Barriers to Distance Education in Rural Schools

Despite the sharp increase in use of distance education at all education levels and across various geographic areas, several types of barriers to distance education have been, and still are, evident. These barriers can serve to prevent and limit the use of distance education to resolve some challenges rural schools face. Berge and colleagues (e.g., Berge, 1998; Berge & Mrozowski, 1999) have been at the forefront of research on and have identified several types of barriers common to distance education in organizations and various postsecondary settings: academic barriers (e.g., lack of student time, large class size, lack of teacher support for student learning to use technology), cultural barriers (e.g., lack leadership support for, faculty expertise to use, and understanding of distance education), and technical barriers (e.g., inadequate connectivity/access, availability computers and necessary programs). Factor analytic and case studies have subsequently identified and examined 10 clusters of barriers (Berge, Muilenberg, & Van Haneghan, 2002; Cho & Berge, 2002; Muilenburg & Berge, 2005). Financial issues are less of a barrier; but organizational support (e.g., strategic planning), technical expertise, and faculty support are often barriers identified in Berge's research (e.g., Berge et al., 2002; Cho & Berge, 2002). However, a national study of postsecondary academic institutions found that distance education program development costs were frequently a barrier (43%) Lewis, Alexander, & Farris, 1997). Nonetheless, these were not considered a serious impediment. Technological factors (e.g., infrastructure, equipment problems and maintenance) were likewise often cited as barriers but not considered major problems (Lewis et al., 1997).

In other research on barriers, Zirkle (2001, 2002, 2004) has differentiated several types of distance education barriers including institutional barriers (e.g., availability of courses, student support services, required materials, cost), student barriers (e.g., computer skills, contact with and feedback from instructor, isolation from other students), and instructional barriers (e.g., faculty training, time commitment, and technical expertise). Zirkle (2002) examined these barriers among college students enrolled in a trade and industrial teacher education program at a Midwestern university. Similar to many rural secondary schools, the university was offering numerous distance education courses in part because of geographic isolation (Zirkle, 2002). Results indicated that course availability and scheduling were ranked highly as institutional barriers. Problems with technical assistance and support were also more of a barrier. The availability of computers, internet connectivity, and students' computer skills were among the lowest barriers. Zirkle (2004) then examined the perceptions of teacher educators regarding the barriers to distance education at their institution. All of the career and technical teacher educators across a Midwestern state (i.e., Ohio) participated. In terms of institutional barriers, costs and funding issues ranked highly. A lack of strategic planning and common vision were also more of a barrier. Technical support and technology-enhanced classrooms were less of a barrier in this study. Instructors training for, teaching skills with, and technical expertise for distance education were higher ranked barriers. For students, ability to learn and the isolation or limited interaction with instructors were considered barriers, but students' technical expertise was not.

Approximately one third of the public schools and 10 million students in the United /States are rural (Johnson & Strange, 2007; Provasnik et al., 2007). Rural students more often attend very small schools (i.e., less than 200 students), and over 50% of rural secondary schools have fewer than 400 students. While attending small rural schools has many benefits and may be protective (Howley, Strange, & Bickel, 2000; Huang & Howley, 1993; Johnson & Strange, 2007; Nye, Hedges, & Konstantopoulos, 2000), it can also present some challenges that are not found as often in urban and suburban schools. One of the most pressing challenges for small, remote, and low-income rural schools is attracting and retaining certified and highly qualified teachers (Barbour, 2007; Barley & Brigham, 2008; Beeson & Strange, 2000; Herzog & Pittman, 1995; Hobbs, 2004; Holloway, 2002; Lowe, 2006; Monk 2007).

When staffing difficulties combine with community population losses and resultant economic decline, some rural districts have elected to close or consolidate (Hobbs, 2004; Jimerson, 2006; Schafft, Alter, & Bridger, 2006; Seal & Harmon, 1995). Closure or consolidation not only removes the benefits of attending rural schools from students, but also takes the community social center and primary employment source away (D'Amico, Matthes, Sankar, Merchant, & Zurita, 1996; Jennings, Swidler, & Koliba, 2005; Lyson, 2002; Schafft et al., 2006). Thus, many want to avoid school closure or consolidation. Distance education, principally in the form of online courses, has been proposed as an alternative to closure or consolidation as well as providing a comprehensive curriculum and advanced courses (Barbour, 2007; Barbour & Mulcahy, 2006; Burney & Cross, 2006; Hobbs, 2004; Jimerson, 2006).

