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Journal of Education for Business
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A Comparison of Online (High Tech) and
Traditional (High Touch) Learning in Business
Communication Courses in Silicon Valley
Mary F. Fortune , Bethany Shifflett & Robert E. Sibley
To cite this article: Mary F. Fortune , Bethany Shifflett & Robert E. Sibley (2006) A Comparison of Online (High Tech) and Traditional (High Touch) Learning in Business Communication Courses in Silicon Valley, Journal of Education for Business, 81:4, 210-214, DOI: 10.3200/ JOEB.81.4.210-214
To link to this article: http://dx.doi.org/10.3200/JOEB.81.4.210-214
Published online: 07 Aug 2010.
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ABSTRACT.In this study, the authors
explored differences in perceptions of skill development and the value of face-to-face interaction between students taking a busi-ness communication course offered online and in the traditional, on-campus classroom environment. They hypothesized that there would be no statistically significant differ-ence in face-to-face interaction or perceived learning scores between students enrolled in online sections and those enrolled in tra-ditional, in-class sections of business com-munication courses. In both cases, the authors observed a statistically significant difference. Findings suggest that the two groups of students were quite similar with respect to perceptions of skill development; however, the authors observed differences in the area of face-to-face interaction.
Copyright © 2006 Heldref Publications
n academia today, students have several choices regarding how they pursue their education goals. An increasingly popular and newer mode of study is to enroll in online classes that do not require students’ presence on campus. This change is challenging the traditional notion that education needs to take place in the classroom. To engage in this innovative activity, many professional educators have modified their current teaching styles to incorporate new technology into their curricula (Savenye, Olina, & Niemeyk, 2001; Walker, 2003). Given these developments, one area of inquiry is to compare student percep-tions in online and on-campus teaching environments.
Our study was unique because the participants live in Silicon Valley, a very techno-centric environment driven by innovation and entrepreneurial-like attitudes. Because of the technology-rich environment, students may feel more comfortable with diversified teaching modalities. Previous resear-chers (Arbaugh, 2001, 2002; Black, 2001; Long & Javidi, 2001; Prater & Rhee, 2003) began to explore the dif-ferences between traditional in-class and online pedagogy. Prater and Rhee found that differences in learning, based on collaborative media tools and techniques, showed that individuals can learn business-writing skills with little
support through direct instruction. They also discovered no differences in learning between those working in face-to-face groups as opposed to those working online using electronic collab-orative work (ECW) systems. Arbaugh (2002) examined the technological and pedagogical structure of business courses in relationship to student learn-ing and satisfaction. Results indicated that technological characteristics were significant indicators of perceived stu-dent learning and delivered minimal satisfaction, yet pedagogical character-istics (e.g., face-to-face instruction in the classroom) were more noteworthy indicators of course satisfaction over time. One possible explanation for suc-cess in an online environment could be that students who select an online sec-tion may be more independent and pre-fer a more flexible learning environ-ment (Worley & Dyrud, 2003).
Purpose
Our purpose in this study was to compare students’ perceptions of the value of face-to-face interaction and perceived learning between online (high tech) and traditional, in-class (high touch) teaching modalities. Each setting covered the same business communication course content, but was approached in two fundamentally different ways. One section was
A Comparison of Online (High Tech)
and Traditional (High Touch) Learning
in Business Communication Courses
in Silicon Valley
MARY F. FORTUNE BETHANY SHIFFLETT ROBERT E. SIBLEY
SAN JOSE STATE UNIVERSITY SAN JOSE, CALIFORNIA
I
instructed online, using highly techni-cal Internet tools such as WebCT, video streaming, instant messaging, and e-mail, whereas the other course was taught on campus in a traditional classroom environment.
Research Hypotheses
Face-to-Face Interaction
For this study, we defined face-to-face interaction as the instructional methods that use immediacy behaviors (e.g., feedback, communication) to reduce social distance and alleviate information overload (Hughes, Ryan-Jones, Smith, & Wickersham, 2002; Hutchins, 2003). In studies supporting the idea that students enrolled in an online course desire less face-to-face interaction than those in traditional in-class sections, researchers have noted other student characteristics and prefer-ences that will increase an instructor’s effectiveness (Hutchins; Worley & Dyrud, 2003). These included new instructional approaches and a role shift between the student and instructor.
