Students’ Reactions to the Introduction
of Videoconferencing for Classroom Instruction
Marjorie Armstrong-Stassen
Faculty of Business Administration, University of Windsor, Windsor, Ontario, Canada
Margaret Landstrom and Ramona Lumpkin
Division of Continuing Education, University of Windsor, Windsor, Ontario, Canada
This study examined how students who had no prior experience with videoconferencingwould react to the use of videoconferencing as an instructional medium. Students enrolled in seven different courses completed a questionnaire at the beginning of the semester and again at the end of the semester. Students at the origination and remote sites did not differ in their reactions toward videocon-ferencing but there was a signi® cant difference for gender. Women reacted less favorably to videoconferencing. Compared to the be-ginning of the semester, students reported signi® cantly less posi-tive attitudes toward taking a course through videoconferencing at the end of the semester. There were no signi® cant differences in students’ attitudes toward videoconferencing across courses at the beginning of the semester but there were signi® cant differences across the courses at the end of the semester. The results suggest the need for better preparation for both students and instructors.
Keywords gender differencesin attitudestoward videoconferencing, reactions to videoconferencing, videoconferencing and distance education, videoconferencing as a classroom instructional tool
Its large expanse, the remoteness of many of its com-munities, and the rapid rate of technological development suggest that Canada provides the ideal setting for the cre-ation of a virtual society. New technologies have brought increasing pressures on many organizations, including al-most every educational institution, to change the way they
Received 13 December 1996; accepted 4 December 1997. Addresscorrespondenceto MarjorieArmstrong-Stassen,University of Windsor, Faculty of BusinessAdministration,Windsor, Ontario N9B 3P4, Canada.
function. The introductionof new technologiesposes chal-lenges for all aspects of the Canadian society, including the ways in which we teach and learn (Paul, 1995). Paul argues that changing the way we teach or expect students to learn will ultimately require us to change our universities. There has been a renewed interest in distance education on university campuses in Canada (Paul, 1995). Accord-ing to Tobin (1995), the future for distance education holds immense promise for the large-scale adoption of techno-logically based methods. And the recent advances in the application of technology to distance education have made it possible to create new forms of distance learning. Virtual classrooms are now a reality. This paper focuses upon one type of virtual classroom: classroom instruction through the use of videoconferencing. Only a handful of univer-sities in Canada are currently using videoconferencing as an instructional tool but this is expected to change as the technology improves and the cost of videoconferencing equipment becomes more affordable (Johnson, 1993).
The study was conducted at a small universityin Ontario. In their efforts to remain competitive, the dilemma faced by smaller universities where resources are more limited is deciding how, and how much, to invest in new technol-ogy (Paul, 1995). In 1995, the university, with the help of donors who were interested in the use of technology in education, out® tted three videoconferencing roomsÐ one at the main campus and one in each of two satellite campuses in communities located within a 60-mile radius of the main campus. The goals of the university were to provide off-campus students with more variety in course offerings, to provide an additional on-campus opportunity for main-campus studentsin an extra section of a course, or to link two centers to provide the necessary enrollment to
The Information Society, 14:153±164, 1998 Copyright c
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1998 Taylor & Francisoperate the class. Seven second-year courses were offered via videoconferencing: two English courses and one each in political science, communication studies, history, psy-chology, and religious studies. Except for one course, the courses originated in classrooms at the university’s main campus, although the instructors did travel to the remote sites and lecture from there two to three times during the semester. Students at the origination site had the professor present in the classroom. Students at both the origination and remote sites could see, hear, and talk to the students at the other site and students at the remote sites could also interact with the professor at the origination site.
One of the major factors that will in¯ uence the effec-tiveness of videoconferencingis student acceptance of this medium of instruction. Leidner and Jarvenpaa (1995) ar-gued that it is important to examine student-related factors before committing to widespread investment in advanced technology. These authors suggested that the lack of en-abling conditions, such as student experience and student effort, may outweigh any gains to be achieved by the new technology. The purpose of the present study was to as-sess students’ attitudes toward, and reactions to, the use of videoconferencing for classroom instruction and to iden-tify some of the factors that may in¯ uence students’ accep-tance of this technology. Speci® cally, we examined how location (origination site versus remote site), gender, and the particular course may affect these attitudes. We drew upon the existing research literature on technology and, in particular, the empirical studies on students’ acceptance of computer technology, to design the study. Attitudes have been found to play an important role in the adoption of technology (Bagozzi et al., 1992). We assessed atti-tudes toward technology and videoconferencing in gen-eral as well as attitudes toward taking a course through videoconferencing. We measured the students’ attitudes in the ® rst week of the semester and again at the end of the semester. We expected that students who had a more pos-itive attitude toward videoconferencing in general and to-ward taking a course through videoconferencing would be more likely to accept videoconferencingas an instructional tool.
