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Publications

Book Chapters

  1. Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2012). CELTS: A Cognitive Tutoring Agent with Human-Like Learning Capabilities and Emotions. In Ayala, A. P. (Ed.) Intelligent and Adaptive Educational-Learning Systems: Achievements and Trends, Springer.  
  2. Fournier-Viger, P., Nkambou, R. & Mephu Nguifo, E. (2010). Building Intelligent Tutoring Systems for Ill-Defined Domains. In Nkambou, R., Mizoguchi, R. & Bourdeau, J. (Eds.). Advances in Intelligent Tutoring Systems, Springer, p.81-101. 3 citations
  3. Fournier-Viger, P., Nkambou, R. & Mephu Nguifo, E. (2010). Learning Procedural Knowledge from User Solutions To Ill-Defined Tasks in a Simulated Robotic Manipulator. In Romero et al. (Eds.). Handbook of Educational Data Mining, CRC Press, p. 451-465.  
  4. Fournier-Viger, P., Nkambou, R., Faghihi, U. & Mephu Nguifo, E. (2009). Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies. In Cao, L. (Ed.) Data Mining and Multiagent Integration, Springer, p.77-92. 2 citations

Journal Papers

  1. Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2012). CMRules: Mining Sequential Rules Common to Several Sequences. Knowledge-based Systems, Elsevier, 25(1): 63-76.  
  2. Faghihi, U., P. Fournier-Viger & Nkambou, R. (2012). A Computational Model for Causal Learning in Cognitive Agents. Knowledge-Based Systems, Elsevier, 30, 48-56
  3. Faghihi, U., Poirier, P., Fournier-Viger, P, & Nkambou, R. (2011). Human-Like Learning in a Cognitive Agent. Journal of Experimental & Theoretical Artificial Intelligence, Taylor & Francis, 23(4): 497-528.
  4. Nkambou, R., Fournier-Viger, P. & Mephu Nguifo, E. (2011). Learning Task Models in Ill-defined Domain Using an Hybrid Knowledge Discovery Framework. Knowledge-Based Systems, Elsevier, 24(1):176-185. 4 citations
  5. Faghihi, U., Fournier-Viger, P, Nkambou, R., Poirier, P. (2011). Identifying Causes Helps a Tutoring System to Better Adapt to Learners During Training Sessions. Journal of Intelligent Learning Systems and Application, Scientific Research Publishing, 3(3): 139-154.  
  6. Fournier-Viger, P., Faghihi, U., Nkambou, R. & Mephu Nguifo, E. (2010). Exploiting Sequential Patterns Found in Users’ Solutions and Virtual Tutor Behavior to Improve Assistance in ITS. Educational Technology & Society, 13(1):12-24. 1 citation
  7. Nkambou, R, Fournier-Viger, P. & Mephu Nguifo, E. (2009). Improving the Behavior of Intelligent Tutoring Agents with Data Mining. IEEE Intelligent Systems, 24(3):46-53. 5 citations
  8. Fournier-Viger, P., Nkambou, R. & Mayers, A. (2008). Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System. IEEE Transactions on Learning Technologies, 1(1):63-74. 1 citation
  9. Fournier-Viger, P., Najjar, M., Mayers, A. & Nkambou, R. (2006). A Cognitive and Logic based Model for Building Glass-box Learning Objects. Interdisciplinary Journal of Knowledge and Learning Objects, 2:77-94. 13 citations
  10. Najjar, M., Mayers, A. & Fournier-Viger, P. (2006). A Novel Cognitive Computational Knowledge Model for Virtual Learning Environments. The WSEAS Transactions on Advances in Engineering Education. 3(4):246-255. 1 citation

