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Article Excerpt
Tools to support knowledge elicitation are used more and more in situations where employees or students collaborate using the computer. Studies indicate that differences exist between experts and novices regarding their methods of work and reasoning. However, the commonly preferred approach tends to deal with team members as a single system with "common," shared preferences. The question is, to what extent this approach is optimal. Potential difficulties with uniform knowledge elicitation support for workplace or work-place-like settings of teamwork can be derived from the literature. We carried out two studies to investigate whether or not support tools for knowledge elicitation should explicitly take into account the expertness of team members.
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To gather qualitative data concerning critical factors of effective knowledge elicitation support for professional teamwork, a Delphi study with known experts was conducted. The experts accentuate the significance of a context-fit of supportive action over content or functionality. In their opinion, prompting must be tailored to the task at hand, team characteristics, team culture, and context. In a second study we gathered qualitative insights into user-elicitation preferences, especially in relation to a user's proficiency in the field. Subjects of this study were graduate students studying for a profession as social worker. Respondents' elicitation preferences didn't correlate significantly with the expertness dimension. Further interpretation and comparison of the results from both studies appear to indicate that it is not so much the proficiency of the team members, but the attunement of context, that is critical for the effect of elicitation support.
More and more, professional learning takes place in open learning environments, co-operating with peers. Computer-mediated forms of collaboration between distributed team members are becoming part of daily practice. Cases in professional practice and computer supported learning show that effective knowledge communication in computer-mediated groups doesn't emerge automatically. Many problems with information and knowledge exchange during collaborative task execution are reported (Alpay, Giboin, & Dieng, 1998; Mulder, Swaak, & Kessels, 2002; Zack, 1998). Underperformance might occur when relevant knowledge of individual team members cannot be taken into account, since it isn't clearly articulated or understood (Allee, 1997; Buckingham Shum & Hammond, 1994).
High quality output of multidisciplinary teams within tight time constraints becomes crucially important in our society (Davenport & Prusak, 1998; Drucker, 1992; Kogut & Zander, 1992; Brown & Duguid, 1998). With the growing importance of virtual collaboration on knowledge intensive tasks both in professional and educational practice, systematic investigation into enablers for effective knowledge articulation is needed (Brown & Duguid, 2000; Johnson & Johnson, 1994; Plotzner, Dillenbourg, Preier, & Traum, 1999; Conklin, Selvin, Buckingham Shum, & Sierhuis, 2001). The implicit knowledge of individuals is seen as crucial for the collective performance (Polanyi, 1967; Leonard & Sensiper, 1998; Nonaka, 1991; Von Krogh, Ichijo, & Nonaka, 2000; Boisot, 1998). So, within the collective quest for a shared solution in multi-disciplinary, multi-expertise teams it is relevant to focus on augmentation of adequate knowledge articulation and communication. Hence, investigation into the first resource for team knowledge building is needed to understand processes that lie at the roots of emerging common ground and collective action in the team process (Beers, Boshuizen, & Kirschner 2003; Selvin, 1999).
Insight is needed to incite knowledge articulation by way of support scenarios which augment collaborative elicitation. Before a well-founded scenario for augmentation of collaborative elicitation can be designed, more insight is needed regarding the impact of specific variables such as levels of expertise.
Current research regarding knowledge communication in teams, concentrates on actions enabling the emergence of a common framework of understanding (Conklin et al., 2001; Mulder et al., 2002). Several elicitation methods are proposed to facilitate elicitation for collaborative work and learning (Selvin, 1999; Conklin et al., 2001). Many incentives propose an explicit structured expression of one's proposition in some structured format (Baker & Lund, 1997; Chi, Glaser, & Farr, 1988; Plotzner et al., 1999; Fischer & Mandl, 2001; Jonassen & Carr, 1999; Van Bruggen, Boshuizen, & Kirschner, 2003). Orientation, representation, and modality of the elicitation prompts, as well as their formats and structure vary. Multiple formulas have been proposed to structure externalisation of ideas (Bell, 1997; Kirschner, Buckingham Shum, & Carr, 2003; Ostwald, 1995; Puntambekar & Kolodner, 1998). Some suggestions are content driven and connect directly to the domain structure. Others propose scaffolding the learning process in a more generic way (Scardamalia & Bereiter, 1996). Mulder, for example proposed the insertion of a question mark to trigger further inquiry (Mulder et al., 2002). Yet other authors recommended prompting for argumentation structure (Toulmin, Rieke, & Janik, 1984): eliciting claims, underlying arguments plus evidence (Carr, 2002), or rationales (Ostwald, 1995). Positive effects of active elicitation have already been reported (Chi et al.; Jonassen & Carr, 1999; Plotzner et al., 1999; Jeong & Chi, 2000). Chi et al. found positive results with triggers enhancing students to elicit self-explanations. Plotzner et al. observed similar results when they stimulated peer-to-peer explanations. Fischer and Mandl (2001) investigated the use of cues and scripts and observed how supportive scripts intensified the collaborative knowledge discourse.
Many structuring scaffolds are still text-based. Several researchers however proposed structuring by way of graphical representations for cognitive reasons (Suthers, 1999; Sumner, Domingue, & Zdrahal, 1998; Van Bruggen et al., 2003). Graphical schemes are supposed to match better with the mental schemata of individual cognition then the linearity of text. Methods to enhance graphical representations are often used in the field of science, engineering, and business studies. In these domains practitioners are already accustomed to communicating through predefined formalisms, since unequivocally typifying a problem in both entities and relations is quite common there.
Studies of elicitation triggering report positive effects mostly in rather "closed," well-designed and defined learning environments with fairly homogeneous groups of students or experts (Plotzner et al., 1999). Most problem-solving activities in natural settings however take place in very fluid open contexts with ill-structured problems. Because reported empirical findings in literature are often derived from well-defined experimental settings, a literature study alone doesn't provide sufficient insight into the decisive factors influencing the successful knowledge flow in collective task fulfillment. They don't explicitly focus on knowledge-building processes in real life multi-disciplinary, multi-expertise teams. Hence, it is questionable whether the same positive effects will occur in these natural settings with ill-structured problems. Can the same support be applied? And what effects can be achieved for collaborating project members from different domain backgrounds, possessing miscellaneous levels of expertise? Insight into specific requirements for elicitation prompting in mixed virtual teams is still insufficient for the design of elicitation support for work-based learning of both professionals and students immersed in authentic work. The design of support for such complex processes as learning and knowledge development in real life settings requires capturing essential determinants from both academic research and professional practice.
Positive effects of elicitation prompting are reported for specific instantiations of collaborative learning and working. A common denominator of these studies seems to be that students or professionals work on a well-defined task within a rather homogeneous team setting (Scardamalia & Bereiter, 1996). The question is whether reported positive effects of elicitation prompting will also occur with ill-structured problems in teams with heterogeneous expertise backgrounds and levels.
It is well known that individual's knowledge building processes differ according to a person's proficiency in the field. Knowing that problem-solving methods and knowledge building vary according to an individual's expertise (Feltovich, Spiro & Coulson, 1997; Bromme & Nuckles, 1998) we assume that elicitation support of mixed groups might require a different approach than the "one-size fits all" triggering approach used so far. We expect that supportive action for collaborative teamwork requires different support modalities for individuals according to their expertness. After all, it is known that individual knowledge building processes differ according to a person's expertise in the field. At present there isn't much empirical evidence with respect to accommodation of support to personal expertness. According to findings on the differences in working habits and learning processes, we...
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