However, the inexperience of the university and the unfamiliarity of both instructors and students with video-conferencing could have a negative effect on students’ attitudes. If this is the case, we would expect to see a less positive attitude toward videoconferencing at the end of the semester compared with the ® rst week of the semester. Changes in the way we teach and learn require concomitant changes in the university’s infrastructure and the resources needed (Besser, 1996). A distance education system in-volvinga same-time/different-place delivery encompasses a relatively complex set-up that requires signi® cant tech-nician support. In the present case, it was incumbent upon the instructor to become adequately pro® cient in operating
the equipment. Videoconferencing also requires a change in presentation style from that of the conventional class-room, including changes in delivery style and the redesign of presentation materials (Besser, 1996). Suf® cient train-ing is essential to the successful implementation of video-conferencing (Gowan & Downs, 1994). Faculty are gen-erally provided with training in the technology and how to operate the equipment although the extent of such training may vary dramatically. However, instructors receive little training in the change in teaching style required (Tobin, 1995). In an exploratory study of students’ reactions to distance telelearning, Webster and Hackley (1996) found that instructor mastery of the equipment and use of an interactive teaching style were associated with more pos-itive attitudes toward the technology and toward distance telelearning. In the present study, instructors were given a relatively brief training session that focused upon how to operate the equipmentwhich includeda four-page handout, ª Hints for Videoconferencing Instructors,º that again em-phasized technical skills but did include a section on per-sonal presentation (looking at the camera, projecting one’s voice, not moving too quickly) and a section on encour-aging interaction.
We also assessed students’ perceptions of the effect of videoconferencing on various aspects of the course, such as course content and opportunities for interaction. We expected that there would be a positive relationship be-tween the attitudinal variables and perceived effect. Dif-ferent methods of transmission limit to varying degrees access to information that is available in face-to-face con-versations and these limitations affect social and cognitive processes (Fussell & Benimoff, 1995). Storck and Sproull (1995) suggest that because of differences in information availability,social inference processes are not the same for face-to-face communication and video. Much of the lit-erature on videoconferencing emphasizes the restrictions the technology itself places on communication processes and content. The technical aspects of videoconferencing, e.g., transmission lags and network bandwidth limitations, tend to interfere with key communication processes such as listener feedback and signals and signs (eye contact, gazing behavior, gestures) related to turn-taking (Fussell & Benimoff, 1995; O’ Conaill et al., 1993; Tang & Isaacs, 1993; Whittaker, 1995). Fussell and Benimoff contend that to develop videoconferencing so that it is the ª next best thing to being thereº requires careful attention be paid to the dynamics of interpersonal communication.
We also assessed the level of anxiety students felt re-lated to taking a course through videoconferencing. There is evidence in the computer technology research literature that negative attitudes may be caused by computer anxi-ety (Torkzadeh & Koufteros, 1993). Extrapolating from this evidence, we expected that there would be a negative relationship between anxiety related to taking a course
through videoconferencing and attitudes toward video-conferencing.
The ® nal dependent variable we examined was toler-ance of ambiguity. People with a low tolertoler-ance of am-biguous situations are more likely to feel threatened by unfamiliar situations than people with a high tolerance of ambiguity (Keinan, 1994). We expected that students with a higher tolerance of ambiguity would have more positive attitudes toward videoconferencing and less anx-iety related to taking a course through videoconferencing. The independent variables we examined were site, gen-der, and course. Students who were in the classroom at the point of origination (origination site) had the professor present in person in the classroom whereas students at the remote site viewed the professor on the video screen ex-cept for the two or three times when the instructor travelled to the remote site. The question we wanted to address was whether or not there would be signi® cant differences in at-titudes and reactions between students at these two sites. It may be that students at the remote site have more positive attitudes as well as a more favorable reaction to video-conferencing because it provides them with accessibility to courses and reduces travel time and costs, whereas stu-dents at the origination site may ® nd the set-up distracting and thus they may have less positive attitudes and reactions toward videoconferencing. On the other hand, students at the remote site may feel they are at a disadvantage be-cause they do not have as easy access to the professor as well as to other resources such as the library. If this is the case, then we would expect students at the remote site to have a less positive attitude toward videoconferencing. Webster and Hackley (1996) found that students felt it was a disadvantage to be at a remote site. Remote students felt less included in the course, had dif® culties in contacting an instructor, and reported diminished interaction with the instructor during the class.
Men and women may have different attitudes toward technologyÐ the so-called ª technological gender gapº (Canada & Brusca, 1991). In their review of the evidence on gender differences, Canada and Brusca noted that studies of college students’ computer-related attitudes and behaviors have shown that women have a less positive attitude toward computers than do men. But this gender difference disappears when researchers control for actual computer experience. It was therefore dif® cult to predict whether there would be differences in the attitudes and reactions of the male and female students.
