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Knowledge Cartography pp 491–517Cite as

Collaborative Knowledge Modelling with a Graphical Knowledge Representation Tool: A Strategy to Support the Transfer of Expertise in Organisations

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

This chapter presents a strategy for collaborative knowledge modelling between experts and novices in order to support the transfer of expertise within organisations. The use of an object-typed knowledge modelling software tool called MOT is advocated, to elaborate knowledge models in small groups composed of experienced and less experienced employees within organisations. A knowledge model is similar to a concept map, except that it is based on a typology of links and knowledge objects. This technique is used to help experts externalise their knowledge pertaining to concepts, principles, procedures and facts related to their work and to support the sharing of knowledge with novice employees. This chapter presents the rationale behind this strategy, the tool used, the applications of this method and the manner in which it can be integrated into a global knowledge management strategy within organisations.

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Notes

  1. 1.

    Represented by the icon [Image] attached to knowledge objects developed further in a sub-model.

  2. 2.

    The term “declarative” when applied to the term “knowledge” comprises two different meanings which are often confused. In a first sense, all knowledge that is overtly “verbalised” (that is, expressed with words) is said to have a declarative format. In a second sense, the term “declarative” defines a specific type of knowledge (declarative knowledge), that is, knowledge about objects and on properties of objects (the know-what), as opposed to “procedural” knowledge or knowledge on actions (the know-how). Procedural knowledge can then be represented in a declarative format.

  3. 3.

    This research project is supported by the CEFRIO (Centre francophone de recherche sur l’informatisation des organisations), which is a liaison and transfer centre that comprises university, industrial and governmental members and researchers in Quebec, Canada.

  4. 4.

    Two other groups recently participated in the study.

  5. 5.

    MISA is a French acronym (Méthode d’Ingénierie d’un Système d’Apprentissage), which stands for “Engineering Method for Learning Systems”.

  6. 6.

    IMS-LD is a standardized language used for the specification of e-learning instructional scenarios (LD stands for “Learning Design”). These scenarios are machine-readable: they can be delivered on different elearning platforms that are compliant with IMS-LD.

References

  • Alpert, S. (2004). Flexibility of expressiveness: state of the practice. In P. Kommers (Ed.), Cognitive Support for Learning: Imagining the Unknown (pp. 253–268). Amsterdam: IOS Press.

    Google Scholar 

  • Apostolou, D., Mentzas, G., Young, R., and Abecker, A. (2000). Consolidating the Product Versus Process Approaches in Knowledge Management: The Know-net Approach. Paper presented at the Conference Practical Application of Knowledge Management (PAKeM 2000) – April 12–14 2000, Manchester, UK.

    Google Scholar 

  • Ausubel, D. (1968). Educational Psychology: A Cognitive View. New York: Rhinehart and Winston.

    Google Scholar 

  • Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Basque, J., Imbeault, C., Pudelko, B., and Léonard, M. (2004). Collaborative knowledge modeling between experts and novices: a strategy to support transfer of expertise in an organization. In A. J. Canas, J. D. Novak and F. M. Gonzalez (Eds.), Proceedings of the First International Conference on Concept Mapping (CMC 2004), Pamplona, September 14–17, vol. 1, (pp. 75–81). Pamplona: Universidad Publica de Navarra.

    Google Scholar 

  • Basque, J. and Lavoie, M.-C. (2006). Collaborative concept mapping in education: major research trends. In A. J. Canas and J. D. Novak (Eds.), Concept Maps: Theory, Methodology, Technology – Proceedings of the Second International Conference on Concept Mapping, vol. 1, (pp. 79–86). San Jose, Costa Rica: Universidad de Costa Rica.

    Google Scholar 

  • Basque, J. and Pudelko, B. (2003). Using a concept mapping software as a knowledge construction tool in a graduate online course. In D. Lassner and C. McNaught (Eds.), Proceedings of ED-MEDIA 2003, Wold Conference on Educational Multimedia, Hypermedia and Telecommunications, Honolulu, June 23–28 2003, (pp. 2268–2264). Norfolk, VA: AACE.

