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Halatchliyski I.,Knowledge Media Research Center | Moskaliuk J.,App Media | Kimmerle J.,Knowledge Media Research Center | Cress U.,Knowledge Media Research Center
International Journal of Computer-Supported Collaborative Learning | Year: 2014

This article discusses the relevance of large-scale mass collaboration for computer-supported collaborative learning (CSCL) research, adhering to a theoretical perspective that views collective knowledge both as substance and as participatory activity. In an empirical study using the German Wikipedia as a data source, we explored collective knowledge as manifested in the structure of artifacts that were created through the collaborative activity of authors with different levels of contribution experience. Wikipedia's interconnected articles were considered at the macro level as a network and analyzed using a network analysis approach. The focus of this investigation was the relation between the authors' experience and their contribution to two types of articles: central pivotal articles within the artifact network of a single knowledge domain and boundary-crossing pivotal articles within the artifact network of two adjacent knowledge domains. Both types of pivotal articles were identified by measuring the network position of artifacts based on network analysis indices of topological centrality. The results showed that authors with specialized contribution experience in one domain predominantly contributed to central pivotal articles within that domain. Authors with generalized contribution experience in two domains predominantly contributed to boundary-crossing pivotal articles between the knowledge domains. Moreover, article experience (i.e., the number of articles in both domains an author had contributed to) was positively related to the contribution to both types of pivotal articles, regardless of whether an author had specialized or generalized domain experience. We discuss the implications of our findings for future studies in the field of CSCL. © 2013 International Society of the Learning Sciences, Inc. and Springer Science+Business Media New York.

Cress U.,Knowledge Media Research Center | Held C.,Knowledge Media Research Center | Kimmerle J.,App Media
Computers and Education | Year: 2013

Tag clouds generated in social tagging systems can capture the collective knowledge of communities. Using as a basis spreading activation theories, information foraging theory, and the co-evolution model of cognitive and social systems, we present here a model for an extended information scent, which proposes that both collective and individual knowledge have a significant influence on link selection, incidental learning, and information processing. Two experimental studies tested the applicability of the model to a situation in which individual knowledge and collective knowledge were contradictory to each other. The results of the first experiment showed that a higher individual strength of association between a target in demand and a tag led to a higher probability of selecting corresponding links, combined with less thorough information processing for non-corresponding links. But users also adapted their navigation behavior to the collective knowledge (strength of associations of tags) of the community and showed incidental learning during navigation, which resulted in a change of their individual strength of associations. The second experiment confirmed these results and showed, in addition, that the effects also occurred for indirect associations. Altogether, the results show that the extended information scent is an appropriate and fertile model for describing the interplay of individual knowledge and the collective knowledge of social tags. © 2012 Elsevier Ltd. All rights reserved.

Kimmerle J.,App Media | Cress U.,Knowledge Media Research Center
Cyberpsychology, Behavior, and Social Networking | Year: 2013

In computer-supported information exchange, people frequently tend to withhold their own information and free-ride on the others' contributions. In doing so, they save costs (time and effort) and maximize their own benefit. However, if everyone behaved in this way, there would be no information sharing at all. In this experiment, we tested if the presentation of a random number could serve as a cognitive anchor and influence the amount of shared information. The experimental setting had all the features of an information-exchange dilemma. Before participants could share information, a random generator presented a random number. It was found that this number served as a cognitive anchor and influenced both the participants' behavioral intentions and their actual behavior. Particularly, the high anchor increased cooperation, even though enhanced cooperation was obviously detrimental to the individual's own benefit. © Mary Ann Liebert, Inc.

Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase II | Award Amount: 706.31K | Year: 2009


Kimmerle J.,App Media | Moskaliuk J.,App Media | Cress U.,Knowledge Media Research Center
Educational Technology and Society | Year: 2011

Computer-supported learning and knowledge building play an increasing role in online collaboration. This paper outlines some theories concerning the interplay between individual processes of learning and collaborative processes of knowledge building. In particular, it describes the co-evolution model that attempts to examine processes of learning and knowledge building by working on wikis. We report an experimental study that aimed at testing some predictions of this model empirically. The results support the assumption that accommodative knowledge building and a development of conceptual knowledge takes place particularly when there is incongruity at a medium level between people's knowledge and the information contained in a digital artefact. In contrast, assimilative knowledge building and the development of factual knowledge depends largely on people's prior knowledge. Concluding, the consequences of these findings on educational uses of wikis are discussed. © International Forum of Educational Technology & Society (IFETS).

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