Some courses may better lend themselves to videocon-ferencing than others and some professors may be more pro® cient at videoconferencing than others. To examine this issue, we compared students’ attitudes and reactions across the seven courses. We did not expect to see a dif-ference in attitudes and reactions to videoconferencing across courses at the beginning of the semester. However,
if aspects of the course or the instructor have an effect, then there would be signi® cant differences in students’ at-titudes and reactions across the courses at the end of the semester.
We included two control variables in the study: age and prior experience. Professors who are using videoconfer-encing to teach courses at the Universit Âe Laval in Canada suggest that this type of multimedia instruction is the per-fect teaching tool to hold the interest of a generation raised on television and video games (Ford, 1995). Does this mean that older students will be less receptive to videocon-ferencing for classroom instruction? And what are the im-plicationsfor those universities that are now offering MBA degrees to primarily midcareer professionals? Older peo-ple have been found to have less positive attitudes toward new technology than younger people have (Brick® eld, 1985). Age has also been found to be positively related to computer anxiety (Nelson & Kletke, 1990). In the present study, there was a signi® cant age difference between the students at the two sites. Students at the remote site were signi® cantly older on average than the students at the orig-ination site so it was important to control for the effects of age.
Prior experience has been shown to be an important variable in the computer literature. In a review of the cor-relates of computer anxiety, Maurer (1994) concluded that amount of computer experience had the clearest relation-ship to computer anxiety of any of the variables stud-ied. Many of the signi® cant gender and age differences in attitudes toward computers and computer anxiety dis-appear when researchers have controlled for the effects of prior experience (cf. Canada & Brusca, 1991; Chen, 1986; Colley et al., 1994; Dyck & Smither, 1994; Maurer, 1994).
METHODS
Participants and Procedure
Students enrolled in three courses in the fall semester 1994 and four courses offered in the winter semester 1995 were asked to complete a questionnaire during the second class of the semester (time 1) and again at the end of the semester 13 weeks later (time 2). A total of 130 studentsparticipated at time 1 and 186 students completed the questionnaire at time 2. The response rates for time 1 and time 2 were 55% and 79% respectively. For time 1, there were 32 (25%) men and 97 (75%) women (1 missing value) and their av-erage age was 30.65 years (SD = 12.09). Of the students, 67% were in the classroom at the point of origination with the professor present and 33% were located at the remote-site classrooms. At time 2, there were 59 (32%) men and 124 (68%) women (3 missing values) and their average age was 26.97 years (SD = 9.48). Seventy (67%) were
at the origination site and 34 (33%) were at the remote sites.
The respondents were asked to provide a code number for the ® rst questionnaire and to put this same code num-ber on the second questionnaire. Of the 97 students who indicated they had completed both the time 1 and the time 2 questionnaires, it was possible to match 67 of the questionnaires. For this matched sample, 15 (22%) were men and 52 (78%) were women and their average age was 29.15 years (SD = 10.00). Forty-® ve (67%) were at the origination site and 22 (33%) were at the remote sites.
Measures
Attitudes Toward Videoconferencing in General. We developed a nine-item measure to assess the respondents’ overall attitudes about videoconferencing. Examples of these items are: ª Videoconferencing is bringing us into a new era of learningº and ª I feel that technology like videoconferencing is a necessary tool in educational set-tings.º The reliability coef® cients for this measure at time 1 and time 2 were .85 and .89, respectively. Principal fac-tor analysis yielded a single facfac-tor with facfac-tor loadings ranging from .47 to .76 with an average factor loading of .62.
Attitudes Toward Taking a Course Through Videocon-ferencing. We developed two measures to assess the re-spondents’ feelings about taking a course offered through videoconferencing. The ® rst measure, VIDEO-1, consis-ted of seven-items using a ® ve-point Strongly Agree/ Strongly Disagree Likert response format. The instruc-tions stated, ª In this section, we are interested in what you personally think about taking a course offered through video-conferencing.º Example items are: ª I like the idea of learning through videoconferencingº and ª I think learning through videoconferencing will be stimulating.º The reli-ability coef® cients for this measure were .92 for time 1 and .95 for time 2. A principal factor analysis yielded a single factor with factor loadings ranging from .75 to .91 with an average loading of .84.
The second measure, VIDEO-2, was a semantic dif-ferential scale consisting of ® ve bipolar pairs of adjectives such as positive/negative and bene® cial/harmful. Respon-dents were asked to respond to the statement ªAll things considered, taking a course through videoconferencing is....º The reliability coef® cients for this measure were .94 for time 1 and .95 for time 2. A principal factor analysis yielded a single factor with factor loadings ranging from .84 to .93 with an average loading of .88. The correlation between the two measures wasr = .80, p< .001 at time 1 andr = .82, p < .001 at time 2.
