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Learning Out of the Box:

Perceived relative advantages of on-line distance learning against the norm!

By

Chad DeFerrari

cdf@mail.sdsu.edu

San Diego State University

Submitted to:

International Communications Association

Panel:

Communication and Technology

November 2, 1998

Distance education programs are designed to fit the needs of the "nontraditional" student. Hoping to meet the demand for more courses that students can take at their own pace, many institutions are turning to "virtual" or on-line courses. Distance education is not necessarily a new trend, but using the Internet as a primary delivery system for college courses is new. The Internet is an innovation itself, but on-line courses can also be considered an innovation within an innovation (Eastmond, 1995; Rogers, 1995). There are a number of advantages for students who take courses on the Internet. Some of those advantages include time and space flexibility, and easier access to course materials.

Purpose of the Study

The purpose of this study is to determine which relative advantages of on-line distance education are perceived as relative advantages by current and potential students and how relative advantage impacts student decisions about taking an online course. This study looks at how traditional students (on campus between the ages of 18 and 25) and nontraditional students (work/professional, have families, geographic separations, etc.) perceive claims of relative advantage in online learning courses. The objective attributes of an innovation determine the range of possibilities of what might be considered relatively advantageous about an innovation. The sub-dimensions of relative advantage include the social prestige, a savings in time and effort, and the immediacy of the reward (Rogers, 1995). On the other hand, some sub-dimensions of relative advantages are preventative. Without the advantage some other attribute of the innovation, those attributes would be considered a disadvantage. Often preventative innovations (innovations that are designed as preemptive) are not as easily adopted because they are not immediately seen as an advantage. However, in this study, the claims of relative advantage and the perceptions thereof, should be obvious to survey respondents. Respondents are able to correlate the benefits of flexibility of scheduling, work, family, perceived cost benefits and convenient access together as being more advantageous than their current situation.

Relative Advantage

Rogers (1995) defines relative advantage as "the degree to which an innovation is perceived as being better than the idea it supersedes" (p. 212). In other words, to a great extent, relative advantages are perceived as an attribute of the potential student rather than a "pure" attribute of the innovation itself. A student’s perception of an advantage is based on need or how the "advantage" can benefit the student. Relative advantage can be expressed as benefits such as economic profitability, social prestige, or—in this case—the ability to learn any time anywhere (Rogers, 1995). One can live in Sacramento and take a course from San Diego State University without having to leave home or pay the cost of moving. With distance learning, students do not have to pay for parking passes or worry about getting to class on time because they cannot find a place to park or the traffic was backed up. According to Rogers (1995), "the relative advantage of a new idea, as perceived by members of a social system (in this case, students), is positively related to its rate of adoption" (p. 216). A relative advantage of on-line distance learning to students is that they can learn anytime, anywhere. Thus, the students who can apply this advantage to their personal lives are more likely to adopt it as an innovation.

The Internet and Virtual Universities

Four years ago, Peterson's College Guide listed 93 "cyberschools;" the 1997 Distance Learning Guide includes 762 (Gubernick & Ebeling, 1997). Robert Tucker, head of InterEd, a research firm that keeps tabs of distance learning courses, estimates that 55% of the United States’ 2,215 four-year colleges and universities have courses available off-site. Over 1 million students are now plugged into the virtual college classroom, which compares with 13 million attending traditional schools (Gubernick & Ebeling, 1997). According to Gubernick and Ebeling (1997), the number of cyberstudents will more than triple by the turn of the century. There are several new motivational factors that many students were not previously required to consider.

There are three motivating factors that contribute to the growth of virtual universities and distance learning courses. First, students must obtain a degree to make a living; second, schools are in competition with each other for students; and third, students are increasingly members of the paid work force rather than full-time students. These working students require school on their time (Witherspoon, 1997; Gubernick & Ebeling, 1997). Cyberlearning is a means of obtaining education for people who can not afford to interrupt a career. Gubernick and Ebeling (1997) state "the beauty of cyberlearning is that you can pursue it while working at a full-time job and living miles from a college" (p. 1).

