Attitude as predictor of success in online training.

This is the second article in a series of articles published with findings on student perceptions of asynchronous web-based courses (Perez Cereijo, Young, & Wilhelm, 2001). This portion of the study examines the independent relationships between various student characteristics and student's perceived advantages and disadvantages of the asynchronous web delivery of the course and seeks to find a predictor that will help determine students most likely to enjoy taking asynchronous courses online. In the asynchronous web delivery format of this course, students had access to the class's lecture videos and textual course materials stored in the school's server, at any time and from anywhere. The data presented in this study provides supporting evidence, which reaffirms universities' commitment to offering online courses to meet students' needs. The study also points to attitude, work schedule, and distance from school as possible predictors of student success in this environment.

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Training departments of major corporations and institutions of higher education are rapidly developing online courses and full degree programs, which students can complete in the virtual world of the Internet. The first wave of courses was well received by students limited by distance and life commitments to continue their studies (Perez Cereijo, Young, & Wilhelm, 2001). Most students in this category saw the possibilities of finishing a college degree or mandated corporate training as the end that justified the means without truly assessing the prerequisites they needed to successfully "attend" classes in cyberspace. Nor did they quite understand the differences between the face-to-face courses and this new nontraditional online approach to classes. Years after the newness of the concept has worn off, educators, administrators, and even students are starting to ask the right questions:

* How convenient, feasible, and truly adequate is distance learning?

* Are online courses meeting the academic standards of the institution?

But more importantly, in a perfectly designed and implemented online course,

* Are all students equally able to successfully complete, enjoy, and even thrive in this new course environment?

* Are there predictors to help corporate and businesses choose the "right" employees to receive training over the Internet as opposed to face-to-face?

To answer these questions researchers first need to uncover what student characteristics (personality, lifestyle, psychographics, and demographics) are most important for determining the most effective method of instruction? In this study, the student characteristics revealed were found to be predictors of student preferences for classroom environments. This article presents the findings.

BACKGROUND

Distance Education: Definition and Need

Distance education (DE), as defined by the National Center for Education Statistics (NCES) (U.S. Department of Education, 2001), is instruction or training courses delivered to remote (off-campus) sites by way of audio, video (live or pre-recorded), or computer technologies, including both synchronous (simultaneous) and asynchronous (not simultaneous or delayed) instruction. The Higher Education Act defines DE as an educational process where the student is separated in time or place from the instructor. DE has evolved from the use of correspondence as the primary delivery medium, to the more common use today of video, audio, phone, and computer technologies (Schamber, 1988; Warner, 2001). With these technologies, multiple variations and hybrid uses have also been adopted, which have allowed for the delivery of live lectures and multimedia materials to enhance the DE environment.

Changes in the fiber of our nation's economic and social structure as well as new developments in technology have made DE over the Internet more appealing. According to Wonacott (2001), short product cycles, a fast-expanding knowledge base, and the rapid obsolescence of existing knowledge put tremendous pressure on employers to upgrade worker skills in a timely, effective, economical manner--"just in time" training. Workers are not able to accommodate the demands of a traditional education in the classroom and thus, students are increasingly demanding flexibility to accommodate changing personal demands. Warner (2001) and many other researchers in the field (Perez Cereijo, et al., 2001; Sujo di Mones & Gonzalez, 2000; Mood, 1995; Moore & Thompson, 1990) all concurred that distance education avails students of educational opportunities which otherwise would not be possible because of personal (work, family, etc.) or geographical limitations. Therefore, the Internet has now become an information and communication technology (ICT) medium for learning, which has created resurgence in DE (Wonacott, 2001). In 2000, Sistek-Chandler reported that universities in the United States were offering well over 54,000 online courses with over 1.6 million students. During the 2000-2001 academic year, the National Center for Educational Statistics (NECS) recorded that 56% of all two and four-year degree-granting institutions offered distance courses. Among these institutions, 90% offered asynchronous web-based courses. Of these institutions and others planning to offer distance courses within the next three years, at least 88% were planning on increasing and/or offering for the first time asynchronous web-based courses as the primary mode for their distance education courses (U.S. Department of Education, NECS, 2001).