Anxiety Related to Videoconferencing. We developed a four-item measure to assess the respondents’ degree of anxiety related to taking a course offered through video-conferencing. Two of the items were taken from the Com-puter Anxiety Rating Scale (Heinssen, et al., 1987) and modi® ed to re¯ ect anxiety concerning videoconferencing instead of computers. Sample items for this measure are: ª I feel somewhat apprehensive about the idea of taking a course taught through videoconferencingº and ª I am con-® dent that I can learn through videoconferencingº (reverse scored). The reliability coef® cients were .87 at time 1 and .88 at time 2. Principal factor analysis yielded a single factor with factor loadings ranging from .71 to .91 with an average loading of .79.
Perceived Effect of Videoconferencing. We developed a four-item measure to assess what effect the students felt videoconferencing would have on the organization and content of the course, interaction among students and be-tween students and instructor, and social aspects such as group coherence, consultation, and friendships. The re-sponse categories were positive, no effect, and negative. A high score re¯ ects a positive effect and a low score rep-resents a negative effect. The reliability coef® cients were .69 for time 1 and .84 for time 2. Principal factor analysis yielded a single factor with factor loadings ranging from .67 to .77 with an average loading of .72.
Tolerance for Ambiguous Situations. We used one item from the MYSTAT-I tolerance for ambiguity scale (McLain, 1993) to assess tolerance for ambiguous situa-tions: ª I don’ t tolerate ambiguous situations well.º
Demographic Variables. We assessed several demo-graphic variables, including age, gender, hours employed per week, number of semester courses completed at the university, location of the respondent (origination or re-mote site), and prior experience with videoconferencing.
Prior Concerns About the Use of Videoconferencing in University Courses. At the end of the time 1 question-naire, we included an open-ended item: ª Tell us about any concerns you have about the use of videoconferencing in university courses.º
Data Analysis
We used multivariate analysis of covariance (MANCOVA) with gender, site, and course as the independent variables and age as the covariate to compare the students’ attitudes and reactions. For the matched sample, we used repeated-measures multivariate analysis of covariance, with time as the within-subjects variable and gender and site as the between-subjects variables and with age as the covariate. There were not enough students in the matched sample to
allow meaningful comparisons across the seven courses. Tukey’s Studentized range (HSD) post hoc comparisons were used to identify signi® cant differences between the groups.
RESULTS
We ® rst compared the two samples, i.e., the fall semester time 1 with the winter semester time 1 and the fall semester time 2 with the winter semester time 2. There were no signi® cant differences between the two samples for any of the measures so we collapsed the two samples together.
Ninety-threepercent of the students had no prior ence with videoconferencing. To control for prior experi-ence, we omitted from the subsequent data analyses those students who indicated that they had some prior experi-ence with videoconferencing (n = 10 for time 1,n = 13 for time 2, andn= 4 for the matched sample).
We compared students at the remote sites with students at the origination sites on the demographic variables. Stu-dents at the remote sites were more likely to be female (time 1v 2 = 6.96, p < .01, time 2v 2 = 7.15, p < .01, matched sample v 2 = 5.77, p < .05) and to be older (time 1t = ¡ 2.22, p < .05, time 2t = ¡ 3.24, p < .01, matched samplet = ¡ 2.29, p< .05) than the students at the origination site.
The MANCOVA analyses indicated no signi® cant ef-fect for site for either time 1 (Wilks’s lambdaF(6, 83) = 1.74, p = .12) or time 2 (Wilks’s lambda F(5, 103) = 1.49, p = .20). The means, standard deviations, and
uni-TABLE 1
Means, standard deviations, and ANCOVAF-values for site comparisons
Time 1 Time 2
Origin. Remote Origin. Remote mean mean mean mean
Measures (SD) (SD) F (SD) (SD) F General attitudes 3.76 3.89 < 1.00 3.62 3.64 < 1.00 (.63) (.59) (.75) (.70) VIDEO-1 attitudes 3.37 3.54 2.47 3.10 2.96 < 1.00 (.78) (.82) (.98) (1.16) VIDEO-2 attitudes 3.65 3.90 2.50 3.57 3.39 < 1.00 (.91) (.90) (.99) (1.18) Anxiety 2.67 2.47 2.48 2.74 2.91 < 1.00 (.76) (.79) (.98) (1.10) Perceived effect 2.17 2.09 1.06 2.22 2.02 2.08 (.49) (.57) (.54) (.72) Tolerance for 3.06 2.77 < 1.00 Ð Ð Ð ambiguitya (1.00) (1.04)
aThis was measured at time 1 only.
variate F-values for the comparisons between sites are shown in Table 1.
The MANCOVA results for gender indicated no signi-® cant differences for time 1 (Wilks’s lambdaF(6, 83) = 1.54, p = .17) but a signi® cant overall gender effect for time 2 (Wilks’s lambdaF(6, 71) = 3.20, p < .01). The means, standard deviations, and univariate F-values for the gender comparisons are presented in Table 2. At time 2, there were signi® cant genderdifferences for general atti-tudes toward videoconferencing, attiatti-tudes toward taking a course through videoconferencing, anxiety, and perceived effect. The post hoc comparisons indicated that the fe-male students were less positive about videoconferencing in general, were less positive about taking a course through videoconferencing, expressed greater anxiety related to videoconferencing, and felt that videoconferencing had a less positive effect than did the male students.