Distance education is as effective as traditional classes in terms of learning outcomes (Bernard et al., 2004; Cavanaugh, Gillan, Kromrey, Hess, & Blomeyer, 2004; Hobbs, 2004; Waxman, Lin, & Georgette, 2003). Recent improvements in infrastructure and affordability have made rural Internet availability comparable to nonrural areas and distance education viable (Hobbs, 2004; Jimerson, 2006; Malecki, 2003). Accordingly, rural districts are increasingly using distance education and perhaps even more so than urban and suburban schools. Specifically, Setzer and Lewis (2005) reported that the proportion of rural districts (46%) with students taking distance education is nearly twice that of urban (23%) and suburban (28%) districts. In addition, 85% of rural schools classified as small and low income, according to the Rural Education Achievement Program (REAP), have been found to be currently using or previously had used distance education (Hannum, Irvin, Banks, & Farmer, 2009). Furthermore, 81.3% reported that they needed distance education to provide advanced or enrichment courses for students and 92.1% were satisfied (i.e., somewhat or very satisfied) with the distance education that had used or were using.

Nonetheless, rural schools likely encounter difficulties and barriers when implementing and using distance education (Hannum et al., 2009; Hobbs, 2004). These barriers may limit the ability of rural schools that need to use and to effectively capitalize on distance education in order to overcome challenges they may face (e.g., teacher shortages, difficulties recruiting certified teachers, teaching subjects outside of their certification). Given the necessity for many rural schools to use distance education and that they use distance education more often than nonrural schools, there is a need for research to identify barriers to distance education in rural schools. Furthermore, small remote and low-income rural schools are likely to encounter more challenges involving recruiting and retaining certified and highly qualified teachers. Thus, the overarching purpose of this investigation is to identify barriers to distance education in small and low-income rural schools as well as factors related to these barriers.

Similar to many rural schools, agriculture education has been experiencing declining enrollments, funding shortages, and difficulties in hiring qualified instructors (Miller & Miller, 2000). Likewise, it has been suggested that agriculture education may be able to overcome challenges and offer courses via the use of distance education. As a result, some have investigated barriers to the use of distance education in agriculture education. Murphy and Terry (1998) completed a nationwide study with a panel of experts in agricultural education programs using distance education. A lack of commitment and preparation by instructors as well as administrative support (i.e., providing time to learn how to use technologies) were identified as barriers to integrating distance education into agricultural education. The availability of necessary hardware, facilities to use, and costs were less frequently barriers. Miller and Miller (2000) examined the usefulness of a synchronous (i.e., live two-way interactive audio and video system) distance learning network for delivering agricultural education to secondary school students across Iowa. A random sample of secondary agriculture teachers completed questionnaires and indicated that scheduling problems were most often barriers. A lack of local support staff and training were also common barriers.

While distance education barriers have been well documented and are widely recognized (Zirkle, 2001, 2004), most research has examined distance education barriers in postsecondary settings. These results may or may not be applicable to high schools. One factor that questions the applicability of postsecondary research in distance education to high schools is the different distance education models that are often used in each of these settings. Distance education in high school often features students working on their distance education courses during an assigned period in the school day, often under the supervision of a facilitator who is in the room with them. In postsecondary institutions, students complete distance education courses in a more independent fashion working on their own whenever and from wherever they choose. It is likely that different barriers are at play in these two scenarios. Thus, extrapolating from the postsecondary research on barriers to distance education use and applying this directly to high schools is questionable at best.

