given laptop computers with wireless network access during the course of a semester. ..... Perceived and Experienced Advantages of Wireless Computing.
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION, 13(2), 257–276 Copyright © 2001, Lawrence Erlbaum Associates, Inc.
The Effects of Wireless Computing in Collaborative Learning Environments Geri Gay, Michael Stefanone, Michael Grace-Martin, and Helene Hembrooke Human–Computer Interaction Group Cornell University Eighty-four students distributed between two different courses at a major research university (one a communication course, the other a computer science course) were given laptop computers with wireless network access during the course of a semester. A wide variety of data (from questionnaires, e-mail logs, proxy server logs, and diaries) regarding students’ use of the laptops for electronic communication, Web browsing, and local application use (e.g., word processing) was collected and analyzed. The influences of course, network (wireless–wired), student population, and the passage of time were investigated in relation to the prevalence and nature of social computing (e.g., e-mail, instant messaging, chat, discussion boards, online annotations) in students’ laptop usage. The relative prevalence of social computing increased and became more exclusive for students in the communication course, especially on the wireless network. Social computing and use of the wireless network were less prominent and influential for students in the computer science course.
1. INTRODUCTION Wireless computing is becoming an integral component of learning environments in higher education and in the world of work, particularly with the increasing number of “laptop universities” and distributed-learning communities. However, little research has been conducted on how wireless computing affects learning experiences or learning environments. In light of extensive research revealing the transformative influence of technology on learning, we can assume that mobile and wireless computing could significantly transform how students learn, the content of courses, learning-related practices, classroom dynamics, and relationships among students and faculty. These technologies will bring about fundamental changes in the ways that the university creates and disseminates ideas, knowledge, and understanding. In an attempt to explore these issues, Cornell University’s Human–Computer Interaction Group (HCI Group) studied the use of wireless computing in two inforRequests for reprints should be sent to Geri Gay, Human–Computer Interaction Group, 209 Kennedy Hall, Cornell University, Ithaca, NY 14850.
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mation-intensive courses—an upper level communication course “Computer-Mediated Communication” and an upper level computer science course “Digital Libraries”—during the Spring 2000 semester. Students and instructors within these courses were provided information services and laptop computers equipped for wireless networking. During this 1st semester of a 2-year study, the HCI Group documented the influence of the technology on both courses. In particular, we explore how the pedagogical framework of the course and access to the wireless network influenced students’ computing behavior. We defined computing behavior with respect to where they were doing much of their computing, their social network, and their tools of choice, as well as a change in the frequency with which they engaged the use of those tools. The data, which was analyzed from a variety of sources, indeed indicated a significant trend toward the use of social applications, as well as an increase in the frequency of social computing over the course of the semester.
1.1. Computer-Supported Collaborative Learning (CSCL) The study described here has been guided in part by research that has focused on the sociological implications of pervasive communication tools on many aspects of life, including work and education. One such area is CSCL. CSCL is defined as a computer-based network system that supports group work in a common task and provides a shared interface for groups to work with (Ellis, Gibbs, & Rein, 1991). Collaborative learning is defined as groups working together for a common purpose (Resta, 1995). The benefits of collaborative learning are widely recognized (Bannon, 1989; Crook, 1989; Koschmann, 1996). The Internet and World Wide Web are well suited for hosting CSCL environments (Hiltz & Wellman, 1997). Many CSCL systems designed for group support on projects and communication are currently available (Koua & De Diana, 1998; McManus & Aiken, 1996). Common characteristics include the ability of students to contribute to an existing database of peer work, as well as the option of commenting on the contributions of others. Research has shown that moderated discussions or structured interactions are more effective than unmoderated or undirected interaction (Jackson, 1994; Klemm & Snell, 1996; Lai, 1997). With the addition of wireless computing technology comes increased support for the CSCL environment, due to sustained interaction and the resulting creation of closer interpersonal bonds (Harasim et al., 1995; Kaye, 1995). This in turn facilitates a wide sharing of knowledge and the creation of a community of learners sharing common goals (Bruffee, 1993). Educational researchers (Brown, Collins, & Duguid, 1989; Resnick, 1987; Soloway et al., 1999) have argued that students learn best when given the opportunity to learn skills and theories in the context in which they are used, then construct their interpretations of a subject and communicate those understandings to others. Wireless computer-mediated learning environments may support this constructive learning process by helping students find and organize information in context, construct their understandings, and communicate those understandings to others. Wireless computers also support “just-in-time” learning, an adoption by educators of a successful industry technique that involves delivery of parts and finished products at precisely
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the time in which they are needed (Schorr, 1995). Transferred to education, this technique may provide context—appropriate information or complete a skill-building task—at the most appropriate teachable moment to students.
