adaptive support for distributed collaboratio

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P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 573 – 595, 2007.© Springer-Verlag Berlin Heidelberg 2007

19

Adaptive Support for Distributed Collaboration

Amy Soller

Institute for Defense Analyses4850 Mark Center Drive

Alexandria, Virginia, [email protected]

Abstract. Through interaction with others, a person develops multiple per-spectives that become the basis for innovation and the construction of new

knowledge. This chapter discusses the challenges facing emerging web-basedtechnologies that enable distributed users to discover and construct new

knowledge collaboratively. Examples include advanced collaborative and so-cial information filtering technology that not only helps users discover

knowledge, peers, and relevant communities, but also plays a powerful role infacilitating and mediating their interaction. As the internet extends around the

world and interconnects diverse cultures, the adaptive web will be challenged

to provide a personalized knowledge interface that carries new perspectives todiverse communities. It will play the role of an interface for knowledge con-

struction, a mediator for communication and understanding, and a structuredchannel through which knowledge is created, interpreted, used, and recreated

by other users.

19.1 Introduction

Methods for individual adaptation on the web, such as content selection and sequenc-

ing, navigation support, and presentation adaptation, focus on helping the user findand apply the knowledge he needs in the most efficient manner. These methods are

effective if the knowledge is available somewhere on the web. What if it is not? Dis-

covery, meaning-making or sensemaking, understanding, and innovation are emergent

processes that develop over time through experiences and the interpretation of interac-

tion with others [36]. This chapter discusses the challenges facing emerging web-

based technologies that help users discover and construct new knowledge by facilitat-

ing the interaction between groups of internet users. Examples include integrated

combinations of distributed performance support and collaborative and social infor-

mation filtering technology that not only help users discover knowledge, peers, andrelevant communities, but also play a powerful role in facilitating and mediating their

interaction. As the internet extends around the world and interconnects diverse cul-

tures, the adaptive web will be challenged to provide a personalized knowledge inter-

face that helps different communities interpret and understand alternative perspec-

tives. It will play the role of an interface for knowledge construction, a mediator for



574 A. Soller

communication and understanding, and a structured channel through which knowl-

edge is created, interpreted, used, and recreated by other users.

Through interaction with others, a person develops the multiple perspectives that

become the basis for innovation and the construction of new knowledge. The adaptive

web has the potential to facilitate this process of collaborative knowledge construction by assisting in the discovery of new business or learning partners, promoting the de-

velopment of existing and new professional and social communities, and supporting

and mediating the interaction between these new relationships.

The first step in developing support for distributed collaboration is enabling people

to exchange the right information, at the right level of detail, using the right language,

at the right time, in the right context, with the right people. Examples of tools that

assist people in finding the right knowledge and expertise at the right times include

collaborative filtering and social matching algorithms [31, 39] (also see Chapter 9 of

this book [30]). Examples of tools that provide appropriate contexts for informationsharing and learning include online communities and virtual spaces for meeting, col-

laborating, and constructing knowledge online [40].

The second step is effectively mediating the participants’ cognitive and collabora-

tive processes. Adaptive collaboration environments that move beyond content or

social-based recommender system approaches to support the innovative processes of

knowledge construction will be challenged to address the complex interplay between

physical, cognitive, and social variables. These factors affect the way in which infor-

mation flows between the collaborating participants, shaping their interaction. In

knowledge domains such as those involving peer help or the development of trustingrelationships, efforts in mediating and maintaining compatibility between collabora-

tive processes should parallel efforts in matching static traits and attributes.

The potential for joint understanding and meaning-making is greatly affected by

the degree of trust and motivation for collaboration and the policies or rules that gov-

ern these processes. People rarely follow up on face-to-face encounters unless busi-

ness process, economic, political, or other factors play a role in maintaining the inter-

action. Throughout the examples in this chapter, incentive is provided though the in-

trinsic motivational characteristics of distance and organizational learning situations.

The research methods and environments presented here should scale more generallyto distributed collaborative environments that encourage persistent collaboration and

active knowledge construction.

The next section in this chapter discusses the challenges of extending user and

group modeling technology to connect people with knowledge and provide support

for complex collaborative processes. The third section discusses research progress in

developing, maintaining, and mediating adaptive online knowledge-sharing communi-

ties. The fourth section summarizes the adaptive collaboration support technology

possibilities within the framework of a theoretical collaboration management cycle

[35]. The final section discusses future trends in managing and supporting web-based

collaboration.



19 Adaptive Support for Distributed Collaboration 575

19.2 From Social Matching to Adaptive Collaboration Support

Individual user models (also called user profiles) store information about a user’s per-

sona, behavior, and preferences. They can be used to recommend products or services

that fit the user’s interests or to provide help and guidance (see Chapter 1 of this book [3]). Collaborative filtering techniques traditionally compute the similarity between

elements in individual user models or group models and attributes of available content

to suggest appropriate information, products, services, activities, or advice [31] (also

see Chapter 9 of this book [30]). Social matching systems apply similar algorithms to

compute the similarity between users or groups, given their interests or information

needs. These systems introduce people to each other, recommend communities or

experts, and suggest opportunistic times for collaboration [39]. The next few para-

graphs briefly introduce these basic concepts in more detail before moving into a dis-

cussion of more advanced methods, and can be skipped if the reader is familiar withthese methods.

Collaborative content-based filtering methods aim to match individuals or groups

to appropriate content, products, services, or activities. For example, content-based

filtering can be used to recommend web sites that would be of interest to a team of

students with different backgrounds and experiences collaboratively navigating the

web together (see [10] for an overview of social navigation). The algorithm would

attempt to find similarities within the student models and select those web sites that

would be appealing and appropriate for a majority of the team members. The recom-

mended items and the consequential student reactions are sometimes stored in groupmodels. Group models characterize the group as a whole, including elements such as

group performance and history. They may also contain individual member profiles.

User and group models are examples of tools that help adaptive collaboration tech-

nology determine the best way to mediate and support online collaboration. Later in

this section, we will see how user and group models can be used to model and medi-

ate dynamic collaborative processes.

If the students in our hypothetical web site recommender example were to rate the

web sites that they found most useful in their work, collaborative social filtering could

then be used to recommend the most popular or most useful sites to other groups of

students. For example, users of the Ringo system [31] rate musical artists. The systemthen recommends new artists to users with similar preferences, automating the “word-

of-mouth” phenomenon. Users can also write reviews that might be useful to other

users with similar tastes or receive lists of the “top 20” or “bottom 10” rated artists.

