Roger Nkambou

Roger Nkambou

Professeur
Liens d'intérêt
Informations générales

Cheminement académique

1er juin 2009 - Actuellement: Professeur titulaire
1er juin 2002 - 31 mai 2009: Professeur agrégé
1er juin 1997 - 31 mai 2002: Professeur adjoint
juin 1996 - mai 1997: Chercheur postdoctoral

Unités de recherche

  • Laboratoire de recherche en gestion, diffusion et acquisition de connaissances (GDAC)

Projets de recherche et/ou de recherche-création en cours

  • e Leveraging the potential of large language models for a new approach to intelligent tutoring systems engineering

    NSERC Discovery Grant (2025 - 2029)

  • Cognitive Pilot (C-Pilot): UQAM, UdeM, UQAC, ETS, Bombardier, CRSNG, CRIAQ, BMU, Cognitive Group

    NSERC Alliance Grant (2024 - 2027)

  • AI Augmented Pilot (Pilot-AI): UQAM, UdeM, Bombardier, CAE, CRSNG, BMU, CRIAQ

    NSERC Alliance Grant (2020 - 2023)

  • Methods and Tools for Active Adaptation in Serious Games Based on a Rich User Model

    NSERC Discovery Grant (2018 - 2024)

  • Algorithmes d'apprentissage profond pour la classification et la prédiction de comportements à partir de données multimodales

    Research Contract (2017 - 2019), BMU inc.

Partenaires (organismes, entreprises)

  • FRQNT, CRSNG, CRIAQ, Bombardier, CAE, BMU inc., Cognitive Group Inc., Agence Spaciale Canadienne

Affiliations externes principales

Enseignement
Participation à l’édition d’une revue
Distinctions
Services à la collectivité

2026- ... - Directeur de l'unité de programmes de cycles supérieurs en informatique pour l'intelligence et la gestion des données
2023-2024 - Diecteur de l'unité de programmes des certificats en informatique
2023 - ...- Directeur du Centre de Recherche en Intelligence Artificielle
Octobre 2018 - Decembre 2019: Directeur du Centre de Recherche en Intelligence Artificielle, Faculté des Sciences, UQAM
2016-Actuellement: Directeur du Laboratoire de recherche GDAC (http://gdac.uqam.ca)
2009-2014: Directeur du Doctorat en Informatique Cognitive

General Chair:
- Educational Data Mining (EDM 2019) - http://educationaldatamining.org/edm2019/
- Intelligent Tutoring Systems (ITS 2018) - http://its2018.its-conferences.com/committees/conference-committee/
- International World Wide Wed Conference (WWW 2016) - http://www2016.ca
- User Modeling, Adaptation and Personalization (UMAP 2012)
- Intelligent Tutoring Systems (ITS 2008)

Senior Programme Committee Member:
- Artificial Intelligence in Education Conferences (AIED)
- Educational Data Mining (EDM)
- Intelligent Tutoring Systems (ITS)