1.2. Social Navigation and Information Seeking Affiliation within a collaborative learning community has benefits that extend beyond that of knowledge building. People located in social networks offer guidance with regard to information seeking as well. Relying on metadata for navigation through information spaces greatly reduces chances for successful landfall (Dieberger, 1997; Lynch, 1997). The ocean of information accessible through the Internet and the dynamic nature of content fuel each other, creating a rather unfriendly information-searching environment. The community around an individual can be a valuable resource by helping to guide information seeking. Numerous strategies are available for information seeking online. The hyperlinked nature of the World Wide Web promotes more or less aimless wandering, or browsing, which lacks utility when seeking specific information (Dieberger, 1997). Having a standardized format for text and multimedia documents has led to the exponential growth of Internet Web pages (Dieberger). Social navigation, collaborative filtering, and recommendation systems are tools with the potential to increase efficiency regarding information-seeking behavior by using information available within social networks (Dourish & Chalmers, 1994). Social navigation is conceptually different from recommendation systems. Recommendation systems employ collaborative filtering techniques based on aggregate data collected from many users over time (Resnick, Neophytos, Mitesh, Bergstrom, & Riedl, 1994; Rose, Borenstein, & Tiene, 1995). The Tapestry System, a software application, used this idea, allowing users to exploit collective experience by offering interest matches when searching a database containing e-mail messages (Terry, 1993). Matches are determined by user “votes” on the utility, or interest of the message. Profiles are garnered from this data and used to help navigate other users with similar profiles. Research based on recommendation systems focusing on books and music has graduated to the commercial realm, readily available on sites such as Amazon.com. Visitors are presented with satisfaction evaluations (both numerical and open-ended response), navigation aids to guide future decisions. This aggregation of user data is alarmingly easy to capture and offers an accurate representation of “trends” in user behavior, presenting a plethora of privacy issues reaching beyond the scope of this article. Spatial navigation (semantic navigation), in contrast, relies on actual two- or three-dimensional spatial metaphors (Dourish & Chalmers, 1994). Virtual reality simulators, and Multi User Domains such as ActiveWorlds, rely on multidimensional space for user navigation, which provides a richer experience of presence (Rheingold, 1993). The spatial metaphor is useful because it is an experience users universally share. Spatial navigation relies on actual semantic relations within the structure of the space, found most pervasively in hypertext environs.
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Social navigation is a phenomenon of interaction (Dourish, 1999). It occurs in the context of many different systems facilitating computer-mediated communication. It is important when considering the dynamics of social navigation to take into account the social context of the user. Users share very different sorts of experiences. Hoijer (1992) distinguished between universal, cultural, and personal levels of experience. Universal experiences are those we all share, such as eating and sleeping. Cultural experiences are products of society, such as gender-related behavior, social or professional memberships, or academic backgrounds. Experiences unique to an individual are classified as private, typically a result of distinctive abilities or personalities. These experiences as a whole shape peoples’ cognitive structures. The resulting configuration is not strictly a reflection of the environment, nor solely a mental creation. They are all socially founded. The product is a unique interpretive frame applied to new stimuli encountered while navigating through space. Combining the work just mentioned with Vygotskian theory of sociocultural learning, as well as components of activity theory (Luria, 1976; Vygotsky, 1978), suggests that context, in the form of classroom structure, tools, and tasks, all contribute and influence the experience of learning. In this research, we endeavored to determine how these contextual influences would effect computing behavior. Once known, this information could be used to inform pedagogical approaches and curriculum development that seeks to integrate technologies in the classroom. This research is a natural extension of our earlier work on the development of computer-supported learning environments (Gay & Lentini, 1995; Gay, Sturgill, Martin, & Huttenlocher, 1999). In this study, we hypothesized that compared with a more traditional approach to classroom teaching, a collaborative learning environment would encourage the use of more social computing among students and group members. Social computing is defined here as the use of social communicative tools such as e-mail, instant messaging, and chat. It further encompasses where, when, and with whom this computing occurs. We hypothesized that the social network within groups would become increasingly cohesive as work on the group portal projects became the focus of their classroom experience. This cohesiveness was operationalized as an increase in social communication among group members with a concomitant decrease in communication with members outside their individualized group. The “where” and “when” aspects are defined in terms of whether or not students accessed the network to do their work. Thus, we further hypothesized that students needing to collaborate would use the availability of the network connections at home and on campus more than the comparison group, and that this behavior would increase over the course of the semester.
