# BRIDGES AND BARRIERS TO TEACHING ONLINE COLLEGE COURSES: A STUDY OF EXPERIENCED ONLINE FACULTY IN THIRTY- SIX COLLEGES

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5 who were not teaching online were excluded, and these faculty members undoubtedly have a somewhat different (and more negative) view of motivations and demotivations. While controversy exists regarding the choice of parametric or non-parametric statistics to analyze ordinal data (e.g. [47]), the more conservative approach is to treat such data as non-parametric in nature. Examination of differences in motivational influences conducted in this paper therefore relies on the use of Pearson chi-squares and standardized adjusted residuals resulting from cross tabular analysis. Standardized adjusted residuals are the observed minus expected value for a table cell divided by an estimate of its standard error. The resulting value is expressed in standard deviation units above or below the group mean. Generally results that indicated differences of more than one standard deviation above or below the mean for an item were considered to be important. This is exploratory research. We therefore set the significance threshold somewhat high. Three chi-square results are reported here: Pearson chi-square, likelihood ratio and linear-by-linear association. In most cases all three tests were below the.05 level of significance indicative of significant differences, i.e. those unlikely to have occurred randomly or by chance. However, in certain cases we chose to include suggestive results where only one or two tests met that threshold. So, results included here have at least one chi-square test that was at or less than the.05 level of significance. Finally, motivational differences were not considered significant for table cells with expected values less than 5 except in instances where the expected value was for a neutral response, i.e. where there was an indication that a difference did exist because very few respondents responded with a neutral choice. These three criteria guided efforts to identify significant motivational differences for online teaching by demographic and contextual factors. V. RESEARCH QUESTIONS 1) What are the advantages of online teaching that recent online instructors report to increase their motivation to teach in online environments? 1a) Do the ranking of these motivators vary based on contextual and/or demographics such as gender, age, faculty rank, online experience or other factors? 2) What are the disadvantages that recent online instructors report as decreasing their motivation to teach in online environments? 2a) Do these demotivators vary based on contextual and/or demographics such as gender, age, faculty rank, online experience or other factors? 3) Do items in the survey used in this study cohere into statistical factors suggesting that they reflect latent constructs interpretable as motivators and demotivators for teaching online that may be useful in future research? VI. RESULTS Research Question 1) What are the factors that recent online instructors report to increase their motivation to teach in online environments? The results of the survey presented in Table 2 provide an initial answer to this question. As can be seen from these results the motivator rated most highly by respondents included a more flexible work schedule. Following closely were a number of factors that reflect interests in taking on a new challenge, addressing student needs, learning about technology and pedagogy, and providing access to new student populations. Statements that suggested that online education might have monetary or other professional benefits were 77

