Introduction
Distance education is playing an ever-growing role in the education industry. As such, it is prudent to explore and understand driving conditions that underlie this growth. Understanding these drivers and their corresponding concerns (Table 1) can help educators in the distance education field better prepare for the industry.
background
Distance education’s primary driver is that it is the major growth segment in the education industry. In 1999, nearly 80% of the public, four-year institutions and over 60% of the public, two-year institutions offered distance education courses. Over 1.6 million students are enrolled in distance courses today. Over 90% of all colleges are expected to offer some online courses by 2004 (Institute of Higher Education Policy, 2000). Corporations envision online training warehouses saving large amounts of training dollars. Combined, the virtual education market and its sister market, corporate learning, are predicted to grow to over $21 billion by the end of 2003 (Svetcov, 2000).
A second major driver is employer expectations. Fundamental job market expectations are changing. Today, employees are not expected to stay in the same job for long periods of time; 20-plus year careers are not expected. The current modes of careers include multiple careers, combinations of part-time work in multiple jobs, telecommuting, leaving and re-entering into the full-time work force, switching jobs, and so forth, and today’s employee easily accepts the need to maintain a level of knowledge current with the career demands (Boyatzis & Kram, 1999). To complement these changes in employer expectations, employees have begun to accept the need for life-long learning.
A third driver is the profit potential. Cost savings may be obtained and if significant enough may drive up demand and costs may be lowered. For example, elective classes that do not have enough students enrolled in them on-campus may pick up enough distance students to make teaching the course more feasible (Creahan & Hoge, 1998). A final driver is the institution’s mission. Most educational institutions serve a geographical region, either by charter or mission, and a distance-learning program may be a practical method to help satisfy this strategic mission (Creahan & Hoge, 1998).
However, the “commercialization” of education raises its own concerns about the basic process of learning (Noble, 1999). For example, are there any problems fundamental to the distance environment because of limited social interaction?
Retention may be one such problem. Carr (2000) reports a 50% drop-out rate for online courses. Tinto (1975) compared the learning retention of distance groups with traditional groups and found that the social integration was a key factor in successful retention of traditional groups. Haythornthwaite et al. (2000) think they found another one.
They looked at how social cues such as text without voice, voice without body language, class attendance without seating arrangements, and students signing in without attending Internet class impacted students “fading back.” They found that the likelihood of students “fading back” is greater in distance-learning classes than in face-to-face classes. From the United Kingdom, Hogan and Kwiatkowski (1998) argue that the emotional aspects of this teaching method have been ignored. Similar concerns are raised from Australia, where technology has been supporting distance- teaching for many years, as Hearn and Scott (1998) suggest that before adopting technology for distance teaching, education must acknowledge the social context of learning. Finally, two other factors, trust and isolation, have been researched by Kirkman et al. (2002), whereby communication helped improve the measures of trust in students using the virtual environment.
| Table 1. Influences on the distance education industry | |
| Drivers | Concerns |
| Growth segment in education industry | Retention |
| Job market expectations | Fading Back |
| Life-long learning as an education paradigm | Less social learning |
| Profit center for educational institutions | Trust & isolation |
| Possible strategic competence | Impact of technology |
By definition, the paradigm of distance education changes the traditional education environment by expanding it to cover geographically dispersed learning. In turn, this means that students will probably respond differently to this environment than they do to the traditional classroom. In addition, academic researchers have always been interested in explaining how people react to the introduction of technology. This body of work can be useful to the distance education environment.
Poole and DeSanctis (1990) suggested a model called adaptive structuration theory (AST). The fundamental premise of the model is that the technology under study is the limiting factor or the constraint for communication. It further proposes that the users of the technology, the senders and the receivers, figure out alternative ways to send information over the channel (technology). A good example here is how a sender of e-mail may use combinations of keyboard characters or emoticons (i.e., :) – sarcastic smile, ;) – wink, :o – exclamation of surprise) to communicate more about their emotion on a subject to the receiver.
Ultimately, the key to realizing the potential of distance education is trading off the benefits and the concerns to produce a quality product. In the new Malcolm Baldridge evaluation criteria, companies are asked to better show a program’s effectiveness through customer satisfaction. In turn, Gustafsson et al. (2000) show customer satisfaction linked significantly to quality at Volvo Car Corporation. Finally, in their more broad analysis of well-run companies, Peters and Waterman (1982) deemed customer satisfaction as a key factor contributing to the companies’ performance.
With these perspectives in mind, we suggest that these areas interact to identify satisfaction as one important measure of quality for distance education programs. Therefore, one of the key factors to a program’s success will be the satisfaction of one of its key stakeholders – its students. If one can identify what helps satisfies students in a distance education environment, one has a better chance to develop a successful program.