Conference Papers

  1. Fournier-Viger, P., Wu, C.-W., Tseng, V. S. (2012). Mining Top-K Association Rules. Proceedings of the 25th Canadian Conf. on Artificial Intelligence (AI 2012), Springer, LNAI 7310, pp. 61-73.[source code]  
  2. Fournier-Viger, P., Wu, C.-W., Tseng, V.S., Nkambou, R. (2012). Mining Sequential Rules Common to Several Sequences with the Window Size Constraint. Proceedings of the 25th Canadian Conf. on Artificial Intelligence (AI 2012), Springer, LNAI 7310, pp.299-304 [source code]  
  3. Fournier-Viger, P., Nkambou, R., Mayers, A., Mephu Nguifo, E & Faghihi, U. (2012). Multi-Paradigm Generation of Tutoring Feedback in Robotic Arm Manipulation Training. Proceedings of the 11th Intern. Conf. on Intelligent Tutoring Systems (ITS 2012), LNCS 7315, Springer, pp.233-242.  
  4. Wu, C.-W., Fournier-Viger, P., Yu., P. S. & Tseng, V. S. (2011). Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets. Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2011). IEEE CS Press,10 pages. acceptance rate: 12 %
  5. Fournier-Viger, P. & Tseng, V. S. (2011). Mining Top-K Sequential Rules. Proceedings of the 7th Intern. Conf. on Advanced Data Mining and Applications (ADMA 2011). LNAI 7121, Springer, pp.180-194. [source code] [ppt] acceptance rate: 18 %
  6. Fournier-Viger, P., Nkambou, R. & Tseng, V. S. (2011). RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. Proceedings of the 26th Symposium on Applied Computing (ACM SAC 2011). ACM Press, pp. 954-959. [source code] [ppt]  
  7. Fournier-Viger, P., Nkambou, R., Mayers, A., Mephu Nguifo, E & Faghihi, U. (2011). An Hybrid Expertise Model to Support Tutoring Services in Robotic Arm Manipulations. Proceedings of the 10th  Mexican International Conference on Artificial Intelligence (MICAI 2011), LNAI 7094, Springer, pp. 478-489.  
  8. Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2011).A Cognitive Tutoring Agent with Episodic and Causal Learning Capabilities. Proceedings of the 15th Intern. Conf. on Artificial Intelligence and Education (AIED 2011). IOS Press, pp. 72-80.  
  9. Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2011). Implementing an Efficient Causal Learning Mechanism in a Cognitive Tutoring Agent. Proceedings of the 24th Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2011), LNAI 6704, Springer, pp.27-36.  
  10. Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2011). A Cognitive Tutoring Agent with Automatic Reasoning Capabilities. Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2011), AAAI press, pp.448-449.  
  11. Fournier-Viger, P., Faghihi, U., Nkambou, R. & Mephu Nguifo, E. (2010). CMRules: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences. Proceedings of the 23th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010). AAAI press, pp. 410-415. [source code] [ppt] 1 citation
  12. Faghihi, U., Fournier-Viger, P., Nkambou, R. & Poirier, P. (2010). The Combination of a Causal Learning and an Emotional Learning Mechanism for an Improved Cognitive Tutoring Agent. Proceedings of the 23th Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2010), LNAI 6097, Springer, pp. 438-449.  
  13. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. & Mayers, A.(2010). ITS in Ill-defined Domains: Toward Hybrid Approaches. Proceedings of the 10th Intern. Conf. on Intelligent Tutoring Systems (ITS 2010), LNCS 6095, Springer, pp. 749-751. 2 citations
  14. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. (2009). Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains. Proceedings of the 14th Intern. Conf. on Artificial Intelligence and Education (AIED 2009). IOS Press. pp. 383-390.  
  15. Faghihi, U., Fournier-Viger, P., Nkambou, R. & Poirier, P. (2009). A Generic Episodic Learning Model Implemented in a Cognitive Agent by Means of Temporal Pattern Mining. Proceedings of the 22nd Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2009), LNAI 5579, Springer, pp. 545-555.  
  16. Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. & Faghihi, U. (2009). Building Agents that Learn by Observing other Agents Performing a Task - A Sequential Pattern Mining Approach. Proceedings of the 22nd Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2009), SCI 214, Springer, pp. 279-284.  
  17. Faghihi, U., Fournier-Viger, P., Nkambou, R., Poirier, P. & Mayers, A. (2009). How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent. Proceedings of the 2009 IEEE Symposium on Intelligent Agents, pp. 23-30. 4 citations