9 decreased their while those with lower computer skill levels were underrepresented in these categories (Table 58 and 59). VII. FACTOR STRUCTURES FOR MOTIVATORS AND DEMOTIVATORS 3) Do items in the survey used in this study cohere into statistical factors suggesting that they reflect latent constructs interpretable as reliable motivators and demotivators for teaching online that may be useful in future research? To understand whether the items in the survey measure latent constructs that can be interpreted as motivators and demotivators for online teaching, we conducted a factor analysis. First, a maximum likelihood estimate with direct oblique rotation was used to test the factor construct of the items that reflected advantages or presumed motivators for teaching online. The inter-correlation coefficients for the items were greater than.30 and the KMO sampling adequacy (.90) and Bartlett s test of sphericity (chisquare is , p <.001) supported the applicability of conducting factor analysis. For the motivators, five factors were extracted with eigenvalues greater than 1. Using this model, 64.6% of the total variance could be explained by these factors. The overall reliability (Chronbach s alpha) was.94 with individual reliability measures between.78 and.91. This analysis led to an interpretable factor structure and we labeled the factors learning, profession, flexibility, access and novelty, reflecting the nature of the items and concerns that each contained (Table 62). For the demotivators the same procedure was followed. The inter-correlation coefficients for the items were greater than.30 and the KMO sampling adequacy (.91) and Bartlett s test of sphericity (chi-square is , p <.001) again supported the applicability of conducting factor analysis. Five factors were extracted with eigenvalue greater than 1. In all, 71.5% of the total variance could be explained. The overall reliability (Chronbach s alpha) was.96 with individual reliability measures between.83 and.93. These factors were labeled compensation, reputation, complexity, promotion and technology, reflecting the nature of the items and concerns that each contained (Table 63). VIII. DISCUSSION The results presented here advance our understanding of the issues that support and undermine faculty willingness to teach in online environments and thus our ability to make higher education more accessible through this modality. Given the increased demand and historic benefits of higher education, coupled with the changing nature of the college student population, providing alternative options for access to college will continue to be a critical strategy to satisfy societal needs. Gaining insight into the factors that enable and constrain faculty acceptance and ongoing participation in the e-learning enterprise is a crucial piece of the puzzle. In this section we will first discuss motivators and then demotivators, reflecting results presented in the previous section. A. Motivators From these results we see that faculty in the state university systems studied here value online teaching for a number or reasons. Flexibility is among the most appealing advantages reported by this group of faculty who are experienced with online teaching. In light of this finding, it seems sensible to highlight and to preserve this aspect of the online teaching experience as fully as possible. Helping other faculty to understand that online teaching can provide greater control over their work life (as reported by these 81

11 Age A number of differences in ranking of motivators were associated with age. These mirror other differences that were associated with academic status and experience with online teaching. Results suggest that younger faculty, perhaps naturally, appear more concerned with opportunities to advance in their careers and seem to be pinning some of their hopes to advantageous experiences gained through online course development and teaching to accomplish this goal. Much of the culture of higher education is incompatible with these hopes; however, new faculty in certain institutional contexts are warned that such activities may actually be detrimental, taking away from more important responsibilities such as research and publication. It seems clear that if younger faculty are to play a role in the furtherance of quality in online education, reward structures need to be aligned with that objective. Employment Status Other motivational differences were associated with employment structures. Full and part-time faculty ranked motivators differently. It is no secret that part-time instructors play a significant role in the academic offerings of many institutions of higher education, and are thus, by default, stewards of the quality of online education. Results suggest that part-time instructors are more appreciative of the benefits of flexibility associated with online teaching, ranking highly its capacity to accommodate other life needs, provide free time for other activities and reduce commuting time or hassle. Flexibility and convenience are well known advantages of online education, but again we need to take care that these attributes do not take precedence over pedagogical quality, learner engagement, and innovation. Flexibility and convenience can become ends rather than means and given the large and increasing number of part-time faculty involved in higher education, both online and in the classroom, we need to be aware of the potential pitfalls. That part-time faculty were over represented as a group that identified flexibility and convenience as a primary motivator may be a cause for concern in this regard. Voluntariness and Institutional Context Faculty who taught in two year colleges were more likely to volunteer to than were faculty employed by four year colleges. It appears likely that cultural distinctions in these institution types favor online teaching for community college faculty. Given that voluntariness is associated with a range of other positive variables, this result may account for the relative over representation of community colleges among the ranks of online providers. Volunteers (and thus community college faculty) were also over-represented among faculty who ranked pedagogical value of online course development as a motivator, highlighting opportunities to reflect on classroom instruction, experiment with new forms of pedagogy, gain new knowledge, and renew interest in teaching. Nonvolunteers associated the potential for material incentives with their. Four year college faculty were over represented among those who gave high marks to flexibility and convenience indicators such as benefits associated with child care or other family needs. Again it must be stressed that such convenience benefits need to be balanced against pedagogical quality issues. Given that voluntariness appears associated with such a broad range of factors likely to increase quality, these results suggest we need to work to ensure that faculty feel ownership over the decision to. Computer Skill Level Faculty with higher reported computing skills appeared less motivated by the notion that online teaching might be a new challenge and more motivated to act as a mentor to others. Providing such opportunities through professional development programs has some obvious potential benefits in terms of engaging additional faculty in the quest for quality. Better computing skills may also be a prerequisite to the teach in a new subject area online; respondents with lower computer skills did not identify this possibility as motivating as those with higher abilities. It seems likely that the struggle associated with mastering the technical aspects of online teaching may be a sufficient challenge without adding new subject matter into the mix. A potential lesson for faculty development professionals keep it simple, especially with computer novices. We turn now to a discussion of the demotivators. 83