Methodology

Using survey methodology as suggested by Fowler (1993), this study examines a cross section of students in a random sample. Participants were instructed to give their opinions on various claims of relative advantage and whether or not they would be influenced to take an on-line course based on those claims of relative advantage. The study seeks to determine what are strong motivating factors for taking an on-line course amongst both traditional and nontraditional students. It is posited that claims of relative advantage are directly related to this innovation's rate of adoption.

Research Proposition and Hypotheses

The study seeks answer the following research proposition:

Based on the scale of nontraditionalism, students who are more nontraditional are more likely to state a willingness to adopt on-line distance learning.

The results of the survey tested each hypotheses below. To confirm or disconfirm each of the hypotheses, a nontraditional scale is used. To construct the nontraditional scale, the z scores of the most significant demographic items (age, hours worked, marital status, etc.) are combined. In addition, the mean score of the groups of survey respondents are used. The depth of individual responses are weighed along with the percentage of respondents. For example: Both traditional and nontraditional students are likely to respond to a question positively. However, members of one group are more likely to respond in a strongly positive manner than are the others.

On-line education is making it possible for students from around the world to study at prestigious United States’ schools without leaving their homes (Gubernick & Ebeling, 1997). The positive attributes of on-line learning will outweigh the negative concerns for nontraditional students. Gubernick & Ebeling (1997) write that students who cannot afford to interrupt careers are now able to excel in education through on-line distance learning. Students value the fact that it gives them control of when they study and take classes, while at the same time it tests their competency rather than their personal seat time. However, traditional students will be less likely to state a willingness to adopt because of factors explored in the propositions below.

Most professors would agree that interacting with students in very small classes produces excellent results. The ability to discuss issues, dissect problems, work through questions and engage in free association is far better with a small group of learners than in a classroom containing a large number of students. Face-to-face interactions permit a professor (leader, facilitator) to rapidly change the direction of the discussion and to directly satisfy the needs of the learners (Bourne et al., 1997). Currently on-line courses are not easily able to adapt to a change in material as with traditional classrooms.

When students and instructors can meet together in small groups, are continuously accessible to each other in a face-to-face setting, and cost is not a concern, on-line courses would not likely be the instructional paradigm of choice (Bourne et al, 1997). In on-line courses the student is socially isolated.

The potential of asynchronous learning networks for changing the way education is delivered and the way people learn is tremendous (Bourne et al, 1997). Out of necessity of time or location, nontraditional students are more readily acceptable to use an innovation that will solve their problem of not being able to sit in a seat on a campus. They are more likely to be accepting of its shortcomings to allow themselves to continue with their education. Traditional students are more accustomed to the traditional time schedule involved in education.

On-line distance learning is not only a more flexible method of learning in time and space, but on-line learning is also cost effective. "The cost effectiveness of ALNs is hypothesized to be much greater than the traditional lecture or laboratory model of instruction" (Bourne et al., 1997). Not only does on-line learning save money for the school by cutting reproduction cost, but the student also saves time and travel. According to Jain (1997), "The main advantage of this system (distance learning) is the savings in travel costs and time" (p. 3). Economically on-line distance learning is more likely to appeal to nontraditional students than to traditional students.

Importance of Study

The importance of this study is to help educators better understand and study the perceived advantages of specific attributes of on-line learning. A social trend in the modern workplace is to communicate and work with computer technology (Gubernick & Ebeling, 1997). E-mail, chat rooms, bulletin boards, and websites are all part of the new company workplace. As Marshall McLuhan (1968) was fond of saying "the medium is the message." McLuhan (1968) predicted the rise of a "global village" (Doyle, 1997). This was to be a world that was linked by technology, effectively shrinking distance through a medium. With today’s satellite technology, Internet, and elaborate telephony system, a single person can reach to the other side of the world in a matter of seconds. "The explosive development of telecommunications and computers, particularly during the last decade, has homogenized many of life’s common experiences into what demographers call a ‘global lifestyle’" (Doyle, 1997, p. 78). Why should education not follow the same path as the professional workplace? Why should the "global village" not extend to learners around the world?