Social matching systems bring people together to satisfy explicit information

needs, curiosity, or community-oriented or interpersonal interests. For example, the

Expertise Recommender system [26] helps people in an organization locate other us-

ers who have specific expertise. The user can search and sort candidate profiles ac-

cording to several criteria, including a social network that incorporates the results of

personal interviews. The I2I system [4] attempts to find appropriate partners by track-ing users’ actions on documents. It uses this information to dynamically identify users

who are working on similar documents and who might be interested in collaborating.

As a user is working on a document, he is presented with visualizations depicting the

other users who are working within the conceptual space defined by the document.



576 A. Soller

Users can also leave “calling cards” on documents to let other users know that they

are interested in chatting about the document.

Content-based, social filtering, and social matching systems can be combined into

hybrid systems (see Chapter 12 in this book [5]) and can also filter implicit, tacit

knowledge. For example, OWL [24] dynamically profiles individuals as they work,attempting to capture the tacit knowledge that describes users’ behavior—that about

which the users themselves may not even be aware. The system observes as users

apply sequences of tool functions to satisfy task-related goals. For example, using

Microsoft Word, a user might select the menu items Table Convert Text to Ta-

ble to convert a segment of tabbed text to a table. As the system observes groups of

users over time, it identifies differences between individuals’ behaviors, skills, and

activities, and suggests further learning to each user based on these (knowledge gap)

differences.

OWL also provides learning recommendations to users about software functionsthat their peers in communities of interest have found useful (e.g., support, research,

or managerial staff communities). As we will see in section 19.3.2, knowledge and

expertise can take on a different character when viewed through the lenses of different

communities. Communities might be based on project teams, organizational roles,

background, experience, community membership, or culture.

The systems described thus far identify and introduce people who may have shared

interests, and recommend opportunistic times for them to collaborate within a shared

context. They accomplish this through collaborative content and social filtering,

matching, navigation, and visualization tools. Navigation tools follow the user as he

navigates the web and inform him of other users who have navigated similar paths,

while visualization tools represent the activities or characteristics of communities of

users so that the user can decide for himself which communities he might like to join.

The first two sections in this book discuss these systems at great length. The remain-

der of this chapter discusses the challenges in building upon these collaborative filter-

ing and social matching technologies to provide adaptive support for the underlying

collaborative, cognitive, and social processes involved in distributed information shar-

ing and knowledge construction.

19.2.1 Beyond Social Matching

Once access to information or expertise on the web is obtained (e.g., via internet

search, collaborative filtering, or social matching), sustained collaboration is neces-

sary for the development of understanding, knowledge construction, and coordinated

action. Supporting persistent collaboration requires attention to more than individual

attributes and traits. As individuals interrelate and collaborate, levels of interdepen-

dency increase, and people begin to feel and act less like isolated individuals and

more like group members [23]. Over time, it becomes more difficult to predict group

performance based on individual members’ characteristics. The significance of these

characteristics lessens as group dynamics and process become core contributing fac-

tors in predicting group outcome. The complex ways in which information and inter-

pretations flow between collaborating participants ultimately shapes the group’s inter-

action and the collaboration’s outcome.




Barriers to effective collaboration and knowledge sharing are pervasive. Research

in social psychology has consistently shown that group members tend to discuss in-

formation that they share in common instead of discussing the knowledge they

uniquely possess [37]. Hatano and Inagaki [16] showed that when knowledge is con-

structed during group discussions in context, individuals may have difficulty assimi-lating this knowledge without the support and collaboration of the other group mem-

bers. This is particularly true when the information is presented by those who hold a

minority opinion. Even when it is not, the way that the information is represented and

the context in which it was created may prevent the receivers of the information from

easily incorporating it into their own mental representations. Group productivity has

been positively linked to such group processes as peer helping, hypothesis development

and testing, management of competition and conflict, ability to use different viewpoints,

mutual support, and ability to produce detailed, elaborated explanations [8].

Approaches to supporting and sustaining effective distributed collaboration rangefrom systems that assist in locating experts or teammates combined with feedback and

reputation updating processes [e.g., see 40] to systems that provide dynamic team

facilitation and coaching. These approaches aim to promote effective collaboration in

distributed knowledge environments by drawing upon user and group models in dif-

ferent ways.

In the first approach, user and group models are consulted, filtered, generalized, or

aggregated, and a group is constructed by selecting members with the most compati-

ble knowledge, skills, and behaviors. Because individuals may behave differently in

groups, individual user models may include behaviors prevalent and productive dur-

ing prior group interactions. This process of constructing the best possible group is

intended to influence the team dynamics positively and increase the likelihood of

group success.

In the second approach, a (human or computer) facilitator analyzes the group inter-

action after the users have begun to work collaboratively, and dynamically attempts to

either facilitate the group interaction or modify the environment appropriately. User

and group models help the facilitator determine the most effective mediation methods

and record how well the users respond to the interventions. Later in this chapter, sec-

tion 19.4 returns to these concepts by describing a cyclic phase-based model of col-

laboration management. The model starts from the user and group modeling phasesand moves through the behavior analysis and knowledge visualization phases to the

adaptive group facilitation phase. During the final phase, the users’ responses to the

environmental feedback are interpreted and used to update the group models in prepa-

ration for the next cycle. The system described in the next section introduces the no-

tion of combining user modeling and adaptive facilitation to support online collabora-

tive learning activities and illustrates some of the challenges in this area.

19.2.2 Strategic Pairing and Adaptive Support for Distributed Collaborative

Learning

IMMEX™ (Interactive Multi-Media Exercises; http://www.immex.ucla.edu) is a

web-based multimedia learning environment designed to help groups of students learn

how to develop and evaluate hypotheses, and analyze laboratory tests while solving

real-world problems. The single-user version has been used for over 13 years in science



578 A. Soller

classes across U.S. middle and high schools, universities, and medical schools, and

has logged over 250,000 student problem-solving performances [38]. The collabora-

tive version of IMMEX includes general-purpose collaborative web navigation and

synchronization facilities, and a structured chat interface [29] (see Fig. 19.1).

Fig. 19.1. The IMMEXTM Collaborative problem-solving environment runs within students’

web browsers. The left-hand panel enables and displays student chat communication. The bot-tom panel shows which student has control of the mouse. The main window is a shared, syn-

chronized multimedia and hypertext workspace

In IMMEX, individual user profiles describe students’ learning performance, pro-gress, gender, preferred problem-solving strategies, and predicted future strategies[38]. Student ability is modeled using Item Response Theory (IRT), which estimatesthe likelihood that a student will correctly solve a problem given the characteristics of the problem and the characteristics of the individual. While traditional IRT has his-torically provided a good estimation of students’ overall abilities within a domain,other approaches have been more successful in modeling the development of complexcognitive processes [see 27 for a discussion of modeling evidentiary reasoning].