In addition to this distinction between postsecondary and high schools, to our knowledge none have investigated barriers to distance education specifically in rural schools. With well-documented differences between rural schools and their urban and suburban counterpoints, generalizing distance education research done in urban and suburban settings to rural schools is questionable. Beyond geographic location, rural schools are distinct in that they have, as previously mentioned, difficulties attracting and retaining certified instructors (Barley & Brigham, 2008; Beeson & Strange, 2000; Herzog & Pittman, 1995; Hobbs, 2004; Holloway, 2002; Lowe, 2006; Monk, 2007). Until more recently, there has also been a rural digital divide regarding connectivity (Hobbs, 2004; Jimerson, 2006; Keane, de la Varre, Irvin, & Hannum, 2008; Malecki, 2003). Though the connectivity problems have lessened, it is likely that staffing shortages and more widespread recent acquisition of comparable connectivity may mean that the experience with and personnel to support distance education is limited. Rural students also may be less prepared for distance education. Given the role distance education may have for possibly preventing closure and consolidation of rural schools as well as the potential for distance education to compensate for constrained curricular offerings and shortages of qualified teachers in some subjects, identifying barriers to distance education in rural schools is needed.

Finally, research on barriers to distance education has not examined how different barriers are related to other factors that may be relevant (e.g., district characteristics, course subject area, and delivery format). That is, the research to date has largely sought to describe the presence of barriers or degree to which various barriers are apparent. It is important to identify the types of barriers and the extent to which these are evident. Taking this a step further and investigating the relationship between various barriers and other pertinent factors may provide additional insights. Such work may identify related issues that may need to be addressed when implementing distance education. For example, if some barriers were related to low levels of student preparation this would suggest that the distance education effort should consider how to enhance students' preparation when planning and implementing a program. Thus, research on barriers that takes other factors into account may clarify not only barriers to distance education in rural schools but also related factors that should be taken into account for successful use of distance education.

The primary purpose of this investigation was to examine barriers to the use of distance education that rural school administrators identified and to explore related factors in small and low-income rural schools. The secondary purpose of this study was guided by five specific aims. The first aim was to examine the relationship between barriers and district characteristics. The second aim was to explore the association between barriers and course subjects offered via distance education. The third aim was to investigate the relationship of barriers to delivery format and student preparation. The fourth aim was to explore the relationship between barriers and course completion. The fifth aim was to examine the association between barriers and student completion of distance education courses, satisfaction with distance education, and the degree to which distance education was meeting students' needs.

The data used in this study were collected via a telephone survey with administrators or other qualified personnel in a sample of randomly selected rural school districts. The survey was developed by research staff to measure several factors and issues related to distance education in rural schools. Trained interviewers administered the survey over the phone. A telephone survey was used in order to increase the response rate.

We randomly selected 417 districts from those qualifying for the 2004-2005 Rural Education Achievement Program (REAP). REAP initiatives include the Small Rural School (SRS) and Rural Low Income School (RLIS) programs. SRS districts have fewer than 600 students, a county with fewer than 10 people per square mile, and all schools in locale code 7 or 8 communities (i.e., fewer than 2,500 residents). RLIS districts have at least 20% of students from families with incomes below the Federal poverty line and each school is in a local code 6, 7, or 8. To ensure adequate representation of rural districts eligible for both programs, 10% of SRS (n = 311) and RLIS (n = 106) districts were randomly selected. We conducted this research with SRS and RLIS districts because they are small, remote, and low income. Thus, these districts are more likely to need distance education to overcome staffing shortages and other challenges in providing a comprehensive curriculum.

The study utilized a survey developed for this project entitled the Rural Distance Education Survey (RDES). This 43-item questionnaire assessed various aspects of distance education in rural schools. The RDES assessed the prevalence of distance education in rural districts by course type (e.g., math, science, foreign language etc.,) and level (i.e., general and honors, Advanced Placement, credit recovery). The survey also examined general issues related to distance education and district needs. Open- and closed-ended items were designed to identify distance education delivery format (e.g., web-based/online course, cable television, two-way videoconferencing, etc.), providers (local, state, regional), barriers to distance education (e.g., funding, connectivity, facilitators), and district needs (e.g., lack of AP courses and foreign language).