13 that faculty with better computing skills were more motivated by opportunities to mentor others than by more general new challenges may be useful in this regard. Leveraging the assistance of such more able peers represents one promising strategy for helping less experienced online instructors to confront the challenges they identified as demotivating. Factor Analysis The factor analysis presented here suggests that the data has an interpretable factor structure. Relatively clear factors emerged, reflecting faculty concerns compatible with previous empirical and conceptual research in this area. These results suggest that motivational items reflect latent constructs important to understanding bridges and barriers to online teaching. Bridges include faculty learning, professional advancement opportunities, flexibility and convenience, provision of access, and benefits associated with novelty and innovation. Barriers reflect issues associated with inadequate compensation relative to time investment, lack of recognition for and negative reputation of online teaching, complexities of technology and online pedagogy, and reward structure misalignments with online teaching. We encourage other researchers to use this instrument in future investigations to provide additional checks of validity and reliability regarding bridges and barriers to online teaching. IX. LIMITATIONS AND FUTURE RESEARCH As an exploratory study the research approach utilized here sought to generate questions as well as answers. While it is useful to attempt to generate new hypotheses, examination of so many individual variables can result in Type I errors and thus spurious findings. Therefore these results need to be replicated through additional research. This is a preliminary study of a relatively small range of faculty (fewer than 400) who are experienced in teaching online, at 36 campuses that are part of the same state university system. We need to have data on faculty from different settings and in different states in order to determine the extent to which motivators and demotivators are shaped by the other contexts, or to which they are similarly perceived in terms of their importance at all types of institutions. We also need a larger and more nationally representative set of responses in order to validate the generalizability of the factor structures observed for these data. The participants in this study appeared to be highly committed to online teaching. Therefore, most importantly, we need to study faculty who have rejected or not had an opportunity thus far to in order to compare their ratings of motivating and demotivating aspects of teaching online with those of more experienced online instructors. X. ACKNOWLEDGEMENTS This research was partially supported in part by a grant from the Alfred P. Sloan Foundation, for which Starr Roxanne Hiltz is a co-pi. The author is grateful for her contributions to this paper. The author also wishes to thank the Office of SUNY Learning Environments for its direct support of this research and to express gratitude to the Director of SUNY Learning Network and the SLN team. SUNY Learning Environments is an office of the Provost and Vice-chancellor of the State University of New York. Finally special thanks to Chun Sau Li for her assistance with statistical factor analysis. XI. ABOUT THE AUTHOR Peter Shea, former director of the SUNY Learning Network, has a joint appointment with the Department of Educational Theory and Practice and the College of Computing and Information at the University at Albany, State University of New York. He is currently principal investigator on a Sloan Foundation funded study of faculty motivation for teaching online. He was also recently awarded a grant 85