2. METHODS 2.1. Participants In this study, we compared the computing behavior between two upper level courses at Cornell University. Both classes met as a whole one time a week, and each half of each class met for an additional laboratory session each week.
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The total number of participants in the study was 84. Table 1 shows the composition of the communications class (hereafter referred to as Comm) by major. The computer science (hereafter referred to as CS) class consisted only of computer science majors. In the Comm, 68% were men and 32% were women. In the CS, 94% were men and 6% were women.
2.2. Procedure All students were issued a high-end laptop computer at the beginning of the semester. It was explained that these computers could be used by them in any manner that they chose, for purposes related to this or any other class they were currently taking. All students were informed that they were part of an experimental testbed designed to examine the effects of wireless computing in the classroom. They were made fully aware that their participation in the experiment would require them to keep journals at times, fill out surveys and questionnaires, and be interviewed at different times throughout the semester. Finally, it was explained to the students that we would be asking them to conduct their computing through a proxy server so that we could track their information seeking and tool use. Care was taken to ensure that students understood that their participation was completely voluntary and would in no way influence their final grades. All students were required to sign an informed consent form before their participation. Data collection efforts included self-report questionnaires that students were asked to complete at the beginning and end of both the Comm and CS course. These surveys assessed student attitudes and behavioral dimensions of computer and software use. In addition, students in the Comm were asked to keep journals regarding their daily computing behavior with regard to specific questions such as application use, computing location, specific difficulties, and tasks on which they were working. To supplement these more subjective measures, we compiled and categorized all of the URL sites searched and visited over the course of the entire semester, as well as tracked other tools the students were using (i.e., discussion board threads, e-mails, instant messaging, etc.; a list is presented in the Appendix). These data allowed us to more objectively substantiate the self-report data, make comparisons between the two classes, and document changes in these behaviors over time.
Table 1: Percentage of Majors Within Comm
a
Communication Class Composition by Department Department Communication Computer Science Design and Arts a
Percentage of Students 32 16 52
Comm = communications class course used in the study.
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2.3. Classroom Structure The Comm was designed and structured to emulate the principles of computer-mediated communication and computer-mediated collaborative learning environments. The format of the class meeting was a combination of open discussion and lecture. The function of the lecture was to mediate and facilitate discussion and group assignments. Less emphasis was placed on traditional methods and roles of teaching and the dissemination of information per se. A Web site was developed for the class that functioned as a portal where students could find specific information about the class, as well as posted contributions by either teaching staff or other students. These posted contributions were information about topics related to the class or popular media issues, or both, of relevancy and could take the form of comments, references, and URL recommendations. Students also had access to a class Listserv and bulletin board in which their online participation was required throughout the semester. The class portal also included Web folders for students and groups to store and share information. These folders provided space on the class server designed to provide a central location for students to leave and share documents and objects. Early in the semester the class was divided into six groups, each responsible for the development and implementation of a Web-based portal. One student in each group was arbitrarily designated portal leader, and the design and content decisions related to the project were left entirely to the group. The final project was not due until the end of the semester; presentations were held during the last 2 days of class meeting. Unlike the Comm, the context of the CS was more traditional in format with structured lectures and exams. Although not forbidden of course, group work and collaboration were not specifically designed in the goals of the course. Both classes had equal access to the wireless network connections across campus that afforded students ready use of both campus e-mail and Internet connection.
2.4. Apparatus An Intel® grant made it possible to purchase Dell® laptop computers (Dell Latitude CPt) that functioned in conjunction with the NOMAD wireless modem network. The software installed on each computer consisted of collaborative tools such as Microsoft® Netmeeting. Students were allowed to install other software for both personal and course-related purposes. A series of wireless transceivers (access points) connected together over the campus composed the NOMAD network infrastructure, exchanging low-power microwave signals of limited range. Each access point keeps track of the wireless devices within its range while communicating with other access points. This enables automatic assignment of devices based on best reception, allowing users’ movement from place to place throughout the network, much like a cell phone moves between cell phone towers. As long as the laptop remains in range of any of the access
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points, users are able to seamlessly move from place to place. During the course of the Spring 2000 semester, new locations were added periodically to the network.
