The Relationship of Bandwidth, Interaction and Performance in Online Courses: A Study
Yan Wu, Ph.D.,
Student, Interdisciplinary Program in Information Sciences
University of North Texas
Email: ywu@lis.admin.unt.eduPhilip Turner,
Vice Provost for Learning Enhancement
University of North Texas
Denton , Texas
Center for Distributed Learning
University of North Texas P.O. Box 310889
Denton , TX 76203-0889
Email:pturner@unt.edu
Abstracts
Although it is often assumed that the larger the bandwidth of the connection to the Internet, the better, few studies have actually been conducted to investigate the impact of various bandwidth connections on student performance in online courses. The goals of this study were to compare student online behaviors under different bandwidth connections and investigate whether the type of course might be related to the impact of bandwidth for the behaviors. The present findings suggest that, while the use of “dial-up” versus “broadband” was related to some effects of behavioral differences, this relationship was not consistent across courses with different learning tasks. The study suggests that for online courses that are heavily learner-to-learner oriented, special considerations for students using dial-up access be considered
IntroductionThe increasing use of technologies, such as computers, the Internet, the World Wide Web in higher education are shaping the current generation of distance learning. Among them, broadband technology has had a great impact on distance education due to its drastic increase of availability and use in the past five years. High speed Internet connection provided by the broadband technology does not only have a significant effect on reducing class time allocated by students searching for information on the Internet, but also provide them with more chance of synergy for knowledge construction by facilitating browsing, scanning, searching, transferring, and comparison of information on Web-based courses. It creates great potential for making more and more online interaction possible.
While the distribution of broadband access has proceeded rapidly, there are still many students taking online classes that are utilizing dial-up to access the Web. This may be because they cannot pay for the higher cost of broadband or because this service is not available where they live. Accessing an online class at dial-up speeds hampers the delivery of sound, video, and graphics and creates a divide among students. Some course providers restrict their users to only those who can access the course via broadband. Others utilize only text and simple graphics so that the lowest bandwidth can be accommodated. Interestingly, there is almost no research on the impact of bandwidth on student behavior and performance.
Relevant literature
1. Dial-up connection and broadbandBandwidth is the capacity of information carriage in unit time which directly affects the flow size and flow rate of the data transfer on the Internet. “Dial-up”, or narrowband Internet access, offers access through traditional telephone lines at speeds of between 28 and 56 kilobits per second (Kbps) (or 53Kbps set by current FCC regulations). The broadband Internet access or high-speed Internet access offers connection at 200 Kbps or higher through a variety of transmission media, including Digital Subscriber Line (DSL), Cable Modem, Wireless, Satellite, Optical Fiber, and Power Line. Narrow bandwidth is most often utilized for text-based information, as congestion is easy to occur if workload is over the capacity. Broad bandwidth, which has high transmission capacity and is much faster than a 56K modem, allows users to send and receive data in terms of megabits, or millions of bits, per second (Mbps) and makes the use of large files, real-time audio and video transfer possible.
Worldwide broadband penetration growth has increased at compound annual growth rate (CAGR) of 155 percent from 1999 till the end of 2004. In the United States, the number of residential broadband users increased the highest ever by 36 percent in 2004, accounting for more than half of the total residential online users. The number of users migrating from dial-up to broadband continues to grow, reaching at 58.82 percent in May 2005, and the momentum is supposed to continue through the end of the decade (Point Topic Ltd., 2005).
2. Speed and performance
Speed has been considered as one of the highest rated problems in using the Web (Kehoe, Pitkow, Sutton, Aggarwal, & Rogers, 1998; Lightner, Bose, & Salvendy, 1996). Slow system response may lead to both serious psychological and economical consequences. In some cases, performance declined when delays exceeded only 1 second (Thadhani, 1981), and 2 seconds delay time is regarded as “loss of conversational nature (Miller, 1968). Delay of 8 seconds is commonly regarded as the threshold for adverse psychological and performance consequences (Kuhmann, 1989; Ramsay, Barbesi, & Preece, 1998; Shneiderman, 1998). The anxiety and stress characterized by increased heartbeat, respiratory rate, blood pressure or perspiration of users due to extended waiting are easily detectable. Some researchers (Lazarus & Flokman, 1984) believe that these negative symptoms are due to the general sense of waste induced by idleness and by the uncertainty associated with the total waiting time.