The Internet provides a viable medium for the delivery of course curriculum to students and workers in need of continuing education or updated training that will keep them current in this competitive job market. The Internet's lower connection costs and accessibility to more people than ever before have been factors in its increased usage. According to the Pew Research Center, 73% of American adults use computers and of these 128 million use the Internet, and the numbers are steadily increasing (Augstums, 2004). The Internet's faster speeds and higher data transfer make textual and multimedia course content delivery viable for online courses, thus enhancing their appeal and effectiveness. Distance education courses with hybrid modalities of content delivery are rapidly being developed at universities all over the world. Course content in textual and multimedia format can now be organized, stored, and delivered over the Internet either: (a) synchronously--trainees/students and educators "meeting" in cyberspace at a prearranged time; (b) asynchronously--prerecorded lecture videos and other materials being accessed online at a time more convenient to the student; or, 3) hybrid--a mixture of synchronous and asynchronous delivery of text and multimedia where some of the lectures are delivered "live" and others "prerecorded" in the online environment. Still, basic communication tools and other text media within all these online environments maintain their same synchronous "feel" in the chat-rooms and asynchronous "feel" in the bulletin boards, e-mail, course notes, tests, and handouts.

Course Effectiveness

One of the difficult challenges educators and institutions face is determining the appropriate design and development of effective online courses. No data exists to unequivocally support the assumption that online courses are more effective than face-to-face instruction, nor that both environments are as effective. However, the assumption that both environments are as effective is generally accepted as fact (Wilson & Hord, 2000) especially when effectiveness is measured by the learning outcomes (Carswell, Thomas, Petre, Price, & Richards, 2000; Shulman & Sims, 1999; Goldberg, 1997; Gubernick & Ebeling, 1997; McCollum, 1997; Vasarhelyi & Graham, 1997). Some researchers further support the belief that the course-delivery medium is rarely the determining factor of educational outcomes such as student satisfaction, perceptions, and learning (Russell, 1999).

A review of the literature reveals that studies performed to measure effectiveness in distance education were based on a comparison of grades between the DE and the conventional classroom environments. Findings suggest that both classroom environments yield similar or not-significantly different outcomes (Sechrest, 1998; Hoey, Pettit, & Brawner, 1998; Brawner, 1997; Marsh, 1997). Earlier studies on the effectiveness of correspondence courses reported that students perform just as well as their classroom counterparts (Valore & Diehl, 1987). Comparison studies on teleconferencing and correspondence courses when compared to their face-to-face counterpart environments showed they are all equally effective (Kuramoto, 1984). A similar study comparing a face-to-face classroom to a class taught by way of satellite broadcasting concluded that students in the distance-learning environment performed better (Souder, 1993). Other, more general assumptions have been made by researchers such as Porter (1997) and St. Pierre (1998) who contended that distance education is at least as effective as traditional education when comparing student performance on grades, activities, and exams. In light of the literature findings uncovered in the course of conducting the research of this study, it is plausible to affirm that Porter's and St. Pierre's statements do stand as true. Most studies performed concur that both educational environments are at least equally effective when effectiveness is measured by grade performance between classroom environments.

Student Perception of Course Effectiveness

When effectiveness is measured by student performance, results seem to lean towards both environments, distance education, and face-to-face, as being comparably effective. From an educator and an administrator's perspective these results reinforce the belief that distance education is a viable medium of instruction. However, when determining effectiveness at the level of individual student preferences, what factor or characteristic of a student's personality or study habit can be identified as an indicator of success in a distance-learning environment? Studies conducted to uncover these factors have found that from a student's perspective convenience, flexibility, learning enhancement and psychology are all major factors of online courses. In a study conducted in 1999 by Perez Cereijo (2001), these factors were reported as major categories and more specific characteristics of each were outlined for each category. In that study, categories and factors were discovered, but no level of importance or preference was attached to any of the factors.

Few studies have researched any one specific factor. In an earlier study, Harasim (1990) pointed out that course flexibility of time and space is what first draws students to online courses. Course flexibility accommodates students with hectic lifestyles and commitments. Online courses allow the student to set their own schedule and to complete the work from the convenience of their home or workplace (Perez Cereijo, et al., 2001). Bates (1995) reported that equity was considered an advantage of online courses as students felt their online participation shielded their physical appearance and made participation easier than in a face-to-face environment.