The MANCOVA analyses for the course comparisons showed no signi® cant overall effect of course at time 1 (Wilks’s lambda F(36, 372) · 1.00, p = .60) but a signi® cant difference across courses at time 2 (Wilks’s lambda F(30, 530) = 2.56, p = .0001). The means, standard deviations, and univariateF-values for the course comparisons are given in Table 3 for time 1 and in Table 4 for time 2. Tukey post hoc comparisons indicated that students in Course D had a more negative attitude toward videoconferencing in general as well as a more negative attitude toward taking a course through videoconferen-cing, reported higher anxiety related to taking a course through videoconferencing, and were more likely to feel
TABLE 2
Means, standard deviations, and ANCOVAF-values for gender comparisons
Time 1 Time 2
Men Women Men Women mean mean mean mean
Measures (SD) (SD) F (SD) (SD) F General attitudes 3.82 3.83 < 1.00 3.74 3.51 8.20¤ ¤ (.58) (.62) (.83) (.75) VIDEO-1 attitudes 3.54 3.43 1.89 3.46 2.88 15.25¤ ¤ ¤ (.70) (.81) (.93) (.98) VIDEO-2 attitudes 3.94 3.74 1.37 3.85 3.39 9.49¤ ¤ (.76) (.94) (.93) (1.03) Anxiety 2.48 2.59 < 1.00 2.42 2.95 11.80¤ ¤ ¤ (.62) (.80) (.84) (.97) Perceived effect 2.03 2.15 < 1.00 2.30 2.15 3.40 (.57) (.51) (.61) (.59) Tolerance for 2.89 2.97 < 1.00 Ð Ð Ð ambiguitya (1.06) (.99)
aThis was measured at time 1 only. p= .06,¤ p< .05,¤ ¤ p< .01,¤ ¤ ¤ p< .001
TABLE 3
Time 1 means, standard deviations, and ANCOVAF-values for course comparisons
Course
A B C D E F G
Mean Mean Mean Mean Mean Mean Mean Measure (SD) (SD) (SD) (SD) (SD) (SD) (SD) F General attitudes 3.66 3.91 4.12 3.80 3.56 3.90 3.85 1.21 (.59) (.48) (.38) (.71) (.50) (.53) (.77) VIDEO-1 attitudes 3.05 3.75 3.83 3.39 3.34 3.48 3.50 1.11 (.78) (.88) (.42) (.88) (.65) (.70) (.92) VIDEO-2 attitudes 3.37 4.18 4.16 3.80 3.44 3.90 3.73 1.17 (.73) (.64) (.65) (1.11) (.74) (.88) (1.01) Anxiety 2.83 2.20 2.23 2.58 2.85 2.66 2.47 1.28 (.69) (.45) (.47) (.98) (.55) (.77) (.85) Perceived effect 1.83 2.10 2.07 2.18 1.98 2.38 2.18 1.57 (.45) (.43) (.42) (.54) (.54) (.48) (.54)
Tolerance for ambiguity 2.69 2.90 3.00 3.05 2.67 3.17 2.91 < 1.00 (.85) (1.10) (.77) (1.07) (1.05) (1.11) (.99)
that videoconferencing had an overall negative effect than did students in other courses. On the other hand, students enrolled in Course B and Course G had more positive at-titudes toward videoconferencing in general and taking
a course through videoconferencing and reported lower anxiety than did students in the other courses.
To ensure that the matched sample was representative of the time 1 and time 2 samples, we compared the students
TABLE 4
Time 2 means, standard deviations, and ANCOVAF-values for course comparisons
Course
A B C D E F G
Mean Mean Mean Mean Mean Mean Mean
Measure (SD) (SD) (SD) (SD) (SD) (SD) (SD) F
General attitudes 3.40a 3.95ab 3.63 3.16bc 3.48 3.53 4.17c 4.40¤ ¤ ¤
(.54) (.80) (.65) (.82) (.39) (.89) (.35)
VIDEO-1 attitudes 2.82d 3.70def 3.16g 2.34egh 2.83 2.98f 3.71h 7.36¤ ¤ ¤
(.77) (.93) (.73) (1.06) (.93) (.93) (.56)
VIDEO-2 attitudes 3.25i 4.23ijkl 3.79n 2.79jmn 2.91k 3.59l 3.95m 7.12¤ ¤ ¤
(.98) (.71) (.76) (.94) (1.16) (1.02) (.76)
Anxiety 3.02r 2.23ors 2.54p 3.44opq 3.18 2.85s 2.15q 6.85¤ ¤ ¤
(.82) (.72) (.70) (.99) (1.12) (.96) (.69)
Perceived effect 2.05 2.51t 2.28u 1.77tuvw 1.78 2.43v 2.56w 6.79¤ ¤ ¤
(.45) (.55) (.53) (.51) (.72) (.51) (.39)
Note. Means with the same superscript are signi® cantly different.