3. RESULTS The data are broken down by type of comparison. As mentioned earlier, we made comparisons between the CS and the Comm, as well as comparisons within the Comm only. Our rationale for this approach was both practical and theoretical. Ethically, we could not consider having one group with access to the laptops and one group with no such access; so our comparisons are between courses with different content and structure, not between experimental and control groups. From a theoretical perspective, this approach lends itself well to addressing the question at hand concerning context. Comparisons between the classes allowed us to investigate tool use as a function of the class structure, expectations, and dynamics within the community “at large.” In some cases we were able to explore where and when students were using the wireless network on campus, giving us yet another indication of social computing differences. Comparisons within the Comm enabled us to look at changes in tool use over time as groups congealed and the task became increasingly focused.
3.1. Comparisons Between Classes Several survey questions we gave to students in both the CS and the Comm queried students about how they discovered URLs that, once visited, were determined to be useful to them. As part of another study on social navigation, a subsection of the results are relevant here also. Interestingly, Comm students reported finding most of their best URLs as a result of social methods of relaying and exchanging data, namely e-mail, discussion boards, instant messaging, and chat clients. CS students indicated that the URLs they found most useful were discovered primarily by nonsocial means, such as search engines and other Web sites they visited. A chi-square analysis of this data revealed these self-reported modes of information exchange were significantly different. In addition to this survey data indicating differences between classes regarding the manner in which URLs came recommended, χ2(1, N = 84) = 3.939, p < .047, df = 1, other data we collected allowed us to look the content of the URLs students were browsing, as well as the amount of Web-based email, instant messaging, and chat students engaged in over the course of the entire semester.
3.2. URL Content Analyses The laptop computers given to students for the NOMAD experiment were configured so that all Web-based activity that occurred on them went through a proxy server and was recorded in its log files—regardless of whether they connected to the
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FIGURE 1
URL content for Comm.
Internet via the wireless network on campus, or alternatively, through any hard-wired connection (e.g., ethernet, phone lines [modem/DSL], or cable) they might use outside the range of the wireless network.1 Approximately 1.7 million URLs were collected or recorded during the course of the semester. We categorized the content represented by nearly 2,000 URL hosts accounting for approximately 87% of the total URL content browsed by students. URLs were segregated by course (Comm or CS) and network (wireless or wired). URLs were further separated into those that occurred during the middle 4 weeks and of the semester (“Time Period 1” or “T1”) and those that occurred during the final 4 weeks of the semester (“Time Period 2” or “T2”). Figures 1 and 2 show the content profiles for the URLs students browsed in each course from the beginning of Time Period 1 through the end of Time Period 2.
1
Students were given the option to change the proxy settings in their browsers (via an explicit option on the Windows Start menu) to bypass the proxy server. However, each time they rebooted their laptop computers, their browsers were reset to the original proxy settings. Indications are that few students chose to bypass the proxy server consistently—that is, according to student interviews and to the generous appearance of socially stigmatized content, like pornography sites.
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FIGURE 2
URL content for CS.
One prevailing trend was a decrease in the total number of URLs browsed by students from T1 to T2 regardless of course or network. Students from both courses viewed 279,866 total categorized URLs during T1 versus 177,637 total categorized URLs during T2; this is a 36.5% decrease. Students may have had less time for Web browsing as the semester wore on and final projects and exams came near. As indicated earlier, part of our definition of social computing included where and when students were engaging in the use of more social forms of computing such as e-mail, instant messaging, and chat. The wireless network allowed students access to these tools in numerous locations on campus, and even a few locations off campus toward the end of the semester. We looked at the amount of Web-based messaging that occurred as a percentage of the total URLs browsed both on and off the network for each class over time, which we refer to as “messaging rate” (see Table 2). One thing to notice is that Comm students browsed more than twice as many URLs on the wireless network than they did off it (149,104 wireless vs. 69,241 wired). The pattern for CS students was just the opposite: More than twice as many of their URLs were browsed off the wireless network than on (71,879 wireless vs. 167,279 wired). One potential reason for this reversal was that CS students have more generous and readily accessible computer laboratory facilities on campus
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Course/Network Comm/Wireless Comm/Wire CS/Wireless CS/Wire Note.
