Web-based Learning Solutions for Communities of Practice

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Mining Unnoticed Knowledge in Collaboration Support Systems Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data. New York: John Wiley & Sons. Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632. doi:10.1145/324133.324140 Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press. Mehlhorn, K., & Näher, S. (1999). The LEDA platform of combinatorial and geometric computing. Cambridge, UK: Cambridge University Press. Meila, M., & Pentney, W. (2007). Clustering by weighted cuts in directed graphs. In Proceedings of the SDM 07. Mika, P. (2005). Flink: Semantic Web technology for the extraction and analysis of social networks. Journal of Web Semantics, 3(2), 211–223. doi:10.1016/j.websem.2005.05.006 Millen, D. R., Fontaine, M. A., & Muller, M. J. (2002). Understanding the benefit and costs of communities of practice. Communications of the ACM, 45(4), 69–73. doi:10.1145/505248.505276 Nanopoulos, A., Theodoridis, Y., & Manolopoulos, Y. (2001). C2P: Clustering based on closest pairs. In Proceedings of the 27 th International Conference on Very Large Data Bases (pp. 331- 340). Palloff, R. M., & Pratt, K. (1999). Building learning communities in cyberspace. San Francisco: Jossey-Bass Publishers. Paolillo, J. C., & Wright, E. (2004). The challenges of FOAF characterization. In Proceedings of the 1 st Workshop on Friend of a Friend, Social Networking and the Semantic Web. Robey, D., Khoo, H. M., & Powers, C. (2000). Situated-learning in cross-functional virtual teams. IEEE Transactions on Professional Communication, 43(1), 51–66. doi:10.1109/47.826416 Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age. New York: McGraw-Hill. Sabidussi, G. (1966). The centrality index of a graph. Psychometrika, 31, 581–603. doi:10.1007/ BF02289527 Sheikholeslami, G., Chatterjee, S., & Zhang, A. (1998). WaveCluster - a multi-resolution clustering approach for very large spatial databases. In Proceedings of the 24 th VLDB conference (pp. 428-439). Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge, UK: Cambridge University Press. Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada (pp. 103-114). Zhang, W., & Storck, J. (2001). Peripheral members in online communities. In Proceedings of the Americas Conference on Information Systems, Boston, MA. KEY TERMS AND DEFINITIONS Community of Practice: refers to the process of social learning that occurs and shared social and cultural practices that emerge and evolve when people who have common goals interact as they strive towards those goals. Clustering: clustering is an algorithmic concept where data points occur in bunches, rather than evenly spaced over their range. A data set which tends to bunch only in the middle is said to possess centrality. Data sets which bunch in several places do not possess centrality. What they do possess has not been very much studied, and there are no infallible methods for locating 93
the describing more than one cluster in a data set (the problem is much worse when some of the clusters overlap) Data Mining: is the process of autonomously extracting useful information or knowledge (“actionable assets”) from large data stores or sets. Data mining can be performed on a variety of data stores, including the World Wide Web, relational databases, transactional databases, internal legacy systems, pdf documents, and data 94 Mining Unnoticed Knowledge in Collaboration Support Systems
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www.dbebooks.com - Free Books & mag
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Director of Editorial Content: Kris
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Editorial Advisory Board Anil Aggar
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Table of Contents Preface .........
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Chapter 11 An Organizational Knowle
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Detailed Table of Contents Preface
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Chapter 6 Using Storytelling as the
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Chapter 11 An Organizational Knowle
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Chapter 17 A Process-Oriented and T
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xviii Preface For more than ten yea
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xx (Greece) - by focusing on the pa
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xxii Advances reported in this book
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This chapter discusses the prelimin
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which will ultimately influence the
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Table 2. Facilitator 2 feedback SOL
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“Focus on reading peers’ posts
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• Feedback must be useful and for
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Schunk, D. H., & Lilly, M. W. (1984
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able to others (Wenger, 1998). With
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equire structure transformation att
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are often manually encoded on a cas
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context within the target template.
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tives have been negotiated with CoP
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Brown, H., & Cole, F. C. (1992). Ed
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26 ABSTRACT Chapter 3 Tackling Acce
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Figure 1. Web-based GUI of eLogbook
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Figure 2. System flow for the email
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submission or validation of deadlin
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Figure 7. Body of the email of crea
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The email-based eLogbook interface
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38 Chapter 4 Supporting Communities
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and to the contextualization of thi
Page 65 and 66: such meetings the CM practitioner n
Page 67 and 68: non-virtual spaces, candidate tools
Page 69 and 70: Compendium allows CoP members to cu
Page 71 and 72: lowing synchronous update of both C
Page 73 and 74: around the shared goal to enhance t
Page 75 and 76: community of farmers which is used
Page 77 and 78: REFERENCES Brown, J. S., & Duguid,
Page 79 and 80: est attain the educational goal. Th
Page 81 and 82: And here is where new technologies
Page 83 and 84: tion of such paths delivers a longe
Page 85 and 86: Figure 2. A snapshot of an activity
Page 87 and 88: Figure 3. A view of the course mult
Page 89 and 90: Henry, G. (2004, September). Connex
Page 91 and 92: defines a CoP as follows: “Commun
Page 93 and 94: ested in learning from each other i
Page 95 and 96: nize clearly how to begin a story,
Page 97 and 98: Figure 2. Story Development Phases
Page 99 and 100: Figure 3. Story Designed to move Em
Page 101 and 102: 2. Some differences in JavaScript i
Page 103 and 104: However, bringing in a new practice
Page 105 and 106: Backbase. (2008). AJAX in enterpris
Page 107 and 108: through continuous learning (Rosenb
Page 109 and 110: do with web-based collaboration sys
Page 111 and 112: Artificial Intelligence. More preci
Page 113 and 114: approaches concerning social networ
Page 115: REFERENCES Ackerman, M. S. (1998).
