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  • Exam #3

    Social Considerations in the Development, Deployment and Adoption ofWeb-Based Organizational Memories

    Andrew GormanCenter for LifeLong Learning and Design

    Department of Computer Science andInstitute of Cognitive Science

    University of ColoradoBoulder, CO 80309-0430

    Abstract

    This paper begins with the premise that people want toparticipate in designing their future. While an individualunaided human mind is powerful, real power is derived fromhumans working in conjunction with tools and other hu-mans. Developing computational support for collabora-tion among groups of individuals is a difficult task, but itspotential benefit is tremendous. This paper will describethree broad models of collaborative systems along withexamples of systems that typify each model. The socio-technical aspects of collaboration and participation will bediscussed as they relate to the development, deployment,and adoption of an organizational memory system intendedto support the collaborative design of a new building.

    IntroductionThe Discovery Learning Center (DLC) is a new building

    being constructed on the campus of the University of Colo-rado at Boulder. This process gathers groups of people whohave varying backgrounds and interests. The goal that iscommon to all is the construction of a new building. How-ever, each has his or her own unique agenda. In order to sup-port such a process, it is desirable to create an informationspace that can be useful to all stakeholders as they partici-pate in the design and construction of the new building. TheDLC information space began as a static repository of de-sign documents and background information. It has sincegrown into a more dynamic system for disseminating designalternatives and gathering feedback from stakeholders. Theadoption of such a system by stakeholders who have littleprior exposure to one another can be a very complicatedprocess, which is affected by both technical and social fac-tors.

    Information Access is Necessary but notSufficient

    In today's workplace, people need to know how to accessinformation. However, simply knowing how to access in-formation falls short of what is truly needed in todaysworkplace. For example, in the Presidents InformationTechnology Advisory Committee report (PITAC, 1999),there is a call for ubiquitous information access. This call

    for accessibility needs to be extended to include the under-standing that the key to the future lies not only in greateraccess to information, but in greater support for knowledgeconstruction (Scardamalia & Bereiter, 1994). Although thereis value in such an access model, its focus is incomplete. Itis based on an impoverished view that relevant knowledgealready exists, waiting to be accessed. In order to truly gainthe benefits of information technology, what is needed is notsimply greater access to information, but a greater ability foraverage people to construct and distribute new knowledge(Arias, 1999 (in press)).

    People Want to Participate

    How can more than 261 million individual Americans defineand reconcile their needs and aspirations with community val-ues and the needs of the future? Our most important finding isthe potential power of and growing desire for decision proc-esses that promote direct and meaningful interaction involvingpeople in decisions that affect them. Americans want to takecontrol of their lives (PCSD, 1996, p.7).

    This finding of the President's Council on SustainableDevelopment (PCSD) supports the claim that somethingmore is needed than access alone. Therefore, an importantchallenge for future information technology is to enablestakeholders of problems to become involved informed par-ticipants (Brown, Duguid, & Haviland, 1994).

    To make informed participation a reality, we need supportfor new forms of knowledge creation, integration, and dis-semination. People seldom explore large repositories of in-formation in the abstract (Fischer, Lemke, McCall, &Morch, 1996; Moran & Carroll, 1996). Instead, informationis typically sought in response to breakdowns encounteredduring meaningful, real-life activities (Fischer, 1994; Pop-per, 1965). By overcoming such breakdowns, new knowl-edge is created, which then must be integrated with anyknowledge that may have been generated during prior break-downs. This cycle leads to the creation and evolution of richinformation spaces that can empower interested communitymembers as they take control of their lives.

  • Exam #3

    2

    M1-M3 Models of ParticipationA typical model of information sharing (e.g., (Ackerman

    & Malone, 1990; Ackerman & McDonald, 1996)) focuseson experts sharing information with non-experts. This is

    represented by the M1 model (seen in figure 1) in which aclass of experts controls the production of information andindividuals act as consumers whose only need is that of ac-cess.

