Including malicious agents into a collaborative learning environment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2363, Issue , 2002, Pages 51-60

  López, Natalia ^a   Núñez, Manuel ^a   Rodríguez, Ismael ^a   Rubio, Fernando ^a  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Cooperative systems; Distributed learning environments

Indexed keywords

FUNCTIONAL PROGRAMMING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; STUDENTS;

CO-OPERATIVE SYSTEMS; COLLABORATIVE ENVIRONMENTS; COLLABORATIVE LEARNING ENVIRONMENT; DISTRIBUTED LEARNING ENVIRONMENTS; LEARNING ENVIRONMENTS; PROGRAMMING ENVIRONMENT; PROGRAMMING PROJECTS; RELEVANT FEATURES;

COMPUTER AIDED INSTRUCTION;

EID: 84943260933     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47987-2_10     Document Type: Conference Paper

References (14)

1. E. Aïmeur, H. Dufort, D. Leibu, and C. Frasson. Some justifications for the learning by disturbing strategy. In AI-ED 97, World Conference on Artificial Intelligence and Education, 1997.
2. E. Aïmeur and C. Frasson. Analyzing a new learning strategy according to different knowledge levels. Computer and Education. An International Journal, 27(2):115–127, 1996.
3. E. Aïmeur, C. Frasson, and H. Dufort. Co-operative learning strategies for intelligent tutoring systems. Applied Artificial Intelligence. An International Journal, 14(5):465–490, 2000.
4. G. Ayala and Y. Yano. GRACILE: A framework for collaborative intelligent learning environments. Journal of the Japanese Society of Artificial Intelligence, 10(6):156–170, 1995.
5. H.U. Hoppe. The use of multiple student modeling to parameterize group learning. In 7th Conference on Artifical Intelligence in Education, pages 234–241, 1995.
6. A. Inaba, T. Supnithi, M. Ikeda, R. Mizoguchi, and J. Toyoda. How can we form effective collaborative learning groups? –Theoretical justification of”opportunistic group formation” with ontological engineering–. In ITS 2000, LNCS 1839, pages 282–291. Springer, 2000.
7. P. Jermann, A. Soller, and M. Muehlenbrock. From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. In 1st European Conf. on Computer-Supported Collaborative Learning, pages 324–331, 2001.
8. N. López, M. Núñez, I. Rodríguez, and F. Rubio. WHAT: A Web-based Haskell Adaptive Tutor. Submitted for publication, 2002.
9. N. López and M. Núñez. A testing theory for generally distributed stochastic processes. In CONCUR’2001, LNCS 2154, pages 321–335. Springer, 2001.
10. M. McManus and R. Aiken. Monitoring computer-based problem solving. International Journal of Artificial Intelligence in Education, 6(4):307–336, 1995.
11. M. Núñez and I. Rodríguez. PAMR: A process algebra for the management of resources in concurrent systems. In FORTE 2001, pages 169–185. Kluwer Academic Publishers, 2001.
12. S.L. Peyton Jones and J. Hughes. Report on the programming language Haskell 98, 1999. http://www.haskell.org.
13. A. Vizcaíno, J. Contreras, J. Favela, and M. Prieto. An adaptive, collaborative environment to develop good habits in programming. In ITS 2000, LNCS 1839, pages 262–271. Springer, 2000.
14. M. Wooldridge and N.R. Jennings. Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2):115–152, 1995.