Page 119 and 120: eing ‘online’ or ‘offline’
Page 121 and 122: communication tools, see Weinberger
Page 123 and 124: learn community booked 147 meetings
Page 125 and 126: 102 Live Virtual Technologies to Su
Page 127 and 128: Annotating FM text chat transcripts
Page 129 and 130: community members were discussing t
Page 131 and 132: chats taking place at this time, on
Page 133 and 134: ing its course. During an extended
Page 135 and 136: Dourish, P. (2006). Re-space-ing pl
Page 137 and 138: INTRODUCTION 114 Individual Learnin
Page 139 and 140: 116 Individual Learning and Emotion
Page 141 and 142: Emotional Processing 118 Individual
Page 143 and 144: The Paradigm of CSA 120 Individual
Page 151 and 152: 128 Chapter 10 From ‘Collecting
Page 153 and 154: aforementioned gap. In particular,
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Page 157 and 158: high degree of support for emergenc
Page 159 and 160: When resources are used in a collab
Page 161 and 162: Figure 7. Instance of the collabora
Page 163 and 164: Transformation Rules During the tra
Page 165 and 166: Grudin, J. (1996). Evaluating oppor
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legitimacy provided by online educa
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have all of the modules implemented
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Figure 5. Process used to classify
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Table 1. Number and percentage of l
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152 An Organizational Knowledge Cir
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CONCLUSION Our practical goal is to
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156 Chapter 12 Using Web-Based Tech
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visual clues and the ability to eas
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should focus on developing engaging
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experience, particularly in an onli
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CONCLUSION Distance education repre
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Roblyer, M. D., Davis, L., Mills, S
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to further increase.” (Allen & Se
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Figure 1. in moving from a real-tim
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Catalonia, Spain. The institutions
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Discussion It is clear that adjustm
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Table 3. Adjustment to Teaching Pre
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Comparisons between face-to-face an
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neous, as noted by this instructor.
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11. Ensure that instructors and stu
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Swan, K., & Shih, L.-F. (2005). On
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INTRODUCTION Museums of the past so
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ties (including internet resources)
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Figure 4. The mobile AR system. ©
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untold potential, as the individual
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in extended episodes of playful lea
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8 at the left shows a real experime
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spectrum in order to specifically d
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Knowledge Learning: Pre-test High a
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eceiving better learning results. T
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Figure 18. Figure 19. learning appr
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The teachers felt that the visit to
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Ilola, L., Lakkala, M., & Paavola,
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210 Chapter 15 A Proposed Framework
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212 A Proposed Framework for Design
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230 Chapter 16 An Agent System to M
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common place (Hillery, 1955). The d
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the local area. Because of this, as
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Figure 3. General architecture cons
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Deliberative-Social Level In this l
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- The person uses knowledge previou
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Figure 7. Showing and sorting resul
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EVALUATION OF THE PROTOTYPE Once th
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support knowledge management but it
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Malhotra, Y. (2000), «Knowledge Ma
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INTRODUCTION 250 A Process-Oriented
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252 A Process-Oriented and Technolo
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exploration and review the attempt
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Defining Features of the IntelCitie
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Figure 1. Integrated eGov services
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portals to merely provide links to
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experiential learning and, therefor
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274 decision-making and develop the
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towards the back-office functions,
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Table 4. Step-wise logic of the ser
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Table 6. Feedback responses to the
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tals, but as particular types of eG
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software consists of a set of enabl
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solve problems associated with the
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ecruitment was organized mainly aro
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The global structure of the activit
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ITPF (during the 2002 crisis) and f
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growth of tax revenue generated som
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and so forth. They confront the cha
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community of practice members. Faci
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It is my belief that these networks
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Tuomi, I. (1999). Corporate knowled
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to accomplish meaningful interchang
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306 Chapter 20 Conditions and Key S
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308 Conditions and Key Success Fact
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330 Compilation of References Abdul
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Balasubramanian, S., & Mahajan, V.
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Buckingham Shum, S. (2007). Hyperme
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Curwell, S., Deakin, M., Cooper, I.
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Dretske, F. (1991). Explaining beha
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Gebert, H., Geib, M., Kolbe, L., Br
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Han, J., & Kamber, M. (2001). Data
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Jain, A. K., & Dubes, R. C. (1988).
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Klijn, E., & Coppenhan, J. (2000).
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Loyarte, E., & Rivera, O. (2007). C
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O’Reilly, T. (2005). What is Web
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Roberts, J. (2006). Limits of commu
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Sharda, N. (2006). Applying movemen
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Tailby, R., Dean, R., Milnerm, B.,
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Wenger, E., White, N., Smith, J., &
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360 About the Contributors empirica
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362 About the Contributors is also
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364 About the Contributors Loïc Me
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366 About the Contributors 1984) he
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368 About the Contributors Christin
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CoPs, key success factors in the cu
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R Reactive Level 236 REGRET reputat
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