This study serves as an exploratory study to precede future experimentation on the predicted rate of adoption or the willingness to adopt. From this study educators should have a better idea of what advantages are important to students (traditional and nontraditional). With this knowledge educators can better serve students by designing learning modules for on-line courses that are suited to the needs of the student. This study seeks to enhance the educational process for all students who are likely to use the Internet as a means of bettering their future through on-line courses.

History of Distance Education

Education is a resource for increasing personal income, obtaining information, and as a base for networking. Distance education is often the only opportunity for nontraditional students to obtain a degree. Recently distance education has benefited from computer-based learning through the Internet. However, distance education started several hundred years ago with correspondence education. Later, in the 1920s, radio broadcasting became the first electronic means of distance education, followed by television and now on the Internet.

Just as the current trend in business is to consolidate telecommunication industries, education has begun to blend electronic communication and distance learning. The Internet incorporates audio, visual, text, and mail. On-line distance learning is a more efficient means of delivering education to those who have limitations on their ability to attend traditional classes. While the means of delivery is new, the roots of on-line distance education started with correspondence education.

Correspondence Education

Several historians have suggested that early cave paintings, St. Paul’s letters to the Corinthians, and tribal talking drums are forms of distance education (Mood, 1995). However, most historians trace the roots of distance education to correspondence education (Mood, 1995). In the United States, correspondence education could not be established reliably until the postal service had been constructed, which provided the basic system of transport for students’ tests, papers, and lessons to and from back and forth to the teacher.

Some early efforts of correspondence education using the new technology of regular mail date back to an advertisement in the Boston Gazette on March 20, 1728 (Mood, 1995). In the advertisement, a local teacher, Caleb Philips, offered to send weekly shorthand lessons to prospective students (Mood, 1995). Later, in 1840, Sir Isaac Pitman, began teaching correspondence courses by mail (Phillips, 1998). For nearly two hundred years, correspondence education was the primary means of delivering distance education in the United States. Over time, correspondence education became more refined and better managed.

In 1891, Thomas J. Foster provided pamphlets by mail to teach mine safety (Mood, 1995). He organized a tutoring staff who helped grade assignments. Earlier still, Anna Eliot Ticknor organized a correspondence school: "Society to Encourage Studies at Home," based in Boston Massachusetts (Mood, 1995, p. 1). Ticknor’s school offered instruction in 24 subjects organized within six departments: history, science, art, literature, French, and German (Mood, 1995). Many of her students were young women, confined to the home because of the conventions of a predominately patriarchal society (MacKenzie & Christensen, 1971).

In the early years, entrepreneurs who worked alone dominated distance education. However, it was not long before organized formal education institutions began to enter the market for correspondence education. Universities in Great Britain, such as Oxford and Cambridge, began to develop extension services in the mid-nineteenth century. These services included not only traveling lectures, but also a system of correspondence education (Mood, 1995

Modern Correspondence Education

While some researchers suggest Pitman, the father of shorthand, pioneered correspondence education (Phillips, 1998), other scholars identify William Rainey Harper as the father of modern correspondence teaching (Mackenzie & Christensen, 1971; Mood, 1995). Harper helped organized the Chautauqua College of Liberal Arts in the early 1880s, then went on to establish the first university correspondence teaching department when he became the president of the University of Chicago (Mackenzie & Chrsitensen, 1971; Mood, 1995). Harper said "the day is coming when the work done by correspondence will be greater in amount than that done in the classrooms of our academies and colleges; when the students who shall recite by correspondence will far outnumber those who make oral recitations" (Mackenzie & Christensen, 1971, p. 7). Harper was visionary in his predictions about thoughts on the future developments of correspondence learning.