Information on distance education barriers was collected by 13 dichotomous (“yes”/”no”) questions assessing each potential barrier. As shown in Table 1, barriers were conceptually grouped into the following categories: district, logistical, personnel, and technology barriers. District barriers included that distance education was not needed for curriculum requirements, not a district priority, not part of a strategic plan, and a lack of sufficient funding. Logistical barriers referred to problems or difficulties in scheduling, implementing, and finding distance education courses. Personnel barriers involved not having personnel trained to support distance education and lacking technical expertise. Technology barriers included lacking technology enhanced classrooms, inadequate maintenance of technology, and insufficient connectivity.

Most district information (e.g., size, percentage of students from various racial/ethnic backgrounds, percentage of students eligible for free/reduced lunch) was gathered directly from respondents. Specifically, district size was obtained by asking to provide the number of students that were currently enrolled in their district. Respondents were also asked to provide the percent of student that were African American, White, Hispanic, and qualified for a free or reduced lunch. SRS and RLIS designations were obtained from the online REAP eligibility files.

Information about the course subjects and levels of courses offered by each district was assessed. The level of courses included general or honors, Advanced Placement (AP), and credit recovery. The current study collected information indicating whether students in respondents' districts were taking any courses in science, math, English, foreign language, history, and psychology or sociology.

Delivery formats were assessed by asking respondents to indicate whether each format was used in the currently offered distance education courses. Delivery formats were classified into asynchronous and synchronous. Asynchronous delivery formats included mail, e-mail, self-instructed computer based tutorials, and web-based online courses. Synchronous delivery included one- and two-way video conferencing, satellite, and cable television.

Student preparation in terms of the academic background, study skills, and computer skills was measured have having respondents indicate on a 4-point Likert-type scale (1 = “not very well” to 4 = “very well”) how prepared their high school students were for distance education courses.

Course completion was measured by asking respondents to provide the percent of students that completed distance education courses in which they enroll.

To assess satisfaction with distance education, respondents were asked to rate on a 4-point Likert-type scale (1 = “very dissatisfied” to 4 = “very satisfied”) how satisfied they were with the distance education courses their district had used.

Respondents also rated on a 4-point Likert-type scale (1 = “not very well” to 4 = “very well”) how well they thought that their current distance education courses were meeting their students' needs.

In the spring of 2005, a contact from the district website or central office was identified and sent a letter describing the survey and indicating that the district had been randomly selected to participate. The letter stated that they would receive a call about participating in the study. Trained interview coordinators called to confirm receipt of the letter and to answer any questions. Contacts were asked who was most qualified in the district to answer questions about distance education. If another person was recommended, they were contacted and the survey was described. After informed consent was obtained from the most qualified district contact, the phone interview coordinator transferred the individual to a trained phone interviewer to conduct the interview. Interviews took an average of 20 minutes. A total of 394 district contacts completed the survey for a 95% participation rate. As previously mentioned, before completing the telephone survey respondents were informed that questions concerned distance education, which refers to a wide variety of approaches used to provide courses to students by an instructor at a remote location. These approaches include courses taught using cable or satellite television, videoconferencing, web-based or online courses, and correspondence courses.

As most of the survey involved a singleitem format, establishing construct reliability and validity is difficult. Nonetheless, many survey items had been used in or were adapted from surveys previously administered by research team members. We also addressed reliability and validity in terms of survey administration procedures. Each telephone interviewer participated in a half-day training session conducted by an experienced director of survey research projects who had both trained and managed telephone interviewers in large-scale studies for several years. During the training, numerous scenarios related to the RDES were used so that the telephone interviewers would learn to be consistent in how they recorded survey responses from the school administrators. During the extended practice in the training session, responses were explained and any discrepancies or misunderstandings regarding how to administer the survey in a standardized manner, including asking follow-up probes and recording responses, were resolved. The telephone interviewers participated in role-playing exercises during training to prepare them for the variety of responses they may encounter and to ensure that each interviewer would record survey responses in an identical fashion. Following the training, the project coordinator acted as a school administrator and had each telephone interviewer conduct a complete interview with him to fill out the survey. The project coordinator followed an identical script when completing the survey for each interviewer to determine the degree of agreement among telephone interviewers and to provide them with feedback on their performance. The project coordinator supervised the telephone interviewers when they were collecting the survey data and was available to handle any issue that arose. These steps help to ensure consistency in data collection among the telephone interviewers.