While improving page load speeds from 8 seconds to 2-5 seconds doubles site traffic (Wonnacott, 2000), slow response time means reduced level of trust and causes a loss of traffic as users seek alternatives (Nielsen, 2000; Hoxmeier & DeCesare, 2000). Both the actual and the perceived waiting are the top reasons that lead to user dissatisfaction (Hornik, 1984; Katz, Larson, & Larson, 1991; Bickford, 1999). The financial loss as the result of delay can also be very high. Research (Zona, 1999) shows that over one third of the Web users may simply give up trying to buy an item over the Internet after encountering excessive delays, resulting in the estimated loss of as much as $4.35 billion in e-commerce sales each year.
Several studies conducted in laboratory situations confirmed the linear relationship between speed and user performance (Morfield, Wlesen, Grossberg, & Yntema, 1969; Goodman & Spence, 1978; Doherty & Kelisky, 1979; Thadhani, 1981; Bergman, Brinkman, & Koelega, 1981; Dannenbring, 1984; Martin & Corl, 1986). However, these studies were conducted on tasks that are rather simple (Butler, 1983), and the results may not transferable to online courses. A limited number of studies on the effects of download speed on performance of more complex tasks have yielded mixed results. A study conducted by Turner and collaborators (Turner, Kaske, & Baker, 1990) showed that though subjects searching at 2400 baud rate did better than those searching at 300 baud rate for the same assigned online tasks, the difference was not significant when subjects’ search experience is considered.3. Interaction and bandwidth
Interaction is regarded as an important component of successful learning (Kearsley, 1995; Keegan, 1988; Thompson, 1990). Either in the form of learner's direct engagement with the course content through self-study or communication with the instructors or other students during the online study, interaction has been found to contribute to both online learning achievement and student satisfaction (Fulford & Zhang, 1993; Kearsley, 1995; Keegan, 1988; Kirby, 1999; Zhang & Fulford, 1994; Swan, 2001).
Moore (1989) suggested that effective distance education courses include all members of the learning community in educative interaction and defined interaction by dividing it into three categories: learner – content, learner – instructor and learner – learner interaction, with each having different effects on learner performance. Later learner-interface interaction was added as a type for online educational environments, and is described as the interaction taking place between the learner and the technology (Hillman, Willis, & Gunawardena, 1994). These four types of interaction are not mutually exclusive but may overlap in an online course (Kirby, 1999). Among many developed models of interaction, Moore 's three types of interaction construct is one of the most influential, and the three types of interaction are described below.
Learner – content interaction results from student studying the content through participating in various class activities. The main function of education is to engage the learner with the content in a planned process. According to Moore , it is “a defining characteristic of education,” because it changes learners' behavior toward an educational goal (p.2).
Learner – instructor interaction occurs between learners and instructors, while the instructors stimulate and guide learners' engagement with the subject content. The importance of learner – instructor interaction has been widely acknowledged both in online and traditional face-to-face learning environment (Garrison, 1990; Holmberg, 1995; Moore & Kearsley, 1996; Muirhead, 2001).
Learner – learner interaction occurs among learners of an online environment with or without the presence of instructors. Interaction among learners is helpful for both pedagogical reasons because exchange of ideas of learners could promote understanding of the content and building an online learning community that supports the sharing of goals, interests, and knowledge among learners. According to Moore , Learner – learner interaction is “sometimes an extremely valuable resource for teaching, and sometimes even essential” (p.4). Empirical evidence suggests that interaction among students as one of the most influential features of online courses (Swan, Shea, Fredericksen, Pickett, Pelz, & Maher, 2000), and it is positively correlated with student's satisfaction and performance with their online learning (Gilbert & Moore, 1998; Moore & Kearsley, 1996; Picciano, 1998; Rourke & Anderson, 2002; Soo & Bonk, 1998; Swan, 2001).
Moore 's theoretical framework of interaction has been investigated in several online education studies on learning outcomes and student satisfaction (Chen, 2001; Kelsey, & D'souza, 2004; Sherry, Fulford, & Zhang, 1998; Swan, 2001). However, there is a paucity of research on Moore 's typology of interaction with the consideration of either the course design or delivery, where learning tasks, content-structure, and technology support are concerned. These factors are believed relevant to distance learning research because they define the nature of the online learning environment that online interaction is carried out. As Garrison (1999) also implied that it is the design of the educational experience that include the transactional nature of the relationship between instructor, learners, and content is of significance to the learning experience.
