Resultats

Cette sous-section présente les résultats des expériences des membres de l’équipe au fur et à mesure que ceux-ci sont produits. Ces résultats sont aussi organisés et classés en fonction des thèmes de recherche de la subvention. La coordination des membres permet ce travail de concert avec tous les étudiants en mettant régulièrement le site à jour suite à la transmission de rapports de progrès par les membres de chaque équipe.

Le projet Intelligence émotionnelle dans l’apprentissage visait une programmation de recherche s’articulant autour de trois thèmes :

  • T1 : l’expression et la reconnaissance des émotions
  • T2 : la capacité de générer des émotions
  • T3 : les composantes du modèle émotionnel de l’apprenant.

Globalement les travaux ont fait l’objet de 144 publications et ont conduit à la formation de 45 étudiants, 22  PhD, 17 MSc et 6 Postdoc

MSc : Kiared Abou Sofiane (2006), Guillaume Asselin (2012), Patrick Hohmeyer (2006), Moez Mabrouk (2006), Adebola Razaki (2007), Usef Faghihi (2007), lIusca Lopez de Menezes (2008), Mohamed Gaha (2008), Antoine Tsimi (2008), Khadija Benadada (2009), Alina Cioboiu (2009), Ramla Ghali (2010), Ilian Cruz-Panesso (2010), Amine Trabelsi (2010), Eric Poitras (2010), Jean-François Quintal (2010), Aurelie Jonquet (2013)

PhD : Emmanuel Blanchard (2007),  Daniel Dubois (2007), Carlos Nakamura (2007), Jingyan Lu (2007), Lucy Cumyn (2008), Soumaya Chaffar (2009), Andrew Chiarella (2009), Pierre Chalfoun (2012), Francois Courtemanche (2013), Lotfi Derbali (2013), Genevieve Gauthier (2009), Salman  Mufti (2009), Fethi Guerdelli (2011), Alicia Heraz (2010), Hicham Hage (2010), Imene Jraidi (2013), Philippe Fournier-Viger (2010), Usef Faghihi (2010), Genevieve Chaput (2011), Gregory Petit (2013), Sandrine Prom (2014), Amal Zouaq (2008)

Post-doc : Delia Rogozan (2010), Amine Rouane-Hacene (2013), Emmanuel Blanchard (2010), Alicia Heraz (2010), Daniel Dubois (2010),  Fethi Guerdelli (2013)

Liste des publications

Références produites par l’équipe (depuis mai 2006)

Abozeid, A., Abdel Razek, M., Frasson, C. (2012), Towards Social Mobile Blended Learning. Poster. The 11th International Conference On Intelligent Tutoring Systems (ITS 2012). Chania, Greece. June 14-18, 2012.

Belghith, K., Nkambou, R., Kabanza, F., Hartman, L. (2012). An Intelligent Simulator for Tele-robotics Training. IEEE Transactions on Learning Technologies (TLT), 5(1): 11-19.  

Benadada, K., Chaffar, S., Frasson, C. (2008a): Towards Selection of Tutorial Actions Using Emotional Physiological Data. Proceedings of the Emotional and Cognitive Issues in ITS (WECITS'2008) workshop. Montréal, Canada, June 2008.

Benadada, K., Chaffar, S., Frasson, C. (2008b): Using Tutorial Actions to Improve the Learner’s Emotional State. AACE World Conference on E-learning in Corporate, Government, Healthcare, & Higher Education: E-LEARN 2008, Las Vegas, Nevada, November 2008.

Blanchard, E., Frasson, C. (2006a). Faciliter la création d'environnements virtuels d'apprentissage s'inspirant des jeux vidéo. TICE'2006, Toulouse, France

Blanchard, E., Frasson, C. (2006b). Motivation and Evolutionary Pedagogical Agents. Workshop on Motivational and Affective Issues in ITS. Held in conjunction with the International Conference on Intelligent Tutoring System (ITS’2006), Jhongli, Taiwan.

Blanchard, E., Frasson, C. (2006c). Easy Creation of Game-Like Virtual Learning Environments. Workshop on Teaching with Agents, Robots, and NLP. Held in conjunction with the International Conference on Intelligent Tutoring System (ITS’2006), Jhongli , Taiwan.

Blanchard, E. G., Frasson, C. (2007a). Un système tutoriel intelligent inspiré des jeux vidéo pour améliorer la motivation des apprenants, Science et Technologies de l'Information et de la Communication pour l'Éducation et la Formation (STICEF), special tracks on emotional dimensions of interactions in e-Learning.

Blanchard, E., Frasson, C. (2007b). Cross-cultural adaptation in eLearning. In T. Kidd and H. Song (Eds). Handbook of Research on Instructional Systems & Technology, Hershey, PA: Idea Group Publishing, Inc.

Blanchard, E., Chalfoun, P., Frasson, C. (2007). Towards advanced Learner Modeling: discussions on quasi real-time adaptation with physiological data. 7th IEEE conference on Advanced Learning Technologies: ICALT 2007, Niigata, Japan.

Blanchard, E. G., Mizoguchi, R. (2008). Designing Culturally-Aware Tutoring Systems : Towards an Upper Ontology of Culture, 1st Workshop on Culturally-Aware Tutoring Systems (CATS2008), held in conjunction with International Conference on Intelligent Tutoring Systems (ITS2008), Montréal, Canada.

Blanchard, E. G., Volfson, B., Hong, Y.-J., Lajoie, S. P. (2009). Affective AIED: From detection to adaptation, 14th International Conference on Artificial Intelligence in Education (AIED2009), Brighton, UK.