T1 No. Messages
%
1,964/94,036 2,476/42,306 920/42,608 1,947/100,916
2.09 5.85 2.16 1.93
T2 No. Messages
%
1,788/55,068 1,708/26,935 295/29,271 1,391/66,363
3.25 6.34 1.01 2.10
T1 = Time Period 1; T2 = Time Period 2.
than do Comm students and tended to use them instead of their laptops while on campus. To analyze these data, we ran a repeated measures (or “longitudinal”) logistic analysis via the Statistical Analysis System (SAS), using the PROC GENMOD application. Independent variables were course, network, and time period; the binary dependent variable was messaging (i.e., either a URL represented Web-based messaging or it did not). Partially due to our large sample size (N = 457,503), every main effect, two-way interaction, and three-way interaction was statistically significant at α = .05. On the whole, Comm students were over three times as likely (3.16) to be doing Web-based messaging compared to CS students (log estimate = 1.1513, Z = 3.99 × 1015, p < .0001). Focusing on the wireless network reveals a statistically significant increase in Web-based messaging for Comm students from T1 to T2 (2.09% → 3.25%) as a percentage of their total URLs, in parallel with a statistically significant decrease for CS students (2.16% → 1.01%).2 It is unclear whether differences in student populations or course format may have contributed to these reverse patterns. When not on the wireless network, patterns of Web-based messaging were quite similar between the Comm and CS groups, with both increasing slightly percentage wise from T1 to T2 (5.85% → 6.34% and 1.93% → 2.10%, respectively). To test whether the presence of communication majors in Comm was having an inordinate effect on the prevalence of social computing, we recomputed totals and reran the statistical analysis after removing the communication majors from the Comm group3 (11 of the 32 students that completed the course were communication majors). Again, all main effects and interactions were statistically significant at α = .05 (see Table 3). With the communication majors removed, Comm students were just under two times as likely (1.98) to be doing Web-based messaging compared to CS students (log estimate = 0.6838, Z = 4.04 × 1015, p < .0001). This is less than the 3.16:1 odds that existed with the communication majors included but is still a large significant difference.
2 3
13
For the Course × Network × Time Period interaction, log estimate = –1.2260, Z = –249 × 10 , p < .0001. N = 349,882 for this second analysis.
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Table 3: Percentage of Web-Based Messaging With Communications Majors Removed Web-Based Messaging as Percentage of Total URLs Course/Network Comm/Wireless Comm/Wired Comm/Wirelessa Comm/Wireda CS/Wireless CS/Wired
T1 No. of Messages 1,964/94,036 2,476/42,306 1,111/54,542 388/19,609 920/42,608 1,947/100,916
%
T2 No. of Messages
%
2.09 5.85 2.04 1.98 2.16 1.93
1,788/55,068 1,708/26,935 1,160/30,995 227/5,578 295/29,271 1,391/66,363
3.25 6.34 3.74 4.07 1.01 2.10
Note. T1 = Time Period 1; T2 = Time Period 2. a Communication majors removed.
The raw counts reveal that the 11 communication majors did a majority of the Web-based messaging that occurred off the wireless network during both T1 and T2 (4,184 messaging URLs for communication majors vs. 615 for the remaining 21 students in class). In terms of percentage of total URLs, communication majors had a messaging rate of 8.10%4 when off the wireless network, compared to a messaging rate of 2.44%5 for the other 21 students. Communication majors’ Web-based messaging was less of a “dominating force” on the wireless network: Communication majors’ messaging rate was 2.33%6 versus 2.65%7 for the noncommunication majors, implying that messaging was a more uniformly used tool for Comm students as a whole while at school on the wireless network. When comparing messaging between T1 and T2 on the wireless network, removing the communication majors from Comm actually accentuated the pattern toward more Web-based messaging as a percentage of total URLs (i.e., 2.04% → 3.74% compared to 2.09% → 3.25%) and remained statistically significant.8 In summary, differences in course format between the CS and Comm courses, as well as differential effects of being on the wireless network versus not being on it, appear to explain some of the differences observed in the prevalence and patterns of Web-based messaging within and between the two student groups. This last analysis, however, suggests that characteristics of the students themselves (i.e., individual differences) can also have a significant influence on the presence of social computing in a mobile computing context.