A very limited number of published studies conducted on user online activities under different bandwidth access are unfortunately not in academic environment. One study conducted by an ISP (Newman, 2001) indicated that people using high-speed connections usually spent more time online and were anxious for updates to the applications. Berchtold and collaborators (2001) conducted their study on detecting difference of time spent between broadband users with narrowband users and found that users spend 27 percent more time online than when they only had narrowband access. They also found that these users spent more time on email, chat, and downloading music, and there was an increased use of game sites by broadband access users. More recent studies also found that broadband and dial-up users differ in their behavior, though others found that the effects were not statistically significant (Rappoport, Kridel, & Taylor, 2002).
4. Bandwidth and media study
Bandwidth affects types of medium that could be provided for teaching. It therefore also determines what kinds of interaction could be carried out, and how the interactions could be carried out.
Some researchers suggested that lower bandwidth of collaboration channels creates disadvantages for users because bandwidth or capacities of transmitting information in each communication medium determine the effectiveness of networked collaboration in learning and problem solving. Users’ ability to address uncertainty and acquire information is severely limited because nonverbal and contextual cures are reduced or eliminated. For example, early researches argued that presence is diminished by text-based computer-mediated communication, since it is devoid of visual and vocal cues that higher-bandwidth and face-to-face systems provide. A conservative theory of Short, Williams, and Christie (1976) also argued that involvement, warmth, and immediacy that communicators experience during interaction may be limited by bandwidth, or cue-carrying capacity of the system. This social presence theory along with media richness theory (Daft & Lengel, 1986) and task-media fitness theory (McGrath & Hollingshead, 1994) all argued that the bandwidth or capacities of transmitting information in each communication medium determine the effectiveness of networked collaboration in learning and problem solving. However, other researchers have challenged this contention, arguing that behaviors of online learners are amenable and spontaneously adaptable to the social communication that takes place (Fulk, Schmitz, & Steinfield, 1990). They pointed out that these early theories emphasize channel effects but failed to consider the active role of individuals in the communication process. A study conducted on two groups of student assigned to either asynchronous text-based computer conferencing system or face-to-face conventional classroom setting on a discussion of three decision-making tasks over six weeks found no significant differences between the two groups (Walther & Burgoon, 1992). So far empirical studies have found that bandwidth-constrained environments do not necessarily seem to have a negative impact on user’s educational experience (Dennis & Kinney, 1998; Ghinea & Chen, 2003).
There is no doubt that future development of broadband will make online learning more engaging. It can be assumed that more bandwidth is better. However, at the current stage, the advantages of more bandwidth for the success of Web-based systems are not apparent to us until user needs to which they apply are identified and targeted. Therefore, the goal of this study is to investigate whether accessing an online class through higher bandwidth alters the online learning experience and whether the interaction emphasized in the course influences the impact of bandwidth.
Research Questions and Hypotheses
The purpose of this study was to investigate whether actual use of bandwidth impacted
student behaviors in online classes, in general. More specifically, the study investigated whether the impact would be differential depending upon where the course fell in two of Moore 's interaction dyads learner – content and learner – learner interaction. The hypotheses tested were:
1) There will be no relationship between level of bandwidth access (dial-up vs. broadband) and student online interactions/behaviors (number of online sessions, total time connected, email read, email sent, discussion read, and discussion posted, etc).
2) There will be no online behavioral difference due to different level of bandwidth access (dial-up vs. broadband) in the online course requiring rich learner – content interaction.
3) There will be no online behavioral difference due to different level of bandwidth access (dial-up vs. broadband) in the online course requiring rich learner – learner interaction.
Methodology
Participants
All participants of this study were graduate student pursuing a master degree in Library and Information Sciences at a major university in Texas. Students identified how they accessed the course and this choice was used to place them into two groups. One group of students used dial-up method, and their online speed was at a maximum 53 kbps; the other group of students had a high bandwidth connection through DSL, Cable, or other broadband methods. A questionnaire was administered through email at the end of each class. Participation was not mandatory, so not all students who enrolled in these courses actually completed the questionnaire. The response rate was more than 90 percent in each of the courses, with a total enrolled student of 330 students.
Courses
Data were collected from two graduate courses delivered using the Learning Content Management System (LMS), WebCT (Campus Edition and later Vista Edition in the second and third iterations of investigation). The first course was a required introductory course in the Masters of Library and Information Sciences. This course was presented in a blended format in which the students met for a full day on campus followed by a semester pursuing the course online. The second course was a graduate research methodology class provided by the same department and was offered 100 percent online.