Blanchard, E. G., Roy, M., Lajoie, S. P., Frasson, C. (2009). An evaluation of sociocultural data for predicting attitudinal tendencies, 14th International Conference on Artificial Intelligence in Education (AIED2009), Brighton, UK.

Blanchard, E. G., Mizoguchi, R., Lajoie, S. P. (2009a). Addressing the interplay of Culture and Affect in HCI: An Ontological Approach, 13th International Conference on Human-Computer Interaction (HCII2009), San Diego, USA., to appear in Springer LNCS/LNAI series.

Blanchard, E. G., Mizoguchi, R., Lajoie, S. P. (2009b). Promoting Cultural Awareness in Information Systems, COGNITIO 2009 - Changing Minds: Cultures and Cognition in Evolution, Montreal, Canada.

Blanchard, E. G., Lajoie, S. P. (2009). Learner-Concerned AIED Systems: Affective Implications when Promoting Cultural Awareness , 2nd Workshop on Culturally-Aware Tutoring Systems (CATS2009), held in conjunction with the International Conference on Artificial Intelligence in Education (AIED2009), Brighton, UK.

Blanchard, E. G., Roy, M., Lajoie, S. P., Frasson, C. (2009b). Critères socioculturels pour la modélisation d'apprenants: une analyse de pertinence, Conférence sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH2009), Le Mans, France.

Blanchard, E. G., Naismith, L., Ranellucci, J., Lajoie, S. P. (2009). EAGLE: Un Système Tutoriel Intelligent pour réguler l'apprentissage interne à un jeu vidéo, Conférence sur les Environnements Informatiques pour l'Apprentissage Humain (EIAH2009), Le Mans, France.

Blanchard, E. G. (2009). Adaptation-Oriented Culturally-Aware Tutoring Systems: When Adaptive Instructional Technologies Meet Intercultural Education, In H. Song and T. Kidd (eds), Handbook of Research on Human Performance and Instructional Technology, Hershey: PA, Information Science Reference

Blanchard, E. G., Mizoguchi, R., Lajoie, S. P. (2010). Structuring the cultural domain with an upper ontology of culture. In E. G. Blanchard, D. Allard (Eds.), Handbook of research on culturally-aware information technology: Perspectives and models. Hershey, PA: IGI Global. 598 pages.

 

Blanchard, E. G., Frasson, C., Lajoie, S. P. (2011). Learning with Games. Encyclopedia of the Sciences of Learning, Springer Verlag, May 2011.

 

Courtemanche, F., Aïmeur, E., Dufresne, A., Najjar, M., & Mpondo, F. (2011). Activity Recognition using Eye-gaze Movements and Traditional Interactions. Interacting with Computers, 23(3), 202-213.

Courtemanche, F., Dufresne, A., & Aimeur, E. (2012). Un Outil Diagnostic de l’Interaction: Intégration de l’oculométrie et des signaux physiologiques. Paper Accepted at Ergo'IHM 2012.

Chaffar, S., Chalfoun, P., Frasson, C. (2006). La prédiction de la réaction émotionnelle dans un environnement d'évaluation en ligne. TICE'2006, Toulouse, France.

Chaffar, S., Frasson, C. (2006). Predicting Learner's Emotional Response in Intelligent Distance Learning Systems. 19th International FLAIRS Conference, AAAI Press, Melbourne, FL, USA.

Chaffar, S., Frasson, C. (2007a). Apprentissage machine pour la prédiction de la réaction émotionnelle de l’apprenant. Revue des Sciences et Technologies de l'Information et de la Communication pour l'Éducation et la Formation (STICEF), numéro spécial sur les dimensions émotionnelles de l’interaction en EIAH., 14.

Chaffar, S., Frasson, C. (2007b). Towards Emotion Prediction in e-Learning Systems. The International Conference on Computing & e-Systems : Hammamet , Tunisia, March 12-15, 2007.

Chaffar, S., Cepeda, G., Frasson, C. (2007). Predicting the Learner's Emotional Reaction towards the Tutor's Intervention. 7th IEEE conference on Advanced Learning Technologies: ICALT 2007, Niigata, Japan, July 2007.

Chaffar, S., (2009) Modélisation des réactions émotionnelles dans un Système Tutoriel Intelligent. Thèse, Université de Montréal, Avril 2009.
 

Chaffar, S., Derbali, L., Frasson, C. (2009a). Inducing positive emotional state in Intelligent Tutoring Systems. AIED'2009: 14th International Conference on Artificial Intelligence in Education, IOS Press, Brighton, UK, July 2009.

Chaffar, S., Derbali, L., Frasson, C. (2009b). Towards Emotional Regulation in Intelligent Tutoring Systems. AACE World Conference on E-learning in Corporate, Government, Healthcare, & Higher Education : E-LEARN 2009, Vancouver, Canada, October 2009.

Chaffar, S., Frasson, C. (2010). Using Emotional Coping Strategies in Intelligent Tutoring Systems. Short paper, 10th International Conference on Intelligent Tutoring Systems (ITS), Springer Verlag, Pittsburgh, Pennsylvania, USA, June 14-18.

Chaffar, S., Frasson, C. (2011). Affective Dimensions of Learning. Encyclopedia of the Sciences of Learning, Springer Verlag, May 2011.

Chalfoun, P., Chaffar, S., Frasson, C. (2006). Predicting the Emotional Reaction of the Learner with a Machine Learning Technique. Workshop on Motivational and Affective Issues in ITS. Held in conjunction with the International Conference on Intelligent Tutoring System (ITS’2006), Jhongli , Taiwan.