Are any of the other factors as appealing or important to distance education students? Research on this and other online course factors needs further study to determine their true individual weight and their weight in relation to other factors. Although students are drawn by the flexibility appeal of the course, according to Harasim (1990) and Perez Cereijo (2001), what personal student characteristic draws and retains a student to online courses? Beyth-Marom (2003) found that students who are more "open to changes" as opposed to having more "conservatism," will choose to participate in Internet-assisted courses over face-to-face traditional courses. Students with more "conservatism" cling to the traditional, the secure, the status-quo and conform to the norm. Beyth-Marom describes students who are "open to changes" as those who seem to follow their interests both intellectually and emotionally guided by their own thoughts and sense of self-direction. Therefore, using the Internet to "attend" classes supports the "open to changes" student with goals of independence and innovation. Online courses are learner-centered and learner-controlled thus requiring that the student be responsible, have self-discipline and a certain amount of motivation to complete the course work (Kerka, 1996). Rintala (1998) cautiously asserts that distance education works best for more mature, motivated, well-organized, and already accomplished learners. Although these statements are presented based on theory and thorough literature review findings, no studies provide any data to support these statements or to prioritize their importance.

RATIONALE

Given the lack of supporting data this study set out to determine, which student characteristics and other factors could help determine and even predict students' preferred classroom environment between the face-to-face in the classroom and the web-based classroom environment. For the past five years, demographics, preferences, and other data have been collected on the students registering in the same graduate-level course. Students enrolled in this course are part of the PhD and Master programs in Educational Technology in the College of Education at a University in the mid-south. In 2000, this study first revealed through qualitative analysis the categories in terms of advantages and disadvantages that the asynchronous online environment offers students. Results were published in Factors Facilitating Student Participation in Web Courses (Perez Cereijo, et al., 2001). In this second portion of the study, relationships between personality, lifestyle, psychographic, and demographic variables were studied using quantitative methods seeking to determine direct correlations between these and classroom environment preferences. Of all these variables cross-tabulated with class format preference only three had a relation. Work-hours, distance from campus, and attitude towards classroom environments did reveal a significant relationship with classroom environment preference.

METHODOLOGY

This study was conducted on the students enrolled in a graduate level course at a university in the mid-South. The course is taught over the Internet through a password protected site designed using WebCT. Although students have the choice of meeting on campus three times during the semester for question-answer sessions, students still have to rely completely on the materials posted online to listen and view the professor's lectures and to complete the course requirements. For purposes of this study, the course is being taught completely as an asynchronous online course.

In this study, data has been gathered for three long semesters, through surveys given to students at the end of every semester. All students in this study were enrolled in the same course with the same professor, the same class format, and the same technologies. The professor has consistently received high marks in student evaluations for the past 10 years and is very highly regarded within the department. Careful planning went into minimizing the effects of "environmental" variables such as teaching quality, course being taught, and methodology, to ensure that the study results would be meaningful.

Semester after semester, students were given the same surveys to find out not only demographical information, but also information on their learning styles, personality type, experience and attitude towards technology, and class format preference. Because of problems with missing data and invalid data being entered in the surveys, the study was extended past the initially intended semester. With every semester new ideas were implemented to make the surveys more reliable. First, the paper-based surveys were converted to electronic format, posted online, and directly connected to a database at a school website to minimize human error in the data tabulation process. The following semester, JavaScript code was added to key fields in the surveys (id, sex, age, computer experience, class format preference) to ensure a minimum of fields were filled before the surveys were submitted, and that crucial fields like id were available to link data collected in all four surveys. Finally, in a subsequent semester, JavaScript code was added to all surveys' fields as the missing data problem was making other results highly unreliable. The whole site, where surveys were posted, was also made password protected to ensure only students enrolled in the course under study were able to access the surveys. Subsequently, after three semesters enough complete records were gathered to ensure reliable results would be reported.

The attitude towards technology (TAT) was measured using a modified version of Christensen (1997)'s 10 paired-semantic differential items in 9 different areas of technology or subjects (computers, using computers for my professional productivity, using the World Wide Web, electronic mail, streaming video, V-Tel classes, online asynchronous classes, online synchronous classes, oncampus classes). Although the semantic scales were grammatically the same and also arranged the same as first designed, the topics were adapted to fit the needs of the researcher in the context of this study. From each attitude scale, a single measure was created, by computing the vertical mean response across the 10 pairs of semantic items. Cronbach Alphas computed on each measure ranged from 0.9451 to 0.9787 (Table 1) indicating reliable construct according to DeVellis (1991) proposed reliability guidelines for research instrument scales and Nunnally (1978)'s acceptable rate in sociological measurements. Therefore, these scales can legitimately be used as predictors and measurements.

POPULATION

The graduate level course under study enrolls an average of 30 students every semester. At this point of the study a total of 96 complete records from 96 students have been collected, over a period of 3 long semesters. All 96 students have been admitted to a graduate level program, 21 students are in a PhD program, 74 are in a Masters program and 1 is in a certification program.