Population and Sample

The sample used for this study included subjects from various locations around San Diego. Approximately half of the subjects were surveyed at San Diego State University on July 29, 1998 and July 30, 1998. The other half of the subjects were surveyed at Mission Valley Mall and Fashion Valley Mall in San Diego on July 31, 1998. The locations were chosen because of the likelihood of gathering information from specific categories of subjects: traditional and nontraditional students. The academic location is more likely to have traditional students answering the surveys, while the mall locations are more likely to have a greater population of people who fit the non-traditional student profile. There were a total of 216 completed questionnaires. Seventeen surveys had to be thrown out because of incomplete data. The survey respondents consisted of 44% men and 56% women. The average age of the respondents was 25 years old. Age ranged from 18 to 73 years.

Factor Analysis

The computer was first used to factor analyze all questions regarding the five constructs of relative advantage. Seven factors identified through the exploratory factor analysis (each with an eigenvalue greater than 1.0) were selected as the best data reduction strategy for constructing scales of relative advantage. The factors were carefully interpreted and labeled as Temporal, Positive Financial, Social Ambiance, Convenient Access, Negative Financial, Mandatory Access, and Social Isolate (see Table 1).

Temporal Factor

The Temporal Factor was made up of eight items (see Table 1). Each item was conceptually related directly or indirectly to time. There were 198 valid cases used in the computation of this factor. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the mean of all eight items most heavily loaded on the Temporal Factor) was .83.

Positive Financial Factor

The Positive Financial Factor strongly indicates the perceived economic benefit of on-line learning. Rogers (1995) lists economic benefit as one of the strongest relative advantages of an innovation. It positively relates to the projected rate of adoption, which will be discussed later in this chapter. This factor is made up of four items (see Table 1). Each item in this factor includes statements that suggest that on-line distance learning is less expensive than traditional learning. There were 200 valid cases used in the computation of this factor. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the mean of all four items most heavily loaded on the Positive Financial Factor) was .82.

Social Ambiance Factor

The Social Ambiance Factor was made up of five items (see Table 1). Each item in this factor suggests that traditional college ambiance is significantly important to the learning experience. There were 212 valid surveys used in the computation of this factor. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the mean of all five items most heavily loaded on the Social Ambiance Factor) was .79.

Convenient Access Factor

The Convenient Access Factor was made up of three items (see Table 1). As detailed in Chapter IV, this construct is significant to the projected adoption of the innovation. There were 214 valid surveys in this factor. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the mean of all three items most heavily loaded on the Convenient Access Factor) was .55. The original access construct in the design of the instrument came out split in the factor analysis. This type of access promotes the use of the Internet in a variety of different ways.

Negative Financial Factor

The next 3 factors were split from their original constructs. They are barely relevant, since there are only two items in each factor. They should be considered different levels of the original construct rather than new constructs. The Negative Financial Factor had to be re-coded so that 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, and 7=1. Thus, a negative response was positively correlated to the adoption of the innovation. There were 209 valid cases used in the computation of this factor. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the two items most heavily loaded on the Negative Financial Factor) was .58.

Mandatory Access Factor

The Mandatory Access Factor has only two items (see Table 1). This construct is a derivative of convenient access, but is not statistically significant to the overall projected adoption (see Table 4). There were 214 valid cases used in the computation of this factor. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the mean of the two items most heavily loaded on the Mandatory Access Factor) was .44. The construct suggests that respondents have no other option to obtain higher education.

Social Isolate Factor

The final construct in the factor analysis for this survey is the Social Isolate Factor. This factor is made up of two items, which in the original design was part of the Social Ambiance construct. There were 215 valid cases used in the computation of this factor. The items test the extreme social ramifications of on-line distance learning. Respondents who agree with the statements are likely to be more willing to adopt on-line distance learning as an innovation. The Cronbach’s alpha reliability coefficient for this scale (an additive index created by computing the mean of the two items most heavily loaded on the Social Isolate Factor) was .35.