A network view of learning, as opposed to the traditional, teacher-centric view, can increase the amount of information available to students (Von-derwell, 2003). According to Santovec (2002), information wealth can be a good thing, but it can also create infor-mation overload for the student. By con-trast, the traditional in-class learning environment does not typically generate overload problems because the learning atmosphere is more hierarchical and students’ questions can be immediately addressed (Black, 2001). In a survey conducted by the American Federation of Teachers (2000), learning in an online class was similar to learning in a face-to-face class, but understanding difficult material outside of the class-room was still an issue for students because additional explanations were required of the instructor.
According to Arbaugh (2002), developing and delivering Web-based courses of instruction are becoming more complex. Greenlaw and DeLoach (2003) indicated that electronic discus-sion alters the focus of the learning process, replacing the single view of the instructor, presented by an
on-cam-pus course, with the various student views. This reduced reliance on the instructor increases collaboration and lessens the social distance within the online classroom environment (Arbaugh, 2001). However, online col-laboration does not automatically occur and must be initiated by the instructor in threaded discussions and required group assignments (Diaz & Cartnal, 1999).
Some researchers, such as Greenlaw and DeLoach (2003) and Hutchins (2003), suggest that student comments should be closely monitored so that online instructors adapt their course-work accordingly. This is particularly true for instructors trying to simulate face-to-face relationships or experi-ences rich with increased human inter-action. The challenge is that the nonver-bal immediacy behaviors, such as eye contact, body position, smiling, and moving around the classroom, that are used by on-campus instructors are not as easily duplicated by online instruc-tors (Arbaugh, 2001). However, online instructors can build trust with this non-verbal form of communication through the use of humor, emoticons (i.e., an animated online language using key-board characters), and video clips (Hutchins, 2003; Vaidyanathan & Aggarwal, 2001).
To further examine the topic, we sought to determine whether students in online sections value face-to-face con-tact differently than students in tradi-tional, on-campus sections. Thus, we tested the following hypothesis:
H0: There will be no statistically signifi-cant difference in face-to-face interaction scores between students enrolled in online sections and those enrolled in tra-ditional, in-class sections of business communication courses.
Perceived Learning
For this study, we defined perceived learning as the level of skill or knowl-edge that students believe they have developed while taking a business com-munication course. Halsne and Gatta (2002) examined the differences between instructional modalities in terms of learner-content interaction with a small sample and concluded that there were no significant differences in
learn-ing. This construct has also been the subject of study by other researchers (e.g., White, 2000), who have found no significant differences in learning between online and on-campus teaching modalities.
In one of the first longitudinal stud-ies regarding the effectiveness of online management education, Arbaugh (2001, 2002) and Arbaugh and Duray (2002) suggested that choosing the right technology and software package for an online course is crucial because instructor behavior cannot overcome technical difficulties. Santovec (2002) agreed with this finding and added that eagerly adopted but poorly designed high-tech curricula can negatively affect the learning experience. Hutchins (2003) also noted that both verbal and nonverbal immediacy instances (class-room interaction) affect learning and student satisfaction with content and instructor.
Issues that might influence learning in an online setting include the selection of technical tools and their implementa-tion. LaRose and Whitten (2000) described the computer as a “social actor” (p. 326), a construct that adds functionality to the course, thereby becoming an instructional object that affects the overall perceptions of learn-ing. These perceptions are interesting because students’ learning styles vary. According to Worley and Dyrud (2003), students who choose online over on-campus courses are more independent by nature, and instructors who under-stand this independence can modify their instruction to fit the needs of the student and take advantage of findings indicating that students enjoyed elec-tronic submission of their homework, flexible schedules, and assignment choices.
To further investigate perceptions regarding skill development, we com-pared responses from students enrolled in the online and on-campus business communication courses by examining the following hypothesis:
H0: There will be no statistically signifi-cant difference in perceived learning scores between students enrolled in online sections and those enrolled in tra-ditional in-class sections of business com-munication courses.
METHOD
Instrument Development
According to Churchill (1979), the protocol used for scale development, refinement, and testing includes the following: (a) review of the literature; (b) discussion of concepts and ideas with academic experts and practition-ers; (c) specification of the construct domain; (d) development of sample items and scales; (e) collection of data; and (f) evaluation of measurement properties of scales.
