JILSA> Vol.3 No.3, August 2011

Identifying Causes Helps a Tutoring System to Better Adapt to Learners during Training Sessions

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ABSTRACT

This paper describes a computational model for the implementation of causal learning in cognitive agents. The Conscious Emotional Learning Tutoring System (CELTS) is able to provide dynamic fine-tuned assistance to users. The integration of a Causal Learning mechanism within CELTS allows CELTS to first establish, through a mix of datamining algorithms, gross user group models. CELTS then uses these models to find the cause of users' mistakes, evaluate their performance, predict their future behavior, and, through a pedagogical knowledge mechanism, decide which tutoring intervention fits best.

Cite this paper

U. Faghihi, P. Fournier-Viger, R. Nkambou and P. Poirier, "Identifying Causes Helps a Tutoring System to Better Adapt to Learners during Training Sessions," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 3, 2011, pp. 139-154. doi: 10.4236/jilsa.2011.33016.

References

[1] D. Dubois, P. Poirier and R. Nkambou, “What Does Consciousness Bring to CTS?” Springer, Berlin, 2007, pp. 803-806.
[2] U. Faghihi, P. Poirier, P. Fournier-Viger and R. Nkambou, “Human-Like Learning in a Conscious Agent,” Journal of Experimental & Theoretical Artificial Intelli- gence, in Press.
[3] U. Faghihi, P. Poirier, D. Dubois and M. Gaha, “A New Emotional Architecture for Cognitive Tutoring Agents,” Proceedings FLAIRS Conference, Coconut Grove, 2008, pp. 445-446.
[4] U. Faghihi, P. Poirier, D. Dubois, M. Gaha and R. Nkambou, “How Emotional Mechanism Learn and Helps Other Types of Learning in a Cognitive Agent,” IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2008), IEEE Computer Society Press, Washington, 2008.
[5] D. Purves, E. Brannon, R. Cabeza, S. A. Huettel, K. La-Bar, M. Platt and M. Woldorff, “Principles of Cognitive Neuroscience,” Sinauer Associates, Sunderland, 2008.
[6] C. B. Martin and M. Deutscher, “Remembering,” Philosophical Review, Vol. 75, No. 2, 1966, pp. 161-196. doi:10.2307/2183082
[7] S. Shoemaker, “Persons and Their Pasts,” American Philosophical Quarterly, Vol. 7, No. 4, 1970, pp. 269-285.
[8] J. Perner, “Memory and Theory of Mind,” In: E. Tulving and F. I. M. Craik, Eds., The Oxford Handbook of Memory, Oxford University Press, Oxford, 2000, pp. 297-312.
[9] S. Bernecker, “The Metaphysics of Memory,” Springer, Berlin, 2008. doi:10.1007/978-1-4020-8220-7
[10] A. Maldonado, A. Catena, J. C. Perales and A. Cándido, “Cognitive Biases in Human Causal Learning,” 2007.
[11] P. Maes, “How to Do the Right Thing,” Connection Science, Vol. 1, No. 3, 1989, pp. 291-323. doi:10.1080/09540098908915643
[12] D. A. Lagnado, M. R. Waldmann, Y. Hagmayer and S. A. Sloman, “Beyond Covariation: Cues to Causal Structure,” In: A. S. Gopnik and L. Schultz, Eds., Causal Learning: Psychology, Philosophy, and Computation, Oxford University Press, Oxford, 2007, pp. 154-172.
[13] A. S. Gopnik and L. Schulz, “Causal Learning: Psychology, Philosophy and Computation,” Oxford University Press, Oxford, 2007.
[14] J. R. Anderson, “Rules of the Mind: Mahwah,” Lawrence Erlbaum Associates, New York, 1993.
[15] W. Schoppek, “Stochastic Independence between Recognition and Completion of Spatial Patterns as a Function of Causal Interpretation,” Proceedings of the 24th Annual Conference of the Cognitive Science Society, Lawrence Earlbaum Associates, Mahwah, 2002, pp. 804-809.
[16] R. Sun, “The CLARION Cognitive Architecture: Extending Cognitive Modeling to Social Simulation Cognition and Multi-Agent Interaction,” Cambridge University Press, New York, 2006.