The online graduate introductory course utilized predominantly learner – learner interaction in the form of threaded discussions around common questions proposed by the teacher. The content presented in the course was limited to a set of reading resources and discussion questions.
The graduate research methodology course was predominantly learner – content oriented. There was a full set of instructional materials mounted within the LMS with detailed instructions on assignments and self-tests for most of the modules of content provided. A CD-ROM was provided containing 130 video clips that varied in length from 20 seconds to seven minutes. These clips were of the faculty member demonstrating concepts.Design
The study covers a period from June 2003 to August 2005, with a total participant of 304 graduate students. There were three iterations of data collections and analyses. The first was during the summer of 2003 and involved both the online graduate introductory course and the online graduate research methodology course. These were both delivered using WebCT Campus Edition. The second and third iterations involved only the online graduate introductory course using the Vista Edition of WebCT.
A single factor (bandwidth), two-level design (narrow vs. broadband) was used for each of the two types of courses (learner – learner and learner – content) in the study. The independent variable is bandwidth; the dependent variables are the amount of online course content read, number of discussion posting read, and number of discussion posted for the first data analysis of both courses. For the second and third analysis, which were performed only on the online graduate introductory course, number of times the course was accessed, total time spent in the online course, discussion posting read, discussion posted, and email activities were analyzed.
Data collection
A questionnaire was administrated at the end of each of the courses for the three data analyses to gather information on student bandwidth use, perception of connection speed, and possible technical problems encountered. Detailed statistics on student online learning activities, such as online course content reading (Hits), discussion posting read (Posting_read), and actual discussion posted (Postings) were collected through WebCT and later Vista logs. Times of access to the course (Session), total time spent on the course (Time), E-mail read (ReadM), and sent (SendM) were additionally collected for the second and third data analysis.
Results
To assess the correlation between bandwidth use and student online interactivities, a series of one-way analyses of variance (ANOVA) were performed on SPSS (the Statistical Package for the Social Sciences) version 12.0.
First Iteration Data Analysis: Both Courses Taught in WebCT CE.
For the first iteration data analysis, total number of student is reduced from 101 to 99 for the online graduate introductory course and from 50 to 48 for the online graduate research methodology course due to high kurtosis on the dependent variable of postings of these students. Mean levels of perceived online student activities were compared across two groups of student with different bandwidth connections. Table 1 and 2 present the descriptive statistics of the online graduate introductory course and online research methodology course separately.
Table 1. Descriptive statistics on online graduate introductory course (N=99)
Variable
N/B*
N
Mean
Std. Deviation
Std. Error
Hits
1.00
34
406.1176
155.10129
26.59965
2.00
65
472.7077
203.05131
25.18542
Posting_read
1.00
34
206.0588
122.23696
20.96347
2.00
65
265.5692
121.61504
15.08449
Postings
1.00
34
19.2941
7.66547
1.31462
2.00
65
22.4000
10.40252
1.29027
N/B* : 1= Dial-up; 2 = Broadband.
Table 2. Descriptive statistics on online graduate research methodology course (N=48)
Variable
N/B*
N
Mean
Std. Deviation
Std. Error
Hits
1.00
12
1263.7500
452.59617
130.65326
2.00
36
1262.9167
621.90731
103.65122
Posting_read
1.00
12
405.0000
121.07548
34.95148
2.00
36
393.3333
174.13837
29.02306
Postings
1.00
12
51.5833
13.72097
3.96090
2.00
36
48.0556
15.60759
2.60126
N/B* : 1= Dial-up; 2 = Broadband.Homogeneity test of variance over the variables show no indication of violation for the online graduate introductory course (N=99) (refer Table 3). ANOVA results indicate the bandwidth use is significantly correlated with online discussion posting read, F (1, 97) = 5.327, p < .023, µ 2 = .0521, indicating that students used broadband read significantly more postings on the discussion board than those students used narrowband connection; but show barely statistic significant on online content reading (p= 0.98, µ 2 = .0281) and actual discussion posted (p= .128, µ 2 = .0237).
The results also show that there is no indication of statistical significant influence of bandwidth use on any student online activities on online content reading, F (1, 46) = .000, p = 0.997, online discussion posting read, F (1, 46) = .046, p = .831, and actual discussion posted, F (1, 46) = .486, p = 0.489, for the online research methodology course (refer Table 5).