    Google Scholar 

  • Basque, J., Rocheleau, J., Paquette, G., and Paquin, C. (1998). An object-oriented model of a computer-enriched high school. In T. Ottmann and I. Tomek (Eds.), Proceedings of ED-MEDIA/ED-TELECOM 98. Charlottesville, VA: Association for the Advancement of Computing in Education.

    Google Scholar 

  • Basque, J., Ruelland, D., and Lavoie, M.-C. (2007). A Digital Tool for Self-assessing Information Literacy Skills. In T. Bastiaens & S. Carliner (Eds.), Proceedings of the E-Learn 2007, World Conference on E-Leaning in Corporate, Government, Healthcare, & Higher education, Quebec City, October 16–19, 2007 (pp. 6997–7003). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE).

    Google Scholar 

  • Beazley, H., Boenisch, J., and Harden, D. (2002). Continuity Management: Preserving Corporate Knowledge When Employees Leave. Hoboken, NJ: Wiley.

    Google Scholar 

  • Booch, G., Jacobson, J., and Rumbaugh, J. (1999). The Unified Modeling Language User Guide. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Brisebois, A., Paquette, G., and Masmoudi, A. (2003). Affective attributes in a distributed learning environments. In 9th International Conference on User Modeling. University of Pittsburgh.

    Google Scholar 

  • Bruner, J. S. (1973). Beyond the Information Given. New York: Norton.

    Google Scholar 

  • Bruner, J. S. (1987). Le développement de l’enfant: Savoir faire, savoir dire (2nd ed.). Paris: Presses Universitaires de France.

    Google Scholar 

  • Chen, P. P.-S. (1976). The entity-relationship model: toward a unified view of data. ACM Transactions on Database Systems, 1(1), 9–36.

    Article  Google Scholar 

  • Chi, M. T. H., Feltovitch, P. J., and Glaser, R. (1981). Categorisation and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

    Article  Google Scholar 

  • Chi, M. T. H., Glaser, R., and Farr, M. J. (1988). The Nature of Expertise. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Chi, M. T. H., Glaser, R., and Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the Psychology of Human Intelligence, (pp. 7–75). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Clark, H. H., and Wilkes-Gibbs, D. (1986). Referring as a collaborative process. Cognition, 22, 1–39.

    Article  Google Scholar 

  • Coffey, J. (2006). In the Heat of the Moment… Strategies, Tactics, and Lessons Learned Regarding Interactive Knowledge Modeling with Concept Maps. In A. J. Canas and J. D. Novak (Eds.), Concept Maps: Theory, Methodology, Technology, (pp. 263–271). San Jose, Costa Rica: University of Costa Rica.

    Google Scholar 

  • Coffey, J. W. and Canas, A. (2003). An Internet-based Meta-cognitive Tool for Courseware Development. In A. Rossett (Ed.), Proceedings of E-Learn 2003, November 7–11, Phoenix, Arizona, (pp. 909–912). Norfolk, VA: AACE.

    Google Scholar 

  • Coffey, J. W. and Hoffman, R. R. (2003). Knowledge modeling for the preservation of institutional memory. Journal of Knowledge Management, 7(3), 38–52.

    Article  Google Scholar 

  • Cole, E., and Engeström, Y. (1993). A cultural-historical approach to distributed cognition. In G. Salomon (Ed.), Distributed Cognitions: Psychological and Educational Considerations, (pp. 1–46). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Craik, F. I. M. and Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671–684.

    Article  Google Scholar 

  • De la Teja, I., Longpré, A., and Paquette, G. (2000). Designing adaptable learning environments for the web: a case study. In Proceedings of the ED-MEDIA Conference. Montreal.

    Google Scholar 

  • De Long, D. W. (2004). Lost knowledge: confronting the threat of an aging workforce. New York: Oxford University Press.