After a review of the existing litera-ture, the scale development process began with the collection and review of qualitative data obtained from students enrolled in two on-campus classes (n= 50 total) and one online class (n= 25), offered during the 2002 spring semes-ter. We asked the students enrolled in these classes to respond to two open-ended fundamental questions regarding skill improvement and overall impres-sions (i.e., What was it like taking this class online or on-campus? and Did your overall communication skills get any better?). In the fall of 2002, we presented these findings at the Associ-ation for Business CommunicAssoci-ation (ABC) Conference in Cincinnati, Ohio. We also used this information to guide the development of the High Touch versus High Tech (HTHT) survey instrument. The HTHT survey instru-ment had 51 questions that were orga-nized into several parts: (a) demo-graphic information; (b) learning environment; (c) face-to-face commu-nication; (d) technical skills (e.g., e-mail, WebCT, Internet, computers); (e) amount of time and course content; (f) course requirements; and (g) com-ments section. Demographic informa-tion included grade point average (GPA), enrollment status (e.g., junior, senior), major, section (i.e., on-campus or online), gender, work status, com-puter proficiency, amount of time spent on homework, and class preparation.
We used a 5-point Likert-type scale with the following options: strongly agree (5), agree (4), neither agree nor disagree(3),disagree(2), and strongly disagree (1). Early in the spring of 2003, five business professionals we selected from both the academic and
practitioner communities reviewed the HTHT survey questionnaire instrument, and we administered it concurrently to one online and one on-campus section of a business communications course. On the basis of feedback and student responses, we revised the survey so that it contained 62 items. On the survey, there were 14 items related to face-to-face interaction and 10 related to per-ceived learning.
Sample
The sample for this study comprised students enrolled in four on-campus and four online sections of business commu-nications during the spring and summer semesters of 2003. We distributed 200 survey questionnaires to a total of eight class sections of 25 students per section. We received a total of 90 survey ques-tionnaires from the online students (90% response rate), and 98 survey questionnaires from the on-campus group (98% response rate).
Data Analysis
We used factor analysis (VARIMAX orthogonal rotation) to examine con-tent validity. To examine the reliability of scores from the face-to-face interac-tion and perceived learning factors, we computed coefficient alpha for each factor. To determine factor scores for each part, we used items loading .70 or higher on a factor. We derived the face-to-face interaction and perceived learning scores for each participant by computing an average across the items contained in each factor. We summa-rized the demographic information using frequency distribution tables for categorical data and included type of course, section enrolled, gender, major, and technical skill level. We also obtained measures of central ten-dencies and variability for continuous data such as age, GPA, and number of hours devoted to work and studying. We examined the two hypotheses using a Mann-Whitney test on the face-to-face interaction and perceived learning scores. We used this statistic because the normality assumption associated with an analysis of variance was not met.
RESULTS
Based on factor analysis, the follow-ing items (which loaded .70 or higher) comprised the face-to-face interaction (7) and perceived learning (6) factors.
The Face-to-Face Interaction scale consisted of the following items:
1. I would rather take this class on-campus than online.
2. Face-to-face instruction would help me learn more.
3. Face-to-face instruction would help me understand the communication concepts better.
4. Face-to-face instruction would be a better way for me to learn the content or course materials.
5. The face-to-face learning environ-ment would contribute to my overall satisfaction with the course.
6. Being in a class with face-to-face communication would improve my abil-ity to learn.
7. I would prefer face-to-face instruc-tion.
The six-item scale for Perceived Learning comprised the following items: 1. My interpersonal skills have improved by taking this course.
2. My writing skills are better as a result of taking this course.
3. My writing skills have improved since taking this course.
4. The instruction was interesting, valuable, and meaningful.
5. My oral communication skills have gotten better since taking this course.
6. The class materials support my ability to learn the prescribed communi-cation theories.
Four other items loaded on the face-to-face interaction factor, but we did not include them because the factor loadings were low (−.45 and −.69). An additional seven items loaded on the perceived learning factor, but we did not include them because their factor-loading scores were less than .70 (.35 and −.66).
We assessed scale reliability by using Cronbach’s alpha (Cronbach, 1951). Coefficient alpha for the seven-item Face-to-Face Interaction scale was .95, and the six-item Perceived Learning scale achieved an alpha of
.89, exceeding the generally accepted standard of .70 (Nunnally, 1978).
Demographics
Regarding the type of course, 47.9% of the participants were enrolled in the online section. Overall, the group was composed of predominantly seniors (61%), followed by juniors (29.3%) and sophomores (1.1%). There were slightly more women (55%) in the sample than men, and the mean age of participants was 25 years, with a range of 19 to 27 years. Regarding employ-ment, a majority (47.9%) of partici-pants worked part-time, 26.5% worked full-time, and 24.5% did not work while taking courses. With respect to computer proficiency, 49.5% of partic-ipants indicated that they were very skilled with computers, 38.6% said that they were somewhat proficient, 10.9% said that their computer profi-ciency was okay, and 1.1% of partici-pants claimed that they were not very proficient with computers.