Students' age ranges from 22 years old to 56. Participants live anywhere between 0 miles (campus resident) up to 180 miles from campus. There are 44 males and 52 females. Forty-seven (47) students work part-time and 49 full-time. Forty-one (41) are part-time students and 45 are full-time students. Forty-nine (49) students are single and 47 are married. Of the 96 students, only 34 students reported having children still living at home. As far as dependents (elderly parents and/or children and excluding self or spouse), 50 students reported no dependents living at home, 16 reported 1 dependent, 17 reported 2 dependents, 7 reported 3 dependents and 3 reported having 4 dependents at home.

Overall, for class format method, 30 students preferred the face-to-face in class format method, 60 students chose the online asynchronous class format as their preferred method, 1 student chose V-Tel and 5 students chose the online synchronous class format. Given the small number of students who preferred V-Tel and synchronous, and the direction of this study, statistical analysis was only conducted on the 90 students who chose asynchronous and traditional as their preferred class format.

RESULTS

Finding 1

Chi-square tests were computed to test the association between each of the categorical variables (lifestyle, psychographics, and demographics) and the subjects preferred method of instruction. A significant Chi-square value (p = 0.017) was found between work hours and the class format method chosen by students in those categories (Table 2).

A cross tabulation table of work hours and class format was generated (Table 3), to interpret the results and draw conclusions.

For the variables with continuous values such as age, number of dependents still living at home, distance from school, technology experience, personality type (introvert-extrovert) and others, a t-test was computed. A significant t value (p=.018)was found when comparing the difference in mean distance from school for the two class methods (Table 4). Distance was measured in number of minutes it took a student to drive to campus. Of the remaining variables, no other significant relationships were found.

These findings lead to the assertions that:

1. A greater percentage of part-time workers than full-time preferred traditional classes in a face-to-face environment over the same course offered asynchronously online. Of the students who chose Traditional as their preferred class format, 66.7% were working part-time versus only 33.3% who were working full-time (Table 3). And conversely, a greater percentage of full-time workers (78.3%) than part-time (54.6%) preferred asynchronous courses to traditional, face-to-face courses. Of the students who chose Asynchronous as their preferred class format, 60% were working full-time versus 40% who were working part-time (Table 3).

2. Students who preferred asynchronous class format lived on average further away from campus than the students who preferred the traditional class format. More specifically, students who preferred the asynchronous class lived on average 42.27 miles away from campus (Table 4). And, students who preferred the traditional class format lived on average 23.63 miles away from campus (Table 4).

Finding 2

In this study, a greater percentage of students chose as a preferred class format the asynchronous web-based format over the face-to-face traditional class format. The measures created from each attitude scale were used to classify subjects as to their choice of preferred class instruction method between the face-to-face traditional class format (TF) and the asynchronous online class format (AF). An ANOVA was performed on each measure to determine whether there was a difference in the mean of the measure on those subjects preferring TF and on those preferring AF. Three measures V-Tel classes (VT), online asynchronous classes (OAC), and oncampus classes (OCC) had significant ANOVA F- tests (Table 5). These three measures were then used

as independent variables in a discriminant analysis to predict preference between TF and AF. The analysis yielded a significant (Wilks' Lambda = 0.547, p=0.000) canonical discriminant function:

The function was evaluated at the mean values of each of the independent variables yielding the group centroids of -0.693 for AF and 1.163 for TF. These centroids were then averaged to produce the cut point, C=0.235, used in the classification process. If f(x)>C, then the subject was classified into TF as the preferred class format and if f(x)<C, the subject was classified into AF as the preferred class format. The discriminant function classified the subjects correctly 86.7% of the time.

According to the crosstabulation shown in Table 6, the discriminant function (Figure 1) correctly predicted the 40 students that chose asynchronous as their preferred class format and correctly predicted the 25 students that chose traditional as their preferred class format. Thus, the preferences for 65 of the 75 students that either chose asynchronous or traditional as their preferred class format, were accurately determined.

CONCLUSIONS

This study uncovered three important findings. First and foremost, students' attitude towards technology and classroom environment can be used as a predictor of their classroom environment preference. And, the discriminant function (Figure 1) found and tested in this study, which correctly classified students 86.7% of the time, can be used to predict these student preferences.

