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
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).