Analysis of Research Questions
The first research hypothesis con-cerned differences in students’ percep-tion of the value of face-to-face interac-tion depending on whether they were enrolled in an online or a traditional, on-campus setting. We observed a statisti-cally significant difference in medians (p< .001). The online group median for desired Face-to-Face Interaction was 3.42, whereas the on-campus group median was 4.57 (see Table 1).
The second research hypothesis con-cerned differences in students’ percep-tions of learning between online and on-campus teaching modalities. We
observed a statistically significant dif-ference (p < .001). The online group median for Perceived Learning was 4.0, whereas the on-campus group median was 4.33 (see Table 1). Although statis-tically significant, the practical signifi-cance (η2= .04) was minimal.
DISCUSSION
As discovered in this study, perceived learning among students enrolled in either the online or on-campus sections was similar. We found that students studying at a metropolitan university located in a highly technical environ-ment reported similar responses as researchers noted in smaller studies (Diaz & Cartnal, 1999; Worley & Dyrud, 2003) where the online mode of instruction was just as effective as the traditional in-class delivery of instruc-tion with respect to skill development (Bowman, 2003: Long & Javidi, 2001; Tucker, 2001).
The differences observed in the value placed on face-to-face interaction lend support to the research conducted by Worley and Dyrud (2003). Their work suggests that students selecting the online environment may be more inde-pendent than students who select a tra-ditional on-campus course. To speculate a bit further, perhaps the online group did not feel the need for face-to-face interaction present in the classroom because the instructor was able to simu-late the interaction through actions rec-ommended by others (Arbaugh, 2001; Arbaugh & Duray, 2002). These actions include using personal experiences and humor, addressing students by name, and providing feedback in real-time or with online instant messages (Greenlaw
& DeLoach, 2003). Along the same lines, Vaidyanathan and Aggarwal (2001) suggest that the online learning experience becomes more interactive and engaging by using nonverbal com-munication techniques such as humor and tech media. Because the high-tech environment may not always pro-duce stimulating personal, face-to-face interaction, using the correct type of electronic support is important for suc-cessful communication (Prater & Rhee, 2003).
We suggest several key ideas for future research. One is to explore ways that students evaluate themselves so that they can self-select into the teaching modality most appropriate for them. Such reflection would have to include the perceived need for face-to-face interaction.
Another area to examine would be students’ level of success in an online course based on their frequency and use of instant messaging and interac-tion with public or group discussion boards (Mazzolini & Maddison, 2003). For such a study, success could be defined as the grade the student receives at the end of the course. An additional line of inquiry would be to assess the value of activities such as electronic fieldtrips that require stu-dents to e-mail and communicate with others in the class while exploring a specific topic (Carty, 1998).
The results of this and future studies can help instructors improve upon the way online curricula are developed and implemented while continually assess-ing and maintainassess-ing successful student-learner outcomes. Strengthening the online delivery of instruction has both long and short-term benefits. In the short term, curricula are enhanced and learning facilitated. In the long term, exposing students to the technology associated with online instruction devel-ops the types of skills and experiences employers are seeking (Worley & Dyrud, 2003; Prater & Rhee, 2003).
NOTE
Correspondence concerning this article should be addressed to Mary F. Fortune, Department of Marketing, College of Business, San Jose State University, One Washington Square, San Jose, California 95192-0069. E-mail: DrMFFortune@ aol.com
TABLE 1. Study Results for Face-to-Face Interaction and Perceived Learning
Group
Online On-campus Variable n M Mdn SD n M Mdn SD
Face-to-face
interaction 89 3.43 3.42 .91 97 4.44 4.57 .51 Perceived
learning 90 4.00 4.00 .55 98 4.26 4.33 .67
REFERENCES
American Federation of Teachers (AFT). (2000). Distance education: Guidelines for good prac-tice. Teacher-centered theory and practice in distance education: Cases from higher educa-tion. Washington, DC: Author.
Arbaugh, J. B. (2001). How instructor immediacy behaviors affect student satisfaction and learn-ing in Web-based courses. Business Communi-cation Quarterly, 64(4), 42–54.
Arbaugh, J. B. (2002). A longitudinal study of technological and pedagogical characteristics of Web-based MBA courses. In D. Nagao (Ed.), Academy of Management Best Papers Proceed-ings. Retrieved September 24, 2003, from http://search.epnet.com/direct.asp?an=7898655 2&db=buh
Arbaugh, J. B., & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with Web-based courses: An exploratory study of two on-line MBA pro-grams [Electronic version]. Management Learning, 33(3), 231–247.
