1 VOLUME 2, NUMBER 1, 2013 Predictors of student preference for online courses Louis Charles Glover, EdD The University of Tennessee at Martin Veronica Evans Lewis, PhD The University of Louisiana at Monroe Abstract Online education has increased significantly in recent years. However, a comprehensive examination of factors that predict who will take online courses has not been located. This study explored demographic and opinion influences on preferences regarding current and future online courses for 152 college student participants. Results indicate that students who expressed a desire for more online courses at their university tended to prefer online courses. Additionally, the greatest number of online courses were taken by females who: are older than 20 years of age; work 40 hours or more per week; express preference for online or blended courses; consider online courses to be equivalent in difficulty to other course formats; and, express desire for more online course offerings at their university. As such, when marketing online courses, university personnel would be wise to target these groups and focus on the question of whether members of these groups desired more online course offerings at their universities. Key Words: online courses, face-to-face courses
2 2 Introduction In recent years, online education in university settings has increased significantly for both undergraduate (Allen & Seaman, 2007; Palloff & Pratt, 2001; Waits & Lewis, 2003) and graduate (Karber, 2003; Lyons, 2004) levels. Although large public educational institutions are more likely to offer online education than small private institutions (Allen & Seaman, 2006), approximately 29% of all college students in the United States are enrolled in one or more online courses (Farkas, 2011). Online education is critical to the long-term strategy of an institution (Lytle, 2011) as a result of student demands, issues of access to campus-based courses, and enrollment concerns (Farkas, 2011). Jenkins (2011) indicated that colleges and universities are offering more online courses because they can provide online courses much more cheaply than traditional courses while charging roughly the same tuition for both types. Due to increasingly positive perceptions regarding the effectiveness and relevance of online courses (Ulmer, Watson, & Derby, 2007) methods for offering a diversity of online course content are being explored (Menon & Rubin, 2011). Students tend toward taking online courses due to the flexibility of scheduling (Angiello, 2010; Coyner & McCann, 2004; Reynold, 2012), employment and family obligations (Karber, 2003; Lyons, 2004), past experience with online courses (Glover & Lewis, in press), and geographic proximity to degree granting institutions (Deal III, 2002). Some students prefer the anonymity of online classes as well as the increased student interactions that are facilitated by use of discussion board and course forums (Coleman, 2005). According to Harrington and Loffredo (2010), a majority of introverts prefer online classes (perhaps as a result of student anonymity) in comparison to extraverts who tend to prefer face-to-face classes.
3 LOUIS GLOVER and VERONICA LEWIS 3 In discussing some of the disadvantages to taking online courses Jenkins (2011) identified lower success rates in online courses (50%) as compared to face-to-face classes (70-75%). Bejerano (2008) and Jain, Jain, and Jain (2011) questioned the appropriateness of using online courses for all course types and academic disciplines. Glover and Lewis (in press) conducted a student survey, which indicated that history, sociology, and computer science courses were best offered online, whereas theoretical mathematics, applied mathematics, and engineering courses were least appropriate for the online format. Online courses have been criticized on the basis of issues regarding cultural effectiveness (Lee, 2009), difficulty in addressing differences in student learning styles (Gallagher-Lepak, Reilly, & Killion, 2009), lack of a collaborative instructional environment (Kurth, 2012), and the need for a level of student maturity that often is not found in young college students (Brown, 2011). Many online courses are content heavy and require completion of more assignments than are required in face-to-face courses (Deal III, 2002; Dykman & Davis, 2008; Jock, 2010) perhaps because of concern for the level of rigor associated with online instruction (Buck, 2001, Federal Trade Commission, 2006) A minimal level of technical competence is necessary for completion of online courses (Jenkins, 2011). The assumption that the current generation of students is technologically sophisticated is not necessarily true (Muilenburg & Berge, 2005) and may especially be inaccurate for low income, disadvantaged, and older students (Brown, 2011) who either are not computer literate or do not have personal computers (Davis, 2011). Online courses require student self-discipline (Allen & Seaman, 2006). However, lack of motivation and self-discipline are problems identified in today s students (Taylor, 2003). As a