    In the M2 model (figure 2), all information is funneled

    through a single person or small committee. This is typicalof information generated in an open source model of soft-ware development (Raymond, 1999). Here there are manycontributors, but only a few (or one) that integrates feedbackfrom the community back into a coherent structure. Therehas been much success using this model in open sourcemovements (Fielding, 1999; Torvalds, 1999) and while thisis an advantage over the M1 model, in terms of collaborativeconstruction, it can lead to problems of scalability. Fur-thermore, there needs to be a highly dedicated person that hasthe full-time responsibility of analyzing and structuring allof the feedback.

    The M3 model (figure 3) can theoretically support distrib-

    uted collaboration in a more direct way. In this model, thereis no gatekeeper. All contributors have the ability to addcontent directly. Developing systems that support this typeof collaboration can be extremely difficult. One way of pro-viding structure to collaboratively constructed information isby codifying the knowledge and expertise of the gatekeeperdescribed in the M2 model. Another approach is to distrib-ute this responsibility among the community members byestablishing policies (Edwards, 1996) to govern the con-struction, organization and use of information.

    Human Cognition and TasksThe memory of an individual can be roughly divided into

    two categories: short-term memory (STM) (also described as

    Figure 1 - The M1 Model of Collaboration

    Figure 3 - The M3 Model of Collaboration

  • Exam #3

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    working memory) and long-term memory (LTM).1 STM isrelatively small, typically thought to have a capacity of 7 2 chunks of information (Miller, 1956). In contrast, LTM isvirtually unlimited (Matlin, 1998). In an information proc-essing theory of human cognition (Pinker, 1997), dataneeded for a given task is typically activated and retrievedfrom LTM and then held in STM while it is actively used.In this process, information is constantly being swapped inand out of STM as new information is constructed and en-coded into LTM. This model of human memory is analo-gous to register and disk storage used in modern-day com-puters.

    Expert behavior is often based on a well-developed tech-nique, or pneumonic, for encoding and retrieving informa-tion. Intelligent behavior, therefore, is often attributed inlarge part to being able to effectively transfer informationinto LTM so that it can later be activated for future use inSTM. Because of the limitations of STM and human atten-tion, cognition can be viewed as a limited scarce resourcethat needs to be allocated as properly during task perform-ance. This view is analogous to operating systems thatallocate computer resources during the execution of a processor sub-process.

    Given that human cognition is limited, it makes sensethat the nature of a task (i.e., the cognitive resources it de-mands) affects our ability to perform the task. Over thecourse of human history, cultures have invented tasks thatnot only push, but also transcend the limits of human cog-nitive capabilities. Because of this, humans have needed todevelop cognitive artifacts (Norman, 1991) and systems toaid in their artificial tasks. For example, long division is anartificial system that produces such a cognitive load that it isdifficult to solve even a moderately advanced problem with-out the aid of cognitive artifacts such as memory aids likepaper and pencil. With the advent of computers, some ofthese artificial tasks have been codified so that they maynow be performed with computational devices such as hand-held calculators.

    Distributed CognitionDistributed cognition (Brown, et al., 1993; Fischer, 1995;

    Hewitt & Scardamalia, 1996; Hutchins, 1993; Norman,1993; Salomon, 1993) emphasizes that the heart of intelli-gent human performance is not the individual human mindin isolation but the interaction of the mind with tools andartifacts as well as groups of minds in interaction with eachother. It is important to understand the fundamental differ-ence between these two forms of distributed cognition.When distributed cognition is at work between the individualhuman mind and cognitive artifacts, it often functions wellbecause the knowledge an individual needs is distributed be-tween her/his head and the world (e.g., calculators, addressbooks, e-mail messages, filing cabinets). On the other hand,when cognition is distributed among groups of minds, agroup has no head, no single mind to store the informationabout this distribution of knowledge, which is available to

    1 Sensory memory is also described in the cognitive psy-

    chology literature, but this is outside the scope of this paper.For more information on sensory memory see (Matlin, 1998)

    all members of the group. In this case, externalizations arecrit

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