Projected Adoption

The projected adoption construct of the survey was factored separately. The original projected adoption construct was designed with five statements regarding the different levels of perceived adoption. That is, items were designed to measure differing probabilities of the likelihood that the respondent would or would not adopt on-line courses as an educational innovation. One item had low factor loadings across both factors. That item stated: "I feel I need more information before I could commit to an on-line course." Because of its poor performance as an indicator of either construct embedded in the two-factor solution, that item was excluded from all subsequent analysis. The factor analysis procedure in SPSS was used again to force a single-factor solution, utilizing the four remaining items. The resulting single-factor solution permitted construction of an additive scale (the mean of the four items with high loadings on the factor) with a reliability coefficient of .64 (Cronbach’s alpha). There were 214 valid cases used to compute the factor solution. This factor solution constitutes the operationalization of the key dependent variable in the present study: projected adoption of the innovation of on-line courses.

Findings

As predicted, nontraditional students are more likely than traditional students to perceive on-line distance learning as relatively advantageous over traditional forms of instruction. Computer statistical analysis, using SPSS 6.1 for Macintosh, shows a linear increase in levels of projected adoption as nontraditional characteristics of students increase. Additional data analysis (shown in the tables below) provide a post hoc analysis of market advantages of on-line learning among nontraditional students.

Characteristics of the Sample

The respondents range from age 18 to 73, with an median age of 25 (rounded to the nearest year). The respondents average 38.2 hours of work per week. Some 84 percent of the respondents have cars. Most of the respondents are not married; 23.8 percent are married, 76.2 percent are not married, and 2.3 percent were missing values. A majority of the respondents do not have children under the age of 18 living with them. Only 8.8 percent of the respondents have one child in the home, 5.6 percent have to in the household, 2.3 have three or more children in the home, and 82.9 have no children under 18 years of age at home. Some 58 percent of the respondents are currently enrolled in college, of those 95 percent are taking courses to obtain a degree of some sort. A majority of respondents have at least some experience with the Internet. Approximately 85% say they have surfed the Internet. The average time spent on the Internet weekly is one hour.

Confirmation or Disconfirmation of Hypotheses

Using the Nontraditional Student Scale, the hypotheses below were tested for confirmation or disconfirmation. The Nontraditional Student Scale is based on the combined sums of the z-scores of Age, Marital Status, Number of children under 18 living in the household, and number of work hours per week. This study had an adequate sample of 216 respondents, so a 95 percent decision rule was utilized.

This hypothesis is confirmed at a 99.9% level of confidence. Students with higher scores on the Nontraditional Student Scale are more likely than students with low scores on the scale to state they are willing to adopt on-line distance learning. The Pearson correlation coefficient is .26, based on 168 valid cases (see Tables 3 and 4).

Social ambiance posts a -.25 Pearson correlation coefficient based 168 valid cases (see Tables 3 and 4), indicating students that score lower on the Nontraditional Student Scale perceive traditional on-campus ambiance as an advantage. Thus, hypothesis two is confirmed at a 99.9% level of confidence. It can be assumed, based on the questions in the survey, that respondents who scored higher on the Nontraditional Student Scale are more comfortable with adapting to the social isolation of on-line distance learning (see Table 4).

This hypothesis is confirmed at a 99.9% level of confidence, that students with higher scores on the Nontraditional Student Scale are more likely than students with low scores on the scale to state they are willing to adapt to the social isolation of on-line learning. The Pearson correlation coefficient is .23 based on 168 valid cases (see Tables 3 and 4).

Hypothesis four is confirmed with a 99.9% level of confidence, that students with higher scores on the Nontraditional Student Scale are more likely than students with low scores on the scale to perceive relative advantages in distance learning based on their need for the flexibility of on-line learning. This hypothesis is confirmed by using the Temporal Factor as a measure of flexibility since it is inclusive of Time and Space (location) flexibility, as well as the Social Isolate Factor. The Social Isolate Factor is representative of solo work. Thus, the respondent has only to work around his or her schedule. The Pearson correlation coefficient for the Temporal Factor is .15, based on 168 valid cases (see Tables 3 and 4). The Pearson correlation coefficient for the Social Isolation Factor is .26, based on 168 valid cases (see tables 3 and 4).