¤ ¤ ¤ p< .001
from the matched sample with those students who had completed only one of the questionnaires. There were no signi® cant differences between the groups for attitudes towards videoconferencing in general, attitudes toward taking a course through videoconferencing, and perceived effect of videoconferencing. The only signi® cant differ-ence found was for anxiety at time 1 (t = ¡ 2.32, p =
.02). At time 1, students in the matched sample reported slightly lower anxiety related to taking a course through videoconferencing compared to those students who either did not complete the time 2 questionnaire or whose time 1 questionnaires could not be matched to a questionnaire at time 2 (M = 2.41 andM = 2.72, respectively).
The means, standard deviations, and correlations be-tween the time 1 and time 2 measures for the matched sample are shown in Table 5. There was a signi® cant pos-itive correlation between the attitudinal variables at time 1 and the attitudinal variables at time 2 and a signi® cant negative correlation between anxiety at time 1 and gen-eral attitudes toward videoconferencing at time 2. Those students who had a positive attitude toward videoconfer-encing at the beginning of the semester continued to have a positive attitude toward videoconferencing at the end of the semester. Those students who expressed anxiety about taking a course through videoconferencing at the beginning of the semester reported more negative attitudes toward videoconferencing at the end of the semester. Attitude toward videoconferencing in general was signi® -cantly related to perceived effect of videoconferencing at time 2. Students who had more favorable attitudes toward videoconferencing at the beginning of the semester
re-ported a more positive effect of videoconferencing at the end of the semester. Althoughtolerance for ambiguity was signi® cantly related to the two time 1 measures assessing attitudes toward taking a course through videoconferen-cing (r = .29, p < .05 andr = .33, p < .01, respec-tively) as well as to perceived effect (r = .34, p< .01) at time 1, it was not signi® cantly related to any of the time 2 variables.
The means, standard deviations, and repeated-measures univariate F-values for site comparisons are presented in Table 6. None of the F-values for the site and time comparisons and the site£ time comparisons were sig-ni® cant. The means, standard deviations, and repeated-measures univariateF-values for gender comparisons are shown in Table 7. There were no signi® cant differences for gender and for the gender£ time interactions. How-ever, there were signi® cant time effects for attitudestoward taking a course through videoconferencing and anxiety. Compared to the beginning of the semester, the students in the matched sample reported signi® cantly less posi-tive attitudes toward taking a course through videocon-ferencing and expressed greater anxiety at the end of the semester.
For the matched sample, there was also a signi® cant time£ age effect for VIDEO-2 (F(1, 56) = 3.78, p < .05) and for anxiety (F(1, 58) = 4.16, p = .05). To ex-amine these differences further, we divided the female students in the matched sample across the two sites into those 25 years old or younger and those over 25 years and graphed the means for the two age groups for time 1 and time 2. There were no signi® cant differences between the
TABLE 5
Matched sample time 1 and time 2 means and standard deviations and correlations
Time 2 measures
General VIDEO-1 VIDEO-2 Perceived Time 1 measures Time 1 mean (SD) Time 2 mean
(SD) attitudes attitudes attitudes Anxiety effect General attitudes 3.84 3.66 .39¤ ¤ .23 .34¤ ¤ ¡ .22 .32¤ (.61) (.69) VIDEO-1 attitudes 3.52 3.12 .35¤ ¤ .42¤ ¤ ¤ .39¤ ¤ ¡ .31¤ ¤ .29 (.80) (.98) VIDEO-2 attitudes 3.85 3.43 .43¤ ¤ ¤ .40¤ ¤ ¤ .44¤ ¤ ¤ ¡ .32¤ ¤ .24 (.84) (1.04) Anxiety 2.12 2.75 ¡ .23 ¡ .28¤ ¡ .23 .23 ¡ .21 (.57) (1.02) Perceived effect 2.15 2.19 .12 .16 .15 ¡ .10 .47¤ ¤ (.54) (.68) Tolerance for 3.02 Ð .08 .12 .00 ¡ .06 .17 ambiguity (1.09) ¤ p< .05,¤ ¤ p< .01,¤ ¤ ¤ p< .001. TABLE 6
Matched sample repeated measures results for site comparisons
Time 1 Time 2 Origin. Remote Origin. Remote
F
Site mean mean mean mean F F £
Measure (SD) (SD) (SD) (SD) Site Time Time General attitudes 3.83 3.84 3.64 3.70 < 1.00 < 1.00 < 1.00 (.61) (.63) (.74) (.58) VIDEO-1 attitude 3.46 3.63 3.09 3.16 < 1.00 < 1.00 < 1.00 (.80) (.81) (.95) (1.05) VIDEO-2 attitude 3.80 3.97 3.36 3.56 < 1.00 1.29 < 1.00 (.86) (.80) (1.00) (1.12) Anxiety 2.09 2.18 2.77 2.70 < 1.00 < 1.00 < 1.00 (.55) (.63) (1.01) (1.05) Perceived effect 2.19 2.07 2.27 2.09 < 1.00 < 1.00 1.10 (.50) (.62) (.67) (.70)
age groups at time 1 on VIDEO-2 and anxiety. At time 2, the younger group’s scores on these measures were similar to their time 1 scores. However, compared to their time 1 scores, the older group showed a signi® cant decrease in their attitudes toward taking a course through videoconfer-encing (VIDEO-2), with older women at the origination site having the least favorable attitudes of the four groups at time 2. Similarly, there were no signi® cant differences
among the four groups at time 1 for anxiety, but the older women at the origination site reported a signi® cant in-crease in anxiety at time 2, especially compared to the younger women at the same site.