4 4 result, administrators in some educational settings advise students of the need for selfmotivation, problem solving abilities, and time management skills in order to successfully complete online courses (Are online classes for you, 2008). Despite the fact that some software companies now market products designed to assess whether students can handle the demands of the online environment, few schools pre-screen online courses to determine which students have the best chance of success (Fertig, 2012) in this course format. Much research relative to the general topic of online education concerns faculty preferences for teaching online courses (Farkas, 2011), student preferences for taking courses online rather than in traditional settings (Fillion, Limayem, Laferriere, & Mantha, 2009; Glover & Lewis, in press; Harrington & Loffredo, 2010; Why do students like online learning, 2005), and problems associated with the rapid growth of online course offerings (Bejerano, 2008; Brown, 2011; Jain et al., 2012; Kurth, 2012). However, a comprehensive examination of factors that predict student preference for online courses has not been located. The purpose of the study was to explore participant demographics and preferences associated with a completion of online courses and to identify which of those demographic variables best predict which students will take additional online courses. Colleges, universities, and other secondary institutions of higher learning can use the findings of this study to make informed decisions about proposed changes to their online course offerings and the audience to which these courses might best be marketed. Participants Method College students from three universities in the southeast participated in this research study. The majority of the 152 participants was female (n = 106, 69.7%), between 20 and 29 years of age (n = 64, 42.1%), and identified themselves as living in rural areas (n = 62, 40.3%).
5 LOUIS GLOVER and VERONICA LEWIS 5 Most participants were full time undergraduate students working toward a degree (n = 97, 63.8%) and indicated that they worked between 1 and 39 hours per week (n = 58, 38.2%). Instrument Data obtained from a survey developed and used by Glover and Lewis (in press) for a previous study was used in the current study. The survey contained five demographic questions and nine questions that assessed participants preferences and opinions regarding online courses. Nine demographic and opinion variables were used in the current study. These variables included the participant s geographic location (i.e., rural, suburban, urban), gender, age, educational status (i.e., level of current enrollment in college), work status, course-type preference (i.e., online vs. face-to-face), desire for more online courses, need for faculty training to teach online courses, and comparative course difficulty for online versus face-to-face courses. Examples of responses to questions in the survey that were not used in the current study included open ended reasons for course preference and indications of which courses could best and least be offered online. Procedure After the authors gained Institutional Review Board approval, data from the previously used survey were accessed. In that survey process, course instructors from the three participating institutions using and course management systems solicited participation. Potential participants were directed to access the survey via surveymonkey.com. A consent statement was included in the initial correspondence and also was provided on the initial page of the survey. The consent statement described the purpose of the study and participants rights. Completion of
6 6 the survey was voluntary, and no incentives were offered for participation. Personally identifying information was neither collected nor reported for participants. The authors of this study had to assume that participants gave honest answers to all questions, only completed the survey once, and were either currently enrolled or had recently been enrolled in college. The consent statement also indicated that the survey should not be completed by anyone under the age of 18, and the authors had to assume that participants adhered to that requirement as well. Results Four opinion variables (i.e., course-type preference, desire for more online courses, need for faculty training, comparative course difficulty) were initially subjected to Goodness of Fit chi-square in an effort to determine whether observed response frequencies met expectations. Significant differences in response frequencies were identified for two of the four variables. For the desire for more online courses variable, a majority (n = 86, 57%) of participants indicated a desire for more online course offerings at their university, χ2 (2, N=152) = 39.49, p =.00. Likewise, for the need for faculty training variable, a majority of participants (n = 106, 70%) indicated a for faculty training relative to teaching online courses, χ2 (2, N=152) = 97.32, p =.00. In contrast, for the course-type preference variable, a relatively equal number of participants indicated preference for online (n = 53, 35%) and face-to-face courses (n = 51, 34%), χ 2 (2, N=152) =.250, p =.88. Also, for the comparative ease of course completion variable, a relatively equal number of participants indicated that online courses were either more difficult to complete (n = 59, 39%) or about the same level of difficulty (n = 54, 36%) as face-to-face courses, χ 2 (2, N=152) = 4.28, p =.12. Table 1 provides a summary of participant responses for all four opinion variables.