The telephone interviewers entered the responses to the survey directly into an Access database as they were administering the survey by telephone. The software was programmed to prompt them on an item-by-item basis and make any necessary adjustment in subsequent questions as a result of responses to prior questions. That is, the database automatically handled any skip patterns in the survey, e.g. if the person completing the survey said they were not teaching any mathematics courses by distance education in their schools, it would go to another category rather than ask which specific mathematics courses were taught by distance education. The data were kept secure and were backed-up each night.

The frequency and rank order of each barrier is presented in Table 1. As noted in this table, the five most common barriers were not having a need for distance education (i.e., not needed for curriculum requirements), funding, scheduling, not being a district priority, and personnel not trained. The least mentioned barriers related to technology including maintenance and connectivity. The remaining results of this study are presented in five sections reflecting the guiding aims of this study. The first section examines the relationship between barriers and district characteristics. The second section discusses the association between barriers and distance education course subjects. The third section describes the relationship of barriers to delivery format and student preparation. The fourth section discusses the relationship between barriers and course completion. The fifth section examines the association between barriers and student completion in, schools satisfaction with distance education, and the degree to which distance education was meeting students' needs.

The results from correlations between barriers and district characteristics are presented in Table 2. Results indicated that district, logistical, and personnel barriers had a positive relationship to district size and RLIS designation. Specifically, distance education not being part of a strategic plan was related to district size and RLIS designation (r = .17 and r = .15, respectively). Distance education being difficult to implement was also associated with district size and RLIS designation (r = .10 and r = .12, respectively). Not having personnel trained to use distance education (r = .14 and r = .17, respectively) and personnel to support distance education (r = .15 and r = .18, respectively) related to size and RLIS classification. These results indicated that larger districts and RLIS districts were more apt to experience several distinct types of the district, logistical, and personnel barriers.

Several other correlations in Table 2 also seem noteworthy. First, not having personnel to support distance education and lacking the technical expertise are personnel barriers that had a positive relationship to the percentage of African American students (r = .12 and r = .13, respectively). This suggests that rural districts serving high proportions of African American youth are more likely to have these personnel barriers. Second, two district barriers had an inverse relationship to student poverty (i.e., percentage of students eligible for a free or reduced lunch). Specifically, not needing distance education for curriculum requirements and not being a district priority related to student poverty (r = −.17 and r = −.13, respectively). Thus, rural districts serving low concentrations of impoverished youth indicated that they did not have barriers in terms of distance education being needed for curriculum requirements and a district priority. Conversely, these results also suggested that rural districts with high percentages of poverty have barriers regarding distance education being needed for curriculum requirements and a district priority.

Results in Table 3 indicated that some district barriers were consistently related to lower provision of most subjects via distance education. Specifically, distance education not being a district priority and part of a strategic plan were related to lowered use of distance education for science (r = −.11 and −.17, respectively), math (r = −.14 and −.13, respectively), English (r = −.20 and −.15, respectively), foreign language (r = −.12 and −.15, respectively), and history (r = −.12 and −.18, respectively). Some logistical barriers were also related to being less likely to use distance education for several subjects. In particular, distance education being difficult to implement and difficulty finding courses needed were associated less with use of the following courses: science (r = −.11 and −.18, respectively), math (r = −.16 and −.11, respectively), and foreign language (r = −.14 and −.11, respectively). One personnel barrier, not having personnel trained to use distance education, was related to less use of science (r = −.20), math (r = −.21), English (r = −.19), foreign language (r = −.20), and psychology or sociology (r = −.13). Taken as a whole, these results suggested that when district, logistical, and personnel barriers are evident rural schools are likely to not use distance education to provide most or any course subjects.