Chalfoun, P., Ilusca, L., Frasson, C. (2007). Emotional Retention Agent for Foreign Language E-Learning. AACE World Conference on E-learning in Corporate, Government, Healthcare, & Higher Education : E-LEARN 2007, Quebec City, Canada, October 15-19 2007.

Chalfoun, P., Frasson, C. (2008a). Subliminal Priming Enhances Learning and Performance in a Distant 3D Virtual Intelligent Tutoring System. AACE World Conference on E-learning in Corporate, Government, Healthcare, & Higher Education : E-LEARN 2008, Las Vegas Nevada, USA, November 17-21 2008.

Chalfoun, P., Frasson, C. (2008b). Positive emotional impact and increase in performance observed in a 3D subliminal teaching ITS. Workshop on Emotional and Cognitive Issues in ITS. International Conference on Intelligent Tutoring System (ITS), Montreal, Canada , June 2008.

Chalfoun, P., Frasson, C. (2008c). Subliminal priming enhances learning in a distant virtual 3D Intelligent Tutoring System. IEEE Multidisciplinary Engineering Education Magazine : Special Issue on Intelligent Tutoring Systems, Vol. 3, No. 4, pp 125-130. December 2008.

Chalfoun, P., Frasson, C. (2009), Optimal Affective Conditions for Subconscious Learning in a 3D Intelligent Tutoring System. 13th International Conference on Human-Computer Interaction (HCII 2009), San Diego, CA, USA. July 19-24 2009.

Chalfoun, P., Frasson, C. (2010). Showing the Positive Influence of Subliminal Cues on Learner’s Performance and Intuition: an ERP Study. Short paper, 10th International Conference on Intelligent Tutoring Systems (ITS), Pittsburgh, Pennsylvania, USA, June 14-18.

 

Chalfoun, P., Frasson, C. (2011). Subliminal cues while teaching: HCI technique for enhanced learning. Advances in Human Computer Interaction: Special Issue on Subliminal Communication in Human-Computer Interaction, Hindawi publishing corporation, Vol 2011, Article ID 968753, 15 pages.

 

Chalfoun, P., Frasson, C. (2012). Cognitive priming: assessing the use of non-conscious perception to enhance learner's reasoning ability. Short paper (acceptance rate 28%), The 11th International Conference On Intelligent Tutoring Systems (ITS 2012). Chania, Greece. June 14-18, 2012.

Chaouachi, M., Jraidi, I., Heraz, A., Frasson, C. (2009). Influence of Dominant Electrical Brainwaves on Learning Performance. World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education. Vancouver, Canada.

Chaouachi, M., Chalfoun, P., Jraidi, I., Frasson, C. (2010). Affect and Mental Engagement: Towards Adaptability to Intelligent Systems. The 23th International FLAIRS Conference, AAAI Press, Daytona Beach, Florida, USA, May 19-21.

Chaouachi, M., Frasson, C. (2010). Exploring the Relationship between Learners EEG mental Engagement and Affect. Short paper, 10th International Conference on Intelligent Tutoring Systems, Springer Verlag, Pittsburgh, Pennsylvania, USA, June 14-18.

Chaouachi, M., Jraidi, I., Frasson, C. (2011), Modeling Mental Workload Using EEG Features for  Intelligent Systems. User Modeling, Adaptation and Personalization, UMAP 2011, Girona, Spain, July 2011.

Chaouachi, M., Frasson, C. (2011), Automatic Detection of User’s Uncertainty in Problem Solving Task: A Multimodal Approach. The 24th International FLAIRS Conference, AAAI Press, Palm Beach, FL, USA, May 18-20, 2011.

Chaouachi, M., Frasson, C. (2012), Mental Workload, Engagement and Emotions : an Exploratory Study for Intelligent Tutoring Systems. Short paper (acceptance rate 28%). The 11th International Conference On Intelligent Tutoring Systems (ITS 2012). Chania, Greece. June 14-18, 2012.

Chiarella, A., Lajoie, S. P. (2010a). A self-organizing systems approach to history-enriched digital objects. In D. Ifenthaler, P. Pirnay-Dummer, & N.M. Seel (Eds.). Computer-Based Diagnostics and Systematic Analysis of Knowledge, New York : Springer, pp. 131-158.

Chiarella, A., Lajoie, S.P. (2010b). Social annotation : Emergent text signals through self-organization. AAACE-Ed-Media World Conference on Educational Multimedia, Hypermedia & Telecommunications. Lisbon, Portugal, June 27 – July 1.

Derbali, L., Frasson, C. (2010). Prediction of Players' Motivational States using Electrophysiological Measures during Serious Game Play. 10th IEEE International Conference on Advanced Learning Technologies (ICALT), Sousse, Tunisia: July 5-7.

Derbali, L., Chalfoun, P., Frasson, C. (2011a), A Theoretical and Empirical Approach in Assessing Motivational Factors: From Serious Games to an ITS. Short paper. The 24th International FLAIRS Conference, AAAI Press, Palm Beach, FL, USA, May 18-20, 2011.

Derbali, L., Chalfoun, P., Frasson, C. (2011b), Assessment of Learners’ Attention while Overcoming Errors and Obstacles: an Empirical Study. AIED'2011: 15th International Conference on Artificial Intelligence in Education, LNCS, Auckland, Australia, July 2011.

Derbali, L., Frasson, C. (2011), Physiological Evaluation of Attention Getting Strategies during Serious Games Play. AIED'2011: 15th International Conference on Artificial Intelligence in Education, LNCS, Auckland, Australia, July 2011.