7 LOUIS GLOVER and VERONICA LEWIS 7 Each of the five demographic geographic (i.e., location, gender, age, educational status, work status) and four opinion (i.e., course-type preference, desire for more online courses, need for faculty training to teach online courses, comparative course difficulty for online versus faceto-face courses) variables were further analyzed using one way analysis of variance (ANOVA) to evaluate whether significant differences existed in the number of online courses taken based on responses provided for categorical options within each variable. Participants had taken an average of 4.28 online courses with 10 or more courses being the most frequently endorsed choice. See Table 2 for a summary of endorsements for number of online courses taken. Oneway ANOVA results were mixed with significant differences being identified for three demographic and three opinion variables. Significant differences in the number of online courses taken were identified by age, F (4, 151) = 6.55, p =.000, η 2 =.151; gender, F (1, 151) = 15.93, p =.000, η 2 =.096; employment status, F (3, 151) = 10.52, p =.000, η 2 =.176; desire for more online courses, F (2, 151) = 16.71, p =.000, η 2 =.184; course-type preference, F (2, 151) = 36.89, p =.000, η 2 =.33; and, comparative course difficulty, F (2, 151) = 5.14, p =.007, η 2 =.065. No other significant differences were identified. Examination of means and post hoc analyses using Tukey identified the following group differences. Participants in the age range (m = 2.38) took significantly fewer online classes than did members of each of the other age groups. Females (m = 4.99) took significantly more online classes than males (m = 2.65). Participants who desired to take more online courses (m = 5.58) had already taken significantly more online courses than those who either did not desire more online courses (m = 2.4) or those who had no opinion on this issue (m = 2.71). Participants who preferred taking online courses (m = 6.85) had taken significantly more online
8 8 courses than participants who preferred face-to-face (m = 2.10) or blended courses (m = 3.77), and participants who preferred blended courses had taken significantly more online courses than participants who preferred face-to-face courses. Participants working 40 or more hours per week (m = 6.86) had taken significantly more online courses than participants from each of the other employment groups (m = 3.72 or less). Participants who indicated that online courses were equivalent in difficulty to face-to-face courses (m = 4.57) had taken significantly more online courses than participants who indicated that online courses were either more (m = 2.60) or less (m = 2.72) difficult than face-to-face courses. In an effort to determine which individual variable or combination of demographic (i.e., location, gender, age, employment status, educational status, number of online courses previously taken) and opinion (i.e., desire for more online courses, need for faculty training, comparative course difficulty) variables would best predict the grouping variable (i.e., preference for online courses), stepwise discriminant analysis was performed. Two discriminant functions were identified with a combined χ 2 (4) = 96.92, p =.000, Wilkes Lambda =.521. After removal of the first function, the second function failed to provide significant predictive contribution, χ 2 (1) =.13, p =.72, Wilkes Lambda =.99. The canonical correlation for function 1 (desire for more online courses) was good, R c =.69, and the canonical correlation for function 2 (number of online courses previously taken) was unimpressive, R c =.03. Function 1 has the greatest predictive ability accounting for 99.9% of the between-group variability, whereas function 2 only accounted for 0.1%. Examination of classification results indicated that 59.2% of all cases were correctly classified with preference breakdowns as follows: Preference for online courses, 40 of 53 cases (75.5%); preference for face-to-face courses, 34 of 51 cases (66.7%); and, preference for blended courses, 16 of 48 cases (33.3%).