4
100% × [(2,476 + 1,708) – (388 – 227)]/[(42,306 + 26,935) – (19,609 – 5,578)] = 8.10%. 100% × [(388 + 227)/(19,609 + 5,578)] = 2.44%; a two-sample test of proportions indicated a significant difference between the 8.10% and 2.44% messaging rates (Z = 34.86, p < .0001); the comparable messaging rate for CS students was 2.00% (which was significantly less than the 2.44% rate). 6 100% × [(1,964 + 1,788) – (1,111 – 1,160)]/[(94,036 + 55,068) – (54,542 – 30,995)] = 2.33%. 7 100% × [(1,111 + 1,160)]/[54542+30995)] = 2.65%; even though this was a smaller difference than the messaging rate discrepancy off the wireless network between the communication and noncommunication majors, a two-sample test of proportions still indicated a significant difference between the 2.33% and 2.65% messaging rates (Z = –4.00, p < .0001); CS students’ comparable messaging rate was 1.69% (which was significantly less than the 2.65% rate). 8 For the Course × Network × Time Period interaction, log estimate = –0.7414, Z = –5.38 × 1015, p < .0001. 5
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3.3. Comparisons Within Comm One of the goals of this research was to track changes in tool use over the course of the semester to gain some insight in user behavior as a function of collaborative group goals. To this end, we amassed several different kinds of data: qualitative data derived from student journals on the kinds of tools students were using throughout the semester; self-report from a pretest and posttest questionnaire regarding students’ perceived and experienced advantages of the wireless computers; URL recommendations and hits over time; and finally, e-mails sent or received between and among group members. We report first on the quantitative data and end with some examples of student journal entries that illustrate tool and location aspects of computing from a more personal perspective.
3.4. Perceived and Experienced Advantages of Wireless Computing At the beginning and end of the semester, students were asked to fill out a questionnaire regarding their predicted use of the wireless computers (at the beginning of the semester), and then, for what they actually used their computers (at the end of the semester). These open-ended survey response questions were analyzed via a content analysis software package. The results of these analyses are presented in Figures 3 and 4. An explanation for the procedure and its interpretation are presented first.
FIGURE 3 Perceptual map of anticipated advantages of wireless computing (pretest).
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Perceptual map of perceived advantages of wireless computing (posttest).
3.5. CATPAC (Woelfel, 1990) Research based in biological nervous systems conducted by neuroscientists, physicists, computer scientists, psychologists, linguists, and communication scientists has resulted in a new class of computer software. Artificial Neural Networks have characteristics of human analysts, but maintain perfect reliability. Biological brains consist of neurons, which essentially are “switches.” CATPAC (Woelfel, 1990) also begins with a set of (artificial) neurons, one for each word in the text being analyzed. The neural-development process involves a “scanning window” that passes through the text, consisting of n consecutive words (default is 7, about as many words as a human looks at in a glance). The window moves through the text one word at a time, so that for an n of 7, the first window contains Words 1 through 7, the next window 2 through 8, then 3 through 9, and on and on. Whenever two or more neurons are simultaneously active, the connection between them is strengthened by a small amount. On completing this process, all or nearly all the neurons in CATPAC will become positively connected. The software also incorporates a “forgetting force,” which reduces the strength of connections by a constant value, simulating forgetting in biological systems. This means that only connections that are frequently reinforced will grow very strong. The result is a set of neurons that have some strong positive associations and some strong negative associations. Mathematically, the structure of the resultant network can be represented by a square matrix of numbers, where each row and
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column of the matrix represents a neuron or word, and each number represents the strength of connection of the neurons that correspond to the row and column of the number. The output is a perceptual map, in which objects are represented by points in a spatial model in such a way that features of the data are revealed in the geometrical relations among points. This differs from simple unidimensional scales in that the points are allowed to assume positions within a two-dimensional plane, or threeor four-dimensional space. When interpreting perceptual maps, the fundamental concept involved is that of psychological distance. The axes on the map outputs are arbitrary. By treating psychological distances like physical distances, we are able to create a map of the way people structure similarities or differences among attitudes in a given domain. Short distance represents similarity and larger distances represent disagreement. Figure 3 depicts the results of the content analysis performed on responses regarding anticipated advantages of mobile computers. Three distinct clusters, or concepts, emerge. Centrally located above the x axis, class, work, e-mail, and accessibility group together. This summarizes the value of mobile computers as a tool for class work. Below the x axis, Internet access, online resources, flexibility, and connection group together. This cluster is