As shown in Table 4, the relationship between barriers and delivery format was more complex. Several district barriers were related to lower use of asynchronous and synchronous delivery formats. Specifically, results indicated that when distance education was not needed for curriculum requirements rural districts were less apt to use either asynchronous or synchronous formats (r = −.11 and −.12, respectively). Likewise, when distance education was not a district priority, rural districts were less apt to use either an asynchronous or synchronous delivery format (r = −.11 and −.12, respectively). Distance education not being part of strategic plan was related to a lowered use of synchronous formats (r = −.17), whereas a lack of funding related to an increased use of asynchronous formats (r = .12). Overall, results suggested that when distance education is not needed for curriculum requirements and is not considered a priority or part of district plans, then rural schools are less apt to use either delivery format.

The logistical barrier that distance education was difficult to implement related to higher use of asynchronous formats (r = .10) but less use of synchronous formats (r = −.15). Personnel not being trained to use distance education was associated with lower use of synchronous formats (r = −.20). Finally, lacking technology enhanced rooms was associated with less use of synchronous formats (r = −.18), whereas insufficient connectivity related to an increased use of synchronous formats (r = .14). These latter findings may suggest that rural schools are less apt to use synchronous formats (e.g., videoconferencing, cable or satellite TV) when lacking technology enhanced rooms but more use of synchronous formats when connectivity is a barrier as this would limit asynchronous formats which are largely internet-based (e.g., web-based courses, e-mail).

In terms of student preparation, distance education not being a district priority was the only district barrier related to student preparation. Specifically, this barrier was associated with lower student preparation in academic skills (r = −.12), study skills (r = −.16), and computer skills (r = −.18). Likewise, distance education being difficult to implement was also related to lower student preparation in academic skills (r = −.22), study skills (r = −.21), and computer skills (r = −.19). Personnel or instructional support barriers were associated with lower student preparation in several similar respects as well. That is, not having personnel trained to use distance education related to lower student preparation in academic skills (r = −.15), study skills (r = −.18), and computer skills (r = −.14). Finally, not having personnel to support distance education was also associated with lower student preparation in academic skills (r = −.12) and study skills (r = −.14). These results indicated that rural schools without personnel trained to use or available to support distance education tend to have students who are also less prepared for distance education. However, it should be noted that students were reported to be highly prepared overall.

As shown in Table 5, distance education being seen as difficult to implement was the only barrier related to the percent of students that complete distance education courses (r = −.13, p < .05). Specifically, difficult distance education implementation was related to lower rates of course completion by rural students.

Results in Table 5 also indicated that similar barriers were related to lower satisfaction with distance education and the degree to which distance education met students' needs. Specifically, distance education not being a district priority was related to lower satisfaction (r = −.23) and meeting students needs with distance education (r = −.16). Distance education not being part of a strategic plan was likewise associated with lower satisfaction (r = −.21) and meeting students needs with distance education (r = −.15). In terms of logistical barriers, distance education being difficult to implement was also related to lower satisfaction (r = −.30) and meeting students needs with distance education (r = −.35). Finally, personnel barriers were also associated. Specifically, personnel not trained to use distance education and not having personnel to support distance education were related to lowered satisfaction (r = −.25 and −.13, respectively) and meeting students' needs with distance education (r = −.21 and −.16, respectively). Taken together, these results indicated that when district, logistical, and personnel barriers are evident rural schools also have lowered satisfaction with distance education and perceptions that students' needs are not met via distance education.

To our knowledge, this study is the first to examine rural schools' barriers to distance education and the relationship of those barriers to other factors. As small and low income rural schools may need to use distance education to overcome staffing issues and other constraints that could limit their ability to meet students' academic needs, clarifying these barriers and related factors may ultimately facilitate their access to and effective use of distance education. While a main focus of the current study was the relationships between barriers and related factors, findings regarding the prevalence of different barriers and how these compare to prior research are notable.