A review of the literature reveals that other studies may have found a significant relation between attitude and preference for online courses, but in every one of those studies, attitude has been defined either differently than in the present study, or measured instead of as a predictor of online course environment preference, as a predictor of the use of an online course component. Thus, in some studies, attitude was defined as a disposition of the student towards the course's ill-prepared and unresponsive faculty and administration (Armstrong-Stassen, Landstrom, & Lumpkin, 1998) and towards the poor design of the course (Ponzurick, Russo France, & Logar, 2000). Elvers, Polzella, & Graetz (2003) found that the effects of a student's procrastination habits affected a student's attitude towards an online course. These studies focused on the significance of how attitude was affected while in a distance learning environment.

In other studies, attitude was referenced as a psychological characteristic, defined in terms of personal independence, creativity, tough-mindedness, sociability, risk-taking, stimulus, and sensation-seeking (Katz, 2002). Katz found that teachers with a positive psychological attitude about themselves where more likely to use information and communication technology, ICT. In that same journal article, Katz references previous studies (Katz & Offir, 1990; Dunn & Ridgway, 1991; Katz, 1993, 1995) where similar findings had been reported but once again psychological attitude was defined slightly differently, this time in terms of self-image, social image, independence, self-confidence, learning satisfaction, internal locus of control, learning control, creativity, learning motivation. In summary, Katz and Offir found that a positive personal psychological attitude facilitates efficient use of distance learning components.

As a predictor of the use of an online course component, attitude was studied by Tello (2004) who conducted a large-scale study with 1,645 students enrolled in 76 fully online courses in the University of Massachusetts System. Tello reported a significant relation between student attitudes toward online learning experience and their interaction with their instructor and with the use of communication tools in the distance-learning environment.

Table 7 shows a summary of some attitude related studies in online course environments discussed in this article. All studies shown including the present study, have adopted a different perspective on attitude thus making research in this area that much richer. Aside from the new finding that attitude towards the class format's technology setting can be used as a predictor of student preference for online class format, this study offers the discriminant function (Figure 1) as a prediction tool to help students determine if an online course will be their preferred course format. When students' attitudes are measured with the semantic scales described in the methodology section, the coefficients calculated can then be used to solve the discriminant function and the final value becomes the predictor of students' classroom-environment preference.

As mentioned at the beginning of this section, this study uncovered three findings. A second important finding was that a greater percentage of part-time workers than full-time, prefer traditional classes in a face-to-face environment over the same course offered online. This may seem reasonable given that most full time workers may have less time to devote to travel to and from campus. Thus, full time workers would seek alternative methods of distance education (such as online, correspondence, and others) due to the time element of their schedules. Similarly, a greater percentage of full-time workers than part-time, prefer asynchronous online courses than traditional, face-to-face courses. As per this rationale, this latest finding is consistent with the time-sensitivity argument given previously for full time workers. Full time workers have many time-sensitive issues that affect their schedules such as work hours, business travel, family, and distance from the university. These time-sensitive issues may lead full time workers to prefer a more flexible asynchronous learning environment to a time and space dependent face-to-face course.

As found in the literature review of this study, Kerka (1996) rationalized that students in online courses need to be responsible, have self-discipline and a certain amount of motivation to complete the course work. Rintala (1998) went further and cautiously asserted that distance education works best for more mature, motivated, well-organized, and already accomplished learners. These statements were based on theory and thorough literature review findings, but no data to support these statements or to prioritize their importance was presented. The findings of this study seem to give a preliminary support to both, Kerka's and Rintala's postures. The population of this study were all graduate level students ("... more mature,.... already accomplished learners ..."), and with a level of motivation given by their need for a degree pursued under the pressures of hectic lifestyles where work, family and studies are all played in a balancing act. Schuemer (1993) stated that to be a successful learner the student must have a high-degree of motivation. As accomplished learners the population of this study is all part of a graduate level program, either in a Masters or PhD program.

Finally, the third important finding of this study revealed that the workload status (part-time or full-time jobs) of the students in this study, and the distance from school were independently found to be strongly related to their classroom environment preference. Sankaran and Bui (2001) conducted a study, which tested motivation and performance between two groups taking a class, one group on the Web and another in the face-to-face environment. The group taking the class online had a higher correlation between motivation and performance than the group taking the class in the face-to-face environment. Sankaran and Bui concluded that students "... in distance learning settings, undergo many sacrifices to get an education and motivation is a driving factor that influences their performance." The students in the present study have a high level of motivation to pursue their educational goals in an online environment, as the highest preference for the online environment was found with students working full-time and the furthest away from school.

