Web-based Learning Solutions for Communities of Practice

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A Document Reuse Tool for Communities of Practice Figure 2: Examples of templates Automatic Matching and Transformation Service As seen in the beginning of this section, a serious obstacle for translating directly between two structured documents is that a mapping between both templates needs to be carefully specified by a human expert. Manual mapping is known to be a time consuming and error-prone process. One response to this problem is schema matching. Schema matching is the task of semiautomatically finding correspondences between two heterogeneous schemas. Several applications relying on schema matching have arisen and have been widely studied by the database, AI communities and more recently document engineering community (Rahm & Bernstein, 2001), (Cali, Calvanese, Giacomo, & Lenzerini, 2002), (Popa, Velegrakis, Miller, Hernandez, & Fagin, 2002). However, these works generally deal with relational schemas. Little research has been dedicated to XML structures matching. Boukottaya (2004) and Boukottaya & Vanoirbeek (2005) establish a state of the art of available XML structures matching techniques. In our work, the goal is to identify the parts to be transformed in the reusable documents and to describe how to place their content in appropriate 19
context within the target template. The result is a set of mappings (correspondences) relating facts from the source documents with the target template by encapsulating all necessary information to transform source documents into instances of the target template. In order to achieve this goal, we adopt a multi-criteria matching process. Each criterion will be represented by a service. These services are extensible. As new criterion becomes available, a new service is created. Examples of services include: (1) Label matching: measures the similarities between entities based on the meaning inferred from their names (e.g. author and writer are synonyms). This service uses CoPs specific vocabularies (thesauri), local dictionaries (that define the common set of abbreviations, acronyms, and commonly used substring/short hand notations for a given CoP) as well as CoPs ontologies; (2) Constraint matching: relates entities based on their respective constraints. Such constraints include the use of Datatypes, occurrence constraints, etc. and (3) Structural matching: relates entities based on the similarity of the structural context in which they appear. For this templates are modelled as trees (treated as a collection of paths). This has two advantages: (1) a match between two paths can be defined as long as there are some matching nodes along the paths; and (2) a match between two nodes can be defined irrespective of where they occur in the tree as long as there is at least a partial match between their paths. We have designed an algorithm to combine all the above criteria and produce a mapping result that clearly defines source and target mapped entities, required transformation operations, and conditions under which the mapping can be executed. Boukottaya (2004) summarizes an evaluation study of the proposed algorithm. The algorithm was evaluated in term of precision, recall, F-measure and overall using real world application: bibliographic date description. Globally it gives good result and only presents some limitations in some cases where complex matches are required. The proposed structural matching technique is based on 20 A Document Reuse Tool for Communities of Practice the notion of node context. We define three kinds of contexts for a given node: the ancestor context, the child context and the leaf context. We show through a comparative study with other matching algorithms that the combination of these contexts highly improves the structural matching. Experience suggests that fully automated schema matching is infeasible, especially for complex matches that involve complex transformation operations. For this, we designed an efficient user interface where the templates (source and target) are represented as trees and a set of mapping operations have been made available to the user to modify the system mappings. Figure 3 depicts the user interface used for mapping validation. Once the user validates the mapping, the system generates automatically the appropriate transformation scripts (XSLT programs). For this, we first have defined a model for structuring mapping results according to a mapping schema that describes the five dimensions of a mapping result: entity, cardinality, structural, transformation and constraint dimensions. Second we have designed an algorithm that generates automatically XSLT scripts based on the above mapping structure. For each matching node pair, the algorithm traverses the target template tree in a depth-first manner and generates progressively transformation rules. Templates, produced mapping results as well as the transformation scripts are stored. When new instances are available, the transformation process is done automatically without re-applying the entire matching process. Template-Driven Evolution In a community of practice context, templates continuously evolve to reflect a change in the practices, to adhere to new users’ requirements, to correct initial design errors, to allow expansion of the template scope over time or to simply allow for incremental maintenance. However, templates updates have a major consequence: documents being valid for the original template are no more
Page 1 and 2: www.dbebooks.com - Free Books & mag
Page 3 and 4: Director of Editorial Content: Kris
Page 5 and 6: Editorial Advisory Board Anil Aggar
Page 7 and 8: Table of Contents Preface .........
Page 9 and 10: Chapter 11 An Organizational Knowle
Page 11 and 12: Detailed Table of Contents Preface
Page 13 and 14: Chapter 6 Using Storytelling as the
Page 15 and 16: Chapter 11 An Organizational Knowle
Page 17 and 18: Chapter 17 A Process-Oriented and T
Page 19 and 20: xviii Preface For more than ten yea
Page 21 and 22: xx (Greece) - by focusing on the pa
Page 23 and 24: xxii Advances reported in this book
Page 25 and 26: This chapter discusses the prelimin
Page 27 and 28: which will ultimately influence the
Page 29 and 30: Table 2. Facilitator 2 feedback SOL
Page 31 and 32: “Focus on reading peers’ posts
Page 33 and 34: • Feedback must be useful and for
Page 35 and 36: Schunk, D. H., & Lilly, M. W. (1984
Page 37 and 38: able to others (Wenger, 1998). With
Page 39 and 40: equire structure transformation att
Page 41: are often manually encoded on a cas
Page 45 and 46: tives have been negotiated with CoP
Page 47 and 48: Brown, H., & Cole, F. C. (1992). Ed
Page 49 and 50: 26 ABSTRACT Chapter 3 Tackling Acce
Page 51 and 52: Figure 1. Web-based GUI of eLogbook
Page 53 and 54: Figure 2. System flow for the email
Page 55 and 56: submission or validation of deadlin
Page 57 and 58: Figure 7. Body of the email of crea
Page 59 and 60: The email-based eLogbook interface
Page 61 and 62: 38 Chapter 4 Supporting Communities
Page 63 and 64: 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
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ested in learning from each other i
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nize clearly how to begin a story,
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Figure 2. Story Development Phases
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Figure 3. Story Designed to move Em
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2. Some differences in JavaScript i
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However, bringing in a new practice
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Backbase. (2008). AJAX in enterpris
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through continuous learning (Rosenb
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do with web-based collaboration sys
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Artificial Intelligence. More preci
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approaches concerning social networ
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REFERENCES Ackerman, M. S. (1998).
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the describing more than one cluste
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eing ‘online’ or ‘offline’
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communication tools, see Weinberger
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learn community booked 147 meetings
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102 Live Virtual Technologies to Su
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Annotating FM text chat transcripts
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community members were discussing t
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chats taking place at this time, on
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ing its course. During an extended
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Dourish, P. (2006). Re-space-ing pl
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INTRODUCTION 114 Individual Learnin
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116 Individual Learning and Emotion
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Emotional Processing 118 Individual
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The Paradigm of CSA 120 Individual
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128 Chapter 10 From ‘Collecting
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aforementioned gap. In particular,
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it into the discussion. This means
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high degree of support for emergenc
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When resources are used in a collab
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Figure 7. Instance of the collabora
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Transformation Rules During the tra
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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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