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Concept Map Based Intelligent Knowledge Assessment System: Experience of Development and Practical Use

  • Janis GrundspenkisEmail author
Chapter

Abstract

Concept maps (CMs), as pedagogical tools, have well established uses to support teaching, learning, and knowledge assessment. This chapter focuses on the use of CMs as knowledge assessment tools. The CM-based adaptive intelligent knowledge assessment system (IKAS) is described. The kernel of the IKAS is the intelligent knowledge assessment agent which is implemented as a multi-agent system consisting of the agent-expert, the communication agent, the knowledge evaluation agent, and the interaction registering agent. The knowledge evaluation agent compares the teacher’s and the learner’s CMs on the basis of graph patterns and assigns score for a submitted solution. Five-year long experience of developing and using IKAS has resulted in improvements and extensions of the system’s functionality and adaptivity. Evolution of IKAS and its characteristics are summarized. This chapter presents student opinions elicited from questionnaires about CMs as knowledge assessment tools. The results of the practical use of four versions of IKAS in different study courses are described.

Keywords

Concept map Knowledge assessment Adaptive intelligent knowledge assessment system Multi-agent system 

References

  1. Ahlberg, M. (2004). Varieties of concept mapping. Proceedings of the First International Conference on Concept Mapping, (Vol. 2, pp. 25–28), September 14–17, 2004, Pamplona, Spain. Retrieved March, 30, 2010, from http://cmc.ihmc.us/papers/cmc2004-206.pdf
  2. Anderson, J. R., & Reiser, B. J. (1985). The Lisp tutor. Byte, 10, 159–175.Google Scholar
  3. Anohina, A., & Grundspenkis, J. (2006). Prototype of multiagent knowledge assessment system for support of process oriented learning. Proceedings of the 7th international Baltic conference on databases and information systems, (pp. 211–219), July 3–6, 2006, Vilnius, Lithuania.Google Scholar
  4. Anohina, A., Pozdnakovs, D., & Grundspenkis, J. (2007). Changing the degree of task difficulty in concept map based assessment system. Proceedings of the IADIS international conference “e-learning 2007”, (pp. 443–450), July 6–8, 2007, Lisbon, Portugal.Google Scholar
  5. Ausubel, D. P. (1968). Educational psychology: A cognitive view (p. 685). New York: Holt, Rinehart and Winston.Google Scholar
  6. Ausubel, D. P. (2000). The acquisition and retention of knowledge (p. 232). New York: Kluwer.Google Scholar
  7. Bloom, B. S. (1956). Taxonomy of education objectives. Handbook I: The cognitive domain (p. 207). New York: David McKay.Google Scholar
  8. Brown, J. S., & Burton, R. R. (1978). A paradigmatic example of an artificially intelligent instructional system. International Journal of Man-Machine Studies, 10, 323–339.CrossRefGoogle Scholar
  9. Brown, J. S., Burton, R. R., & de Kleer, J. (1982). Pedagogical, natural language, and knowledge engineering techniques in SOPHIE I, II and III. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic.Google Scholar
  10. Burton, R. R., & Brown, J. S. (1982). An investigation of computer coaching for informal learning activities. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic.Google Scholar
  11. Cañas, A. J. et al. (2003). A summary of literature pertaining to the use of concept mapping techniques and technologies for education and performance support. Technical report submitted to the chief of naval education and training (p. 108). Pensacola, FL: Florida Institute for Human and Machine Cognition.Google Scholar
  12. Carbonell, J. R. (1970). AI in CAI: An artificial intelligence approach to computer-assisted instruction. IEEE Transactions of Man-Machine Systems, 11(4), 190–202.CrossRefGoogle Scholar
  13. Cimolino, L. et al. (2003). Incremental student modelling and reflection by verified concept-mapping. Supplementary proceedings of AIED 2003: Learner modelling for reflection (pp. 219–227), July 20–24, 2003, Sydney, Australia.Google Scholar
  14. Clancey, W. J. (1982). Tutoring roles for guiding a case methods dialogue. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic.Google Scholar
  15. Twomey, E., Nicol, J., & Smart, C. (2004). Computer-assisted assessment: Using computers to design and deliver objective tests. Computers in Teaching Initiative. Retrieved March 30, 2010, from http://etudeedl.free.fr/annexes/assess.pdf
  16. Conlon, T. (2006). Formative assessment of classroom concept maps: The reasonable fallible analyser. Journal of Interactive Learning Research, 17(1), 15–36.Google Scholar
  17. da Rocha, F. E. L. et al. (2008). An approach to computer-aided learning assessment. Proceedings of the third international conference on concept mapping (pp. 170–177), September 22–25, 2008, Tallinn, Estonia and Helsinki, Finland.Google Scholar