Eighty-one students at time 1 responded to the open-ended question asking them to indicate any concerns they had about the use of videoconferencing in university courses. A content analysis of their responses identi® ed
TABLE 7
Matched sample repeated-measures results for gender comparisons
Time 1 Time 2 Men Women Men Women
F
Gender mean mean mean mean F F £
Measure (SD) (SD) (SD) (SD) Gender Time Time General attitudes 3.81 3.84 3.76 3.63 < 1.00 < 1.00 1.47 (.60) (.62) (.64) (.71) VIDEO-1 attitude 3.58 3.50 3.42 3.03 < 1.00 3.26 < 1.00 (.82) (.80) (.81) (1.01) VIDEO-2 attitude 3.94 3.83 3.66 3.36 < 1.00 6.75¤ ¤ < 1.00 (.77) (.86) (.83) (1.09) Anxiety 1.96 2.16 2.34 2.86 1.87 8.62¤ ¤ 1.92 (.47) (.60) (.79) (1.06) Perceived effect 2.13 2.14 2.18 2.20 < 1.00 1.96 < 1.00 (.57) (.53) (.72) (.69) p= .07,¤ p< .05,¤ ¤ p< .01. TABLE 8
Concerns related to videoconferencing
Concern % Examples of comments
1. Technical dif® culties 31 Background noise Dif® cult to hear Unclear video image
Extensive set-up time or adjusting equipment takes time from lecture What will happen if technology fails
2. Accessibility of instructor 28 Loss of direct contact with instructor Dif® cult to relate to image on the screen Dif® culty in getting together with the professor Can’ t ª connectº with the professor
3. Distracting/impersonal environment 25 Classroom setting impersonal Dif® culty maintaining attention span
Shifts the focus from the subject of the course to the technology Creates a sterile environment
4. Participation constraints 20 Dif® culty asking questions; less willing to ask questions for fear of camera being turned on him/her
Stilted discussion; loss of classroom exchange
People at remote site feel like observers rather than participants Intimidated by camera; self-conscious about seeing self on camera
four major areas of concern: technical dif® culties, acces-sibility to the professor, distracting/impersonal environ-ment, and participation constraints. These concerns, along with details on each of the concerns, are listed in Table 8.
DISCUSSION
Whether students were at the origination site or the re-mote site had little effect on their attitudes and reactions
to videoconferencing. What we did see was a less posi-tive attitude toward videoconferencing in general and to-ward taking a course through videoconferencing as well as an increase in anxiety for students at both sites at the end of the semester compared to the beginning of the semester. It may be that, because these students had had no prior experience with videoconferencing, they did not know what to expect. This suggests that it is as impor-tant to prepare students for the virtual classroom as it is to prepare instructors. Robinson (1995) argued that the vir-tual classroom can be an intimidating place and students must have an informal opportunity to become familiar with it.
It may also be that, because this was the ® rst time the university had offered courses through videoconferencing, there were some technical problems in delivery that needed to be worked out. Besser (1996) noted the importance of providing signi® cant technical support, and this is espe-cially true for novice users. Students did complain about the inordinate amount of time lost at the beginning of each class while the instructor set up the equipment. It some-times took more than 20 minutes to do this.
This is one of the ® rst studies to compare the attitudes of men and women to videoconferencing. The gender com-parisons showed no signi® cant differences between men and women at the beginning of the semester but signi® -cant gender differences at the end of the semester. Women had less positive attitudes toward videoconferencing and taking a course throughvideoconferencingand higher anx-iety than men. We did not ® nd signi® cant gender differ-ences for the matched sample but this could in part be due to the small number of men in the matched sample. The ® ndings showed that both men and women had less positive attitudes toward taking a course through video-conferencing and higher anxiety at the end of the semester but that women were even less likely to ® nd videocon-ferencing a positive experience. The time£ age interac-tions indicated that it was the older women at the orig-ination site who had the least favorable attitudes toward taking a course through videoconferencing and who re-ported the greatest anxiety. These ® ndings suggest that some older female students may have problems adapt-ing to the use of videoconferencadapt-ing as an instructional tool. The fact that the older female students at the remote site did not differ from the younger female students indi-cates that not all older female students will have a more unfavorable attitude to videoconferencing. It is possible that men and women differ in their reactions to the effect that videoconferencing has on social processes, especially communication processes. Women may ® nd the restric-tions posed by the technology to be more disruptive than do men.