  18. Faghihi, U., Nkambou, R, Poirier, P., & Fournier-Viger, P. (2009). Emotional Learning and a Combined Centralist-Peripheralist Based Architecture for a More Efficient Cognitive Agent. Proceedings of the 7th IEEE International Conference on Industrial Technology (ICIT 2009), 6 pages.  
  19. Fournier-Viger, P., Nkambou, R & Mephu Nguifo, E. (2008), A Knowledge Discovery Framework for Learning Task Models  from User Interactions in Intelligent Tutoring Systems. Proceedings of the 7th  Mexican International Conference on Artificial Intelligence (MICAI 2008). LNAI 5317,  Springer, pp. 765-778. *Best Paper Award*
11 citations
  20. Fournier-Viger, P., Nkambou, R. & Mayers, A. (2008). A Framework for Evaluating Semantic Knowledge in Problem-Solving-Based Intelligent Tutoring Systems. Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2008). AAAI press, pp. 409-414.  
  21. Nkambou, R, Mephu Nguifo, E. & Fournier-Viger, P. (2008). Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain. Proceedings of the 9th International Conference on Intelligent Tutoring Systems (ITS 2008). LNCS 5091,  Springer, pp. 395-405.  *In top 5*
6 citations
  22. Fournier-Viger, P., Nkambou, R. & Mayers, A. (2008). Evaluating Spatial Knowledge through Problem-Solving in Virtual Learning Environments. Proceedings of the Third European Conference on Technology Enhanced Learning (EC-TEL 2008). LNCS 5192,  Springer, pp 15-26. *In top 14*
  23. Fournier-Viger, P., Nkambou, R & Mephu Nguifo, E. (2008). A Data Mining Framework for the Acquisition of Procedural Knowledge in Intelligent Tutoring Systems. Proceedings of the 16th International Conference on Computers in Education (ICCE 2008). pp. 153-157.  
  24. Nkambou, R., Mephu Nguifo, E., Couturier, O. & Fournier-Viger, P. (2007). Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System. Proceedings of the 19th Canadian Conference on Artificial Intelligence (AI'2007), LNAI 3501, Springer, pp: 393-404. acceptance rate: 17%
2 citations
  25. Fournier-Viger, P., Nkambou, R., Mayers, A. & Dubois, D. (2007). Automatic Evaluation of Spatial Representations for Complex Robotic Arms Manipulations. Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies (ICALT 2007),  pp: 279-281. 1 citation
  26. Nkambou R., Mephu Nguifo, E., Couturier, O. & Fournier-Viger, P. (2007). A Framework for Problem-Solving Knowledge Mining from Users’ Actions. Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED 2007). pp: 623-625.    
  27. Najjar, M., Fournier-Viger, P., Lebeau, J.-F. & Mayers., A. (2006). Recalling Recollections according to Temporal Contexts - Applying a Novel Cognitive Knowledge Representation Approach. Proceedings of the 5th IEEE International Conference on Cognitive Informatics (ICCI 2006), pp. 424-433. 1 citation
  28. Fournier-Viger P., Najjar, M., Mayers, A. & Nkambou, R. (2006). From Black-box Learning Objects to Glass-Box Learning Objects. Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). LNCS 4053,  Springer,  pp: 258-267. 4 citations
  29. Najjar, M., Fournier-Viger, P., Mayers, A. & Bouchard, F. (2005). Memorising Remembrances in Computational Modelling of Interrupted Activities. Proceedings of the 7th International Conference on Computational Intelligence & Natural Computing (CINC 2005). pp: 483-486.  
  30. Najjar, M., Fournier-Viger, P., Mayers, A.. & Hallé, J. (2005). DOKGETT - An Authoring Tool for Cognitive Model-based Generation of the Knowledge. Proceedings of the 5th IEEE International Conference on Advanced Learning Technologies (ICALT 2005), pp: 371-375. 1 citation
  31. Najjar, M., Fournier-Viger, P., Mayers, A. & Hallé, J. (2005). Tools and Structures for Modelling Domain/User Knowledge in Virtual Learning. Proceedings of the 16th AACE World Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA 2005). pp: 4023-4028. 1 citation
  32. Fournier-Viger, P., Najjar, M., Mayers, A. (2005). Combining the Learning Objects Paradigm with Cognitive Modelling Theories - a Novel Approach for Knowledge Engineering. Proceedings of the ITI 3rd International Conference on Information & Communication Technology (ICICT 2005). pp: 565-578.  
  33. Najjar, M., Mayers, A. & Fournier-Viger, P. (2004). Goal-based Modelling of the Learner Behaviour for Scaffolding Individualised Learning Instructions. Proceedings of the 7th International Conference on Computer and Information Technology (ICCIT 2004). pp: 255-262.  
  34. Najjar, M., Mayers, A. & Fournier-Viger, P. (2004). Representing Correct/Erroneous Knowledge during Learning : A Framework for a Novel Cognitive-based Student Model. Proceedings of the 2004 International Conference on Intelligent Knowledge Systems (IKS 2004). pp: 236-242.  