    Book  Google Scholar 

  • Doise, W. and Mugny, G. (1984). The Social Development of the Intellect. Oxford: Pergamon Press.

    Google Scholar 

  • Dufresne, A., Basque, J., Paquette, G., Léonard, M., Lundgren-Cayrol, K., and Prom Tep, S. (2003). Vers un modèle générique d’assistance aux acteurs du téléapprentissage. Sciences et Technologies de l’Information et de la Communication pour l’Éducation et la Formation, Numéro spécial: Technologies et formation à distance, 10(3), 57–88.

    Google Scholar 

  • Ericsson, K. A. and Charness, N. (1994). Expert performance: its structure and acquisition. American Psychologist, 49(3), 725–747.

    Article  Google Scholar 

  • Ermine, J.-L., Boughzala, I., and Tounkara, T. (2006). Critical knowledge map as a decision tool for knowledge transfer actions. The Electronic Journal of Knowledge Management, 4(2), 128–140.

    Google Scholar 

  • Faletti, J. and Fisher, K. M. (1996). The information in relations in biology, or the unexamined relation is not worth having. In K. M. Fisher and M. R. Kibby (Eds.), Knowledge Acquisition, Organization, and Use in Biology, (pp. 182–205). Berlin, Heidelberg, New York: Springer.

    Chapter  Google Scholar 

  • Fisher, K. (1992). SemNet: a tool for personal knowledge construction. In P. Kommers, D. H. Jonassen, and J. T. Mayes (Eds.), Cognitive Tools for Learning, Vol. NATO ASI Series, vol. 81, (pp. 63–75). Berlin, Heidelberg, New York: Springer-Verlag.

    Chapter  Google Scholar 

  • Fisher, K. and Moody, D. E. (2000). Student misconceptions in biology. In K. Fisher, J. Wandersee, and D. E. Moody (Eds.), Mapping Biology Knowledge, (pp. 55–75). Dordrecht: Kluwer.

    Google Scholar 

  • Fisher, K. M. (1990). Semantic networking: the new kid on the block. Journal of Research in Science Teaching, 27(10), 1001–1018.

    Article  Google Scholar 

  • Fisher, K. M. (2000). Overview of knowledge mapping. In K. M. Fisher, J. H. Wandersee and D. E. Moody (Eds.), Mapping Biology Knowledge, (pp. 5–23). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Gentner, D. and Stevens, A. (1983). Mental Models. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Glaser, R. (1986). On the nature of expertise. In H. Hagendorf (Ed.), Human Memory and Cognitive Capabilities: Mechanisms and Performances, (pp. 915–928). North Holland: Elsevier Science.

    Google Scholar 

  • Gordon, J. L. (2000). Creating knowledge maps by exploiting dependent relationships. In Applications and Innovations in Intelligent Systems, (pp. 63–78) Berlin, Heidelberg, New York: Springer.

    Google Scholar 

  • Henri, F., Gagné, P., Maina, M., Gargouri, Y., Bourdeau, J., and Paquette, G. (2006). Development of a knowledge base as a tool for contextualized learning. AI and Society 20(3), 271–287.

    Article  Google Scholar 

  • Hereen, E. and Kommers, P. A. M. (1992). Flexibility of expressiveness: a critical factor in the design of concept mapping tools for learning. In P. A. M. Kommers, D. H. Jonassen, and J. T. Mayes (Eds.), Cognitive Tools for Learning (NATO Series, vol. F81), (pp. 85–101). Berlin, Heidelberg, New York: Springer.

    Chapter  Google Scholar 

  • Hinds, P. J., Patterson, M., and Pfeffer, J. (2001). Bothered by abstraction: the effect of expertise on knowledge transfer and subsequent novice performance. Journal of Applied Psychology, 86(6), 1232–1243.

    Article  Google Scholar 

  • Holley, C. D. and Dansereau, D. F. (1984). Spatial Learning Strategies. Techniques, Applications, and Related Issues. New York, London: Academic Press.