specifically focusing on networked resource availability. Finally, assignments, convenience, and anywhere cluster on the right. The perceptual map efficiently conveys three unique perceived benefits related to mobile computing. It is significant to note that, at this point in the semester, the Internet as an information and research tool is conceptually central in relation to perceived advantages associated with mobile computing. Comparing these findings to the results of perceived advantages gathered at the end of the semester, the perceptual map, although similar to the results gathered from the beginning of the semester, contains notable differences. Figure 4 shows the cluster above the x axis addresses excitement regarding the expansion of the network to College Town Bagels, a popular student haunt located on the fringe of campus. Below that cluster, centrally located on the map, is a cluster containing great, advantage, communicate, assignments, e-mail, and projects. Most significant is the observation that Internet access, conceptualized as a resource, has become less vital, visually confirmed by its physical location on the perceptual map. Instead, the map depicts social and communication advantages of mobile computers and their use to accomplish class-related tasks. The strength mobile computers have in facilitating social relationships within a higher education context is emphasized. Walther (1992) discussed the ability people have to adapt a medium to fulfill social objectives. It is apparent the students, through the course of the semester, discovered this technology’s utility as a social channel, independent from a strictly academic tool. Other forms of social computing behavior within the Comm class were also investigated. Earlier we referred to social navigation as a means of discovering useful Web sites. Within the Comm class, we looked at discussion board threads and Listserv for explicit recommendations of URLs posted by students and their subsequent “hits” by others in the class. The findings are represented in Table 4. At the outset of the semester, students used the set of communication tools frequently. The discussion board and Listserv functioned well for “umbrella”-type
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Table 4: Frequency of URL Recommendations and Subsequent Hits Frequency of URL Recommendations and Consequent Hits Over Time Time 1 2 3 4 5
No. Recommended 27 11 5 6 0
Note.
No. of Hits 3,849 963 85 0 0
Time blocks represent 22 days.
communication, conveying content of interest to the entire class. For example, the phenomena of social navigation were evident, indicating strong community participation. When comparing the number of URLs hit before and after recommendation, a significant relation was found (M = 50.55, SD = 276.85), t(30) = 2.59, p = .015. This is clear evidence that when students posted a URL of interest to either the discussion board or Listserv, other students in the social network of the class followed and explored the referred URL. Although the portal projects were assigned at the beginning of the semester, class observations indicate groups did not begin serious work on them until past the second half of the semester. As the semester progressed, and as communication between students became increasingly task oriented, the tools designed for larger audiences were replaced with more personal, individual communication tools, namely e-mail and independent discussion boards. Three of the six portal groups implemented discussion boards in their portal design. E-mail communication was analyzed across all groups over time using a 2 × 2 (Time × Within Group, Outside Group) repeated measures analysis of variance. What emerged from this data was a significant main effect of Time, F(1, 27) = 5.629, p < .05, and a near significant interaction effect between Time and Message Type (within or outside the group), F(1, 27) = 3.714, p < .065. Interestingly, e-mails among group members from T1 to T2 did not change, but e-mails to and from other members of the class decreased dramatically. Figure 5 illustrates these patterns. That communication outside their groups dropped off was not surprising. What was somewhat counterintuitive was that within the groups e-mails did not change over time. We decided to run another analyses to see if we could discern some explanation for this. In the second analysis, we analyzed only e-mails from group leaders to other members of their group and to members outside their group. Although no effect was significant due to the very small sample size (N = 5), the opposite pattern emerged. E-mails to members within their group increased sharply, nearly doubling between T1 and T2, whereas communication with members of the class outside their group decreased to nearly none. Figure 6 depicts these patterns. It appears that their role as group leaders was best served by the social communicative benefits of e-mail (e.g., being able to copy the entire group the same mes-
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FIGURE 5
Mean e-mail communication among all group members.
sage). Again, although these trends are only suggestive, the patterns that emerge indicate that tool use was differentially affected by the context within the group, namely the role one assumes or is assigned.
4. DISCUSSION Taken together, the results here suggest an interesting interplay between class structure, task, and leadership roles with tool choice and usage. It is useful to conceptualize the two classes, the groups within the Comm class and the leaders within those groups as independent universes along a time continuum. Differ-
FIGURE 6
Mean e-mail communications from group leaders only.