Student development of problem-solving strategies in IMMEX is modeled though

a self-organizing map neural network approach [20]. The neural network is designedto represent the space of student problem-solving strategies in varying stages of de-velopment. First, the 36-node network topology is developed. Then, it is iterativelytrained with thousands of student performances represented by sequences of problem-solving actions. For the domain of chemistry, a student performance might includeactions such as selecting a flame test, a blue litmus test, and a precipitate test, and



580 A. Soller

By comparing models across different classes of students, the system can strategically

select collaborative learning partners who might help the user see a different point of

view, thus increasing the probability that the user’s future learning trajectory would

follow a more productive course. Although small group research has suggested that

individual characteristics are generally poor predictors of group learning performance[23, 42], the IMMEX approach is unique because it boosts the predictive capabilities

of individual student models. The neural network and HMM analyses project the ef-

fects of individual tendencies into future online collaborative interactions, thus facili-

tating the prediction of future individual and group behavior. The challenge is deter-

mining what combinations of current and future cognitive problem-solving strategies

will be the most productive. Collaborative learning studies suggest that students gen-

erally work best in heterogeneous groups with a combination of abilities, as long as

the heterogeneity is not too wide-ranging [8].

One can imagine several different partnering combinations based on students’ cur-rent and future strategy predictions. For example, the system might recommend that a

student who is using an ineffective strategy (and whom we predict will continue to

use the ineffective strategy) partner with another student who has adopted an efficient

strategy. Alternatively, the system might recommend that two students work together

if they are both using less effective strategies but show a high tendency to shift their

strategies on the following problem set. The collaboration component of IMMEX sets

up on-line collaborative sessions, introduces the team members, and helps to facilitate

and guide the group learning session. Once a group is strategically constructed and

begins a collaborative problem-solving session, the IMMEX neural network-based

modeling software begins to predict the new group problem-solving strategy auto-

matically. This analysis is done by examining and probabilistically modeling the se-

quence of group members’ actions [for more detail, see 38].

Although the group strategy provides some indication of how the group problem-

solving is proceeding, it may provide little information about the individuals’ learn-

ing. For example, a student using an efficient strategy may solve the problem alone

without explaining his actions to his partner, or he may instead give instructions to his

partner about what to do, and his partner may simply follow these instructions without

questioning them. In both cases, the system will recognize the overall group problem-

solving strategy as efficient even though the individual learning outcomes may tell adifferent story. Whether or not the individual with the less efficient strategy adopts a

more efficient problem-solving method depends not only on the combination of prior

individual strategies, but also on the way the collaborative learning process develops

over time. Another possibility is that the student with the more efficient strategy will

regress. For this reason, monitoring and facilitating the collaborative interaction is

important.

Monitoring and assessing collaborative interaction might be done similarly to

Soller’s [33] approach, in which sequences of student chat conversation (coded us-

ing sentence openers such as “I think” or “Do you know”) and actions are analyzedusing HMMs [also see 15]. This approach was shown to predict the effectiveness of

student knowledge-sharing interaction in laboratory experiments with about 74%

accuracy. Preliminary studies [14] have applied a similar approach to determine the

degree to which students’ conversational structures provide evidence about whether

or not the group members are helping each other adopt more efficient problem-




solving strategies. If the structure of students’ discussions reflects the structure of

their decision processes, then problem-solving strategy shifts might be recognized

by modeling and characterizing interaction patterns in the context of various known

strategy applications.

Work is underway to develop web-based pedagogical agents for IMMEX that usethe knowledge of a group’s mix of cognitive strategies to strategically take on behav-

iors that might nurture the development of more efficient group problem-solving

strategies. Playing this complex role will require an understanding of how groups

members collaborate to construct new knowledge, and an understanding of how to

support this process.

Situations in which the student interaction is less likely to produce problem-solving

strategy shifts might be facilitated by targeted mouse control schemes. Previous re-

search has shown that mouse control schemes that change the way in which group

members share their view of the learning environment can have significant effects onstudent learning [17]. For example, Chiu [6] studied the effect of four different

schemes on student performance: assign, in which one student was assigned exclusive

control of the workspace; rotate, in which control automatically shifted to the next

student every 3 minutes; give, in which the student currently controlling the work-

space decided when and to whom to relinquish control; and open, in which any mem-

ber could take control at any time. The results of the study suggest that when one stu-

dent is assigned control of the workspace such that the other group members cannot

anticipate attaining control at some future time, the students not only perform better,

but also engage in more task-oriented dialog. The inability to control the workspace

directly may encourage students to express and justify their ideas in words, rather than

waiting for their turn to take actions.

Modeling users and groups, and using these models to strategically construct and

facilitate online groups is just one way of providing adaptive support for distributed

collaborative web-based applications. The next section discusses how online commu-

nities provide adaptive virtual spaces for meeting, collaborating, learning, sharing,

and constructing knowledge online.

19.3 Knowledge Sharing and Discovery in Online Communities

Professionals across distributed organizations naturally share knowledge by forming

small groups based on similar interests, practices, personal affinity, and trust. These

groups are termed Communities of Practice [2, 21] because they function as cohesive

communities that share a common sense of purpose and interest. Communities of

Practice facilitate the sharing and creation of new knowledge, and are therefore im-

portant to the stability and growth of organizations and the development of knowledge

areas. Their members interact on an ongoing basis, sharing best practices and shaping

the growth and advancement of those practices.

Communities of Interest (CoIs) [43] are less formally structured community net-

works linked by shared interests rather than best practices. Because of the tenuous and

diverse types of links between members’ peripheral relationships, processes within

CoIs are difficult to identify and understand. While formal organizational learning

literature has focused on topics such as understanding how peripheral community



582 A. Soller

members become core members, research in CoIs recognizes the benefits of establish-

ing peripheral community membership in many different communities and helping to

bridge disparate communities into informal social networks. These loosely bridged

networks can explain how innovation happens when community members interact

with members of possibly far-reaching communities that may be able to offer new,different perspectives. Such chance encounters and informally planned interactions

are often encouraged through referrals by peripheral community members. This sec-

tion describes how adaptive collaboration support technology enables, mediates, en-

courages, and guides this natural process.