Table 3. Test of homogeneity of variances (N=99)
Variable
Levene Statistic
df1
df2
Sig.
Hits
2.316
1
97
.131
Posting_read
.004
1
97
.947
Postings
3.618
1
97
.060
Table 4. ANOVA results on online graduate introductory course (N=99)
Variable
Source of
VariationSum of Squares
Df
Mean Square
F
P-Value
Hits
Between Groups
98986.439
1
98986.439
2.797
.098
Within Groups
3432570.976
97
35387.330
Total
3531557.414
98
Posting_read
Between Groups
79057.472
1
79057.472
5.327
.023
Within Groups
1439655.821
97
14841.813
Total
1518713.293
98
Postings
Between Groups
215.341
1
215.341
2.356
.128
Within Groups
8864.659
97
91.388
Total
9080.000
98
Table 5. ANOVA results on online graduate research methodology course (N=48)
Variable
Source Of
VariationSum of Squares
Df
Mean Square
F
P-Value
Hits
Between Groups
6.250
1
6.250
.000
.997
Within Groups
15790181.000
46
343264.804
Total
15790187.250
47
Posting_read
Between Groups
1225.000
1
1225.000
.046
.831
Within Groups
1222598.000
46
26578.217
Total
1223823.000
47
Postings
Between Groups
112.007
1
112.007
.486
.489
Within Groups
10596.806
46
230.365
Total
10708.812
47
Second Iteration Data Analysis: Introductory Course Taught in WebCT Vista
For the second iteration data analysis, which involved only the online introductory graduate course, the total number of students is 48. Mean levels of perceived online student activities were compared across two groups of student with different bandwidth connections. Table 6 presents the descriptive statistics of the online graduate introductory course analyzed. It shows that the means of the online activities (session, total time online, E-mail read, sent, read of discussion postings, and actual discussion posted) of students using broadband are all higher than those using narrowband, indicating more student online activities. This suggests that bandwidth does positively influence student online activities on WebCT Vista for the online graduate introductory course in general.
Table 6. Descriptive statistics on online graduate introductory course (N=48)
Variable
N/B*
N
Mean
Std. Deviation
Std. Error
Session
1.00
7
79.4286
20.18958
7.63094
2.00
41
101.2683
39.30778
6.13884
Time
1.00
7
15.0343
5.54283
2.09499
2.00
41
22.8237
7.48479
1.16893
ReadM
1.00
7
35.7143
17.13393
6.47602
2.00
41
42.4634
17.88449
2.79309
SendM
1.00
7
9.0000
6.80686
2.57275
2.00
41
10.0488
6.45349
1.00787
ReadD
1.00
7
304.5714
230.37495
87.07355
2.00
41
718.6585
486.09169
75.91477
PostD
1.00
7
18.4286
4.96176
1.87537
2.00
41
24.9024
7.68051
1.19949
N/B* : 1= Dial-up; 2 = Broadband.
Homogeneity test of variance over the variables show no indication of violation (N=48) (refer Table 7). ANOVA results indicate bandwidth use is significantly correlated with total time student spent online, F (1, 46) = 6.881, p < .012, µ 2 = .130, indicating that students used broadband spent significantly more time online on the course than those used narrowband. The results also show statistic significant on actual discussion posted, F (1, 46) = 4.827, p < .033, µ 2 = .095; statistically significant on discussion posting read, F (1, 46) = 4.597, p < .037, µ 2 = .091. These also indicate that students used broadband wrote more discussion postings, and viewed more online discussion postings than those used narrowband. But there are no indications of statistical significant influence either on E-mail activities or frequency (session) of access to the online course (refer to Table 8).
Table 7. Test of homogeneity of variances (N=48)
Variable
Levene Statistic
df1
df2
Sig.
