Barriers to Adoption of Technology-Mediated Distance Education in Higher-Education Institutions

Access to postsecondary education is an issue of significant importance both to the individual and society in general. In the United States, estimates of the proportion of future jobs requiring postsecondary education range from 70 to 90% (Gladieux & Swail, 1999). The individuals with a postsecondary degree earn on average 50% more than high school graduates over the course of their lifetime (U.S. Bureau of Census, 1999). Also, increased educational attainments accrue benefits to society, including greater productivity, increased community services, enhanced civic life, and decreased reliance on government financial support (Institute for Higher Education Policy, 1998).

The rapid development of information technology and the Internet have generated broader opportunities for students to access postsecondary education. In the fall 2005 semester, more than 96% of the very largest higher-education institutions (more than 15,000 total enrollments) had online distance offerings, and the enrollment in online courses reached nearly 3.2 million, up nearly 35% over the 2004 figures (Allen & Seaman, 2005, 2006). In 2007, approximately one-third of higher education institutions accounted for three-quarters of all online enrolments (Allen & Seaman, 2007).

While technology has expanded the opportunities for students to access higher education, it is interesting to note that a number of colleges and universities are still hesitant to offer technology-mediated distance education (TMDE), and there is a very uneven distribution of distance education offerings by type of institutions. The questions of interest for the current study focus on the institutional decision of distance education offering. What are the factors that impact a higher educational institution to adopt distance education? The factors in this study include concerns about program cost, faculty participation, and others.

Barriers to distance education exist both in the stakeholders of institution and faculty (Galusha, 1997). The primary institutional barrier is availability of funds, as cost will increase substantially due to utilization and maintenance of technology. Descriptive statistics show that the high cost for program development and maintenance is a significant barrier to widespread adoption of distance education (Allen & Seaman, 2007; National Post-secondary Education Cooperative, 2004; Waits & Lewis, 2003). In fall 2005, about two thirds of the very large institutions had online programs, compared to only about one sixth of the small institutions (Allen & Seaman, 2006). Moreover, the data from the Cost of Supporting Technology Services project show that for 2000-2001, the median spending on information technology was $1,299 for each student at the wealthiest colleges in the study. By contrast, the less endowed colleges showed a median spending of only $459 per student (Warburton & Chen, 2002). Thus, there is an institutional digital divide pertaining to the perceived gaps in access to technology capital among different types of institutions with regard to their funding availability.

Another important factor on institutional adoption of TMDE is faculty participation. Those institutions most engaged in online believe it is a barrier to more wide-spread adoption of online education (Allen & Seaman, 2007). Research has shown that barriers to teaching and learning at a distance often impede faculty from adapting to new educational opportunities. These barriers include technical expertise, faculty compensation and time, attitudes toward technology, and so on (Berge, 2002; Chen, Voorhees, & Rein, 2006). There is a critical need for regular training and support of faculty on the use of technology and adapting it to enrich their curriculum (Rodriguez, Gonzalez, & Cano, 1996).

Thus, two hypotheses on institution barriers and faculty barriers to TMDE adoption were derived based on the theoretical literature review:

The present study was a secondary data analytic research using the National Center for Education Statistics (NCES) public use dataset. The variables of the study come from a nationally representative survey of distance education, the Postsecondary Education Quick Information System (PEQIS), undertaken by the NCES in the academic year 2000-2001. Unfortunately, this is the most recent pubic dataset that the author was able to access. The survey panel employs a standing panel of 1,599 postsecondary education institutions. The panel includes institutions at the 4-year, 2-year, and less-than-2-year level, public and private colleges, and universities that award associate, baccalaureate, master's, and doctoral degrees. Among the eligible institutions, 1,500 responded to the survey, with a response rate of 94%. For the current analysis, 1,485 responses were valid for the selected variables, excluding 15 missing responses.

The dependent variable of this study is the adoption decision of TMDE of higher education institutions. The sample was divided into two groups based on adoption status. As illustrated in Table 1, the first group includes 1,111 institutions that offered TMDE in the academic year 2000-2001. The second group includes 121 institutions that did not offer TMDE in 2000-2001, but planned to offer it in the next 3 years (2001-2002 through 2003-2004), and 268 institutions that did not offer TMDE in 2000-2001, and did not plan to offer it in the next three years.

Institutional characteristics were controlled for in the analyses as five explanatory factors. The first controlling factor is institution type, created from a combination of level (2-year and 4-year) and control (public and private). The four types include: public 2-year, private 2-year, public 4-year, and private 4-year. The second controlling factor is the availability of graduate program. The third factor is based on the standard Carnegie classification code, indicating which level of programs the institution is offering, recorded as doctoral, master's, baccalaureate, associate, specialized, and other/missing. The fourth factor is the degree of urbanization, recorded as city, urban fringe, town/rural, and missing. And the last one is minority-serving institution.

The independent variables are barriers to TMDE adoption. In the survey, the participating institutions were asked to rate 15 factors that kept them from starting or expanding their distance education courses. In the current analysis, two major independent variables of 5 factors were selected from the 15 factors. The first one, program cost, is measured with two distinct factors: program development costs, and equipment failure/costs of maintaining equipment. The second variable, faculty participation, includes three factors. They are concerns about faculty workload, lack of faculty interest, and lack of faculty rewards or incentives. For each factor, the response categories are “not at all,” “minor extent,” “moderate extent,” and “major extent.”