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ences in tool use among these universes exist as a function of the class dynamics (collaborative vs. traditional pedagogical approaches), and changes in tool use and frequency of use within the Comm class occurring as a function of task and role within the group. Within the Comm class, information seeking and exchange occurred primarily though the use of tools that foster communication among several individuals simultaneously. Such communication and interaction was encouraged in the Comm class. As work on the group projects commenced, communication among the class as a whole decreased and the focus became increasingly refined to the group. Group leadership roles were reflected in the differential use of e-mail between leaders and nonleaders within the group. The comparisons between classes indicated distinct differences between how Comm and CS students used both the wireless and mobile technologies. Communication and computer science students sought information from different sources and differed in the tools chosen for exchanging information. The Comm class engaged in significantly more social messaging than CS students. Although we recognize the limitations of our design for making “clearer” comparisons, we are confident that this difference was due to the context of the class and not an artifact. Even with communication majors removed from the analysis, the Comm class was twice as likely to engage in this form of social computing. We do not however, deny the potential for individual differences between certain subject variables such as major. These results suggest that the introduction of wireless computing resources in learning environments can potentially affect the development, maintenance, and transformation of learning communities. Ubiquitous mobile computing allows students to engage in learning-related activities in diverse physical locations, to work on projects supported by multimedia resources at the point of learning, to communicate with distant collaborators, and to access information networks anywhere, anytime. Wireless computing technologies can potentially enhance social learning and can augment the acquisition of tacit knowledge that is part and parcel of socialization in communities of practice. However, learning activities are complex systems of interactions, and the benefits of ubiquity and mobility can easily be lost if that complexity is not appreciated and understood. Not every teaching activity or learning community can or should successfully integrate mobile computing applications. The structure and content of classes, pedagogical and curricular philosophies, and the nature of assignments influence how mobile systems and particular applications are used. Different characteristics and dispositions of learners (such as preferred learning styles, gender, and backgrounds) and of teachers influence how wireless technologies are used. For example, the computer science students in both classes tended to be less social than students from the arts and social sciences. The diverse subject areas and cultural norms of different learning communities—for example, different disciplines or different age-based cohorts—likewise influence the use and success of such technologies. Students and faculty have limited visions of how systems should be integrated in learning environments. Most educational environments are organized to favor independent knowledge acquisition and individual performance. Classes need to
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be organized to take advantage of new technologies for knowledge building. Clearly, the results here indicate that at least within a collaborative learning environment students within groups readily recognize and use social communication tools for the exchange of information. The wireless connection resulted in an increase in the use of such tools, blurring the boundaries between where and when collaborative work can take place. A priority now is to explore complex, concrete, context-dependent learning settings, to identify how ubiquitous mobile computing tools mediate particular relationships and practices for particular learners and learning communities. Through carefully constructed studies, we can begin to address the challenges posed for the HCI community by the anytime, anyplace nature of mobile and ubiquitous computing technologies.
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APPENDIX KEY TO CATEGORIES FOR URL CONTENT • Entertainment: Web sites whose primary purpose was to entertain or disseminate information about the entertainment industry. • Sports: Web sites whose primary purpose was to provide sports-related entertainment or disseminate information about sporting events. • Search: Whenever keywords were input into a search engine (note that this doesn’t include browsing through the pages returned by the search engine). • ProdsSvc: Web pages whose content related primarily to commercial products and/or services. • Cornell: Web pages on Cornell servers, including the course Web sites but excluding “People Pages” (see “People” category). • Messaging: This includes instant messaging, but also Web-based e-mail. • Software: These are pages containing information about various software and/or pages where software can be downloaded from the Web. • People: This category includes students’ Web pages on the Cornell people page server, as well as personal pages at other “community servers” like Geocities™, AOL™, and so forth. • Reference: These are Web pages offering reference-like information, including dictionaries, encyclopedias, Web statistics databases, and so forth. • Special (“special interest”): Web pages for people sharing interests in specific topics, like monkeys, activism, food/cooking, and so forth. • Finance: Web pages highlighting information about stocks and investing; also, “real-time” stock tickers. • News: Web pages whose primary purpose is to disseminate news about current events (e.g., abc.com). • Employment: Web pages that highlight job and internship opportunities. • Regional Info: Web pages providing information about specific geographical areas (e.g., maps and event calendars related to Ithaca, NY). • Other U: Web pages residing on university servers other than Cornell’s. • Pers Port: Web-based communication and/or information management center (e.g., My Yahoo or My AOL). Web-based e-mail transactions were extracted from this category (see “Messaging”). • Multimedia: Web pages featuring various forms and collections of multimedia not specific to the aforementioned categories.