[17] S. Hélie, “Modélisation de L'apprentissage Ascendant des Connaissances Explicites dans une Architecture Cognitive Hybride,” PHD, DIC, UQAM, Montréal, 2007.
[18] A. Gopnik, C. Glymour, D. M. Sobel, L. E. Schulz, T. Kushnir and D. Danks, “A Theory of Causal Learning in Children: Causal Maps and Bayes Nets,” Psychological Review, Vol. 111, No. 1, 2004, pp.3-32. doi:10.1037/0033-295X.111.1.3
[19] M. Braun, W. Rosenstiel and K.-D. Schubert, “Comparison of Bayesian Networks and Data Mining for Coverage Directed Verification Category Simulation-Based Verification,” High-Level Design Validation and Test Workshop, Eighth IEEE International, 2003, pp. 91-95.
[20] S. Franklin and F. G. Patterson, Jr, “The LIDA Architecture: Adding New Modes of Learning to an Intelligent, Autonomous, Software Agent,” Integrated Design and Process Technology, 2006.
[21] D. Hofstadter, R and M. Mitchell, “The Copycat Project: A Model of Mental Fluidity and Analogy-Making,” In: K. J. Holyoak and J. A. Barnden, Eds., Advances in Connectionist and Neural Computation Theory, Ablex, Norwood, 1994.
[22] D. Dubois, “Réalisation d'un Agent Doté D'une Conscience Artificielle: Application à un Système Tutorel Intelligent,” Université du Québec à Montréal, Montréal, 2007.
[23] U. Faghihi, P. Fournier-Viger, R. Nkambou, P. Poirier and A. Mayers, “How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent,” Proceedings of the 2009 IEEE Symposium on Intelligent Agents, Nashville, 2009, pp. 23-30. doi:10.1109/IA.2009.4927496
[24] R. Agrawal, T. Imielminski and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” SIGMOD Conference, 1993, pp. 207-216.
[25] L. R. Squire and E. R. Kandel, “Memory: From Mind to Molecules,” W. H. Freeman, San Francisco, 2000.
[26] V. Goel and R. J. Dolan, “Differential Involvement of Left Prefrontal Cortex in Inductive and Deductive Reasoning,” Cognition, Vol. 93, No. 3, 2004, pp. 109-121. doi:10.1016/j.cognition.2004.03.001
[27] R. Nkambou, K. Belghith and F. Kabanza, “An Approach to Intelligent Training on a Robotic Simulator Using an Innovative Path-Planner,” Proceeding of the 8th International Conference on Intelligent Tutoring Systems (ITS), LNCS, 2006, pp. 645-654.
[28] P. Fournier-Viger, U. Faghihi, R. Nkambou and E. M. Nguifo, “CMRULES: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences,” FLAIRS Conference, 2010.
[29] J. Hipp, U. Güntzer and G. Nakhaeizadeh, “Data Mining of Association Rules and the Process of Knowledge Discovery in Databases,” Industrial Conference on Data Mining, 2002, pp. 15-36.
[30] J. Deogun and L. Jiang, “Prediction Mining―An Approach to Mining Association Rules for Prediction,” Proceedings of RSFDGRC 2005 Conference, Springer-Verlag, Berlin, 2005, pp. 98-108.
[31] L. Li and J. S. Deogun, “Discovering Partial Periodic Sequential Association Rules with Time Lag in Multiple Sequences for Prediction,” Proceedings of ISMIS 2005, Springer, Berlin, LNCS 3488, 2005, pp. 332-341.
[32] P. Fournier-Viger, “Knowledge Discovery in Problem-Solving Learning Activities,” Ph.D. Thesis, University of Quebec in Montreal, 2010.
[33] M. Hegland, “The Apriori Algorithm―A Tutorial. Mathematics and Computation,” Imaging Science and Information Processing, Vol. 11, 2007, pp. 209-262. doi:10.1142/9789812709066_0006
[34] S. Franklin, “A Cognitive Theory of Everything: The LIDA Technology as an Artificial General Intelligence,” Artificial General Intelligence Research Institute (AGIRI) 2006.
[35] J. R. Anderson, D. Bothell, M. D. Byrne, S. Douglass, C. lebiere and Y. Qin, “An Integrated Theory of the Mind,” Psychological Review, Vol. 111, No. 4, 2004, pp. 1036-1060. doi:10.1037/0033-295X.111.4.1036

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