Hypothesis 5 is partially confirmed. The positive financial construct posts a .03 Pearson correlation coefficient based on 168 valid cases, which is insignificant. However, the negative financial construct posts a -.27 Pearson correlation coefficient also based on 168 valid cases (see Table 4). The data suggests a weak relationship between students who post a higher score on the Nontraditional Student Scale are more likely to perceive positive financial benefits from on-line distance learning. The disconfirmation is subject to three interpretations: 1) the theory is wrong, 2) the operational measure is unreliable and invalid as a measure of the construct, or 3) both theory and operationalization are inadequate. Further research is necessary to make a final determination. However, since the theory withstood the other six tests, operational problems seem the more plausible explanation for disconfirmation of the hypothesis.

Yet, students who post lower scores on the Nontraditional Student Scale are more likely to perceive negative financial benefits from on-line distance learning. The Negative Financial Factor is significant with a 99.9% level of confidence thus, indicating a strong perception on behalf of traditional students that on-line distance learning cost more than traditional education.

Summary

Of the five relationships tested, all were found to be statistically significant. The four confirmed hypotheses are: (H1) Nontraditional students are more likely than traditional students to state that they are willing to adopt on-line distance learning; (H2) Students with high scores on the Nontraditional Student Scale are less likely to value the ambiance of traditional on-campus instruction than students with low scores on the Nontraditional Student Scale; (H3) Nontraditional students are more likely than traditional students to be willing to adapt to the social isolation of on-line learning; and (H4) Nontraditional students are more likely than traditional students to perceive relative advantages in distance learning based on their need for the flexibility of on-line distance learning. This indicates that students who are score higher on the Nontraditional Student Scale perceive more overall advantages to on-line learning. That is not to say that traditional students do not perceive advantages to on-line learning, but that there is more statistical significance to students who are nontraditional in nature.

The single hypothesis that is only partially confirmed is hypothesis five: Nontraditional students are more likely than traditional students to perceive on-line distance learning as having positive financial attributes, whereas traditional students are more likely than nontraditional students to perceive on-line courses as having negative financial attributes. Nontraditional students do not necessarily perceive on-line distance learning to be less expensive than traditional courses. The data does suggest that nontraditional students may perceive on-line distance learning to be less expensive, but the data is insignificant, thus, invalid. A larger sample may show a more significant correlation for nontraditional students’ perception regarding the cost benefits of on-line distance learning. The implications of these results will be discussed in Chapter V. The post hoc analysis, which follows this section, delineates relationships detected in this study’s research findings that were not proposed in this study’s original theoretical model.

Post Hoc Analysis

There are several relationships to projected adoption of on-line distance learning that have become apparent through the post hoc analysis of data collected in this study. Age is a significant predictor of projected adoption. The original metric variable (recorded to the nearest year) was recoded so that "age" was split into a two-way categorical variable and a five-way categorical variable. Using 199 valid cases, the two-way variable showed respondents between 18-25 years of age as more "traditional," whereas students ages 26-73 years of age were more "nontraditional." (see Table 7). Split five ways, moreover, age showed very similar results, suggesting that the older a respondent was the more likely he or she was to adopt. To split "age" five ways, cumulative percentages were used to produce five age categories with comparable numbers of cases in each category (see Table 7).

Conclusions and Recommendations

Using the five benefits of relative advantage as a means of testing traditional and nontraditional students’ reactions to on-line distance learning, the results show that students who are more nontraditional are more likely to adopt the innovation of on-line distance learning. The findings confirm that nontraditional students are more likely to adopt on-line distance learning. The Pearson correlation coefficient for hypothesis one is .26, based on 168 cases of current and potential students, arranged along a continuum that measures "nontraditionalism" among college students. Hypothesis one states that nontraditional students are more likely than traditional students to report that they are willing to adopt on-line distance learning. The positive value of the Pearson correlation coefficient indicates respondents who scored higher on the nontraditional scale were more likely to adopt. The nontraditional scale is a metric that combines separate measures of nontraditional attributes of students (age, marital status, number children under 18 who are in the household, and the number of hours worked per week), summed together on the basis of their z-scores. With zero as the mean, any positive score on the scale indicates above-average "nontraditionalism;" any negative score on the scale indicates above average "traditionalism" (i.e., below-average "nontraditionalism").