Derbali, L., Frasson, C. (2012), Exploring the Effects of Prior Video-Game Experience on Learner’s Motivation during Interactions with HeapMotiv. Short paper (acceptance rate 28%). The 11th International Conference On Intelligent Tutoring Systems (ITS 2012). Chania, Greece. June 14-18, 2012.

Derbali, L., Frasson, C. (2012), Assessment of Learners’ Motivation during Interactions with Serious Games: a Study of some Motivational Strategies in Food-Force. Journal of Advances in Human-Computer Interaction, (Submitted: 2012).

Derbali, L., Ghali, R., Frasson, C. Assessing motivational strategies in Serious Games using Hidden Markov Models. FLAIRS 2013, The 26th International FLAIRS Conference, AAAI Press, May 22-24, St. Pete Beach, Florida, USA, 2013.

Dubois, D., Nkambou, R., Hohmeyer, P. (2006). How ‘Consciousness’ Allows a Cognitive Tutoring Agent Make Good Diagnosis During Astronauts’ Training. 8th International Conference on Intelligent Tutoring Systems (ITS’2006). LNCS No 4053, Springer-Verlag, Berlin, pp. 154-163.

Dubois, D., Gaha, M., Nkambou, R. Poirier, P. (2008). Cognitive Tutoring System with ‘Consciousness’.   LNCS No 5091. ITS’2008, pp. 803-806, Springer.

Faghihi, U., Dubois, D., Nkambou, R. (2007a). Learning Mechanisms for a Tutoring Cognitive Agent. Proccedings of the 7th IEEE International Conference on Advanced Learning Technologies (ICALT 2007), pp. 546-547.

Faghihi, U., Dubois, D., Nkambou, R. (2007b). Learning Mechanisms for a Tutoring Cognitive Agent. In C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007 (pp. 3106-3114). Chesapeake, VA: AACE.

Faghihi, U., Dubois, D., Poirier, P., Gaha, M., Nkambou, R. (2008a). A new emotional architecture for cognitive tutoring agents. FLAIRS’2 Actes de la conférence "the 21th International Florida Artificial Intelligence Research Society Conference" (FLAIRS 2008). AAAI press, 2008.

Faghihi, U., Poirier, P., Dubois, D, Gaha, M., Nkambou, R. (2008b). 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 (pp 606-611).

Faghihi, U., Poirier, P., Dubois, D., Gaha, M. and Nkambou, R. (2008c). Implementation of Emotional Learning for Cognitive Tutoring Agents. 7th Mexican International Conference on Artificial Intelligence (MICAI 2008). IEEE Computer Society press (pp 310-316).

Faghihi, U., Poirier, P., Nkambou, R. (2008d). Emotional Learning and a Combined Centralist-Peripheralist Based Architecture for a More Efficient Cognitive Agent. IEEE/ ICIT 2009 International Conference on Industrial Technology (ICIT 2009). IEEE Computer Society press (6 pages).

Faghihi, U., Poirier, P, Dubois, D., Gaha M. (2008). A New Emotional Architecture for Cognitive Tutoring Agents. FLAIRS Conference 2008, AAAI Press (pages 445-447).

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 (IAE-AIE 2009), LNAI 5579, Springer, pp. 545-555.

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 IEEE Symposium on Intelligent Agents, pp. 23-30.

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.Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2010), LNAI 6097, Springer, pp. 438-449.

Faghihi, U., Poirier, P., Fournier-Viger, P., Nkambou, R. (2011). Human-Like Learning in a Cognitive Agent. Journal of Experimental and Theoretical Artificial Intelligence, 23(4):497-528, Taylor & Francis

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 & Applications, vol 3, n3.

Faghihi, U., Fournier-Viger, P., Nkambou, R. (2011a). A Cognitive Tutoring Agent with Episodic and Causal Learning Capabilities. Proceedings of the 15th International Conference on Artificial Intelligence and Education (AIED 2011). IOS Press, pp. 72-80.

Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2011b). Implementing an Efficient Causal Learning Mechanism in a Cognitive Tutoring Agent. Proceedings of the 24th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE 2011), LNAI 6704, Springer, pp.27-36.

Faghihi, U., Fournier-Viger, P., Nkambou, R. (2011c). 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.

Faghihi, U., Fournier-Viger, P., Nkambou, R. (2012a). A Computational Model for Causal Learning in Cognitive Agents. Knowledge-Based Systems, Elsevier. Elsevier Science, 30: 48-56.

Faghihi, U., Fournier-Viger, P. & Nkambou, R. (2012b). 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.

Faremo, S., Lajoie, S., Gauthier, G., Wiseman, J. (2006). Supporting diagnostic problem solving in medical education using an integrated classroom - E-Learning model. World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education (ELEARN 2005), Vancouver, Canada.

Fournier-Viger, P., Nkambou, R., Mayers, A. (2007). Evaluating Spatial Knowledge through Problem-Solving in Virtual Learning Environments. Addendum to conference proceedings of the Second European Conference on Technology Enhanced Learning (EC-TEL 2007), pp: 14-22.

Fournier-Viger, P., Nkambou, R., Mayers, A. and D. Dubois (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.

Fournier-Viger, P., Nkambou, R., Mayers, A. (2008). A Framework for Evaluating Semantic Knowledge in Problem-Solving-Based Intelligent Tutoring Systems. Actes de la conférence "the 21th International Florida Artificial Intelligence Research Society Conference" (FLAIRS 2008). AAAI press, pp. 409-414.

Fournier-Viger, P., Nkambou, R., Faghihi, U., Mephu Nguifo, E. (2009). Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies. Dans Cao, L. (Ed.) Data Mining and Multiagent Integration, Springer, pp. 77-92.