The analysis was done using the PEQIS dataset and SPSS software version 15.0. First, descriptive statistics were used to understand the sample and general information about barriers to distance education adoption. Then, considering the stratified, multistage sampling design of the dataset and the dependent factor as dichotomous, a binary logistic regression was selected for further analysis. With the institutional characteristics kept as constant, the regression was used to predict the institutional TMDE decisions based on two sets of perceived barrier variables.

Table 2 shows the impact of the barrier factors to adoption of TMDE. About 30% of the participants scored “program development costs” as a factor of moderate extent. However, “equipment failure/cost” seems to be a less important factor and over 70% of the participants marked it as “minor extent” or “not at all.” In the faculty participation factors, “concerns about faculty workload” outweighs the other two factors. About one third of the participants regarded the faculty workload as a moderate extent impact, while almost two thirds of the participants thought of faculty interest or reward as factors of minor extent or not at all important.

The results of the binary logistic regression analysis support the hypotheses. The factors of program cost and faculty participation showed statistically significant associations with the institutional adoption of distance online courses (p < .000, R² = .569). The large positive regression coefficient suggests that these factors strongly influence the probability of the adoption decision. First, controlling for institutional characteristics such as institution type, location, and degree of urbanization, institutions with sufficient funding and few concerns about program cost are more likely to adopt TMDE than others. In particular, the adoption decision was associated with increased odds of concerns for program development cost (OR = 1.12; 95% CI = .60-2.11). In other words, for every one unit increase in program development cost, the odds of adoption distance education (versus not adopting) increased by a factor of 1.12.

Second, controlling for institutional characteristics, institutions with few concerns about faculty participation are more likely to adopt TMDE than other institutions. Specifically, whether to adopt distance education was associated with increased odds of concerns for faculty workload (OR = 3.02; 95% CI = 1.57 − 5.79), and lack of faculty incentives (OR = 2.97; 95% CI = 1.41 − 6.25). For every one unit increase in concerns for faculty workload, the odds of adoption distance education (versus not adopting) increased by a factor of 3.02, while for every one unit increase in concerns for lack of faculty incentives, such odds increased by a factor of 2.97.

Besides the hypotheses, it is noted that one of the controlling variables, institution type, has high associations with TMDE adoption. The public 4-year institutions have the highest odds to adopt distance education (OR = 17.62; 95% CI = 6.66 − 46.52). The public 2-year institutions have the second highest odds (OR = 7.91; 95% CI = 4.85 − 12.89). By contrast, the private institutions have much lower odds for TMDE adoption. Therefore, the results agreed to prior research that large, wellfunded institutions have greater access to technology than smaller colleges with fewer resources do.

This study explored associations between the institutional decision of adopting TMDE and the factors that keep them from starting or expanding distance course offerings. The empirical findings support the main hypotheses, including the significance of the program cost and the faculty participation in driving adoption.

Among all factors, program development costs, concerns for faculty workload, and lack of faculty rewards are significant barriers that prevent institutions from offering distance education. To address these factors and help more institutions start their online programs, federal and state education policy makers might allocate funding to encourage institutions and faculty to develop online courses. Other funding sources to roll out online learning initiatives are private foundations and commercial resources, such as Alfred P. Sloan Foundation, which has awarded grants to more than 100 colleges and universities during the past two decades (Parry, 2009).

On the other hand, institutions that are planning online offerings might be able to cut costs by taking advantage of new technologies. For instance, institutions could consider using open source software, such as moodle, to reduce spending on course management systems. In terms of faculty participation, institution and faculty collaboration might be a way to avoid increasing faculty workload. Faculty members across the country can exchange and share courseware, instead of recreating the wheel. A number of world-class universities are offering free online courses to initiate collaborations. Examples are MIT Open Courseware, the University of California at Berkeley Webcasts courses, and the Open University's (UK) OpenLearn Learning Spaces. Free learning objects and course contents are available at multiple educational resource websites, such as Merlot (http://www.merlot.org/) and Jorum (http://www.jorum.ac.uk/).

The limitation of this research is that the PEQIS dataset was collected by the NCES in the academic year of 2000-2001. With the technology advances and an increasing need for alternative learning options, more universities are willing to embrace online education, compared to 8 years ago. Future research should seek a more recent database to reflect the most recent trends and situations in how higher education institutions are adopting technology-mediated distance education in a new Web 2.0 age when it is easier than ever before to create and share instructional materials online. It will be interesting to see if the impact of the faculty participation factor on adopting distance education will change.

It is believed that student demand for online learning in the United States is still growing (Allen & Seaman, 2007). This study addressed an important issue of relationship between access to postsecondary education and the role of technology. It is expected that the results to be of most value to academic administrators and education policymakers who face the problem of designing postsecondary programs and expanding access to higher education to a larger population. Understanding and mitigating the barriers to TMDE adoption will motivate both institutions and faculty to offer distance education, and better facilitate the opportunities for students to enroll in higher education.