Barriers categorized as district barriers were most frequently reported by rural schools. Specifically, distance education not being needed for curriculum requirements, not having sufficient funding, and not being a district priority were three of the most common barriers reported overall. This was similar to prior research demonstrating that barriers in postsecondary settings include organizational support (e.g., Berge et al., 2002; Cho & Berge, 2002), a lack of strategic planning and a common vision (Zirkle, 2004), funding (Lewis et al., 1997; Zirkle, 2004), and administrative support (Murphy & Terry, 1998). However, our results stand in contrast with one study that found costs were less of a barrier (Murphy & Terry, 1998). Our results indicated that logistical barriers were the next most frequent type of barriers in rural schools. In particular, scheduling problems was the third most often cited barrier. Most likely scheduling problems arise when using synchronous distance education delivery. School systems that are on a slightly different time schedule are not able to participate in the same synchronous course because the class time would start at different times in the different schools. Scheduling is likely less of a problem with asynchronous delivery. Distance education being difficult to implement and difficulty finding courses were barriers in some rural districts. Others have reported that similar types of barriers are often apparent including course availability and scheduling (Miller & Miller, 2000; Zirkle, 2002).

Personnel barriers were, as a group, the next type of barriers most apparent in rural schools. Furthermore, not having personnel trained to use distance education was among the five barriers reported most often. Others have found similar barriers in postsecondary agricultural education (e.g., Miller & Miller, 2000; Murphy & Terry, 1998). It also seems worth notice that over 30% of participating rural districts reportedly did not have personnel to support distance education. Few rural districts reported lacking the technical expertise needed, but this has been a barrier in postsecondary settings (e.g., Berge et al., 2002; Zirkle, 2002, 2004). Finally, technology barriers (e.g., lack technology enhanced classrooms, maintenance, or sufficient connectivity) were rarely barriers for small and low income rural schools, which was also consistent with prior research (Lewis et al., 1997; Murphy & Terry, 1998).

The results of this study also extended previous research by identifying relationships between distance education barriers and other relevant factors. In terms of district barriers, distance education not being part of a strategic plan was the only such barrier that related to size and RLIS designation. District barriers were more consistently related to course subject, delivery format, satisfaction with distance education, and distance education meeting students' needs. In particular, district barriers were generally related to lowered use of any course subject or delivery format as well as lowered satisfaction with and degree that distance education met students' needs.

Similar to results concerning district barriers, only one logistical barrier (i.e., distance education being difficult to implement) was related to size and RLIS designation. In addition, this logistical barrier was consistently related to course subject, delivery format, student preparation, course completion, satisfaction, and degree distance education met students' needs. Interestingly, distance education being difficult to implement was associated with higher use of asynchronous formats but less use of synchronous formats. These findings may indicate that rural schools were more likely to use asynchronous formats because these are less difficult to implement. Quite possibly this is an artifact of issues associated with scheduling distance education classes in synchronous delivery modes. Also, rural schools may be less likely to use synchronous formats as these may be more difficult to implement. Moreover, some of the strongest relationships evident in the current study were the associations between the logistical barrier regarding implementation difficulty and lowered student preparation, satisfaction with distance education, and degree distance education met students' needs. This logistical barrier was also the only barrier that related to course completion.

Within personnel barriers, not having personnel trained to support distance education demonstrated the most consistent relationship to other factors. Specifically, this barrier was related to size and RLIS designation. Not having personnel trained to support distance education was also consistently related to lower use of any course subject, but only for synchronous delivery formats. This barrier was related to lowered student preparation, satisfaction with distance education, and degree distance education met students' needs as well. Not having personnel available to support distance education was also related to district size, RLIS designation, and percent of African American students. Thus, not having personnel trained or available to support distance education were less often barriers but correlation results suggested these may be quite important given their associations to other key variables.

Technology-related issues were not only infrequently a barrier but also rarely related to other factors. Nonetheless, lacking technology enhanced rooms was related to less use of synchronous delivery formats. In addition, insufficient connectivity was associated with more use of synchronous delivery formats. These findings may indicate that rural schools are less apt to use synchronous formats (e.g., videoconferencing, cable or satellite TV) when lacking technology enhanced rooms as these are needed for such formats, whereas rural schools may be more apt to use synchronous formats when connectivity is a barrier as this could prevent the use asynchronous formats that are largely Internet-based (e.g., web-based courses, e-mail).