Black, G. (2001, November). A comparison of traditional, online and hybrid methods of course delivery. Paper presented at the Interna-tional Conference on Teaching Online in High-er Education: Synthesizing Teaching Strategies virtual conference. Retrieved March 5, 2004, from
http://www.ipfw.edu/as/tohe/2001/Papers/black 1.htm.
Bowman, J. P. (2003). It’s not easy being green: Evaluating student performance in online busi-ness communication courses. Business Commu-nication Quarterly, 66(1), 73–78.
Carty, S. (1998). The challenge of the educated Web. Searcher, 6(4), 32–33, 36–40.
Churchill, G. A. (1979). A paradigm for develop-ing better measures of marketdevelop-ing constructs. Journal of Marketing Research, 16, 64–73. Cronbach, L. J. (1951). Coefficient alpha and the
internal structure of tests. Psychometrika, 16, 297–334.
Diaz, D., & Cartnal, R. (1999). Students’ learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching, 47(4), 130–135.
Greenlaw, S. A., & DeLoach, S. B. (2003). Teach-ing critical thinkTeach-ing with electronic discussion. The Journal of Economic Education, 34(1), 36–52.
Halsne, A. M., & Gatta, L. A. (2002). Online versus traditionally-delivered instruction: A descriptive study of learner characteristics in a community college setting. Online Journal of Distance Learning Administration, 5(1). Retrieved Octo-ber 11, 2003, from http://www.westga.edu/ distant/ojdla/spring51/halsne51.html
Hughes, S. C., Ryan-Jones, D. A., Smith, S., & Wickersham, L. (2003). Overcoming social and psychological barriers to effective on-line col-laboration. Educational Technology & Society, 5(1), 86–92.
Hutchins, H. (2003). Instructional immediacy and the seven principles: Strategies for facilitating online courses. Online Journal of Distance Learning Administration, 6(3). Retrieved Octo-ber 31, 2003, from http://www.westga.edu/~ distance/ojdla/fall63/hutchins63.html LaRose, R., & Whitten, P. (2000). Rethinking
instructional immediacy for Web courses: A social cognitive exploration [Electronic ver-sion]. Communication Education, 49(4), 320–338. Retrieved October 31, 2003, from http://www.westga.edu/~distance/ojdla/fall63/h utchins63.html
Long, L., & Javidi, A. (2001). A comparison of course outcomes: Online distance learning ver-sus traditional classroom settings. Retrieved March 3, 2004, from http://www.communication .ilstu.edu/activities/NCA2001/paper_distance _learning.pdf
Mazzolini, M., & Maddison, S. (2003). The effect of instructor intervention on student participa-tion in online discussion forums. Computers
and Education, 40(3), 237–253.
Nunnally, J. C. (1978). Psychometric theory(2nd ed.). New York: McGraw-Hill.
Prater, E., & Rhee, H. (2003). The impact of coor-dination methods on the enhancement of busi-ness writing. Decision Sciences Journal of Innovative Education, 1(1), 57–70.
Santovec, M. L. (2002). The seven myths of online learning: Which do you believe? Dis-tance Education Report, 6(21), 1–6.
Savenye, W. C., Olina, Z., & Niemeyk, M. (2001). So you are going to be an online writing instructor: Issues in designing, developing and delivering an online course. Computers and Composition, 18(4), 371–385.
Tucker, S. (2001). Distance education: Better, worse, or as good as traditional education? Online Journal of Distance Learning Administra-tion, 4(4). Retrieved October 11, 2003, from http://www.westga.edu/~distance/ojdla/winter44/ tucker44/html
Vaidyanathan, R., & Aggarwal, P. (2001). Tech-nology-enhanced learning: The next wave. In S. Van Auken & R. P. Schlee (Eds.),Riding the wave of innovation in marketing education(pp. 59). Proceedings of the Marketing Education Association. Madison, WI: Omnipress. Vonderwell, S. (2003). An examination of
asyn-chronous communication experiences and per-spectives of students in an online course: A case study. Internet and Higher Education, 6(1), 77–90.
Walker, K. (2003). Applying distributed learning theory in online business communication cours-es. Business Communication Quarterly, 66(2), 55–67.
White, C. (2000). Learn online: Students and fac-ulty respond to online distance courses at Grant MacEwan community college. T.H.E. Journal, 27(9), 66–70.
Worley, R. B., & Dyrud, M. A. (2003). Grading and assessment of student writing. Business Communication Quarterly, 66(1), 79–96.