Effective knowledge sharing across Communities of Interest with different objec-

tives and perspectives means sharing the right information, at the right level of detail,

using the right language, at the right time, in the right context, with the right people

[13]. A failure related to any one of these factors can lead to a knowledge-sharing

breakdown. Some social psychology research has identified strategies that might en-courage communities to share the information they uniquely possess. Such strategies

include helping participants understand the nature and granularity of the knowledge

held by each Community of Interest, and setting up interactive agendas specifically

for information sharing so that gaps can be more readily identified. This section dis-

cusses tools and methodologies for facilitating knowledge sharing and community

development

Facilitating knowledge sharing across Communities of Interest that do not yet have

established processes for information sharing involves creating the infrastructure,

mindset, and tools needed to support a new culture of collaboration and sharing. Sev-

eral different factors influence community members’ participation, involvement, and

the eventual success of the collaboration. These include (1) the degree to which users

are aware of the various communities, information, and knowledge available in the

environment (awareness), (2) the ability of online communities to maintain knowl-

edge and user interest, and provide access to useful information in a timely manner

(maintenance), and (3) whether community members perceive an immediate benefit

from collaborating with others (motivation). The next three subsections address these

three processes respectively.

19.3.1 Knowledge Discovery and Awareness

The distributed and virtual nature of the adaptive web makes effective collaboration,

knowledge sharing, and an understanding of collaborators’ perspectives essential to

creating meaningful knowledge and achieving complementary objectives. Helping

communities develop their own awareness and understanding of other communities’

knowledge, problems, and goals are some of the most difficult challenges.

In supporting collaborative knowledge discovery and awareness, one of the most

important decisions involves the design of shared workspaces. Each Community of

Interest might have a different set of complementary objectives and may still need to

collaborate effectively to share the information that others need, without necessarily

aiming to attain the same goals. A shared, unified workspace or common view may be

helpful for providing the appropriate context for sharing knowledge but may, in some

cases, also hinder collaborators’ ability to engage in certain specialized activities for

meeting their individual goals (e.g., exploring private databases or web portals, cus-




tomizing views to perform focused analyses). The design of shared workspaces

should consider the degree to which representations of shared artifacts will be viewed

and interpreted differently by participants based on their backgrounds, experiences,

cultures, and values [22, 25].

A simple example with which the savvy international traveler might be familiar isgiven by the litany of traffic signs and symbols in foreign countries. Shinar, Dewar,

Summala, and Zakowska [32] asked 1000 licensed drivers from Canada, Finland,

Israel, and Poland to interpret 31 traffic signs from various countries. They found

highly significant differences in participants’ understanding of the signs. Fig. 19.2

shows examples of two signs (“Dead End” and “No Vehicles Carrying Explosives”)

for which 86% and 78% of the participants answered incorrectly. Ten percent of the

participants actually misinterpreted three of the signs as having the opposite of their

true meaning.

“Dead End”

Australia, Canada

“No Explosives”

Poland

“Dead End”

Australia, Canada

“No Explosives”

Poland

Fig. 19.2. Two traffic signs for which 86% (Dead End) and 78% (No Explosives) of the par-

ticipants interpreted incorrectly

Shared workspaces for supporting online communities should also take into consid-

eration the persistence and validity of information [11]. Activities that involve tran-

sient or uncertain information may be more appropriately conducted within private

workspaces or private chat rooms. Once the information reaches a level of stability

appropriate for a broader audience, it should be migrated to shared community work-

spaces. Community members will perceive information in shared workspaces as sta-

ble and reusable because the nature and affordances of shared workspaces inherently

convey information persistence. This is a common problem on the web, a mediumregarded as persistent, but one in which links are often moved, updated, and deleted.

Distributed collaboration technology adds adaptivity to shared virtual work-

spaces by supporting awareness and tolerance, and helping users understand how

their perspectives differ. Examples of technology for supporting these processes

include knowledge seeking and searching tools that attempt to understand the user’s

core community perspective while guiding her toward the most appropriate knowl-

edge sources tailored to her needs. Other awareness tools help communities frame

their knowledge in terms and languages that are most familiar to other known

communities, developing implicit links between similar concepts and programs, or

suggesting meaningful analogies to facilitate this conceptual translation. Social

awareness and social networking tools can be useful for connecting community

members and enabling them to attach meaning to tacit knowledge that was devel-

oped in specific contexts.



584 A. Soller

Social network theory defines methods and models for analyzing and understand-

ing the linkages between entities in social networks. Concepts such as “cliques” (de-

fined by the interconnections between actors), “centrality and prestige,” and “affilia-

tions” (representing the links between actors and events) provide the building blocks

of this applications-oriented theory [41]. Social network tools provide views of onlinecommunities and their members by drawing upon specialized user profiles that spec-

ify the communities of interest to which each user belongs. Members who enhance

their profile to include more detailed information enable the system to serve them in a

more meaningful way. For example, a member of a funding agency who posts de-

tailed information about his agency’s resources and funding opportunities enables the

technology to assist the member in identifying potential customer communities that

seek such resources. Resources might be linked to individual members’ profiles and

categorized in their corresponding communities (e.g., Training and Simulation com-

munity, Nanotechnology community) so that they can be retrieved either by commu-nity or individual member search criteria.

Web-based social networking technology enables community members to view visu-

alizations of social networks and run content or member-based searches across these

networks. A typical search might begin with a researcher viewing his usual community

of professional colleagues and friends. Clicking on a contact in the social network might

set off two different processes: (1) the system would look to see if the selected person

has an existing profile, and (2) the system would use the selected participant to “grow”

the social network. New contacts might be “discovered” by linking the selected person

with the co-authors on their publications or the partners they list on their Curriculum

Vitae. Examples of systems that apply these or similar ideas include LiveJournal

(http://www.livejournal.com), iVisto [34], Referral Web [19], Friend-of-a-Friend

(FOAF) (http://www.foaf-project.org), and Huminity (http://www.huminity.com).

LiveJournal and IVisTo are described in more detail in the remainder of section 19.3.

Users register with LiveJournal by creating a simple profile. A user profile includes

a mini-bio, a list of interests (used to find other users with similar interests), (option-

ally) a list of friends, and (optionally) a list of communities. Once a user has created a

profile, she can create journal entries that include icons, representing her mood, and

polls that request other users to vote on her ideas. She also has the option to allow

peers to respond (through comments) to the ideas in her journal. Journals can be cus-tomized or embedded in web pages.