Session
3.283
1
46
.077
Time
.864
1
46
.357
ReadM
.167
1
46
.685
SendM
.059
1
46
.809
Posting_read
3.378
1
46
.073
Postings
2.380
1
46
.130
Table 8. ANOVA results on online graduate introductory course (N=48)
Variable
Source of
VariationSum of Squares
Df
Mean Square
F
P-Value
Session
Between Groups
2851.904
1
2851.904
2.042
.160
Within Groups
64249.763
46
1396.734
Total
67101.667
47
Time
Between Groups
362.782
1
362.782
6.881
.012
Within Groups
2425.219
46
52.722
Total
2788.001
47
ReadM
Between Groups
272.355
1
272.355
.861
.358
Within Groups
14555.624
46
316.427
Total
14827.979
47
SendM
Between Groups
6.577
1
6.577
.156
.695
Within Groups
1943.902
46
42.259
Total
1950.479
47
Posting_read
Between Groups
1025236.545
1
1025236.545
4.827
.033
Within Groups
9769840.934
46
212387.846
Total
10795077.479
47
Postings
Between Groups
250.593
1
250.593
4.597
.037
Within Groups
2507.324
46
54.507
Total
2757.917
47
Third Iteration Data Analysis: Introductory Course Taught in WebCT Vista
For the third iteration data analysis, which again involved only the online graduate introductory course, the total of number of student is 105. Mean levels of perceived online student activities were compared across two groups of student with different bandwidth methods. Table 9 presents the descriptive statistics of the online graduate introductory course analyzed. It shows that the means of the online activities (session, total time spent online, E-mail read, sent, and actual discussion posted) of the students using broadband connection are higher than those of students using narrowband, except the online discussion postings read. This suggests that bandwidth does quantitatively positively influence student online activities on Vista on the online graduate introductory course in general.
Table 9. Descriptive statistics on online graduate introductory course (N=105)
Variable
N/B*
N
Mean
Std. Deviation
Std. Error
Maximum
Session
1.00
19
61.2632
22.44460
5.14915
115.00
2.00
86
77.3372
32.09787
3.46120
178.00
Total
105
74.4286
31.11111
3.03613
178.00
Time
1.00
19
21.13526
9.731863
2.232643
49.220
2.00
86
27.94674
15.370275
1.657419
90.080
Total
105
26.71419
14.711205
1.435667
90.080
ReadM
1.00
19
40.2105
16.95936
3.89074
84.00
2.00
86
43.9302
22.91149
2.47061
134.00
Total
105
43.2571
21.92909
2.14006
134.00
SendM
1.00
19
10.2632
7.30177
1.67514
30.00
2.00
86
10.7791
7.57768
.81712
42.00
Total
105
10.6857
7.49656
.73159
42.00
Posting_read
1.00
19
2649.3158
3490.79402
800.84307
11919.00
2.00
86
2600.1163
2279.34750
245.78836
10374.00
Total
105
2609.0190
2521.04673
246.02897
11919.00
Postings
1.00
19
30.4737
12.92443
2.96507
67.00
2.00
86
33.9186
12.61019
1.35979
72.00
Total
105
33.2952
12.67487
1.23694
72.00
N/B* : 1= Dial-up; 2 = Broadband.
Homogeneity test of variance over the variables show moderate indication of violation (N=105) on session and discussion postings read (refer Table 10). ANOVA results indicate bandwidth use is significantly correlated with session of student access to the online course on Vista , F (1, 103) = 4.285, p < .041, µ 2 = .040, indicating that student used broadband method significantly accessed more frequently to the online course than those used narrowband connection. The results also show barely statistic significant on total time spent on the online course, F (1, 103) = 3.414, p < .068, µ 2 = .0331, indicating that students used broadband connection spent more time on the online course than those using narrowband. But there are no indications of statistical significant influence either on E-mail activities or discussion postings activities for the online graduate introductory course (refer to Table 11).
Table 10. Test of homogeneity of variances (N=105)
Variable
Levene Statistic
df1
df2
Sig.
Session
4.112
1
103
.045
Time
3.026
1
103
.085
ReadM
1.520
1
103
.220
SendM
.117
1
103
.733
Posting_read
5.030
1
103
.027
Postings
.028
1
103
.868
Table 11. ANOVA results on online graduate introductory course (N=105)
Variable
N/B*
Sum of Squares
Df
Mean Square
F
P-Value
Session
Between Groups
4020.809
1
4020.809
4.285
.041
Within Groups
96640.905
103
938.261
Total
100661.714
104
Time
Between Groups
722.014
1
722.014
3.414
.068
Within Groups
21785.619
103
211.511
Total
22507.634
104
ReadM
Between Groups
215.318
1
215.318
.445
.506
Within Groups
49796.739
103
483.463
Total
50012.057
104
SendM
Between Groups
4.142
1
4.142
.073
.787
Within Groups
5840.487
103
56.704
Total
5844.629
104
Posting_read
Between Groups
37669.019
1
37669.019
.006
.939
Within Groups
660952698.942
103
6417016.495
Total
660990367.962
104
Postings
Between Groups
184.681
1
184.681
1.151
.286
Within Groups
16523.167
103
160.419
Total
16707.848
104
N/B* : 1= Dial-up; 2 = Broadband.