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 International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IAE-AIE 2009), Springer.

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 International. Conference on Artificial Intelligence and Education (AIED 2009). IOS Press. pp. 383-390.

Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2010a). Exploiting Sequential Patterns Found in Users’ Solutions and Virtual Tutor Behavior to Improve Assistance in ITS. Educational Technology & Society, vol. 13, no. 1, pp. 12-24.

Fournier-Viger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2010b). 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.

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.

Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E., Mayers, A. (2010). ITS in Ill-defined Domains: Toward Hybrid Approaches. Proceedings of the 10th International Conference on Intelligent Tutoring Systems (ITS 2010), LNCS 6095, Springer, pp. 749-751.

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.

Fournier-Viger, P., Nkambou, R., Tseng, V.S. (2011). RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. Proceedings of the 26th ACM Symposium on Applied Computing (SAC 2011). ACM Press, pp. 954-959.

Fournier-Viger, P., Faghihi,  U., Nkambou, R.,  Mephu Nguifo, E.  (2012). CMRules: Mining Sequential Rules Common to Several Sequences. Knowledge-Based Systems. 25(1): 63-76. Elsevier Science.

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 International Conference on Intelligent Tutoring Systems (ITS 2012), LNCS 7315, Springer, pp.233-242.

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 Conference on Artificial Intelligence (AI 2012), Springer, LNAI 7310, pp.299-304.

Frasson, C., Chalfoun, P. (2010). Managing Learner's Affective States in Intelligent Tutoring Systems. In R. Nkambou, R. Mizoguchi, and J. Bourdeau (Eds), Advances in Intelligent Tutoring Systems. Springer. pp. 339-358.

Frasson, C., Heraz, A. (2011). Emotional Learning. Encyclopedia of the Sciences of Learning, Springer Verlag, May 2011.

Frasson, C., Blanchard, E. G. (2011). Simulation-based Learning. Encyclopedia of the Sciences of Learning, Springer Verlag, May 2011.

Gaha, M., Dubois, D., Nkambou, R. (2008b). Proposition d'un traitement émotionnel pour un STI "conscient".  Revue des Sciences et Technologies de l’Information et de la Communication pour la Formation (STICEF),  Vol. 14. Pp. 239-264

Gaha, M., Faghihi, U., Nkambou, R. (2009). Cognitive Tutoring System with deliberation capabilities. FLAIRS Conference 2009, AAAI Press.

Guerdelli, F., Dufresne, A., Martial, O., Droui, M., & Vázquez-Abad, J. (2008). Un dispositif de suivi occulaire pour l'analyse de l'attention et des processus cognitifs des apprenants en physique. Paper presented at the Colloque : L'utilisation des technologies pour la recherche en éducation scientifique à la Conférence de la Société Canadienne pour l'Étude de l'Éducation (SCÉÉ), Vancouver, Canada.

Guerdelli, F., Dufresne, A., Martial, O., Droui, M., & Vázquez-Abad, J. (2010). Un dispositif de suivi occulaire pour l'analyse de l'attention et des processus cognitifs des apprenants en physique. In J. Vázquez-Abad, M. Riopel & P.

Ghali, R., Frasson, C. (2010). Emotional Strategies for Vocabulary Learning. 10th IEEE International Conference on Advanced Learning Technologies (ICALT), Sousse, Tunisia: July 5-7.

Ghali, R., Frasson, C. (2010). Agent émotionnel pour l’apprentissage d’une langue étrangère. TICE 2010, 7ème Colloque Technologies de l'Information et de la Communication pour l'Enseignement, Nancy, 6-8 Décembre.

Hage, H., Aïmeur, E. (2009). The impact of privacy on learners in the context of a web-based test. 14th International Conference on Artificial Intelligence in Education (AIED 2009), Brighton, July 2009.

Hage, H., Aïmeur, E. (2010). Preserving Learners' Privacy in Advances in Intelligent Tutoring Systems by Riichiro Mizoguchi, Jacqueline Bourdeau and Roger Nkambou., Springer Verlag, pp. 465-482.

Hage, H., Aïmeur, E. (2010). E-learning for the new generations, a Web2.0 approach, E-Learning Book Marian Buzzi Editor, pp. 1-18.

Heraz, A., Razaki, R., Frasson, C. (2007). Using machine learning to predict learner emotional state from brainwaves. ICALT 2007: 853-857

Heraz, A., Frasson, C. (2008a). Predicting the Three Major Dimensions of the Learners' Emotions from Brainwaves. International Journal of Computer Science. Volume 2, number 3, pp. 187-193,

Heraz, A., Daouda, T. and Frasson, C. (2008). Decision Tree for Tracking Learner’s Emotional State predicted from his electrical brain activity. Proceeding in the 9th International Conference on Intelligence Tutoring System. Montréal, June 23rd.

Heraz, A., Frasson, C. (2008b). Detecting the Affective Model of Interplay between Emotions and Learning by Measuring Learner’s Brainwaves. Workshop on Emotional and Cognitive Issues in ITS (WECITS'2008) in the 9th International Conference on Intelligence Tutoring System. Montreal, June 23rd

Heraz, A., Frasson, C. (2009a). How Do Emotions Induce Dominant Learners' Mental States Predicted from Their Brainwaves? Agent and Artificial Intelligence (from ICAART 2009), Springer Verlag.

