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Donna Kennedy | Matt Devine | Vito Cangelosi |                                                                                                                         Analysis

     The growth of knowledge sharing amongst educational institutions and personnel has resulted in the need for a systematic and robust method for managing knowledge. Jennex (2005) elaborates on the importance of knowledge management (KM) by indicating that previous experiences can augment current and future decision making activities. The benefit of KM has not gone unnoticed by academic organizations and has led to the development of KM systems designed to encourage collaboration amongst stakeholders and effectively capture tacit and explicit knowledge. An example of a KM system is given by Zhang, Kaschek, and Kinshuk (2005)who designed a Web-based KM support system for the purpose of assisting instructors in teaching database normalization in an introductory database management course. Results of the study revealed strong user preferences for the prototype with a majority of students listing flexibility in learning, increased trust between teachers and students, and satisfaction with knowledge base as primary indicators for future usage. Teachers also indicated that the prototype promoted tacit knowledge sharing in an easy to use environment. Ractham and Zhang (2006) explored the usage of podcasting as a KM student support system that promotes accurate information retrieval, content management, and knowledge sharing. Such a system, according to the authors, relies on the usage of podcast aggregators to successfully capture and disseminate academic content. The confluence of intellectual capital in a central repository promotes new discoveries and reduces problem solving difficulties.

     However, the construction of a KM system cannot be undertaken in a haphazard manner that neglects proper knowledge capture, identification, organization, and management procedures. Lauded as an effective mechanism for managing and utilizing knowledge resources, knowledge management seeks to identify information that can serve the interests of the individual or institutions. According to Mentzas, Kefentzis, and Georgolios (2007), organizations are beginning to recognize that a presence within a global knowledge network is essential for cultivating and exploiting knowledge. To participate in this global initiative, knowledge networks must clearly identify goals and desired outcomes prior to KM system design. Table 1 contains some of the prevalent questions that should be asked prior to KM system development. Once constructed, the framework should be continuously evaluated for conformance throughout the design process.

Table 1
KM System Development Questions
Knowledge Management
  • How can users create, distribute, and share knowledge?
  • Why is it important to capture educational information?
  • Will capturing educational knowledge benefit me or my organization?
  • Collaboration
  • How can academic stakeholders collaborate via the desired KM system?
  • What educational values are to be gained from collaboration?
  • How can we motivate users to participate in the KM system?
  • User Acceptance/Application
  • How can an educational KM system enhance lifelong learning for users?
  • Will the KM system encourage users to share and utilize tacit knowledge?
  • What are the policies and procedures governing the KM system's usage?
  •      Despite its benefits, KM systems pose difficulties that reduce effectiveness and applicability. Marjanovic (2005) discusses the ?informational junkyard? dilemma faced by large scale KM systems that unknowingly capture explicit knowledge while excluding tacit knowledge. A primary reason for this occurrence is failure on the part of designers to include a social network component, such as a blog, forum, or comment module, that permits sharing of experiential knowledge. KM systems such as MERLOT and ARIADNE have attempted to rectify this problem by including collection components that encourage the sharing of personal experiences and knowledge. When tacit knowledge capture mechanisms are made available, individuals assume the role of voluntary contributor rather than information forager. Leung and Chan (2007) confirm the importance of social collection modules for capturing tacit knowledge and promoting knowledge sharing between cultures from geographically diverse areas. The authors suggest chat rooms, blogs, wikis, and mobile devices can democratize the sharing and generating knowledge process. However, the complexities of social networking tools can often be cumbersome to manage and difficult to organize knowledge. Agostini, Albolino, Boselli, DeMichelis, DePaoli, and Dondi (2003) indicate that KM systems fail when there is a decentralized working practice that does not uniformly collect and utilize knowledge. To combat this dilemma, the authors propose an ethnographic and action learning research approach in order to identify common knowledge exchanges, cause for social exchanges, reasons for spontaneous knowledge learning, and people practices. Results generated can then be used to construct an effective socio-technical tool that increases knowledge management performance. The inclusion of collaborative modules also expands organizational opportunities for effective decision making processes and new discoveries.