In summary, results of the three iteration data analyses show occasions of statistically significant correlations between bandwidth access methods with online student behaviors on the online graduate introductory course.
For the first iteration data analysis, students using broadband read significantly more online discussion postings in the online graduate introductory course. The second iteration data analysis shows that students who used broadband spent significant more time in the online course and read more discussion postings as well as posted more discussion items in the online graduate introductory course than those used narrowband. The third iteration data analysis shows that students who used broadband more frequently accessed the online course than those using narrowband and spent considerably more total time on the online learning in the online graduate introductory course.
For the online graduate research methodology course, there was no statistical significant difference on all activities by the two groups of student. In fact, there was little or no difference at all in the variables measured between the students who accessed the class via dial-up versus broadband.
From the results, we conclude that:
- There is some evidence of a relationship between level of bandwidth access and student interaction and behaviors (hypothesis 1).
- This relationship was not found in the course that emphasized learner-content interaction (hypothesis 2).
- The relationship did occur in the class which was heavily weighted toward learner-learner interaction (hypothesis 3).
Discussion
The relationship between speed and performance that has been demonstrated by studies conducted in laboratory environments was not totally confirmed in this study. Moore’s interaction model is useful in explaining the inconsistency of the relationship.
Learner-Learner Interaction.
The course in which students spent most of their online time interacting with each other showed the largest and, in fact, only relationship between bandwidth and behavior. In this class, students were required to participate in discussions around a series of questions that were supplemented by online readings. There was a minimum number of discussion postings required in the class as well as a minimum number of readings. Evidently, students who used dial-up access were less likely to go beyond the minimum postings to interact more broadly with their fellow students. Why might this have occurred? While the slower bandwidth did lengthen page refresh rates, the dial-up students could have interacted as heavily as the broadband students simply by staying online longer. In fact, the students who used dial-up spent less time (significantly less in the second iteration of the study) than those students accessed the course via broadband. The finding does confirm the result of Berchtold and collaborators (2001).
Learner-Content Interaction.
Why was the impact of bandwidth in the courses that emphasized learner-content interaction almost non-existent? Most likely, the students spent enough time on each content segment that the screen refresh rate did not have an impact. In addition, a CD-ROM was supplied that provided the high bandwidth portion of the content off line.
Conclusions and recommendations
Until broadband access is truly ubiquitous, those who offer online courses and programs will need to make decisions regarding course design and access policies. This was an exploratory study limited to post hoc analysis of existing data and to the examination of user behavior only. Despite these limitations, it is apparent that viewing the impact of the choice of narrow versus broadband access is not as simple as it might have appeared. Our data shows that bandwidth has the most impact when interaction among student (or learner – learner interaction) represented the major behavioral requirements in the course, and it had the least when the main function of the Web-based portion of the course was acquisition of large chunks of content (or has a focus on learner – content interaction).
Instructional designers and instructors should integrate interaction into online courses to ensure success of online learning. Whether the course design calls for an emphasis on learner-to-content interaction or student-to-student interaction, the impact of the bandwidth utilized by the students to access the course should be considered. Our first recommendation is that more study of the problem is indicated. These studies should utilize the current learning management systems and courses that represent a wide range of learner – instructor – content – interface interactions with consideration of the learning context. Attention should be paid to determine the range of activities in which students are engaged in while logged into the class, including activities that are not class related.
For courses that are heavily learner-to-content oriented and do not utilize a large amount of bandwidth-heavy applications, having students in the class who are accessing it via dial-up may not pose a problem. In this type of class, course designers should consider providing student video, audio, and sophisticated graphics “offline”.
If a course requires a significant amount of interaction among students, those who are delivering the course may want to urge students to utilize broadband access when available so the access does not impair the learning experience. They may also consider strategies to minimize the number of postings that must be read by grouping students into discussion “subgroups”.
In summary, this study lays the groundwork for more in-depth investigations of the effects of bandwidth access on student online behaviors. With the increasing availability of bandwidth as well as other technologies, major transformation of education becomes possible; the challenge we are facing therefore is to fully exploit these new technologies to support the design of online instruction.References
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Online Journal of Distance Learning Administration, Volume IX, Number I, Spring 2006
University of West Georgia, Distance Education Center
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