The findings also confirm that perceived relative advantage is a powerful indicator of projected adoption. The overall projected adoption table (Table 4) shows the individual constructs of factor analysis and their relationship to projected adoption. There are varying levels of significance for relative advantage constructs. This study found that the construct of convenient access (i.e., Convenient Access Factor at the operational level) was the most desirable perceived relative advantage. The score for the Pearson correlation coefficient of the Convenient Access Factor is .56. The next most significant construct is Temporal Factor with a correlation of .50 with projected adoption. The Temporal Factor is inclusive of perceptions of both time and space flexibility. Those constructs are also linked with a certain amount of cross compatibility at the conceptual and operational level. The items within the Temporal Factor have the highest factor loading in that construct, but may be also loaded in other constructs (see Table 1). Institutions that are looking for ways to market their on-line courses can use this information to increase perception of more specific relative advantages. In this case, promoting on-line courses with marketing messages that reinforce convenient access and time-and-space flexibility should yield higher enrollments (adoption of on-line distance learning). However, the data shows that mandatory access is not significantly related to rate of adoption.. The construct, as measured by only two items on that factor, has a weak reliability at the operational level, so the disconfirmation is subject to three interpretations: 1) the theory is wrong, 2) the operational measure is unreliable and invalid as a measure of the construct, or 3) both theory and operationalization are inadequate. Further research is necessary to make a final determination. However, since the theory withstood the other six tests, operational problems seem the more plausible explanation for disconfirmation of the hypothesis.

The findings also show that prior computer experience with the Internet does indeed influence a respondent’s willingness to adopt. It is conceivable that prior experience yields a certain amount of comfort with regard to the Internet. The data shows that nontraditionalism combined with prior Internet experience together account for about 10% of the variance in projected adoption. Thus, students who have used the Internet are more likely to adopt on-line distance learning, because are more likely to recognize the relative advantages of using the on-line distance learning courses (the "anytime, anywhere" advantages) while, at the same time, do not have to overcome the disadvantage of learning the arcane technology of the Internet. Also, those respondents who report frequently accessing the Internet already have the financial and hardware/software resources to use the Internet. Thus, the psychological, technological, and financial "costs" of accessing the Internet have already been "paid" by these potential adopters.

The disadvantages of on-line distance learning included, as predicted, the lack of traditional college ambiance. The Social Ambiance Factor correlated with projected adoption with a -.25 coefficient. The negative score indicates that students who are more traditional in nature find the ambiance of a campus more important than do nontraditional students. However, the "social isolate" construct correlated with projected adoption at .23. Social isolation means a lack traditional college ambiance. The positive score indicates that nontraditional students are more likely to expect the isolation of on-line learning, than traditional students. Reasons for this response are complex, but younger, more-traditional students may have a higher "comfort level" with traditional methods of learning. Traditional students are less likely to have families and jobs; thus, they lack a social support system. Nontraditional students tend to be older and have families and jobs. Thus, nontraditional students are less likely to need the social support system that traditional students need.

The findings also show a strong relationship between age and projected adoption. The original metric variable (recorded to the nearest year) was recoded so that "age" was split into a two-way categorical variable and a five-way categorical variable. Using 199 valid cases, the two-way variable showed respondents between 18-25 years of age as more "traditional," whereas students ages 26-73 years of age were more "nontraditional." (See table 6). Age, split five ways, showed very similar results, suggesting that the older a respondent was, the more likely he or she was to adopt. The older respondents are more likely to be willing to adopt on-line distance learning than younger students are. This can be useful information for adult education courses. Typically schools that focus on adult education have an abundance of nontraditional students.