  18. Deese, J. (1965). The structure of associations in language and thought. Baltimore, MD: Johns Hopkins Press.Google Scholar
  19. de Souza, F. S. L. et al. (2008). An approach to comparison of concept maps represented by graphs. Concept mapping – connecting educators, proceedings of the third international conference on concept mapping (pp. 205–212), September 22–25, 2008, Tallinn, Estonia & Helsinki, Finland.Google Scholar
  20. Gouli, E. et al. (2004). COMPASS: An adaptive web-based concept map assessment tool. In A. Cañas, J. Novak, & F. González (Eds.), Concept maps: Theory, methodology, technology. Proceedings of the first international conference on concept mapping, (pp. 128–135), September 14–17, 2004, Pamplona, Spain. Retrieved March 30, 2010, from http://cmc.ihmc.us/papers/cmc2004-128.pdf
  21. Graudina, V., & Grundspenkis, J. (2008). Concept map generation from OWL ontologies. Concept mapping – Connecting educators. Proceedings of the third international conference on concept mapping, (pp. 173–180), September 22–25, 2008, Tallinn, Estonia & Helsinki, Finland.Google Scholar
  22. Grundspenkis, J. (2008a). Development of concept map based adaptive knowledge assessment system. Proceedings of IADIS international conference on e-learning (pp. 295–402), July 22–25, 2008, Amsterdam, The Netherlands.Google Scholar
  23. Grundspenkis, J. (2008b). Knowledge creation supported by intelligent knowledge assessment system. Proceedings of the 12th world multi-conference on systemics, cybernetics and informatics (pp. 135–140), June 29-July 2, 2008, Orlando, Florida, USA.Google Scholar
  24. Grundspenkis, J., & Anohina, A. (2005). Agents in intelligent tutoring systems: State of the art. Scientific Proceedings of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, 22, 110–121.Google Scholar
  25. Grundspenkis, J., & Anohina, A. (2009). Evolution of the concept map based adaptive knowledge assessment system: Implementation and evaluation results. Scientific Proceedings of Riga Technical University, 5th Series Computer Science, Applied Computer Systems, 38, 13–24.Google Scholar
  26. Grundspenkis, J., & Strautmane, M. (2009). Usage of graph patterns for knowledge assessment based on concept maps. Scientific Proceedings of Riga Technical University, 5th series Computer Science, Applied Computer Systems, 38, 60–71.Google Scholar
  27. Hsieh, I.-L., & O’Neil, H. (2002). Types of feedback in a computer-based collaborative problem-solving group task. Computers in Human Behaviour, 18, 699–715.CrossRefGoogle Scholar
  28. Lavendelis, E., & Grundspenkis, J. (2010). MIPITS: An agent based intelligent tutoring system. Proceedings of the 2nd international conference on agents and artificial intelligence (ICAART 2010) (Vol. 2, pp. 5–13), January 22–24, 2010, Valencia, Spain.Google Scholar
  29. Lukashenko, R., & Anohina, A. (2009). Knowledge assessment systems: An overview. Scientific Proceedings of Riga Technical University, 5th series Computer Science, Applied Computer Systems, 38, 25–36.Google Scholar
  30. Lukashenko, R., & Grundspenkis, J. (2009). A conception of agents-based user modelling shell for intelligent knowledge assessment system. Proceedings of the international conference “e-learning 2009”, June 17–20, 2009 (pp. 98–140), Algarve, Portugal.Google Scholar
  31. Novak, J. D. (1998). Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations (p. 272). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  32. Novak, J. D., & Gowin, D. B. (1984). Learning how to learn (p. 150). New York: Cornell University Press.Google Scholar
  33. Papanastasiou, E. C. (2003). Computer-adaptive testing in science education. Proceedings of the 6th international conference on computer based learning in science, (pp. 965–971), July 5–10, 2003, Nicosia, Cyprus.Google Scholar
  34. Pirnay-Dummer, P. et al. (2008). Highly integrated model assessment technology and tools. Proceedings of IADIS international conference on cognition and exploratory learning in digital age (CELDA 2008) (pp. 18–28), 13–15 October, 2008, Freiburg, Germany. IADIS.Google Scholar
  35. Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33(6), 569–600.CrossRefGoogle Scholar
  36. Stevens, A., & Collins, A. (1977). The goal structure of a Socratic tutor. Proceedings of the national ACM conference, (pp. 256–263), January 1977, Seattle, WA.Google Scholar
  37. Waterhouse, S. (2004). The power of e-learning: The essential guide for teaching in the digital age (p. 228). Boston, MA: Allyn & Bacon.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Systems Theory and Design, Faculty of Computer Science and Information TechnologyRiga Technical UniversityRigaLatvia

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