9 LOUIS GLOVER and VERONICA LEWIS 9 Discussion Many universities include online course offerings as part of their long-term strategic plans (Lytle, 2011). Institutions seeking to address enrollment concerns (Farkas, 2011) and the financial impact of advancing the online course format (Jenkins, 2011) are encouraged to consider marketing online courses as a solution to declining college enrollment. This study explored participant demographics and preferences associated with a completion of online courses and sought to identify which of those demographic variables best predict which students will take additional online courses. Participants in the current study suggested relatively equal preference for online versus face-to-face courses. However, those participants who preferred online courses had taken, on average, significantly more online courses than participants who preferred face-to-face or blended courses. Similarly, a relatively equal number of current participants suggested that online courses were either more difficult or equal in difficulty to face-to-face courses. This perception could result from the increased workload that often is required in online courses (Deal III, 2002; Dykman & Davis, 2008). A majority (70%) of all participants indicated that faculty who teach online courses should be trained to provide instruction using the online format. Based on differences in the number of online courses taken by members of each demographic and opinion group, a description of the audience most likely to take online courses was summarized. According to results of the current study, the targeted audience to whom online courses should be marketed consists of females who: are older than 20 years of age; work 40 hours or more per week; express preference for online or blended courses; consider online
10 10 courses to be equivalent in difficulty to other course formats; and, express desire for more online course offerings at their university. Results of discriminant analysis confirmed that the best predictor of preference for online courses was the desire for more online course offerings at the participant s university. Although a second discriminant function emerged (i.e., number of online courses taken), it proved to provide minimal contribution (0.1%) to the prediction of preference for online courses. Examination of classification results indicated that a majority (59.2%) of all preference cases were correctly classified with preference for online courses maintaining the highest percentage of correct (75.5%) classifications and preference for blended courses maintaining the lowest percentage correct (33.3%). As such, when marketing materials to the previously described groups that are most likely to take online courses, university personnel would be wise to focus on the question of whether members of those groups desired more online course offerings at their universities. There were several limitations associated with this study. Participation in the survey was limited to students with enrollment histories at three public institutions in the southeast portion of the United States. As such, results may not reflect the opinions and preferences of college students in other parts of the country. The format of the survey limited the types of analyses that could be performed. Therefore, different results might have been derived had the survey questions been formatted in a different manner. Survey data were collected for only 5 months during the academic spring semester of A survey period that included both summer and fall semesters as well as the spring semester might have resulted in different results.
11 LOUIS GLOVER and VERONICA LEWIS 11 Recommendations for further study Because of the increase in online course offerings at universities, continued research into the need for and effectiveness of online education is required. Future studies should continue to examine student and faculty perspectives relative to desires for and satisfaction with online course offerings in order to better clarify the audience to whom these courses should target. A larger and more geographically diverse population that encompasses participants taking courses throughout the academic year should be examined in future studies to increase generalizability of results. Future studies should also focus more readily on collection of data (e.g., Likert scales) that may afford different types of analyses in evaluation of the research questions.
12 12 References Allen, I., & Seaman, J. (2006). Making the grade: Online education in the United States. Needham, MA: Sloan Consortium. Allen, I., & Seaman, J. (2007, October). Online nation: Five years of growth in online learning. Needham, MA: Sloan Consortium. Angiello, R. (2010). Study looks at online learning vs. traditional instruction. The Hispanic Outlook in Higher Education, 20(14), Are online classes for you? (2008). Retrieved May 10, 2012, from Los Angeles Southwest College Web site Bejerano, A. (2008). Raising the question #1: The genesis and evolution of online degree programs: Who are they for and what have we lost along the way? Communication Education, 57, Brown, R. (2011). Community college students perform worse online than face-to-face. Retrieved May 14, 2012, from Students/128281/ Buck, J. (2001). Assuring quality in distance education. Higher Education in Europe, 26, Coleman, S. (2005). Why do students like online learning? Retrieved May 10, 2012 from, Coyner, S., & McCann, P. (2004). Advantages and challenges of teaching in an electronic environment: The accommodate model. International Journal of Instructional Media, 31,
13 LOUIS GLOVER and VERONICA LEWIS 13 Davis, C. (2011). Many students still prefer traditional classes. Retrieved May 10, 2012, from Deal III, W. (2002). Distance Learning: Teaching technology online. The Technology Teacher, 61, Dykman, C., & Davis, C. (2008). Online education forum: Part two teaching online versus teaching conventionally. Journal of Information Systems Education, 19, Farkas, K. (2011). Online education growing as colleges offer more classes to meet student demand. Retrieved May 10, 2012, from Federal Trade Commission. (2006, October). Diploma mills: Degrees of deception. Retrieved June 1, 2012, from Fertig, J. (2012). Critiquing online learning, part two. Retrieved May 10, 2012, from Fillion, G., Limayem, M., Laferriere, T., & Mantha, R. (2009). Integrating information and communication technologies into higher education: Investigating onsite and online students points of view. Open Learning, 24, Gallagher-Lepak, S., Reilly, J., & Killion, C. (2009). Nursing student perceptions of community in online learning. Contemporary Nurse, 32, Glover, L., & Lewis, V. (in press). Student preference online versus traditional courses. The Global elearning Journal.