          The gathering of knowledge within a central repository is not without costs. King, Marks Jr., and McCoy (2002) indicate that a considerable amount of financial expenditures are required for the construction of a KM system. In a study conducted at the University of Pittsburgh, approximately 2,000 top-level personnel involved with KM system construction and maintenance indicated they primarily seek outcomes from cost-benefit analyses prior to allocation of funds towards a KM system. The process generally involves a thorough evaluation from their subordinates and associates on tools, applications, and development costs. Conversely, perceived benefits from the KM system must also be disclosed in order to identify the proportionate distribution of funds on design, development, and implementation. Kankanhalli, Tanudidjaja, Sutanto, and Tan (2003) indicate that a medium-scale KM network can cost approximately $7,500 per employee each year to maintain. The cost is far greater in large-scale organizations such as British Petroleum whose KM system startup price was $434,000 with thousands more being allocated on yearly maintenance. Educational institutions are not shielded from such expenditures either with costs typically being passed on to network participants. For example, the European-based ARIADNE KM system charges members 1,000 Euros per year for access and contribution rights and 5,000 Euros for hosting services.

         The inclusion of a KM system in an educational framework requires significant effort from personnel to create, maintain, and organize knowledge. According to Davies, Duke, and Sure (2003), assigning group experts and knowledge engineers to a specific knowledge domain should be recommended for the purpose of alleviating usability and collection issues. The authors indicate that domain experts can adequately examine collected data and determine necessary modifications. Such an organizational structure would assist in ensuring appropriate analyses and contribute towards the creation of a useful taxonomy. Yordanova (2007) expands upon the scope of support for KM systems by advocating the usage of help desk experts who can assist individuals within a knowledge group with interface and information retrieval problems. Help desk personnel would also verify the accurate delivery of information between individuals and provide expeditious solutions to communication difficulties. Halverson, Erickson, and Ackerman (2004) confirm the importance of a knowledge management help desk by presenting a framework that alleviates user frustration and properly encapsulates and disseminates knowledge. Acting as sentinels, technical experts monitor KM social collaborative modules and quickly forward issues to the appropriate subject-matter expert (SME). The SME then analyzes the issue and, if necessary, initiates a conversation with the client or student in order to clarify any outstanding concerns. Voss and Schafer (2003) discuss support parameters in online KM systems and how they can be used to examine communication processes. Specifically, moderators or knowledge experts analyze event logs for trends and commonalities in tacit or explicit knowledge and then provide feedback to participants and moderators. Although these processes require greater human resources, benefits accrued from a robust evaluative process helps stakeholders in understanding the dynamics of the KM system.

         The success of a KM system undoubtedly falls upon the shoulders of its constituents. According to Tippins (2003), organizational members have a natural inclination to withhold information and experiences. A cause for such resistance may lie in cultural and bureaucratic barriers that prohibit the transfer of knowledge. Shortsighted organizational policies preclude the creation and usage of robust knowledge exchange mediums and promote proprietary knowledge. In order to rectify this dilemma, the author recommends a managerial policy aimed at expunging preconceived proprietary opinions and espousing the value of intellectual knowledge sharing for successful decision making processes. Embracing knowledge sharing is not facile and requires significant support from stakeholders. Loermans (2002) suggests the development of an inclusive and clear training model that emphasizes the benefits and ease of knowledge sharing. Such a model expedites learning processes, reduces user frustration with the KM system, and assists in identifying relevant and useful knowledge. When a KM system is properly constructed, stakeholders develop a sense of ownership and begin to recognize the role of knowledge capture mechanisms in fostering a collaborative learning environment.

    Tacit Knowledge

    Tacit knowledge is often difficult to communicate since it is knowledge typically known by people but usually not documented. Examples of tacit knowledge may include personal problem solving processes, mental models, or insights. Tacit knowledge manifests itself through a problem solving process.

    Explicit Knowledge

    Explicit knowledge is knowledge that can be documented and described to others. It can be organized, shared, and managed by an individual or organization. Examples of explicit knowledge are organizational documents, procedures, and policies.

    Read more on tacit and explicit knowledge (.pdf)

    Educational KM Systems

  • Multimedia Educational Resource Web Site


  • European Multimedia Educational Resource Web Site


  • Institute for the Study of KM in Education


  • Open Source KM Application


  • A Robust Open Source Wiki

  • TikiWiki

  • European Vocational Education and Training KM System

  • eKnowVET