Heraz, A., Frasson, C. (2009b). Predicting Learner Answers Correctness through Brainwaves Assesment and Emotional Dimensions. Workshop in the 14th International Conference on Artificial Intelligence in Education. IOS Press, Brighton, UK

Heraz, A., Frasson, C. (2009c). Detecting Guessed and Random Learners Answers through their Brainwaves. Seventeenth International Conference on User Modeling, Adaptation and Personalization.  UMAP 2009, Springer Verlag LNCS, Trento, Italy

Heraz, A., Frasson, C. (2009d). How Do Emotional Stimuli Influence the Learners' Brain Activity? Tracking the brainwave frequency bands Amplitudes. First International Conference on Agents and Artificial Intelligence ICAART 2009, Porto, Portugal.

Jraidi, I, Heraz, A., Chaouachi, M., Frasson, C. (2009). New Architecture of a Multi Agent System which Measures the Learner Brainwaves to Predict his Stress Level Variation. World Conference on E-Learning in Corporate, Government, Healthcare and Higher Education, Vancouver, Canada

Jraidi, I., Heraz, A., Frasson, C. (2009). Predicting Stress Level Variation from Learner Characteristics and Brainwaves. Workshop in the 14th International Conference on Artificial Intelligence in Education.

Jraidi, I., Chaouachi, M., Frasson, C. (2010). Enhancing Learner Self Esteem for Learning Improvement. Short paper. The 23th International FLAIRS Conference, AAAI Press, Daytona Beach, Florida, USA, May 19-21.

Jraidi, I., Frasson, C. (2010). Subliminally Enhancing Self-Esteem: Impact on Learner Performance and Affective State. 10th International Conference on Intelligent Tutoring Systems (ITS), Springer Verlag, Pittsburgh, Pennsylvania, USA, June 14-18.

Jraidi, I., Chalfoun, P., Frasson, C. (2012). Implicit Strategies for Intelligent Tutoring Systems. The 11th International Conference On Intelligent Tutoring Systems (ITS 2012). Chania, Greece. June 14-18, 2012 (Acceptance rate 16%).

Jraidi, I., Frasson, C. (2012). Student’s Uncertainty Modeling through a Multimodal Sensor based Approach. Journal of Educational Technology and Society (In press, 2012).

Jraidi, I., Chaouachi, M., Frasson, C., A dynamic Multimodal Approach for Assessing learner's Interaction Experience. 15th ACM International Conference on Multimodal Interaction (ICMI2013). Sydney, Australia, December 9-13, 2013.

Lajoie, S. P. (2007a). Aligning theories with technology innovations in education. British Journal of Educational Psychology - Monograph Series II (5), Learning through digital technologies, 27-38.

Lajoie, S. P. (2007b). Developing computer based learning environments based on complex performance models. In B. Shuart, W. Spaulding and J. Poland (Eds.), Nebraska Symposium on Motivation. Modeling complex systems: Vol. 52 (pp. 123-144). Lincoln: University of Nebraska Press.

Lajoie, S. P., Gauthier, G., Lu, J. (2009). Convergence of data sources in the analysis of complex learning environments. Research and Practice in Technology Enhanced Learning, vol 4, no 3, pp. 195–219.

Lajoie, S. P. (2009a). Developing professional expertise with a cognitive apprenticeship model:  Examples from avionics and medicine. In K. A. Ericsson (Ed.), Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments, New York: Cambridge University Press, pp. 61-83.

Lajoie, S.P. (2009b). Can Computers Teach You To Think And Care? Revisiting The Modeling Debates With An Eye To The Future. International Artificial Intelligence and Education Conference. Brighton, UK, July 6-10.

Lajoie, S. P. (2009c). Cognitive tools for facilitating meaningful interactions in complex domains. The annual meeting of the American Educational Research Association, San Diego, CA, April.

Lajoie, S. P. (2009d). Assessing Professional Competence. Faculty of Education, University of Wollongong, November.

Lajoie, S. P. (2010a). Aptitude Treatment Interactions revisited: Implications for designing technologies to increase strategy awareness and spatial skills. National Science Foundation Conference on Sources of Individual Differences in Spatial Skills: The Interaction between the Learner and the Learning Environment. Harvard University, Cambridge, MA, May.

Lajoie, S.P. (2010b). Factors that impact student engagement and learning behaviors in interactive learning environments. Discussion at the Annual meeting of the American Educational Research Conference, Denver, April.

Lajoie, S. P., Lu, J. (2012). Supporting collaboration with technology : Does shared cognition lead to co-regulation in medicine. Metacognition and Learning, vol.7, n.1, Springer.

Lopes de Menezes, I., Frasson, C. (2008). Taking into Consideration Emotional Changes in Business. The International Conference on Computing & e-Systems, Hammamet , Tunisia , March 16-19, 2008.

Lopes de Menezes, I. Frasson, C. (2008). Using an Emotional Intelligent Agent to Reduce Resistance to Change. International Conference of Intelligent Tutoring System (ITS'2008), Montréal, Canada, June 2008. Springer Verlag LNCS 5091.

Lu, J., Lajoie, S.P., Wiseman, J. (2010). Scaffolding problem based learning with CSCL Tools. International Journal of Computer Supported Collaborative Learning, vol 5, no 3, pp. 283-299.

Lustigová,  Z., Dufresne, A., Courtemanche, F., Guerdelli, F. (2009). Mining physiological data for automated educational feedback: new attitude to learning in virtual environments. ICETA 2009 conference proceedings, Elfa, Košice, 2009, p. 116-122, ISBN:978-80-8086-089-9

Lustigová, Z., Dufresne, A., Guerdelli, F. (2009). Mining physiological data for automated educational feedback. ICL 2009 Proceedings, Carinthia tech Institut, Villach, Austria, 2009, p. 159-166, ISBN:978-3-89958-481-3

Lustigova, Z., Dufresne, A., Courtemanche, F., Malach, J., & Malcik, M. (2010). Acquiring physiological data for automated educational feedback in virtual learning environments. The New Educational Review, 21(2), 97-110.