LiveJournal automatically shows the user a hypertext list of the communities re-

lated to his interests and the other members of those communities. Users can then

freely navigate through communities and discover new communities by viewing the

communities to which each user is a member. For example, I list “Education” as one

of my interests and discover that Mary and Bob are both members of that community.

They are also members of the community “Collaborative Learning Technology,”

through which I discover Peter, who is a member of several other communities I did

not know even existed. The trail continues indefinitely.Privacy is handled by allowing the user to control who can view his contact infor-

mation and journal entries, who can send him text messages, who can leave comments

(and whether or not the user wants to screen the comments posted to his journal), and

who can participate in polls. The user also has full control over the communities that

he moderates.




Fig. 19.3. The Touchgraph LiveJournal Browser graphically displays the other members of theuser’s CoI and any other related CoIs

The Touchgraph LiveJournal Browser, shown in Fig. 19.3 enables users to visualize

the virtual communities within the LiveJournal environment.

In the figure, pink and white nodes represent users, and blue nodes represent com-

munities. Pink user nodes are “expanded” to show all the user’s community member-

ships, while white user nodes are “collapsed” to save screen real estate. The user has

the option of either viewing a particular Community of Interest’s members (blue node

surrounded by pink and white nodes) or a particular member’s Communities of Inter-

est (pink node surrounded by blue nodes). Each node includes a link directly to the

LiveJournal web page describing the user or community. For example, clicking on the

green “info” box attached to the “Educational Technology” node in the center of

would automatically bring up the LiveJournal web page for that community.

New nodes can also be added to the display, and the system will automatically

identify the user or community links between the new node and the nodes already on

the display. When the user moves the mouse over a Community of Interest, the sys-tem highlights those other users who share the same interests, thus enabling the user

to identify new friends and communities.



586 A. Soller

19.3.2 Community Maintenance

Like any real-world community, online communities need support and maintenance to

sustain their development and growth. Determining how to provide this support re-

quires an understanding of what to expect over the lifetime of the online community.Communities should generally be motivated to share quality understandable informa-

tion with other communities that repay the good will. The perceived and measured

benefit of collaborating is predictive of the level to which community members con-

tinue to collaborate with each other over time. For example, Cho, Stefanone, and Gay

[7] studied the online interaction of students using listservs and community discussion

boards, and found that less information was shared and processed by the students as

the term progressed. Central/prestigious actors shared more information at the begin-

ning of the term, while less central/prestigious (more peripheral) actors were more

likely to interact and share knowledge later in the term. This suggests that peripheral

actors require time to enter community-based practices, providing a concrete web-

based application of Lave and Wenger’s [21] legitimate peripheral participa-

tion/situated learning theory.

Cho et al. [7] also found that URLs posted to the class listservs (and consequently

emailed to all the participants) were visited significantly more times than those posted

on the discussion boards that the students needed to access explicitly. The “push”

technology was necessary to have the learners fully involved in the community-

based activities. This concept may be particularly important for more established

community members because their motivation for community-supported knowledge

discovery may decrease over time as they reach the knowledge boundaries of thecommunity and perceive a reduced need to use a system to discover things they

think they already know.

By connecting Communities of Interest and providing more information and asso-

ciations at users’ fingertips, we increase the volume of data through which a user must

search to find the most relevant information. Guidelines, roadmaps, metadata, struc-

tures, and tools for finding relevant information in community-based contexts are

essential and must be constantly updated and maintained.

The community moderator role is also key; several moderators may be needed

(e.g., perhaps one from each community). Questions should also be raised regardingthe characteristics that are needed for effective moderation of community-based

knowledge networks. For example, moderators may need domain knowledge or ex-

perience in professional group facilitation, or they may need time to get to know the

collaborating partners personally and establish a level of trust with them.

Cross-community discussion groups that are linked to shared data sources may

help to give more context and meaning to the content. For example, users and groups

could collaborate in online discussion forums that are directly linked to the imagery

and reports they are sharing, commenting and explicitly making linkages (e.g., ar-

rows, highlights) to sections of the shared items being discussed. Rating or voting

tools also help community members determine what information (discussion items,

images, and so forth) was helpful for what purposes. The most useful information can

then be maintained and enhanced as less central knowledge migrates to community

peripheries. The remainder of this section discusses a social networking tool for such

community management.




IVisTo (Interactive Visualization Tool) [34] is a social networking tool that oper-

ates within a peer-to-peer knowledge management environment. It enhances user

and group (community) models by monitoring and analyzing users’ keyword and

ontology-based search behaviors. IVisTo displays a weighted combination of social

networks, where each social network addresses a different user model variable, and theweights are given by the learner’s social and semantic preferences. The interface con-

tains a set of slider bars that represent the social variables in the user model (e.g., Organ-

izational Role, Collaboration Level) (see Fig. 19.4). Using these slider bars, the user can

indicate the importance, or weight , of each variable. Behind the scenes, the system gen-

erates a social network for each of the variables, and then computes one single network

by calculating a weighted sum of the individual networks. For example, by increasing

the importance of the “Organizational Role” slider bar, the tool gives more “credit” to

Fig. 19.4. IVisTo1 interface showing Sally’s social network. Sally is a community manager working in the Mechanical Components division, searching for knowledge about high-speed

train problems

1 IVisTo was developed using the TouchgraphLLC toolkit (see http://www.touchgraph.com).



588 A. Soller

those other Community of Interest members who hold a similar Organizational Role

as the user, displaying those users as more prominent on the interface. In this way,

IVisTo provides each learner across an organization access to a personalized set of

visualizations from his perspective, weighted according to his interests.

As the learner carries on her day-to-day learning and collaborative work, thelengths of the links in IVisTo are recalculated as the elicited and inferred information

in her user model is updated. For instance, when users interact with new colleagues

from different online communities, the system updates the appropriate corresponding

user model values for their level of collaboration. It also reassesses the degree of se-

mantic and lexical similarity between users’ queries and their shared resources using

an ontological matching procedure [1]. These activities help the system intelligently

infer and visualize different types of knowledge-sharing communities and identify

potential future members and items of interest. These kinds of adaptive personalized

social networks may also raise users’ awareness of the social factors that define their Communities of Interest, and facilitate their access to relevant artifacts and other related

communities. The next challenge is evaluating the ability of adaptive social network-

based tools to perform these tasks while maintaining and serving virtual communities.