Workshop Papers

  1. Fournier-Viger, P., Nkambou, R. & Mephu Nguifo, E. (2008). A Sequential Pattern Mining Algorithm for Extracting Partial Problem Spaces from Logged User Interactions. The 3rd International Workshop on Intelligent Tutoring Systems in Ill-Defined Domain: Assessment and Feedback in Ill-Defined Domains  (in conjunction with ITS2008).  June 23-27, Montreal, Canada. 2 citations

Talks & Demonstrations

  1. Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2011), Conscious Emotional Learning Tutoring System (CELTS) : A cognitive architecture. The 6th Young Researchers Conference in Cognitive Science (Cognitio 2011): Nonhuman Minds: Animal, Artificial or Other Minds, 3-5 July, Montréal, Canada.  
  2. Nkambou, R, Fournier-Viger, P. & Dubois, D. (2008). RomanTutor, a Tutoring System for Training Astronauts on Robotic Arm Manipulations. Proceedings of the Demonstration Program of the 9th International Conference on Intelligent Tutoring Systems (ITS 2008), pp. 51-53.  
  3. Fournier-Viger, P., Dubois, D., Nkambou, R. & Mayers, A. (2006). Representing the Knowledge Handled in Robotized Arm Manipulations. The 3rd Young Researchers Conference in Cognitive Science (Cognitio 2006), Aug. 19-21, Montreal, Canada.  

French Publications

  1. Fournier-Viger, P. (2010), Un modèle hybride pour le support à l'apprentissage dans les domaines procéduraux et mal-définis. Ph.D. Thesis, University of Quebec in Montreal, Montreal, Canada, 184 pages.  
  2. Fournier-Viger, P., Mephu Nguifo, E. & Nkambou, R. (2009), Extraction de motifs séquentiels à partir de traces d'utilisation pour la construction automatique de modèles de tâches dans les systèmes tutoriels intelligents. Short paper. Journée thématique : Fouille de données séquentielles et ses applications, Université d'Orléans, Nov. 27, Orleans, France.  
  3. Fournier-Viger, P. (2005). Un modèle de représentation des connaissances à trois niveaux de sémantique pour les systèmes tutoriels intelligents. M.Sc. dissertation, University of Sherbrooke, Sherbrooke, Canada, 194 pages. 13 citations
  4. Fournier-Viger, P. & Mayers, A. (2005). Une représentation des connaissances selon trois perspectives (ontologique, psychopédagogique et objet d'apprentissage) pour les systèmes tutoriels intelligents. Poster, presented at the 4th IMSI (Intelligent Materials and Systems Institute) Colloquium, Oct. 26-27, Sherbrooke, Canada.  
  5. Fournier-Viger, P. & Mayers, A. (2003). Une méthodologie pour intégrer un contenu dynamique dans les objets d'apprentissage. Poster, presented at the 3th IMSI (Intelligent Materials and Systems Institute) Colloquium, Nov. 5-6, Sherbrooke, Canada.  

Other information

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