As attitude was found to be a predictor of classroom environment preferences in the present study, it is safe to assume that a student's high motivation level combined with a positive attitude towards online courses can be used to match students with classroom environments. Can we establish the next connection and assert that an initial positive attitude towards online courses when combined with a high level of motivation, as defined by Sankaran and Bui (2001), can be used to predict not only course environment preference but also successful completion of the course? Are there other intrinsic relationships between class format preference and a specific combination of demographic and psychographic measures? So far, extensive testing of some of these factors has not revealed other relationships. Though in time, familiarity with online course environments and the use of technology may uncover, that some of the now nonsignificant relations do become predictors of course environment preference.

FUTURE WORK

The study will be continued to collect more student records and thus increase the data file. Additional data collected will be used to test the validity of the discriminant function (Figure 1) as a useful instrument for classifying subjects as to their preference in class format depending on their response to attitude measures. The function will then be refined using the additional data. Furthermore, the author will continue to explore other types of measures, which may prove useful in classifying subjects as to their class format preferences. Other measures included may help establish stronger correlations and predictors between motivation, attitude and ultimately successful completion of online courses. A more refined discriminant function (Figure 1) with an increased rate of prediction will ultimately prove to be more adequate to help corporations, businesses and institutions of higher education choose the "right" employees and students who would benefit from web-based training as opposed to the face-to-face training. Understanding learners' needs and offering them appropriate class environments will increase training success, satisfaction and ultimately, improve productivity.

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MARIA VICTORIA PEREZ CEREIJO

University of Texas at Arlington, USA

[e-mail]

Table 1 Cronbach's Alpha for Methods

Online asynchronous  .9583
Oncampus classes     .9451
V-Tel                .9787

Table 2 Chi Square Test for Demographical and Psychographical Variables
by Class Format Method

Variable         Chi-square  p-value

Work hours       5.692       .017
Gender           1.098       .295
Degree seeking    .863       .650
Enrollment type  3.258       .071
Marital          2.690       .101
                             n.s.

Table 3 Cross tabulation of Work hours by Class Format (% of students)

                      Class Format
                Asynchronous  Traditional  Total

Part-time work  24 (40%)      20 (66.7%)   44 (48.9%)
Full-time work  36 (60%)      10 (33.3%)   46 (51.1%)
Total           60 (100%)     30 (100%)    90 (100%)

Table 4 Independent Samples Test

                             Mean               Std. Error Mean
Variable          Asynchronous  Traditional  Asynchronous  Traditional

Age               34.40         35.27        1.245         1.953
Number of           .89           .87         .152          .213
Dependents
Distance from     42.27         23.63        5.139         4.355
campus in min.
Technology         6.67          6.50         .184          .239
experience
Personality type   4.88          5.07         .246          .346

                  t-test for
                  Equality of Means
Variable          t      p-value

Age               -.387  .699
Number of          .108  .914
Dependents
Distance from     2.413  .018
campus in min.
Technology         .538  .592
experience
Personality type  -.431  .667

n=90, df=88

f(x) = (0.343 * VT) + (-0.918 * OAC) + (0.605 * OCC) + 0.150

Figure 1. Discriminant function

Table 5 Tests of Equality of Means for Class Methods

Class Format         Wilks' Lambda  F       df1  df2  Sig.

Online asynchronous  .745           24.967  1    73   .000
Oncampus classes     .776           21.051  1    73   .000
V-Tel                .926            5.819  1    73   .018

Table 6 Predicted Group Analysis for Class Format Crosstabulation

                            Method
Predicted/Actual  Asynchronous  Traditional  Total

Method
Asynchronous      40             3           43
Traditional        7            25           32
Total             47            28           75

                    Chi-Square Test
                    Value   df  Asymp. Sig. (2-sided)

Pearson Chi-Square  39.697  1   .000

Table 7 Attitude Related Studies

Research                             Significant finding

Katz & Offir, 1990; Dunn & Ridgway,  some personal psychological
1991; Katz, 1993, 1995               attitude factors as predictor of
                                     the use of online course components
Armstrong-Stassen, Landstrom, and    attitude towards online courses
Lumpkin, 1998                        based on experience w/faculty and
                                     administration
Ponzurick, Russo France, and Logar,  attitude towards online courses
2000                                 based on course design
Katz, 2002                           different personal psychological
                                     attitude factors as predictors of
                                     the use of online course components
Elvers, Polzella, and Graetz, 2003   attitude towards an online course
                                     affected by procrastination effects
Tello, 2004                          attitude towards online courses
                                     based on communication experienced
                                     in the course