In this regard, the present study provides some useful and unexpected information regarding the adoption characteristics of Internet users. That is, the highest level of Internet usage is reported among respondents in the 24-28 year category (6.62 hours per week). The next highest levels of Internet usage are reported by respondents aged 29-40 (5.83 hours per week) and those aged 41-73 (5.84 hours per week). The lowest levels of usage in the sample are reported by younger respondents in the 21-23 year category (4.73 hours per week) and the 18-20 year category (3.03 hours per week). Thus, the negative relationship between age and the adoption of computer innovations does not hold up for the Internet in the San Diego area. In fact, removing the influence of Internet usage does little to reduce the positive relationship between age and adoption of on-line distance learning. The belief that "fear of technology" among older potential students depresses adoption of on-line distance learning over the Internet among these nontraditional students is not supported by the data. With the exception of the "computer smart" cohort of 24-28 year olds, the older, non-traditional (potential) students post the highest levels of Internet usage.

The relationships between Internet usage, projected adoption, and gender are also revealing. Indeed, as expected, men report significantly higher levels of Internet usage than do women (men=6.75 hours per week; women=3.94 hours per week). However, with regard to projected adoption, men are only slightly more likely than women to adopt on-line distance learning over the Internet. The difference is not significant. Once weekly usage of the Internet is controlled, levels of projected adoption of on-line distance learning over the Internet is virtually the same for both men and women.

Conclusions

The findings in the study indicate that on-line distance learning is more likely to be adopted by nontraditional students: older students, students with spouses, students with children in the home, and students working longer hours than traditional students. However, differences between traditional and nontraditional students are relative. Traditional students may also perceive relative advantages of on-line distance learning, but nontraditional students are more likely to adopt the innovation. Traditional students are slower to adopt a new learning paradigm, because it is significantly different than to what they are accustomed. Perhaps more important, traditional students place higher value on the "ambiance" of traditional college education on campus. Thus, "Relative advantage is the degree to which an innovation is perceived as being better than the idea it supersedes" (Rogers, 1995, p. 212). To increase the enrollment of traditional students in on-line courses, they must be convinced that this new way to learn is substantially better than the traditional method.

Implications

At no other time in history has education meant so much to the well-being of the individual. Surviving in the job market today without a degree is one of the largest limitations of personal careers. "The beauty of cyberlearning is that you can pursue it while working at a full-time job and living miles from a college. In an age when many jobs require continuing education, cyberlearning brings it to people who cannot afford to interrupt a career" (Gubernick & Ebeling, 1997, p. 1). Gubernick and Ebeling (1997) list three motivational factors that will contribute to the growth of virtual universities and distance learning courses: First, students must obtain a degree to make a living; second, schools are in competition with each other for students; and third students are increasingly members of the work force rather than just students and require school on their time. That list suggests that there is likely to be an increase in the number of students who are will to take courses on-line. Marketing those courses to a specific demographic may be the key to which school wins the enrollment race.

This research study should interest any educator or course developer who is building an on-line course. Clearly, it would be valuable to test the research hypotheses over a large sample and longer period of time. However, the initial study is an impartial and practical marketing tool for on-line course development and distribution.

Table 1. –Factor Analysis of Perceived Relative Advantage Items with Varimax Rotation

Table 1. –Continued

Table 2. –Factor Loading for Project Adoption

Table 3.—The survey sample

Table 4. --Pearson Product-Moment Correlation Coefficients and Explained Variance for Seven Constructs of Perceived Relative Advantage with Projected Adoption

*p= 1-tailed test of significance

Table 5. --Partial Correlation Coefficients between Projected Adoption and the

Nontraditional Student Scale, Controlling for Hours Weekly on the Internet

*P= 1 tailed Significance

Table 6. --Projected adoption coefficients on the traditional/Nontraditional Student

Scale controlling for time spent per week on the Internet

*P= 1 tailed Significance

Table 7.—Age range two-way categorical variable and a five-way categorical variable

*Note there were 7 missing cases or 3.2percent.

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