14 by Sloan to implement a series of blended academic programs in the School of Education and other units at the University at Albany, State University of New York. He is author of many articles on student and faculty experiences in online education, and co-author of The Successful Distance Learning Student. XII. REFERENCES 1. The Condition of Education In: U.S. Department of Education, National Center for Education Statistics, NCES , Washington, DC: U.S. Government Printing Office, Day, J. C. and E. C. Newburger. The Big Payoff: Educational Attainment and Synthetic Estimates of Work-Life Earnings. (Current Population Reports, Special Studies, P23-210). Washington, DC: Commerce Dept., Economics and Statistics Administration, Census Bureau, Hill, K., D. Hoffman and R. Rex. The value of higher education: Individual and societal benefits, Institute for Higher Education Policy. Reaping the Benefits: Defining the Public and Private Value of Going to College. The New Millennium Project on Higher Education Costs, Pricing, and Productivity, Washington, DC, Rowley, L. L. and S. Hurtado. The Non-Monetary Benefits of an Undergraduate Education. University of Michigan: Center for the Study of Higher and Postsecondary Education, Perry, W. Forms of Ethical and Intellectual Development in the College Years. San Francisco: Jossey-Bass, Choy, S. Nontraditional Undergraduates. In: U.S. Department of Education, National Center for Education Statistics, NCES , Washington, DC: U.S. Government Printing Office, Distance Education at Degree-Granting Postsecondary Institutions: U.S. Department of Education, National Center for Education Statistics, NCES , Washington, DC: U.S. Government Printing Office, Allen, I. E. and J. Seaman. Growing by Degrees: Online Education in the United States. Needham, MA: Sloan-C, Bourne, J. Introductory remarks. Proceeding of the Sloan-C Summer Research Workshop. Needham, MA:Sloan-C, Anderson, S. E. Understanding teacher change: Revisiting the concerns-based adoption model. Curriculum Inquiry 27(3): , Cheung, D., J. Nattie and N. Davis. Reexamining the stages of concern questionnaire: A test of alternative models. The Journal of Educational Research, 94: , Davis, F., R. Bagozzi and P. Warsaw. User acceptance of computer technology: A comparison of two theoretical models. Management Science 35(8): , Fuller, F. F. Concerns of teachers: A developmental conceptualization. American Education Research Journal 6: , Hall, G. E., R. C. Wallace and W. A. Dossett. A Developmental Conceptualization of the Adoption Process Within Educational Institutions. Austin, TX: Research and Development Center for Teacher Education, The University of Texas, Hall, G. E. and S. M. Hord. Change in Schools. Albany, NY: State University of New York Press, Hall, G. E. and S. M. Hord. Implementing Change: Patterns, Principles, and Potholes. Needham Heights, MA: Allyn & Bacon, Rogers, E. M. Diffusions of Innovations, 1 st Ed. New York: Free Press, Rogers, E. M. Diffusions of Innovations, 5 th Ed. New York: Free Press,

