Hongchi Shi
Abstract
In this paper, we present a multi-agent system for supporting student-centered, self-paced, and highly interactive learning in undergraduate computer science education. The system is based on a hybrid problem-based and case-based learning model, for both creative problem solving and mechanical experience simulation. It aims at enhancing the effectiveness of the undergraduate learning experience in computer science. Implemented using the prevalent Internet, Web, and digital library technologies, the system adopts an open architecture design and targets at large-scale, distributed operations. In the initial implementation of the system, a number of prototypes using different Java-based software environments have been developed. They offer tradeoffs in system performance and design complexity.
- 1 Beck, J. E., and Woolf, B. P. Using a learning agent with a student model. In Intelligence Tutoring System (Proc. $th Int~ Conf. IT3'98), B. P. GoettI, H. M. Halff, C. L. Redfield, and V. J. Shute, Eds. Springer, 1998, pp. 6-15.]] Google Scholar
Digital Library
- 2 Brusilovsky, P., Ritter, S., and Schwartz, E. Distributed intelligent tutoring on the web. In Proc. AI-ED'97 World Conference on Artificial Intelligence in Education (Kobe, Japan, 1997).]]Google Scholar
- 3 Chert, S. Knowledge dissemination = digital libraries + collaboration technology, in Building and Sharing of Very Large Scale Knowledge Bases. IOS Press, 1995.]]Google Scholar
- 4 Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., and Glaser, R. Self-explanations: How students study and use examples to solve problems. Cognitive Science 13 (1989), 145-182.]]Google ScholarCross Ref
- 5 Curtis, R. Web based configuration control system for team projects. SIGCSE Bulletin 28, 1 (i996), 189-193.]] Google ScholarDigital Library
- 6 Farley, J. Java Distributed Computing. O'Reilly Publishers, 1998.]] Google ScholarDigital Library
- 7 Hamburger, H., and Tecuci, G. Toward a unification of human-computer learning and tutoring. In Intelligence Tutoring System (Proc. $th Int'l Conf. ITS'98), B. P. Goettl, H. M. Halff, C. L. Redfield, and V. J. Shute, Eds. Springer, 1998.]] Google ScholarDigital Library
- 8 Hupfer, E. F. S., and Arnold, K. JavaSpaces Principles, Patterns, and Practice. Addison-Wesley, 1999.]] Google ScholarDigital Library
- 9 JATLite. http://java.stanford.edu/java_agent/html.]]Google Scholar
- 10 Laffey, J., and Singer, j. Using mapping for cognitive assessment in project based science. Journal of Interactive Learning Research 8, 3/4 (1997), 363- 387.]] Google ScholarDigital Library
- 11 Owen, G. integrating World Wide Web technology into undergraduate eduation. SIGCSE Bulletin 28, 1 (1996), 101-103.]] Google ScholarDigital Library
- 12 Ritter, S. Communication, cooperation and competition among multiple tutor agents. In Artificial Intelligence in Education: Knowledge and Media in Learning Systems. (Proc. of AI-ED'97, 8th World Conf. on AI in Education), B. D. Boulay and It. Mizoguchi, Eds. 1997.]]Google Scholar
- 13 Weib, G., and Saadip, S. Adaption and learning in multi-agent systems. In Lecture Notes in Artificial Intelligence, Vol. 10~2. Springer-Verleg, Berlin, 1996.]]Google Scholar
- 14 Zhang, D. M., Alem, L., and Yacef, K. Using multiagent approach for the design of an intelligent learning environment. In Agents and Multi-Agent Systems (Lecture Notes in Artificial Intelligence 1441), W. Wobcke, M. Pagnucco, and C. Zhang, Eds. Springer, 1998, pp. 220-230.]] Google ScholarDigital Library
Index Terms
- A multi-agent system for computer science education
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