Table 2. Questions that correlate significantly to satisfaction
| ID | Question Statement | Correlation Coef. Sign. | |
| 16 | I was satisfied with the content of the course | .605 | .000 |
| 17 | The tests were fair assessments of my knowledge | .473 | .000 |
| 18 | I would take another distance course with this professor | .755 | .000 |
| 19 | I would take another distance course | .398 | .000 |
| 20 | The course workload was fair | .467 | .000 |
| 21 | The amount of interaction with the professor and other students was what I expected. | .710 | .000 |
| 22 | The course used groups to help with learning | .495 | .000 |
| 23 | I would like to have had more interaction with the professor. | -.508 | .000 |
| 26 | The course content was valuable to me personally | .439 | .000 |
| 28 | Grading was fair | .735 | .000 |
| 30 | Often I felt “lost” in the distance class | -.394 | .000 |
| 31 | The class instructions were explicit | .452 | .000 |
| 33 | Feedback from the instructor was timely | .592 | .000 |
| 34 | I received personalized feedback from the instructor | .499 | .000 |
| 36 | I would have learned more if I had taken this class on-campus (as opposed to online) | -.400 | .000 |
| 37 | This course made me think critically about the issues covered. | .423 | .000 |
| 38 | I think technology (email, web, discussion forums) was utilized effectively in this class | .559 | .000 |
| 39 | I felt that I could customize my learning more in the distance format | .254 | .001 |
| 42 | The course content was valuable to me professionally | .442 | .000 |
| 43 | I missed the interaction of a “live,” traditional classroom | -.341 | .002 |
| 46 | Overall, the program is a good value (quality/cost) | .258(1) | .017 |
| LOHITECH | Aggregate of Yes votes in Q6 through Q15 | .270(1) | .012 |
| (1) While significant, the low correlation coefficient below .300 should be noted | |||
The Research Study
The distance program used in this study is one of the largest, online, AACSB-accredited MBA programs in the world (US News and World Report, 2001). The methodology used a questionnaire with a battery of 49 questions to gather the data. The questions were developed using the concepts and ideas from literature discussed earlier as a guide.
Once the subject identified his or her reference course, that subj ect’s grade was obtained from administrative records and recorded. In addition, four other demographic questions gathered information on gender, number of courses taken, student status, amount of time expected to spend in the reference course, and the amount of time actually spent in the reference course (Martz et al., 2004).
Two sets of questions were used. The first set asked about the student’s use of different technologies (i.e., chat, e-mail, streaming video, etc.) in the class and if used, how effective (five-point Likert: 1 = LO …. 5 = HIGH) did they believe the technology to be in helping them with the class. We created a new variable, LOHITECH, for analysis purposes. Using LOHITECH, respondents can be placed in one of two groups: one group that reported using three or less technologies, while the second group reported using four or more technologies in their reference class. The second set of questions asked students to rate (five-point Likert: 1 = Strongly Agree …. 5 = Strongly Disagree) their experience with the reference distance course against statements concerning potential influences for satisfaction. These questions associated a five-point rating scale to statements about the issues identified earlier. The order of the questions was randomly determined and the questionnaire was reviewed for biased or misleading questions by non-authors.
The questionnaire was sent to 341 students enrolled in the distance MBA program. In Fall 2002, the program served 206 students from 39 states and 12 countries. The majority of these students are employed full-time. The program used in this study has been running since Fall 1996 and has over 179 graduates. It offers an AACSB accredited MBA and its curriculum parallels the on-campus curriculum. Close to 33% of the enrolled students are female. The oldest student enrolled is 60 years old and the youngest is 22. The average age of all students enrolled is 35. Over 25 PhD qualified instructors participate in developing and delivering the distance program annually. Recently, the news magazine US News and World Report (2001) classified the program as one of the top 26 distance education programs.
There were 131 useable questionnaires returned. The students’ final grade for their reference course was obtained and added to the questionnaire record as a variable. These were separated into two groups: 30 that had not yet taken a course and 101 that had completed at least one course. This second group, those students who had completed at least one course, provided the focus for this study.
research results
Question 24, “Overall, I was satisfied with the course,” was used as the subject’s level of general satisfaction. The data set was loaded into SPSS for analysis. Table 2 shows that 23 variables, including LOHITECH, proved significantly correlated to satisfaction (Q24).
The large number of significant variables leads to the need for a more detailed analysis on how to group them (StatSoft, 2002). Kerlinger (1986, p. 590) suggests the use of factor analysis in this case “to explore variable areas in order to identify the factors presumably underlying the variables”. An SPSS factor analysis was performed with a Varimax Extraction on those questions that had proven significantly correlated to satisfaction. All reliability coefficients (Cron-bach Alpha) are above .7000 and all Eigenvalues are above 1.00, indicating an acceptable level for a viable factor (Kline, 1993; Nunnally, 1978). Finally, the five components explain 66.932% of the variance.
In summary, 22 variables from the questionnaire proved significantly correlated to satisfaction. A factor analysis of those 22 variables extracted five possible constructs. These constructs were labeled: Interaction with the Professor; Fairness; Content of the Course; Classroom Interaction; and Value, Technology & Learning, based upon the key characteristics of the underlying questions. Table 3 shows the results of combining the ratings for the questions in each construct and correlating each of them to satisfaction. As can be seen from the table, the constructs hold up well as five indicators of satisfaction.
