Exploiting partial problem spaces learned from users' interactions to provide key tutoring services in procedural and Ill-defined domains

Frontiers in Artificial Intelligence and Applications

Volumn 200, Issue 1, 2009, Pages 383-390

  Fournier Viger, Philippe ^a   Nkambou, Roger ^a   Mephu Nguifo, Engelbert ^b  

^a UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

^b UNIVERSITÉ CLERMONT AUVERGNE   (France)

Author keywords

Domain knowledge mining; Intelligent tutoring systems; Tutoring services

Indexed keywords

COMPUTER AIDED INSTRUCTION; DATA MINING;

DOMAIN KNOWLEDGE; DOMAIN KNOWLEDGE MINING; ILL-DEFINED DOMAINS; INTELLIGENT TUTORING; INTELLIGENT TUTORING SYSTEM; KNOWLEDGE MINING; PROBLEM SPACE; TUTORING SERVICE; TUTORING SYSTEM; USER INTERACTION;

DOMAIN KNOWLEDGE;

EID: 73149103243     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: 10.3233/978-1-60750-028-5-383     Document Type: Conference Paper

References (17)

1. V. Aleven, B.M. McLaren, J. Sewall, K. Koedinger, The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains. Proc. Intelligent Tutoring Systems 2006, 61-70, 2006.
2. H. A. Simon, Information-processing theory of human problem solving". In W.K. Estes (Ed.), Handbook of learning and cognitive processes: Vol. 5. Human information, 1978.
3. C. Lynch, K. Ashley, V. Aleven, N. Pinkwart, Defining Ill-Defined Domains; A literature survey. Proc. of the Intelligent Tutoring Systems for Ill-Defined Domains Workshop (ITS06), 1-10, 2006.
4. A. Mitrovic, M. Mayo, P. Suraweera, B. Martin, Contraint-based tutors: a success story. Proc. of the Industrial & Engineering Application of Artificial Intelligence & Expert Systems, 931-940, 2001.
5. R. Nkambou, E. Mephu Nguifo, P. Fournier-Viger, Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain. Proc. of ITS 2008, 395-405, 2008.
6. P. Fournier-Viger, R. Nkambou, A. Mayers, Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System. IEEE Transactions on Learning Technologies, 1:1(2008): 63-74.
7. B. M. McLaren et al, Bootstrapping Novice Data: Semi-Automated Tutor Authoring Using Student Log Files, Proceedings of the Workshop on Analyzing Student-Tutor Logs (ITS '2004), 2004.
8. S.B. Blessing, A Programming by Demonstration Authoring Tool for Model-Tracing Tutors. In Authoring Tools for Advanced Technology Learning Environments: Toward Cost-Effective Adaptive, Interactive and Intelligent Educational Software, 93-119. Kluwer Academic Publishers, 2003.
9. M. Jarivs, G. Nuzzo-Jones, N.T. Heffernan, Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems. Proc. Intelligent Tutoring Systems 2006, 541-553. Springer, 2006.
10. P. Fournier-Viger, R. Nkambou, E. Mephu Nguifo, A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems. Proc. 6th Mexican International Conference on Artificial Intelligence, 765-778. LNAI 5317, Springer, 2008.
11. F. Kabanza, R. Nkambou, K. Belghith, Path-Planning for Autonomous Training on Robot Manipulators in Space. Proc. IJCAI 2005, 1729-173, 2005.
12. Y. Hirate, H. Yamana, Generalized Sequential Pattern Mining with Item Intervals, Journal of Computers, 1:3(2006), 51-60.
13. H. Pinto et al, Multi-Dimensional Sequential Pattern Mining, Proc. Int. Conf. Information and Knowledge Management (CIKM2001), 81-88, 2001.
14. R. Agrawal, R. Srikant. Mining Sequential Patterns. Proc. Int. Conf. on Data Engineering, 3-14, 1995.
15. J. Wang, J. Han, C. Li, Frequent Closed Sequence Mining without Candidate Maintenance, IEEE Trans. on Knowledge and Data Engineering, 19:8 (2007),1042-1056
16. P. Songram, V. Boonjing, S. Intakosum, Closed Multidimensional Sequential Pattern Mining, Proc. 3rd Int. Conf. Information Technology: New Generations, 512-517, 2006.
17. T. Barnes, J. Stamper, Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data, Proc. 9th Int. Conf. Intelligent Tutoring Systems (ITS 2008), 373-382, 2008.