This study found a similar pattern of results was apparent across several types of barriers. Specifically, some district barriers (i.e., distance education was not a district priority and not part of a strategic plan), a logistical barrier (i.e., distance education is difficult to implement), and a personnel barrier (i.e., personnel not trained to support distance education) related to course subject, delivery format, student preparation, satisfaction with distance education, and degree distance education met students' needs. Even though these demonstrated a consistent relationship, a key limitation of the study tempers the conclusions that may be drawn from these results.

Perhaps the most important limitation in this study is that causality and directionality cannot be directly inferred from the data because this study was correlational and crosssectional. For example, distance education being difficult to implement was related to lower rates of course completion. It may be that when distance education is difficult to implement this leads to lower course completion by students. Conversely, lower rates of course completion may cause respondents to perceive and report that distance education is difficult to implement. The data in this study do not allow for determination of this causality; they only establish a relationship.

Other limitations relate to measurement issues. Barriers were captured by dichotomous measures in order that the survey could be completed in a relatively short time period. This limited statistical power and likely attenuated the strength of relationships. Second, the data were primarily self-reported. Specifically, district administrators or other appropriate personnel reported data in the telephone survey. Such data may involve memory inaccuracies, perception biases, and social acquiescence.

Finally, there were some limitations regarding analyses. The analyses primarily involved correlations, and the size of many correlations was small. However, it is also likely that these correlations were attenuated by the dichotomous nature of the barriers data. These issues also limited the use of multivariate analyses (e.g., regression analyses), which may have allowed analysis that would be more explanatory. Nonetheless, results from the current study hold some important implications and suggestions for future research.

There are some policy implications stemming from the frequency that different barriers were evident. In general, district barriers were most apparent, suggesting that policies should be enacted to address such issues. More specifically, results indicated that a lack of funding and distance education not being a district priority were among the most common barriers. Policymakers may help rural schools overcome these barriers by enacting policies and providing funds intended to support and stimulate use of distance education in small and low income rural districts. In addition, district characteristics (e.g., district size, RLIS designation, percentage of African American students) were related to personnel barriers. This suggests that policies may also need to address these specific personnel issues in particular types of rural schools.

Perhaps the most broad and significant implication of this study is that results indicate that several barriers are related to other important factors that may affect small and low-income rural schools effective use of distance education (e.g., satisfaction, student preparation). This implies that when barriers are present efforts to implement or to improve distance education should determine the presence of other related factors and seek to manage these effectively. These results support conclusions of others that prior to implementation of distance education there should be consideration of each setting's distance education capabilities and their learners' needs as well as necessary modifications made as needed (Cho & Berge, 2002). Given that a majority of small and low income rural schools are already using distance education (Hannum et al., 2009), the thorough analysis suggested by Cho and Berge (2002) may also be needed to identify factors that could be targeted to improve the effective use of distance education.

Future research should consider other potentially related factors in order to better understand the conditions under which barriers to distance education are apparent and perhaps from which these barriers emerge. Subsequent research should explore multiple sources of information and perspectives regarding barriers to distance education, not just the perceptions of district administrators. This could include, for example, school observations and interviews with other school staff (e.g., teachers, guidance counselors), students, and parents. The use of Likert-type scales to capture barriers could provide more measurement variance and perhaps a more nuanced understanding of the degree to which various barriers are apparent in small and low income rural schools. This may also allow future research to address explanatory questions and employ multivariate analyses. Finally, the use of longitudinal designs and other analytic techniques, such as structural equation modeling, may provide more information about potential causal relationships. Distance education is a growing phenomenon that offers considerable promise for expanding educational opportunities, especially for rural high schools. It is important to conduct additional research aimed at understanding the role barriers play in limiting distance education use.

This work was supported by a Research and Development Center grant (R305A04056) from the Institute of Education Sciences to the National Research Center on Rural Education Support.