Table 3. Correlation of final constructs to satisfaction
| Construct (Component: Loading) | Correlation | Significance |
| Professor Interaction (Q18: .576, Q21: .643, Q33: .794, Q34: .849) | .771 | .000 |
| Fairness (Q17: .722, Q20: .738, Q28: .626, Q31: .512) | .695 | .000 |
| Course Content (Q16: .596, Q26: .850, Q39: .689, Q42: .825) | .588 | .000 |
| Classroom Interaction
(Q23: -.354, Q30: -.514, Q36: -.809, Q43: -.770) |
-.515 | .000 |
| Technology Use & Value (LOHITECH: .508, Q19: .596, Q22: .542, Q37: .494, Q38: .478, Q46: .700) | .624 | .000 |
future trends
As mentioned earlier, the organization, the school in this case, is a key stakeholder in the success of a distance education program. The future success of distance programs depends largely on satisfying these critical success factors. Distance education courses and programs are not only used for providing an alternative delivery method for students but also to generate revenues for the offering unit/college/university. As the number of distance courses and programs increase at an exponential rate, the necessity to enhance quality and revenues also takes prominence. We conclude with a set of operational recommendations that can impact online program success (Table 4).
The data in this study indicate that a timely and personalized feedback by professors results in a higher level of satisfaction by students. The administrators therefore have to work closely with their faculty and offer them ways to enrich the teacher-student relationships. Paradoxically, a faculty member needs to use technologies to add a personal touch to the virtual classroom. For example, faculty should be encouraged to increase the usage of discussion forums, respond to e-mail within 24 to 48 hours, and keep students up-to-date with the latest happenings related to the course.
The data also indicate that good course content and explicit instructions increase student satisfaction in the virtual classroom. It may well be that this basically sets and manages the expectations for the distance student. This result suggests that faculty should have complete Web sites with syllabi and detailed instructions. In turn, this suggests that distance education administrators should focus their attention on providing faculty with support such as good Web site design, instructional designer support, test design, user interaction techniques, and so forth, appropriate for distance learning.
Since distance students’ notion of value intertwines learning and technology, it is imperative that distance administrators offer, and faculty use, the available technology in the distance program. Technology in this case not only refers to the actual software and hardware features of the platform but also how well technology is adapted to the best practices of teaching. The results imply that if technology is available but not used, it lowers satisfaction. So, technology options that are not being used in a course should not appear available. For the program administrator, this would suggest adoption of distance platforms that are customizable at the course level with respect to displaying technological options.
conclusion
This study attempts to identify potential indicators for satisfaction with distance education. A body of possible indicators was derived from the literature surrounding the traditional versus virtual classroom debate. A 49-question questionnaire was developed from the indicators and was administered to MBA students in an established distance education program. One hundred and one questionnaires from students with one or more distance classes were analyzed with the result that 22 variables correlated significantly to satisfaction. A factor analysis of the questionnaire data extracted five basic constructs: Professor Interaction, Fairness, Course Content, Classroom Interaction and Technology Use & Value. Several recommendations for implementing and managing a distance program were extracted from these constructs and discussed.
Table 4. Recommendations to increase online program success
| 1 | Have instructors use a 24-48-hour turnaround for e-mail. |
| 2 | Have instructors use a 1-week turnaround for graded assignments. |
| 3 | Provide weekly “keeping in touch” communications. |
| 4 | Provide clear expectation of workload. |
| 5 | Provide explicit grading policies. |
| 6 | Explicitly separate technical and pedagogical issues. |
| 7 | Have policies in place that deal effectively with technical problems. |
| 8 | Provide detailed unambiguous instructions for coursework submission. |
| 9 | Provide faculty with instructional design support. |
| 10 | Do not force student interaction without good pedagogical rationale. |
| 11 | Do not force technological interaction without good pedagogical purpose. |
| 12 | Collect regular student and faculty feedback for continuous improvement. |
key terms
Classroom Interaction: The interaction that can only be achieved face-to- face in a classroom. For example, the real-time feedback of facial expressions is not (yet) available in a distance course and so would be considered “classroom interaction”.
Concerns of “Commercialization”: The negative factors that the implantation and use of distance education may create.
Course Content: The main themes covered in a course.
Critical Success Factors: The few key areas in which activities must “go right” so that a project of program succeeds (Rockart, 1979).
Exploratory Factor Analysis: A process used to identify statistically significant constructs underlying a set of data.
Fairness: A subjective term defining the level to which a student feels he or she was treated fairly by the professor with respect to the class, including but not limited to test questions, grading, schedule flexibility, and so forth.
Market Drivers for Distance Education: The key elements that seem to be driving the diffusion and usage of distance education in the marketplace.
Professor Interaction: The amount of communication (e-mail, phone calls, video, chat rooms, etc.) that occurs between a student and the professor.
Satisfaction Constructs for Distance Education: Five constructs identified that seem to help identify satisfaction in distance education programs.
Technology Use: The usage of a technology whether it be e-mail, chat rooms, automated tests, software, and so forth.
Technology Value: The user’s benefits (perceived and actual) over the costs (perceived and actual) created by the use of technology.