19.3.3 Motivation and Participation

Distributed communities that are actively engaged and motivated to share knowledge

may experience improved learning and development, and increased productivity and

growth [2]. Motivating community members to interact regularly and maintain their

engagement is key to community development. Communities that experience long-term success reward members for taking actions that improve the health and progress

of the community by providing positive feedback. Feedback can take the form of peer

ratings, an improved reputation, a greater understanding of the domain, or privileged

involvement in planning core community activities.

Online communities might encourage members to participate and interact by ena-

bling them to rate each other and their resources via informal peer review. Ratings

might be weighted and aggregated to compute values for user reputation and resource

value. For example, suppose Professor Arnold searches among her Communities of

Interest, finds Mr. Brown, and discovers through that link that Professor Clark might be a good scientific partner for a project proposal. She should be able to provide the

system with feedback describing the crucial role Mr. Brown played in establishing

this partnership, perhaps even without his knowledge. In Vassileva’s [40] approach, a

user’s reputation is based both on feedback from other users and their level of col-

laboration with respect to the communities to which they belong (e.g., the number of

resources contributed to each community, how many “favors” the user owes to other

users, and whether or not the user is being a “free rider”) An inflation rate allows

“older” activity to decrease in importance and weight over time.

Reputation-based behaviors and processes in online communities mirror our behav-

ior in face-to-face contexts. A strong link appears to exist between a person’s online

reputation according to his peers and his degree of perceived trustworthiness. Esfan-

diari and Chandrasekharan [12] explain that trust has both cognitive and mathematical

foundations. From a cognitive perspective, trust is a function of one’s underlying be-

liefs; from a mathematical perspective, trust is a metric based on variables such as




competence, risk, utility, and importance. In propagating trust through structures such

as social networks, Esfandiari and Chandrasekharan recommend exercising caution

because different paths in the network might produce contradictory values and cycles

in the graph can artificially decrease trust values (e.g., one might loop three times

before reaching a neighboring agent). Using even the most stable and fair algorithmstill means determining the degree to which peers are trustworthy, reliable, or knowl-

edgeable by substituting mathematical procedures for personal judgments based on

experience, culture, beliefs, and values. Depending on one’s point of view, the degree

of fallibility in either case can be seen as variable (poor judgment may be no better

than a mediocre computer algorithm).

As discussed earlier in this section, user and group models might be updated to re-

flect the outcomes of positive knowledge-sharing interactions (e.g., improved reputa-

tion for knowledge sharer, improved understanding of content for knowledge re-

ceiver). They might also serve as resources by which the adaptive web providescommunity members with summative feedback about their participation and collabo-

ration. Augmenting participation and activity statistics with suggestions and com-

ments can also help community participants understand what is working and why (or

why not). Evaluation and assessment should be done at each phase of development

and deployment with a high level of community involvement. For example, each or-

ganization should understand what knowledge was shared and how it was used by

other organizations.

19.4 Practical Collaboration Management

At the beginning of this chapter, we discussed how web-based user and group models

might assist in group construction by selecting members who have the most compati-

ble knowledge, skills, and behaviors. Strategically composing groups may provide a

reasonable way to set up online collaborations, but once the collaboration begins,

variables such as users’ prior knowledge, motivation, roles, language, and group dy-

namics will interact with each other in unpredictable ways, making it difficult to

measure and understand behavioral effects. In the third section of this chapter, the

need for dynamic mediation and facilitation led us to a discussion of awareness,knowledge discovery, and community maintenance tools. Understanding when, how,

and to what extent to employ these tools during online collaboration sessions can

make a significant difference. For guidance on this, we can build upon the Collabora-

tion Management Cycle ([35], see Fig. 19.5), a phase-based model designed to frame

our understanding of how to structure and mediate distributed virtual group activity.

In the first two phases of the Collaboration Management Cycle, the online interac-

tion is observed, recorded, formatted, and logged for later processing. Recording can

happen at many different levels of granularity, from audio and video capture to em-

bedded instrumentation of web-based software applications. Ultimately, user actions

and interactions must take the form of standardized, computer analyzable log files

(e.g., <time: 14:00> , <user: Tom>, <event: click-entity5>, <chat: “I’m going to paste

the image of the bike now”>).

The state of interaction must then be conceptualized and represented using the data

gathered in the first two phases. The way that this model of interaction is conceptualized



590 A. Soller

Fig. 19.5. The Collaboration Management Cycle

From Soller, A., Martínez-Monés, A., Jermann, P., Muehlenbrock, M.: From mirroring to guid-

ing: A review of state of the art technology for supporting collaborative learning. InternationalJournal of Artificial Intelligence in Education 15(4) (2005) 261–290. Copyright 2005 by the

International AIED Society. Reprinted with Permission.

depends on how the performance is to be measured and assessed. Typically, one or

more high-level variables, such as “collaboration” or “skill competency,” are selected

and evaluated by algorithms that dynamically read in the log file data. Although the

methods behind these algorithms vary broadly from simple statistical calculations to

iterative probabilistic models and fuzzy logic, the end result should always reflect a

better understanding of the collaborative process and an improvement in individual

and group performance. Factors such as “group cohesion” or “shared understanding”

are difficult to grasp, and even more difficult to measure quantitatively. Researchers

and practitioners improve collaboration management by both theoretically grounding

the selection of variables and metrics, and comprehensively evaluating the impact of

those variables on human performance.

In the third phase of the Collaboration Management Cycle, the online interaction is

diagnosed, and preparations are made for possible remediation. This phase requires aconception of the desired interaction formulated using the same computational repre-

sentation and/or variables as the current state of interaction. The difference between

the current and desired states should provide the users with an understanding of how

well they are performing and how much more they could potentially achieve. This

phase prepares the system for providing recommendations and advice to the users.




Remediation might be offered by intelligent interfaces, web-based computer

agents, or human facilitators when discrepancies exist between the current and desired

states of interaction. The labels on the arrows pointing inward in Fig. 19.5 show three

different categories of adaptive collaboration support technologies that mediate online

interaction: mirroring tools, metacognitive tools, and guiding systems.Mirroring tools are termed as such because after they collect and log the interac-

tion, they simply reflect this data back to the user. These tools are intended to pro-

voke self-reflection and self-mediation. Users who self-reflect using mirroring tools

however may have more difficulty mediating their interaction than those who self-

reflect using metacognitive tools. Metacognitive tools show users representations of

both their own interaction and their potential interaction, and may also hint at pos-

sible ways to improve performance. Jermann [18] found that these tools positively

affect student performance online by increasing their task-related communication,

and the quality and sophistication of their problem-solving plans. His system displays participation rates to pairs of collaborators as they are solving a traffic light

tuning problem. The display compares the volume of messages sent by each student

to the volume of problem-solving actions taken by each student. The system also

displays a color-coded model of desired interaction next to the observed interaction

state—the students used this standard to judge the quality of their interaction. Jer-

mann studied the behavior of students when desirable interaction was represented as

engaging in a greater proportion of talk relative to the proportion of simulation-

based actions. He found that the metacognitive display positively encouraged the

students to participate more through the chat interface, in particular to engage in

more precise planning activities.