Students in the seven courses expressed similar atti-tudes and reactions to videoconferencing at the beginning
of the semester but there were signi® cant differences in students’ attitudes and reactions across courses at the end of the semester. These ® ndings indicate that either the course content or the instructor or both can have an effect on students’ attitudes and reactions to videoconferencing. Leidner and Jarvenpaa (1993) suggested that course sub-ject matter is related to the effectiveness of instructional innovationsand that different types of technological meth-ods may be best suited for different subject matters. One of the courses in which students exhibited the most positive attitudes towards videoconferencing and the least anxiety was the communications studies course. It is possible that a course in communications is better suited to videocon-ferencing than some of the other courses that were offered. This suggests that not all courses should or can be taught via videoconferencing or, at the very least, some courses may require greater modi® cation than others to make them suitablefor the videoconferencingenvironment. However, it is more likely that someone who teaches communica-tions will be better able to adapt his or her teaching style to different technologies.
One student wrote on her questionnaire at the end of the semester, ª Not all profs will be able to teach through videoconferencing (because of the type of course or their personality) and I’ m afraid that many of those that are un-suitable will be using this method.º This points out the need to train instructors in how to be effective at teach-ing a course usteach-ing videoconferencteach-ing. Researchers inves-tigating the use of computer technology in classrooms have found instructor differences. Leidner and Jarvenpaa (1993) found that the differences in instructor styles gener-ated different outcomes relgener-ated to the amount and type of interaction and student reactions. Hiltz (1995) found that instructors differed a great deal in terms of the amount and effectiveness of the efforts they put into organizing and conducting their online classes. Hiltz concluded that a virtual classroom is not the proper mode for all faculty or all students.
In the present study, the inexperience of the university, the instructors, and the students with videoconferencing, the unfamiliarity of instructors with the technology, and aspects of the technology itself that interfere with social processes may in large measure account for the less fa-vorable attitudes toward videoconferencing at the end of the semester compared to the beginning of the semester. The ® ndings of this study may only generalize to contexts involving novice users. One of the major limitations of the present study was the inability to compare the attitudes and reactions of students with prior experience to those who were novice users. We plan to conduct a follow-up study that would include students with prior exposure to the use of videoconferencing for classroom instruction. It may be that different results would be obtained if the students and instructors have more exposure to videoconferencing and
the university has more experience with delivering courses via videoconferencing.
The virtual classroom has broad employment implica-tions for faculty that have not been addressed in the lit-erature. The use of videoconferencing raises issues of workload and job security. In the past, the university hired instructors to teach the courses at the remote sites. With videoconferencing, the university had the same instructor who was teaching the course on the main campus also teach the students at the remote site (or, for one of the courses, the same instructor at one remote site teach the course to students at the other remote site). The result, of course, was that the instructors’ class size and hence workload had been increased without an increase in teaching assistance support. The faculty at this particular university are union-ized, as are most faculty at Canadian universities. The col-lective bargaining agreement has provisions about course workload and stipulations against increasing course work-load. As university administrators, who are under a great deal of pressure to cut costs, attempt to increase the use of videoconferencing they are very likely to be confronted by the union on this issue. This has already happened at one university. The literature on the virtual classroom fo-cuses upon the technical aspects, faculty and/or student factors, and support systems. However, there are likely to be societal or institutional forces that will play a role in the implementation and diffusion of technology within a speci® c context.
The use of videoconferencingis increasing in the work-place as well as in educational institutions. Companies are increasingly using videoconferencing to conduct business both nationally and globally (Hildebrand, 1995). Students who have been exposed to videoconferencing in their uni-versity courses will be better prepared for the business world. Hildebrand contends that videoconference literacy will be as essential as computer literacy. Given the rapid expansion of videoconferencing, it is important that we identify those factors that both facilitate and inhibit the ef-fectiveness of this technology. There has been a tendency to force users to adjust to the technology rather than de-signing the technology to the needs of the users. Robinson (1995) noted that neither the university nor the telecommu-nications suppliers have yet designed a virtual classroom according to the speci® cations of the teachers using it. Fussell and Benimoff (1995) argued that design decisions are driven by what is technically feasible rather than what will best suit the needs of the users. In addition, these researchers suggested that developers of new telecommu-nications technology need to take into account the social and cognitive processes underlying the technology. As Whittaker (1995) noted, ª The successful application of video technology for interpersonal communications still requires extensive researchº (p. 525).
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