Web-based Learning Solutions for Communities of Practice
Web-based Learning Solutions for Communities of Practice
Web-based Learning Solutions for Communities of Practice
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Mining Unnoticed Knowledge in Collaboration Support Systems<br />
Kaufman, L., & Rousseeuw, P. J. (1990). Finding<br />
groups in data. New York: John Wiley & Sons.<br />
Kleinberg, J. (1999). Authoritative sources in a<br />
hyperlinked environment. Journal <strong>of</strong> the ACM,<br />
46(5), 604–632. doi:10.1145/324133.324140<br />
Lave, J., & Wenger, E. (1991). Situated learning:<br />
Legitimate peripheral participation. Cambridge,<br />
UK: Cambridge University Press.<br />
Mehlhorn, K., & Näher, S. (1999). The LEDA plat<strong>for</strong>m<br />
<strong>of</strong> combinatorial and geometric computing.<br />
Cambridge, UK: Cambridge University Press.<br />
Meila, M., & Pentney, W. (2007). Clustering by<br />
weighted cuts in directed graphs. In Proceedings<br />
<strong>of</strong> the SDM 07.<br />
Mika, P. (2005). Flink: Semantic <strong>Web</strong> technology<br />
<strong>for</strong> the extraction and analysis <strong>of</strong> social networks.<br />
Journal <strong>of</strong> <strong>Web</strong> Semantics, 3(2), 211–223.<br />
doi:10.1016/j.websem.2005.05.006<br />
Millen, D. R., Fontaine, M. A., & Muller, M. J.<br />
(2002). Understanding the benefit and costs <strong>of</strong> communities<br />
<strong>of</strong> practice. Communications <strong>of</strong> the ACM,<br />
45(4), 69–73. doi:10.1145/505248.505276<br />
Nanopoulos, A., Theodoridis, Y., & Manolopoulos,<br />
Y. (2001). C2P: Clustering <strong>based</strong> on closest<br />
pairs. In Proceedings <strong>of</strong> the 27 th International<br />
Conference on Very Large Data Bases (pp. 331-<br />
340).<br />
Pall<strong>of</strong>f, R. M., & Pratt, K. (1999). Building learning<br />
communities in cyberspace. San Francisco:<br />
Jossey-Bass Publishers.<br />
Paolillo, J. C., & Wright, E. (2004). The challenges<br />
<strong>of</strong> FOAF characterization. In Proceedings<br />
<strong>of</strong> the 1 st Workshop on Friend <strong>of</strong> a Friend, Social<br />
Networking and the Semantic <strong>Web</strong>.<br />
Robey, D., Khoo, H. M., & Powers, C. (2000).<br />
Situated-learning in cross-functional virtual teams.<br />
IEEE Transactions on Pr<strong>of</strong>essional Communication,<br />
43(1), 51–66. doi:10.1109/47.826416<br />
Rosenberg, M. J. (2001). E-learning: Strategies<br />
<strong>for</strong> delivering knowledge in the digital age. New<br />
York: McGraw-Hill.<br />
Sabidussi, G. (1966). The centrality index <strong>of</strong> a<br />
graph. Psychometrika, 31, 581–603. doi:10.1007/<br />
BF02289527<br />
Sheikholeslami, G., Chatterjee, S., & Zhang, A.<br />
(1998). WaveCluster - a multi-resolution clustering<br />
approach <strong>for</strong> very large spatial databases. In<br />
Proceedings <strong>of</strong> the 24 th VLDB conference (pp.<br />
428-439).<br />
Wasserman, S., & Faust, K. (1994). Social network<br />
analysis: Methods and applications. Cambridge,<br />
UK: Cambridge University Press.<br />
Zhang, T., Ramakrishnan, R., & Livny, M. (1996).<br />
BIRCH: An efficient data clustering method<br />
<strong>for</strong> very large databases. In Proceedings <strong>of</strong> the<br />
1996 ACM SIGMOD International Conference<br />
on Management <strong>of</strong> Data, Montreal, Canada (pp.<br />
103-114).<br />
Zhang, W., & Storck, J. (2001). Peripheral members<br />
in online communities. In Proceedings <strong>of</strong> the<br />
Americas Conference on In<strong>for</strong>mation Systems,<br />
Boston, MA.<br />
KEY TERMS AND DEFINITIONS<br />
Community <strong>of</strong> <strong>Practice</strong>: refers to the process<br />
<strong>of</strong> social learning that occurs and shared social and<br />
cultural practices that emerge and evolve when<br />
people who have common goals interact as they<br />
strive towards those goals.<br />
Clustering: clustering is an algorithmic concept<br />
where data points occur in bunches, rather<br />
than evenly spaced over their range. A data set<br />
which tends to bunch only in the middle is said<br />
to possess centrality. Data sets which bunch in<br />
several places do not possess centrality. What<br />
they do possess has not been very much studied,<br />
and there are no infallible methods <strong>for</strong> locating<br />
93