Lustigova, Zdena, Dufresne, Aude (2012). Assessing Affective Reactions in E-learning and E-commerce Applications, ICL/IGIP2012 Interactive Collaborative Learning, Conference, Villach, Austria.

Mpondo, F., Courtemanche, F., Aïmeur, E. (2010). Cultural Adaptation of Pedagogical Resources within Intelligent Tutorial Systems, Poster. Tenth International Conference on Intelligent Tutoring Systems, Pittsburgh.

Naismith, L., Lajoie, S.P. (2010). Using Expert Models to Provide Feedback on Clinical Reasoning Skills. 10th International Conference on Intelligent Tutoring Systems (ITS), Springer Verlag, Pittsburgh, Pennsylvania, USA, June 14-18.

Nkambou, R. (2006). Managing Student Emotions in Intelligent Tutoring Systems. Proceedings of the Florida Artificial Intelligence Research Society (FLAIRS’2006), pp. 389-394. AAAI press

Nkambou, R., Mephu Nguifo, E., Dubois, D. (2008). RomanTutor, a Tutoring System for Training Astronauts on Robotic Arm Manipulations. Actes du programme des démonstrations de la conférence "the 9th International Conference on Intelligent Tutoring Systems" (ITS 2008), pp. 49-51.

Nkambou, R., Mephu Nguifo, E., Fournier-Viger, P. (2008a). Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain. Actes de la conférence "the 9th International Conference on Intelligent Tutoring Systems" (ITS 2008). LNCS 5091,  Springer, pp. 395-405. Best paper award nomination.

Nkambou, R., Delozanne, E., Frasson, C. (2008). Les dimensions émotionnelles de l’interaction dans un EIAH. Revue des Sciences et Technologies de l’Information et de la Communication pour la Formation (STICEF),  Vol. 14. Pp. 205-216.

Nkambou, R., Fournier-Viger, P., Mephu Nguifo, E. (2009). Improving the Behavior of Intelligent Tutoring Agents with Data Mining. IEEE Intelligent Systems, vol 24, no 3, pp 46-53.

Nkambou, R., Bourdeau, J., Mizoguchi, R. (2010). Advances in Intelligent Tutoring Systems. Springer Verlag, 1st edition, 525 pages.

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 Science, vol.  24, no 1.

Poitras, E., Lajoie, S. P., Hong, Y. (2011). The design of technology-rich learning environments as metacognitive tools in history education. Instructional Science, Springer.

Rouane Hacene, M., Valtchev, P., Nkambou, R. (2011). Supporting ontology design through large-scale FCA-based ontology restructuring. Proc. of the 19th International Conference on Conceptual Structures (ICCS 2011), LNCS No. 6828, pp. 257-269

Razaki, R., Blanchard, E., Frasson, C. (2006). On the Definition and Management of Cultural Group of e-Learners. Poster. International Conference on Intelligent Tutoring System (ITS), Jhongli, Taiwan, June 2006.

Razek, M., Chaffar, S., Frasson, C. (2006). Using Machine-learning technique to recognize Emotions for On-Line Learning Systems. In Maja Pivec. Affective and Emotional Aspects of Human-Computer Interaction - Game-Based and Innovative Learning Approaches. Netherlands: IOS press, june, 255-265.

Trabelsi, A., Frasson, C. (2010). The Emotional Machine: a Machine Learning Approach to Online Prediction of User’s Emotion and Intensity. 10th IEEE International Conference on Advanced Learning Technologies (ICALT), Sousse, Tunisia: July 5-7.

 

Cliquer sur chaque thème ici-bas (ou sur le + situé à droite de celui-ci) exposera les résultats associés au thème choisi.

T1 : l'expression et la reconnaissance des émotions


Le premier thème comportait lui-même trois sous-thèmes comme la reconnaissance des émotions au moyen de signaux physiques, la mesure de l’intensité des phénomènes émotionnels et l’importance de la culture dans l’expression et la reconnaissance des émotions.

En ce qui concerne la détection et mesure  des émotions au moyen de signaux physiologiques plusieurs résultats ont été obtenus soit par des mesures de signaux physiologiques, soit par des questionnaires posés aux apprenants et sur lesquels nous avons appliqué des techniques d’apprentissage machine [Benadada et al, 2008a; Blanchard et al, 2007; Chaffar et al, 2007; Heraz et al, 2007; Razek et al, 2006].


Figure 1 - Extrait d’un arbre de décision obtenu suite à nos expériences utilisé par un agent intelligent pour prédire l’émotion ressentie par un apprenant dans une session d’apprentissage en ligne.

Figure 2 - Extrait de l’environnement MOCAS utilisé en combinaison aux signaux physiologiques et cérébraux (EEG) afin de détecter les émotions et état mentaux des apprenants dans le but d’adapter en temps quasi-réel la matière enseignée

Nous avons mis en œuvre de nouvelles techniques qui constituent une avancée remarquable car elles ouvrent la voie à de grandes possibilités de détection. Elles sont basées sur les activités électriques du cerveau qui résultent en des manifestations émotionnelles [Heraz et Frasson, 2009a; Heraz et Frasson, 2009b; Jraidi et al, 2009; Chaouachi et al, 2009; Heraz et Frasson, 2009d].