14 14 Harrington, R., & Loffredo, D. (2010). MBTI personality type and other factors that relate to preference for online versus face-to-face instruction. Internet & Higher Education, 13(1), Jain, P., Jain, S. J., & Jain, S. (2011). Interactions among online learners: A quantitative interdisciplinary study. Education, 131, Jenkins, R. (2011). Why are so many students still failing online? Retrieved May 10, 2012, from Jock, M. (2012). Student problems with online courses. Retrieved May 10, 2012, from, Karber, D. (2003). Comparisons and contrasts in traditional versus online teaching in management. Higher Education in Europe, 26, Kurth, H. (2012). Study finds first-generation college students prefer collaborative learning. 94cc-11e1-a3df-001a4bcf6878.html Lee, M. (2011). Culture and distance learning. Retrieved May 10, 2012, from Lyons, J. (2004). Teaching U.S. history online: Problems and prospects. The History Teacher, 37, Lytle, R. (2011). Study: online education continues growth. Retrieved May 10, 2012, from Retrieved May 10, 2012, from Menon, G., & Rubin, M. (2011). A survey of online practitioners: Implications for education and practice. Journal of Technology in Human Services, 29,
15 LOUIS GLOVER and VERONICA LEWIS 15 Muilenberg, L., & Berge, Z. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26, Palloff, R., & Pratt, K. (2001). Lessons from the cyberspace classroom: The realities of online teaching. San Francisco: Jossey-Bass. Reynold, J. (2012). Why do students like online learning? Retrieved May 10, 2012, from Taylor, S. (2003). The endless class. Community College Week, 15, 6-9. Ulmer, L., Watson, L., & Derby, D. (2007). Perceptions of higher education faculty members on the value of distance education. The Quarterly Review of Distance Education, 8, Waits, T., & Lewis, L. (2003). Distance education at degree-granting postsecondary institutions: Washington, DC: U.S. Department of Education, National Center for Education Statistics.
16 16 Appendix A Table1 Participant Responses to Survey Questions Question Response Options N % Which best describes your preference? taking courses online when available taking courses in a face-to-face setting taking a blended course that combines elements of both Would you like to have more online courses offered at your institution? Yes No It does not matter to me Do you feel that faculty should be certified to teach online courses? Yes No It does not matter to me What is your opinion of online courses? They are easier than face to face courses They are more difficult than face to face courses They are about the same as face-to-face courses
17 LOUIS GLOVER and VERONICA LEWIS 17 Appendix B Table 2 Number of Online Courses Previously Taken by Participants Question Response Options N % How many online courses have you taken? or more None
18 18 About the authors Dr. Louis C. Glover is an assistant professor of education at the University of Tennessee at Martin. His areas of interest include STEM education, mathematics education, science education, online education, at risk students, and factors affecting minority students. Prior to teaching higher education in 2011, he taught secondary chemistry, mathematics, and physics in Louisiana and Texas. Dr. Veronica Evans Lewis is a professor of psychology at the University of Louisiana at Monroe. Prior to coming to ULM, Dr. Lewis supervised an educational program for school-aged children with disabilities for 2 years, taught high school for 1 year, and served as a practicing school psychologist for 4 years. Her primary research interests are related to minority issues and activities that enhance and detract from the learning of students.
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. P S Y C H O L O G Y. MASTER OF ARTS DEGREE PROGRAMS IN PSYCHOLOGY As a special-purpose institution of higher education for urban programming, Texas Southern University embraces the concept of liberal
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Evaluation in Online STEM Courses Lawrence O. Flowers, PhD Assistant Professor of Microbiology James E. Raynor, Jr., PhD Associate Professor of Cellular and Molecular Biology Erin N. White, PhD Assistant
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IHE Master's Performance Report Appalachian State University 2004-2005 Overview of Master's Program Appalachian State University is a comprehensive university offering a broad range of graduate programs.
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Kennedy, A., Young, S., & Bruce, M. A. (2012). Instructors perceptions of community and engagement in online courses. The Researcher, 24(2), 74-81. NRMERA 2011 Distinguished Paper Instructors Perceptions
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