    Google Scholar 

  • Inglis, A. (2003). Facilitating team-based course designing with conceptual mapping. Distance Education, 24(2), 247–263.

    Article  Google Scholar 

  • Inhelder, B. and Piaget, J. (1958). The Growth of Logical Thinking from Childhood to Adolescence. New York: Basic Books.

    Book  Google Scholar 

  • Jacob, R. and Pariat, L. (2001). Gérer les connaissances: un défi de la nouvelle compétitivité du 21e siècle. Montrèal: CEFRIO.

    Google Scholar 

  • John-Steiner, V. and Mahn, H. (1996). Sociocultural approaches to learning and development: a vygostskian framework. Educational Psychologist, 31, 191–206.

    Google Scholar 

  • Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Jonassen, D. H., Reeves, T. C., Hong, N., Harvey, D., and Peters, K. (1997). Concept mapping as cognitive learning and assessment tools. Journal of Interactive Learning Research, 8(3/4), 289–308.

    Google Scholar 

  • Kintsch, W. (1996). Mental representations in cognitive science. In W. Battmann and S. Dutke (Eds.), Processes of the Molar Regulation of Behavior. Scottsdale, AZ, USA: Pabst Science Publishers.

    Google Scholar 

  • Lundgren-Cayrol, K., de la Teja, I., and Léonard, M. (2001). Modélisation d’un gestionnaire de ressources. Rapport interne de recherche. Montreal, Canada: Centre de recherche LICEF, Télé-université.

    Google Scholar 

  • McDermott, R. (2001). Designing communities of practice: reflecting on what we’ve learned. In Proceedings of Communities of Practice 2001. Cambridge, MA: Institute for International Research.

    Google Scholar 

  • Mead, G. H. (1934/1974). Mind, Self, and Society from the Standpoint of a Social Behaviorist. Chicago: Chicago University Press.

    Google Scholar 

  • Merrill, M. D. (1994). Principles of Instructional Design. Englewood Cliffs, NJ: Educational Technology Publications.

    Google Scholar 

  • Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The Psychology of Computer Vision. New York: McGraw-Hill.

    Google Scholar 

  • Moody, D. E. (2000). The paradox of the textbook. In K. M. Fisher, J. H. Wandersee, and D. E. Moody (Eds.), Mapping Biology Knowledge, (pp. 167–184). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Newell, A. and Simon, H. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Nonaka, I. and Takeuchi, H. (1995). The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation. New York: Oxford University Press.

    Google Scholar 

  • Novak, J. D. and Gowin, D. B. (1984). Learning How to Learn. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Okebukola, P. A. and Jegede, O. J. (1988). Cognitive preference and learning mode as determinants of meaningful learning through concept mapping. Science Education, 72(4), 489–500.

    Article  Google Scholar 

  • Osmundson, E., Chung, G. K., Herl, H. E., and Klein, D. C. (1999). Knowledge Mapping in the Classroom: A Tool for Examining the Development of Students’ Conceptual Understandings (Technical report No. 507). Los Angeles: CRESST/ University of California.

    Google Scholar 

  • Oughton, J. M. and Reed, W. M. (1999). The influence of learner differences on the construction of hypermedia concepts: a case study. Computers in Human Behavior, 15(1), 11–50.

    Google Scholar 

  • Oughton, J. M. and Reed, W. M. (2000). The effect of hypermedia knowledge and learning style on student-centered concept maps about hypermedia. Journal of Research on Computing in Education, 32(3), 366–382.

    Google Scholar 

  • Paquette, G. (1999). Meta-knowledge representation for learning scenarios engineering. In S. Lajoie and M. Vivet (Eds.), AI and Education, Open Learning Environments. Proceedings of AI-ED 99. Le Mans: France IOS Press.

    Google Scholar 

  • Paquette, G. (2002). Modélisation des connaissances et des compétences. Sainte-Foy (Québec): Presses de l’Université du Québec.