Guiding systems attempt to augment users’ cognitive processes by assessing the

collaboration activities and providing hints, guiding questions, dynamically selected

and structured content, or recommendations for online partners. This guidance might

be presented by a web-based animated agent serving as a coach, group facilitator, or

peer. For example, the pedagogical agents in the COLER system consider the differ-

ences between students’ personal, individual problem-solving workspaces and their

group’s shared workspace [9]. The agent provides feedback and advice to the students

by using a decision tree that considers combinations of these differences and the pro-

gression of the students’ collaboration. Collaboration variables include such factors asoverall participation, the degree to which students have equally contributed to the

shared solution, and whether or not the students would benefit from reflecting on their

work (students are required to state agreement or disagreement when changes are

made to the group’s shared workspace).

Remediation will have an impact on students’ future interactions regardless of

whether or not it is offered by a system or human, and this impact must be evaluated

to ensure that it produces the desired effects. The arrows in Fig. 19.5 that run from

phase 4 back through the center illustration to phase 1 indicate the cyclic nature of the

Collaboration Management Cycle and the importance of continual evaluation andassessment.

In less-structured environments in which goals and objectives are not as clearly de-

fined, adaptive collaboration support technology may give the users more control over

the way that their interaction is mediated. For example, the adaptive web might take

the form of an interactive, personalized social network visualization that enables users



592 A. Soller

to discover knowledgeable peers, online communities, and other resources in cross-

cutting research areas. The user might help the system personalize the discovery proc-

ess by suggesting levels of constraints on criteria such as location, expertise, organiza-

tional role, and online collaboration level. Adaptive collaboration support technology

sometimes also takes the form of distributed teams of socially aware intelligent rec-ommendation agents that might put the user in contact with an online expert or in-

structor from a selected Community of Practice [34, also see section 19.3.2].

19.5 Future Trends

We communicate with each other through the many flavors of voice, text, appear-

ance, behavior, and action, the complex interplay between these forms, and even

through the absence of communication itself. The outcomes of our communication

are sometimes difficult to predict because they depend on the combination of forms

used in a particular context and timeframe. Even the notions of “context” and

“timeframe” in today’s internationally networked knowledge-based society are un-

clear, as are the traditional characteristics that distinguish asynchronous from syn-

chronous communication. Interaction in such a society might be supported and en-

hanced by harnessing the opportunities afforded by the adaptive web as a unique

communication medium. This is however a unique challenge that may require re-

searchers to design new theories of interaction, and develop new performance sup-

port tools that enable the seamless shifting between communication forms, while

providing awareness and a greater understanding of the interaction as it evolves andtransforms contexts over time.

In designing the next generation of collaboration tools for the adaptive web, we

should continue to improve the interoperability and design of collaborative tools for

voice, text, and nonverbal communication and for constructing and annotating docu-

ments, images, and videos. Awareness and support facilities should help people un-

derstand the way that the technology increases learning and work efficiency and

shapes participant roles as they move between social contexts and communication

tools. This suggests that we should invest more in the study of distributed collabora-

tive work and learning processes in context and address our findings through newcollaboration tool paradigms. The technology should be prepared to model and ana-

lyze unpredictable events in new contexts, learn from those events, and effectively

impart its knowledge to its human collaborators.

The web indeed provides a vast knowledge resource and the opportunity to im-

prove individual productivity through advanced filtering and adaptation algorithms,

but it is also an interface for knowledge construction, a mediator for communication

and learning, and a structured channel through which knowledge is created, inter-

preted, used, and recreated by other users.

Acknowledgments. This work was supported by the Institute for Defense Analyses

Central Research Program. The opinions, assertions, and analyses in this chapter are

those of the author alone. They do not necessarily reflect official positions or views of

any U.S. government entity, and they should not be construed as asserting or implying

U.S. government endorsement of this content.




References

1. Bouquet, P., Serafini, L., Zanobini, S.: Semantic coordination: A new approach and anapplication. Proceedings of the 2nd International Semantic Web Conference (2003) 130–145

2. Brown, J.S., Duguid, P.: Organizational learning and communities of practice: Toward aunified view of working, learning, and innovation. Organization Science 2(1) (1991) 40–57

3. Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational

systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): The Adaptive Web: Methods and

Strategies of Web Personalization, Lecture Notes in Computer Science, Vol. 4321.Springer-Verlag, Berlin Heidelberg New York (2007) this volume

4. Budzik, J., Bradshaw, S., Fu, X., Hammond, K.: Supporting online resource discovery inthe context of ongoing tasks with proactive software assistants. International Journal of Human-Computer Studies 56(1) (2002) 47–74

5. Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W.

(eds.): The Adaptive Web: Methods and Strategies of Web Personalization, Lecture Notesin Computer Science, Vol. 4321. Springer-Verlag, Berlin Heidelberg New York (2007) thisvolume

6. Chiu, C.H.: Evaluating system-based strategies for managing conflict in collaborative con-cept mapping. Journal of Computer Assisted Learning 20 (2004) 124–132

7. Cho, H., Stefanone, M., Gay, G.: Social information sharing in a CSCL community. In:

Stahl, G. (ed.): Proceedings of the 2002 ACM CSCL Conference Lawrence Elbaum Asso-ciates, Hillsdale, NJ (2002) 43–53

8. Cohen, E.: Restructuring the classroom: Conditions for productive small groups. Review of

Educational Research 64(1) (1994) 1–359. Constantino-González, M.A., Suthers, D., Escamilla de los Santos, J.: Coaching web-based

collaborative learning based on problem solution differences and participation. Interna-tional Journal of Artificial Intelligence in Education 13 (2002) 263–299

10. Dieberger, A., Dourish, P., Höök, K., Resnick, P., Wexelblat, A.: Social navigation: tech-niques for building more usable systems. interactions 7(6) (2000) 36-45

