
Kessler, G. C. (2007). “Online education in computer and digital forensics: A case study.” Proc. of the 40th Hawaii Int. Conf. on System Sciences, Shidler College of Business, University of Hawai'i, Manoa.
Kummerow, A., Miller, M., and Reed, R. (2012). “Baccalaureate courses for nurses online and on campus: A comparison of learning outcomes.” Am. J. Dist. Educ., 26(1), 50–65.
Langford, D. A., Bilham, T., Fellows, R., and Newcombe, R. (1994). “Case study of development of distance-learning course.” J. Prof. Issues Eng. Educ. Pract., 333–340.
McMullin, K. M., and Owen, G. N. (2002). “Educating students via distance learning for civil engineering design.” J. Prof. Issues Eng. Educ. Pract., 6–11.
McPhee, I., Duffy, T., and Marks, D. (2012). “Comparison of equated learning for online and on campus postgraduate students on academic achievement.” Univ. Fraser Valley Res. Rev., 4(3), 80–88.
McPhee, I., and Söderström, T. (2012). “Distance, online and campus higher education: Reflections on learning outcomes.” Campus-Wide Inf. Syst., 29(3), 144–155.
Means, B., Toyama, Y., Murphy, R., Bakia, M., and Jones, K. (2009). “Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies.” U.S. Dept. of Education, Washington, DC.
Meyers, L. S., Gamst, G., and Guarino, A. J. (2006). Applied multivariate research: Design and interpretation, Sage Publications, Thousand Oaks, CA.
Molenaar, K., Washington, S., and Diekmann, J. (2000). “Structural equation model of construction contract dispute potential.” J. Constr. Eng. Manage., 268–277.
Molenaar, K. R., Park, J. I., and Washington, S. (2009). “Framework for measuring corporate safety culture and its impact on construction safety performance.” J. Constr. Eng. Manage., 488–496.
National Center for Education Statistics (NCES). (1998). “Issue brief: Distance education in higher education institutions: Incidence, audiences, and plans to expand.” 〈http://nces.ed.gov/pubs98/98132.html〉 (Sep. 25, 2012).
Phipps, R. A., and Merisotis, J. P. (1999). “What’s the difference: A review of contemporary research on the effectiveness of distance learning in higher education.” The Institute for Higher Education Policy, Washington, DC.
R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing, Vienna, Austria.
Rosseel, Y. (2012). “lavaan: An R package for structural equation modeling.” J. Stat. Softw., 48(2), 1–36.
Schaefer, D., et al. (2008). “Integration of distance learning technology into traditional engineering physical laboratory exercises.” Proc. of the ASEE Southeast Section Conf., American Society of Engineering Education, Washington, DC.
Sherry, L. (1996). “Issues in distance learning.” Int. J. Educ. Telecommunications, 1(4), 337–365.
Shuman, L. J., Besterfield-Sacre, M., and McGourty, J. (2005). “The ABET ‘Professional skills’—Can they be taught? Can they be assessed?” J. Eng. Edu., 94(1), 41–55.
Smart, K. L., and Cappel, J. J. (2006). “Students’ perception of online learning: A comparative study.” J. Inf. Technol. Educ., 5, 201–219.
Tabachnick, B. G., and Fidell, L. S. (2000). Using multivariate statistics, 4th Ed., Allyn and Bacon, Boston.
Tang, M., and Johnson, L. E. (1999). “Changing the learning paradigm through the STC-Link electronic archive.” J. Prof. Issues Eng. Educ. Pract., 19–24.
Tuckman, B. W. (1999). Conducting educational research, 5th Ed., Harcourt Brace College Publishers, Fort Worth, TX.
Washington, S., Karlaftis, M. G., and Mannering, F. L. (2011). Statistical and econometric methods for transportation data analysis, CRC Press, Boca Raton, FL.
Information & Authors
Information
Published In
Journal of Professional Issues in Engineering Education and Practice
Volume 140 • Issue 1 • January 2014
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Dec 27, 2012
Accepted: May 23, 2013
Published online: May 25, 2013
Published in print: Jan 1, 2014
Discussion open until: Mar 1, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.