    Google Scholar 

  • Paquette, G. (2003). Instructional Engineering in Networked Environments. San Francisco: Pfeiffer/Wiley.

    Google Scholar 

  • Paquette, G. (2006). Building Graphical Knowledge Representation Languages – From Informal to Interoperable Executable Models. Paper presented at the i2LOR-06 Conference, November 8–10, Montreal.

    Google Scholar 

  • Paquette, G., De la Teja, I., Léonard, M., Lundgren-Cayrol, K., and Marino, O. (2005). An instructional engineering method and tool for the design of units of learning. In R. Koper, and C. Tattersal (Eds.), Learning Design – A Handbook on Modelling and Delivering Networked Education and Training, (pp. 161–184). Berlin Heidelberg New York: Springer.

    Google Scholar 

  • Paquette, G., De la Teja, I., Lundgren-Cayrol, K., Léonard, M., and Ruelland, D. (2002). La modélisation cognitive, un outil de conception des processus et des méthodes d’un campus virtuel. Revue de l’Éducation à distance, 17(3), 4–25.

    Google Scholar 

  • Paquette, G., Léonard, M., Lundgren-Cayrol, K., Mihaila, S., and Gareau, D. (2006). Learning design based on graphical knowledge-modeling. Journal of Educational Technology and Society 9(1), 97–112.

    Google Scholar 

  • Paquette, G. and Rogozan, D. (2005). Primitives de représentation OWL-DL – Correspondance avec le langage graphique MOT+OWL et le langage des prédicats du premier ordre. TELOS documentation. Montreal, Canada: LICEF Research Center.

    Google Scholar 

  • Paris, S., Lipson, M. Y., and Wixson, K. K. (1983). Becoming a strategic reader. Contemporary Educational Psychology, 8, 293–316.

    Article  Google Scholar 

  • Polanyi, M. (1966). The Tacit Dimension. London: Routledge and Kegan Paul.

    Google Scholar 

  • Reed, W. M. and Oughton, J. M. (1998). The effects of hypermedia knowledge and learning style on the construction of group concept maps. Computers in Human Behavior, 14(1), 1–22.

    Article  Google Scholar 

  • Rogoff, B. and Lave, J. (1984). Everyday Cognition: Its Development in Social Context. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Romizowski, A. J. (1999). Designing Instructional Systems: Decision Making In Course Planning And Curriculum Design. Sterling, VA: Stylus Publications.

    Google Scholar 

  • Roth, W.-M. and Roychoudhury, A. (1992). The social construction of scientific concepts or the concept map as conscription device and tool for social thinking in high school science. Science Education, 76(5), 531–557.

    Article  Google Scholar 

  • Roth, W.-M. and Roychoudhury, A. (1994). Science discourse through collaborative concept mapping: new perspectives for the teacher. International Journal of Science Education, 16(4), 437–455.

    Article  Google Scholar 

  • Roth, W. and Roychoudhury, A. (1993). The concept map as a tool for the collaborative construction of knowledge: a microanalysis of high school physics students. Journal of Research in Science Teaching, 305, 503–554.

    Article  Google Scholar 

  • Ruelland, D. (2000). Vers un modèle d’autogestion en situation de télé-apprentissage. Université de Montréal, Montréal.

    Google Scholar 

  • Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F., and Lorensen, W. (1991). Object-Oriented Modelling and Design. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Rumelhart, D. E., and Ortony, A. (1977). The representation of knowledge in memory. In R. C. Anderson, R. J. Spiro, and W. E. Montague (Eds.), Schooling and the Acquisition of Knowledge. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Schank, R. C., and Abelson, R. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Erlbaum.

    MATH  Google Scholar 

  • Schmidt, H. G. and Boshuizen, H. P. A. (1993). On acquiring expertise in medicine. Educational Psychology Review, 5(3) 205–221.

    Article  Google Scholar 

  • Schreiber, G., Wielinga, B., and Breuker, J. A. (1993). KADS – A Principled Approach to Knowledge-based System Development. San Diego, CA: Academic Press.