11. Dillenbourg, P., Traum, D.: Does a shared screen make a shared solution? In: Hoadley, C.,Rochelle, J. (eds.): Proceedings of the Third Conference on Computer Supported Collabo-

rative Learning. Lawrence Erlbaum Associates, Mahwah, NJ (1999) 127–13512. Esfandiari, B., Chandrasekharan, S.: On how agents make friends: Mechanisms for trust

acquisition. Proceedings of the 4th Workshop on Deception, Fraud and Trust in Agent So-cieties (2001) Montreal, Canada, 27-34

13. Frank, F., Soller, A.: Collaboration and knowledge sharing across the intelligence commu-nity. In: Sapp, A., Brown, B., Kirkhope, J., Tomes, R. (eds.): The Faces of Intelligence Re-

form: Perspectives on Direction and Form. The Council for Emerging National Security

Affairs (CENSA), New York (2005) 99-10214. Giordani, A., Gerosa, L., Soller, A., Stevens, R.: Extending an online individual scientific

problem-solving environment to support and mediate collaborative learning. Proceedings of

the Artificial Intelligence in Education (AI-ED 2005) Workshop on Representing and Ana-lyzing Collaborative Interactions (2005) 12-22

15. Goodman, B., Linton, F., Gaimari, R., Hitzeman, J., Ross, H., Zarrella, G.: Using dialoguefeatures to predict trouble during collaborative learning. User Modeling and User-Adapted

Interaction 15(1) (2005) 85–13416. Hatano, G., Inagaki, K.: Sharing cognition through collective comprehension activity. In:

Resnick, L., Levine, J., Teasley, S. (eds.): Perspectives on socially shared cognition.

American Psychological Society, Washington D.C. (1991) 331–34817. Inkpen, K., McGrenere, J., Booth, K., Klawe, M.: The effect of turn-taking protocols on

children's learning in mouse-driven collaborative environments. Proceedings of GraphicsInterface '97, Kelowna, BC (1997) 138–145



594 A. Soller

18. Jermann, P.: Computer support for interaction regulation in collaborative problem solving.Unpublished doctoral dissertation, University of Geneva, Switzerland (2004)

19. Kautz, H., Selman, B., Shah, M.: The hidden web. AI Magazine 18(2) (1997) 27–3620. Kohonen, T.: Self-organizing maps. Springer-Verlag, Berlin (2001)

21. Lave, J., Wenger, E.: Situated learning: Legitimate peripheral participation. CambridgeUniversity Press, Cambridge (1991)

22. Lesgold, A.: Contextual requirements for constructivist learning. International Journal of Educational Research 41(6) (2004) 495-502

23. Levine, J.M., Moreland, R.L.: Small groups. In: Gilbert, D., Fiske, S., Lindzey, G. (eds.):The handbook of social psychology. McGraw-Hill, Boston, MA (1998) 415–469

24. Linton, F.: OWL: A system for the automated sharing of expertise. In: Ackerman, M.,Pipek, V., Wulf, V. (eds.): Sharing expertise: Beyond knowledge management. MIT Press,

Cambridge, MA (2002) 383–401

25. Mantovani, G.: Social context in HCI: A new framework for mental models, cooperation,and communication. Cognitive Science 20 (1996) 237–269

26. McDonald, D., Ackerman, M.: Expertise recommender: A flexible recommendation systemand architecture. Proceedings of the ACM Conference on Computer Supported Cooperative

Work (2000) 231–24027. Mislevy, R., Steinberg, L., Almond, R.: On the structure of educational assessments. Meas-

urement: Interdisciplinary Research and Perspectives 1(1) (2003) 3–6228. Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech rec-

ognition. Proceedings of the IEEE 77(2) (1989) 257–28629. Ronchetti, M., Gerosa, L., Giordani, A., Soller, A., Stevens, R.: Symmetric synchronous

collaborative navigation applied to e-learning. IADIS International Journal onWWW/Internet 3(3) (2005) 1-16

30. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): The Adaptive Web: Methods and

Strategies of Web Personalization, Lecture Notes in Computer Science, Vol. 4321.

Springer-Verlag, Berlin Heidelberg New York (2007) this volume31. Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating "Word

of Mouth". CHI'95 - Human Factors in Computing Systems (1995) 210–217

32. Shinar, D., Dewar, R., Summala, H., Zakowska, L.: Traffic sign symbol comprehension: across-cultural study. Ergonomics 46(15) (2003) 1549–1565

33. Soller, A.: Understanding knowledge sharing breakdowns: A meeting of the quantitativeand qualitative minds. Journal of Computer Assisted Learning 20 (2004) 212–223

34. Soller, A., Guizzardi, R., Molani, A., Perini, A.: SCALE: Supporting community aware-ness, learning, and evolvement in an organizational learning environment. Proceedings of

the 6th International Conference of the Learning Sciences, Santa Monica, CA (2004) 489– 496

35. Soller, A., Martínez-Monés, A., Jermann, P., Muehlenbrock, M.: From mirroring to guid-ing: A review of state of the art technology for supporting collaborative learning. Interna-

tional Journal of Artificial Intelligence in Education 15(4) (2005) 261–29036. Stahl, G.: Group Cognition: Computer Support for Building Collaborative Knowledge.

MIT Press, Cambridge, MA (2005)37. Stasser, G.: The uncertain role of unshared information in collective choice. In: Thompson,

L., Levine, J., Messick, D. (eds.): Shared knowledge in organizations Erlbaum, Hillsdale,

NJ (1999) 49–6938. Stevens, R., Johnson, D., Soller, A.: Probabilities and predictions: Modeling the develop-

ment of scientific problem solving skills. Cell Biology Education 4(1) (2005) 42–57(Available at http://www.cellbioed.org/)

39. Terveen, L.G., McDonald, D.W.: Social matching: A framework and research agenda.ACM Transactions on Computer-Human Interaction (ToCHI) 12(3) (2005) 401–434




40. Vassileva, J.: Supporting peer-to-peer user communities. In: Meersman, R., Tari, Z. (eds.):On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE : Con-

federated International Conferences CoopIS, DOA, and ODBASE 2002. Springer-Verlag,Berlin-Heidelberg (2002) 230–247

41. Wasserman, S., Faust, K.: Social network analysis: Methods and applications. CambridgeUniversity Press, Boston (1994)

42. Webb, N., Palincsar, A.: Group processes in the classroom. In: Berlmer, D., Calfee, R.(eds.): Handbook of educational psychology. Simon & Schuster Macmillan, New York

(1996) 841–87343. Wenger, E., McDermott, R., Snyder, W.: Cultivating Communities of Practice: A guide to

managing knowledge. Harvard Business School Press, Boston (2002)

adaptive support for distributed collaboratio

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