15 20. Dziuban, C., P. Shea and J. Arbaugh. Faculty roles and satisfaction in ALNs. In: S. R. Hiltz and R. Goldman (Eds.), Learning Together Online: Research on Asynchronous Learning Networks, Mahway, NJ: Lawrence Elrbaum Associates, Kashy, E., M. Thoennessen, G. Albertelli and Y. Tsai. Implementing a large on-campus ALN: Faculty perspective. Journal of Asynchronous Learning Networks 4(3): , Hartman, J., C. Dzuiban and P. Moskal. Faculty satisfaction in ALNs: A dependent or independent variable. Journal of Asynchronous Learning Networks 4(3): National Education. Focus on distance education. Update 7(2): March 2001, Washington, DC Shea, P., E. Fredericksen, A. Pickett, W. Pelz and K. Swan. Measures of learning effectiveness in the SUNY Learning Network. In J. Bourne and J. C. Moore (Eds.), Online Education, Volume 2: Learning Effectiveness, Faculty Satisfaction, and Cost Effectiveness, Needham, MA: Sloan-C, Smith, L. Faculty satisfaction in LEEP. A web-based graduate degree program in library and information science. In J. Bourne and J. C. Moore (Eds.), Online Education, Volume 2: Learning Effectiveness, Faculty Satisfaction, and Cost Effectiveness, Needham, MA: Sloan-C, Swan, K., P. Shea, E. Fredericksen, A. Pickett, W. Pelz and G. Maher. Building knowledge building communities: Consistency, contact and communication in the virtual classroom. Journal of Educational Computing Research 23(4): , Arbaugh, J. B. Virtual classroom characteristics and student satisfaction in internet-based MBA courses. Journal of Management Education 24: 32 54, Hartman, J. L. and B. Truman-Davis. Factors relating to the satisfaction of faculty teaching online courses at the University of Central Florida. In J. Bourne and J. C. Moore (Eds.), Online Education, Volume 2: Learning Effectiveness, Faculty Satisfaction, and Cost Effectiveness. Needham, MA: Sloan-C, Fredericksen, E., A. Pickett, W. Pelz, K. Swan and P. Shea. Factors influencing faculty satisfaction with asynchronous teaching and learning in the SUNY Learning Network. Journal of Asynchronous Learning Networks 4(3): , Rockwell, K., J. Schauer, S. M. Fritz and D. B. Marx. Incentives and obstacles influencing higher education faculty and administrators to teach. Online Journal of Distance Learning Administration 2(4): Thompson, M. Faculty satisfaction in Penn States World Campus. In J. Bourne and J. C. Moore (Eds.), Online Education, Volume 2: Learning Effectiveness, Faculty Satisfaction, and Cost Effectiveness, Needham, MA: Sloan-C, Hislop, G. and M. Atwood. ALN teaching as routine faculty workload. Journal of Asynchronous Learning Networks 4(3): National Education. A survey of traditional and distance learning higher education members. Washington DC, Shea, P., W. Pelz, E. Fredericksen and A, Pickett. Online teaching as a catalyst for classroombased instructional transformation. In J. Bourne and J. C. Moore (Eds.), Elements of Quality Online Education, Needham, MA: Sloan-C, Shea, P., E. Fredericksen, A. Pickett and W. Pelz. Faculty development, student satisfaction, and reported learning in the SUNY Learning Network. In T. Duffy and J. Kirkley (Eds.), Learnercentered Theory and Practice in Distance Education, Mahway, NJ: Lawrence Elrbaum Associates, Clay, M. Faculty attitudes toward distance education at the State University of West Georgia. University of West Georgia Distance Learning Report, /~distance/attitudes.html. 87

16 37. Schifter, C. C. Faculty participation in asynchronous learning networks: A case study of motivating and inhibiting factors. Journal of Asynchronous Learning Networks 4(1): 15 22, Betts, K. S. Factors influencing faculty participation in distance education in postsecondary education in the United States: An institutional study. Doctoral dissertation, The George Washington University, Dissertation Abstracts International: UMI, Wolcott, L.L. Tenure, promotion, and distance education: Examining the culture of faculty rewards. The American Journal of Distance Education 11(2): 3 18, Twigg, C. Who Owns Online Courses and Course Materials? Intellectual Property Policies for a New Learning Environment, Werry, C. and M. Mowbray. Online Communities: Commerce Community Action, and the Virtual University. Upper Saddle River, NJ: Prentice Hall, Chizmar, J. F. and D. B. Williams. What Do Faculty Want? EDUCAUSE Quarterly 24(1): 18 24, Muilenburg, L. Y. and Z. L. Berge. Barriers to distance education: A factor-analytic study. The American Journal of Distance Education 15(2): 7 22, Berge, Z. L. and L. Y. Muilenburg. Obstacles faced at various stages of capability regarding distance education in institutions of higher learning. Tech Trends 46(4): 40 45, Berge, Z. L., L. Y. Muilenburg and J. V. Haneghan. Barriers to distance education and training: Survey results. The Quarterly Review of Distance Education 3(4): , Hiltz, S.R., Kim, E. and Shea, P. Faculty Motivators and Demotivators for Teaching Online: Results of Focus Group Interviews at One University. Proceedings of the 40th Annual Hawaii International Conference on System Sciences, Velleman, P. and L. Wilkinson. Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician 47(1): 65 72, Larreamendy-Joerns, J. and G. Leinhardt. Going the distance with online education. Review of Educational Research 76(4): , Kramarae, C. The third shift women learning online. American of University Women, XIII. APPENDIX: TABLES AND TESTS Table 1: Demographic Data and Teaching Experience (N=386) Frequency Percent Valid Percent Institution Community College University Center University College College of Technology Specialized College Other Chose not to answer Blank (no answer) Gender Male Female Chose not to answer