Figure 3 - Architecture du système NORA développé qui permet de mesurer, interpréter et corréler les ondes cérébrale des apprenants aux émotions ressenties lors d’une session d’apprentissage.

En ce qui concerne la mesure de l’intensité des émotions nous avons cherché à déterminer l’impact émotionnel des actions du tuteur sur l’apprentissage, autant dans un environnement de classe que de simulation informatique [Benadada et al, 2008b; Chaffar et Frasson, 2007b; Chalfoun et al, 2007; Faghihi et al, 2007a; Faghihi et al, 2008; Gaha et al, 2008a; Hage et Aimeur, 2009; Heraz et Frasson, 2008; Nkambou et al, 2008; Fournier-Viger et al, 2007; Fournier-Viger et al, 2008; Nkambou et al, 2008; Fournier-Viger et al, 2008].

Figure 4 - Extrait du premier environnement web utilisé en combinaison avec des mesures physiologiques (rythme cardiaque, électromyographie (EMG),  réaction électrodermale de la peau,…) pour mesurer l’impact du tuteur sur l’apprenant lors d’une séance d’apprentissage.

Figure 5 – Architecture du CITS (Système tutoriel intelligent Conscient) avec la modélisation des mécanismes émotionnels pouvant avoir un impact important sur la mémoire épisodique lors d’une session d’apprentissage.

En ce qui concerne l’importance de la culture dans la reconnaissance des émotions, les travaux ont porté sur la variation d'aspects affectifs en fonction de spécificités culturelles. Ceci conduit à la mise en place d’un système culturellement adaptatif qui prend la forme d’une ontologie de haut niveau conceptualisant les aspects entrecroisés des domaines affectifs et culturels [Blanchard et Frasson, 2006a, 2006b, 2006c; Blanchard et Mizoguchi, 2008; Blanchard et al, 2009; Blanchard et Lajoie, 2009; Blanchard et al, 2009b].

Figure 6. Formalisation ontologique des concepts de phénomène affectif et de culture.


T2 : la capacité de générer des émotions


Le deuxième thème comporte également trois sous-thèmes comme l’amélioration des techniques existantes d’induction émotionnelles, l’adaptation culturelle et l’induction émotionnelle subconsciente.

Ici l’induction s’est basée sur des techniques positives [Chaffar et al, 2009a, 2009b] et l’adaptation culturelle sur des jeux vidéo [Blanchard et al, 2009; Blanchard et Frasson, 2006, 2006a, 2007a, 2007b; Blanchard et al, 2009a] qui améliorent la motivation basé sur la théorie de l’autodétermination. Une saisie d’écran de cet environnement est présentée au thème T1.  

Tableau 1 - Formes de motivation selon la théorie de l’autodétermination

Figure 7 - Élèves utilisant MOCAS dans le cadre d’un cours d’histoire

L’induction émotionnelle subconsciente a apporté une technique toute nouvelle dans le domaine avec des résultats porteurs de nombreuses possibilités. On utilise des signaux subliminaux produits dans un environnement 3D pour améliorer grandement l’apprentissage [Chalfoun et Frasson, 2008a, 2008b, 2008c].


Figure 8 – Environnement MOCAS transformé et recodé pour fonctionner en plein écran permettant ainsi le sentiment d’une plus grande immersion. La partie de droite représente le mécanisme utilisé pour la projection subliminale d’information pertinentes provoquant des réaction émotionnelle positives pour l’apprentissage.


T3 : les composantes du modèle émotionnel de l'apprenant


Le troisième thème a comporté une étude sur le modèle émotionnel de l’apprenant, le contrôle des émotions et l’indexation avec le subconscient. Les travaux ont ainsi progressivement amené à un renforcement du modèle de l’apprenant par un modèle émotionnel [Blanchard et al, 2007; Blanchard et al, 2009; Chaffar et al, 2006; Chaffar et Frasson, 2006, 2007a; Chalfoun et al, 2006; Dubois et al, 2006; Dubois et al, 2008; Faghihi et al, 2008, 2008b; Heraz et Frasson, 2007; Heraz et al, 2008; Jraidi et al, 2009].

Figure 9 – Le simulateur RomanTutor amélioré avec l’ajout de l’architecture consciente ainsi que la modélisation émotionnelle ayant un impact sur l’apprentissage épisodique des apprenants. On voit sur (B) le suivi de l’évolution des connaissances de l’apprenant et sur (C) l’intervention de l’agent tuteur.

De plus, des travaux dans le domaine de la résolution de problèmes médicaux nous ont permis d’étudier et de mesurer l’impact émotionnel que le choix des mots peut avoir sur un diagnostic. Ce choix se veut donc un outil puissant pour le contrôle émotionnel des étudiants en médecine lorsqu’ils font face à un diagnostic difficile [Faremo et al 2006; Lajoie, S.P. 2007a, Lajoie, S.P. 2007b].

Figure 10 - BioWorld met en jeu une simulation hospitalière où les apprenants apprennent à raisonner sur des infections en visitant des patients, en interprétant leurs symptômes, en menant des test et en collectant les informations appropriées dans la bibliothèque médicale mise à leur disposition pour les aider.

L’indexation émotionnelle avec le subconscient est un axe qui a démarré récemment par des approches subliminales très prometteuses nous permettant d’investiguer l’état émotionnel optimal des apprenants lors d’une séance d’apprentissage. [Chalfoun et Frasson, 2009]. Connaissant cet état, il nous est possible de prédire l’état émotionnel d’un apprenant à un moment précis (grâce aux résultats du thème 1) et induire ainsi un état optimal à l’apprentissage (grâce aux résultats obtenus au thème 2).