    Google Scholar 

  • Sowa, J. F. (1984). Conceptual Structures, Information Processing in Mind and Machine. Reading, MA: Addison-Wesley Publishing.

    MATH  Google Scholar 

  • Star. L. (1989). The structure of ill-structured solutions: boundary objects and heterogeneous distributed problem solving. In L. Glaser et M. N. Huhns (Eds.), Distributed Artificial Intelligence, vol. 2, (pp. 37–54). San Mateo, CA: Morgan Kaufman Publishers.

    Google Scholar 

  • Stensvold, M. S. and Wilson, J. T. (1990). The interaction of verbal ability with concept mapping in learning from a chemistry laboratory activity. Science Education, 74(4), 473–480.

    Article  Google Scholar 

  • Sternberg, R. (1997). Cognitive conceptions of expertise. In R. R. Hoffman (Ed.), Expertise in Context. Human and Machine, (pp. 149–162). Menlo Park, CA/Cambridge, MA: AAAI Press/MIT Press.

    Google Scholar 

  • Sternberg, R. (1999). What do we know about tacit knowledge? making the tacit become explicit. In J. A. Horvath (Ed.), Tacit Knowledge in Professional Practice, (pp. 231–236). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Stoyanova, N. and Kommers, P. (2002). Concept mapping as a medium of shared cognition in computer-supported collaborative problem solving. Journal of Interactive Learning Research, 13(1/2), 111–133.

    Google Scholar 

  • Tennyson, R. D. and Rasch, M. (1988). Linking cognitive learning theory to instructional prescriptions. Instructional Science, 17(4), 369–385.

    Article  Google Scholar 

  • Trabasso, T. and van den Broek, P. (1985). Causal thinking and importance of story events.Journal of Memory and Language, 24, 612–630.

    Article  Google Scholar 

  • Uschold, M. and Gruninger, M. (1996). Ontologies: principles, methods, and applications. Knowledge Engineering Review, 11(2), 93–155.

    Article  Google Scholar 

  • van Boxtel, C., van der Linden, J., and Kanselaar, G. (2000). Collaborative learning tasks and the elaboration of conceptual knowledge. Learning and Instruction, 10, 311–330.

    Article  Google Scholar 

  • Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Process. Cambridge: Harvard University Press.

    Google Scholar 

  • Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Wenger, E., McDermott, R., and Snyder, W. M. (2002). Cultivating Communities of Practice. Boston, MA: Harward Business School Press.

    Google Scholar 

  • Wertsch, J. V. and Stone, C. A. (1985). The concept of internalization in Vygotsky’s accountof the genesis in higher mental functions. In J. V. Wertsch (Ed.), Culture, Communication,and Cognition: Vygotskian Perspectives, (pp. 146–161). Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • West, C. K., Farmer, J. A., and Wolff, P. M. (1991). Instructional Design: Implications from Cognitive Science. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Wilson, T. and Schooler, J. (1991). Thinking too much: introspection can reduce the quality of preferences and decisions. Journal of Personality and Social Psychology, 60, 181–192.

    Google Scholar 

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  1. LICEF Research Center, Tele-universite, West Montreal, QC, Canada

    Josianne Basque, Gilbert Paquette, Beatrice Pudelko & Michel Leonard

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  1. Josianne Basque
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Correspondence to Josianne Basque .

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  1. Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom

    Alexandra Okada

  2. Connected Intelligence Centre, University of Technology, Sydney, Australia

    Simon J. Buckingham Shum

  3. Sheffield Hallam University Centre for Science Education, Sheffield, United Kingdom

    Tony Sherborne

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Basque, J., Paquette, G., Pudelko, B., Leonard, M. (2014). Collaborative Knowledge Modelling with a Graphical Knowledge Representation Tool: A Strategy to Support the Transfer of Expertise in Organisations. In: Okada, A., Buckingham Shum, S., Sherborne, T. (eds) Knowledge Cartography. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-6470-8_22

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