17 Blank (no answer) Age or older Chose not to answer Blank (no answer) Academic Category Teaching Assistant Instructor Lecturer Adjunct Professor Assistant Professor Associate Professor Full Professor Chose not to answer Blank (no answer) Times teaching First time Second time Third time Fourth time Fifth time More than five times Chose not to answer Blank (no answer) Number of Students in Course More than More than Blank (no answer)

18 Computer Skill Low Medium High Chose not to answer Blank (no answer) Table 2: Descriptive Statistics for Motivators to Teach Online Teaching online can provide N Mean SD 14. a more flexible work schedule an opportunity to stretch, - take on a new challenge Students may want online courses an opportunity to learn new technology an opportunity to gain new knowledge, skills, and insights about my teaching an opportunity to experiment with new pedagogical approaches an opportunity to reach students in different geographical locations an opportunity to reach students at different stages of their learning lives (e.g. more mature/experienced, older, younger, etc.) an opportunity to reach students with different cultural backgrounds an opportunity to reflect upon and rethink classroom teaching an opportunity to experiment with alternative means of assessment accommodate other life needs (child care, transportation, other family needs) reduce commuting time, or hassle to renew interest in teaching (overcome staleness, apathy) a higher level of interaction with my students Online courses/programs can allow an institution to maintain or increase enrollment/revenue and therefore promotes job security." provide more free time for other professional activities (e.g. attend conferences, consulting, etc) become a mentor or to assist others to learn about online teaching Colleagues may refer to online teaching in a positive way participate in a collaborative professional development activity (e.g. training) which enhances relationship with peers to teach a new subject area Teaching online can provide an additional opportunity to demonstrate competencies important for tenure and promotion Other material incentives may be available for online course development Teaching online may be a condition of your employment (hired to teach online) Note: Range = 1 (not a motivator) to 7 (strongest motivator) 90

19 Table 3: Motivator Differences by Gender: Online teaching can accommodate other life needs such as child care, transportation, etc. Gender Male Female Life Needs Does not increase my Increases my Neutral Count Expected Count Adjusted Residual Count Expected Count Adjusted Residual Pearson Chi-Square 6.148(a) N of Valid Cases 325 (a) 0 cells (.0%) have expected count less than 5. The minimum expected count is Table 4: Motivator Differences by Gender: Online teaching can reduce commuting time or hassle. Gender Male Female Reduce Commuting Time or Hassle Does not increase my Neutral Increases my Count Expected Count Adjusted Residual Count Expected Count Adjusted Residual Pearson Chi-Square 7.500(a) N of Valid Cases 321 (a) 0 cells (.0%) have expected count less than 5. The minimum expected count is

20 Table 5: Motivator Differences by Age: Online teaching can provide opportunities to experiment with new forms of pedagogy. Experiment with New Pedagogy Age 2 Under or older Does not increase my Increases my Neutral Count Expected Count Adjusted Residual Count Expected Count Adjusted Residual Pearson Chi-Square 6.215(a) N of Valid Cases 325 (a) 0 cells (.0%) have expected count less than 5. The minimum expected count is Table 6: Motivator Differences by Age: Online teaching can provide opportunities to demonstrate competencies important for promotion or tenure. Demonstrate Competencies Age 2 Under or older Does not increase my Increases my Neutral Count Expected Count Adjusted Residual Count Expected Count Adjusted Residual Pearson Chi-Square (a) Likelihood Ratio N of Valid Cases 280 (a) 0 cells (.0%) have expected count less than 5. The minimum expected count is

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