Directions de thèses et mémoires

Thèses de doctorat
Mémoires
Rapports d'activités et projets d'intervention
  • Luc, Attilia. (2015). Synthèse et analyse des techniques de fouilles de données : perspectives pour les environnements numériques d'apprentissage. (Rapport d'activités et projet d'intervention). Université du Québec à Montréal.
  • Ouadihi, Khalid. (2006). Multimédia pédagogique. (Rapport d'activités et projet d'intervention). Université du Québec à Montréal.
Autres directions et supervisions
  • Kameni Homte, Jaures Styve. (2023). Optimisation des systèmes de recherche d'information pour des contextes d'apprentissage. (Thèse de doctorat). Université de Yaoundé I.
  • Brisson, Janie. (2019). Si Alors et plus encore: Identification des compétences visée et évaluation automatique du raisonnement déductif chez les adultes. (Thèse de doctorat). Université du Québec à Montréal.
  • Kenmogne, Edith Bélise. (2018). Contribution to the sequential and parallel discovery of sequential patterns with an application to the design of e-learning recommenders. (Thèse de doctorat). Université de Dschang.
  • Luccioni, Alexandra. (2018). STI-DICO : an intelligent tutoring system to foster dictionary skills for french teachers-in-training. (Thèse de doctorat). Université du Québec à Montréal.
  • Tawamba, Emile. (2016). Vers une approche holistique de gestion de l'évolution des ontologies. (Thèse de doctorat). Université de Yaoundé I.
  • Larue, Othalia. (2015). Une architecture cognitive inspirée des théories des processus duaux pour une interaction fluide des comportements réactifs et délibératifs. (Thèse de doctorat). Université du Québec à Montréal.
  • Zouaq, Amal. (2008). Une approche d'ingénierie ontologique pour l'acquisition et l'exploitation des connaissances à partir de documents textuels : vers des objets de connaissances et d'apprentissage. (Thèse de doctorat). Université de Montréal.
  • Benaicha, Mohamed. (2017). Identification des concepts pour la ré-ingénierie des ontologies. (Mémoire de maîtrise). Université du Québec à Chicoutimi.
  • Asselin, Guillaume. (2013). Une approche multi-agents pour le développement d'un jeu vidéo. (Mémoire de maîtrise). Université de Montréal.
  • Szymoniak, Karl. (2010). Générateur de phrases basé sur une ontologie syntaxique. (Mémoire de maîtrise). Université du Québec à Montréal.
  • Lidong, Wang. (2002). Distributed Authoring tool based on XML technology. (Mémoire de maîtrise). Université de Montréal.
  • Xin, Zhao. (2002). Knowledge representation for restriction digestion and reconstructing DNA in a genetic lab. (Mémoire de maîtrise). Université de Sherbrooke.
  • Tchoumtchoua, Jacob. (2002). Using fuzzy logic in student modelling. (Mémoire de maîtrise). Université de Sherbrooke.
  • Meudja, Kaufmann. (2002). An interface for web-based courses. (Mémoire de maîtrise). Université de Sherbrooke.
  • Mguedmini, Chawki. (2002). Distributed multimedia learning resource server for ITSs. (Mémoire de maîtrise). Université de Sherbrooke.
  • Amen, Ajroud. (2002). XML-Based resource for Distributed ITS. (Mémoire de maîtrise). Université de Sherbrooke.
  • Hamadouche, Mohamed. (2001). Modèle de communication entre un laboratoire virtuel réflexif et un tuteur intelligent. (Mémoire de maîtrise). Université de Sherbrooke.
  • Laporte, Yan. (2001). Agents intelligents pour la formation. (Mémoire de maîtrise). Université de Sherbrooke.

Publications

Articles scientifiques
  • Kameni, J.S.H., Batchakui, B. et Nkambou, R. (2025). Optimization of information retrieval systems for learning contexts. International Journal of Artificial Intelligence in Education, 35(1), 65–95. http://dx.doi.org/10.1007/s40593-024-00415-z.
  • Kenmogne, E.B., Tetakouchom, I., Tayou Djamegni, C., Nkambou, R. et Tabueu Fotso, L.C. (2024). An Improved Algorithm for Extracting Frequent Gradual Patterns. Informatica, 35(3), 577–600. http://dx.doi.org/10.15388/24-INFOR566.
  • Batchakui, B., Nkambou, R. et Tawamba, E. (2023). A Hybrid Approach to Ontology Modularization. SN Computer Science, 4, article 634. http://dx.doi.org/10.1007/s42979-023-02066-8.
  • Zarglayoun, H., Laurendeau-Martin, J., Tato, A., Vera-Estay, E., Blondin, A., Lamy-Brunelle, A., Chaieb, S., Morasse, F., Dufresne, A., Nkambou, R. et Beauchamp, M.H. (2022). Assessing and optimizing socio-moral reasoning skills: Findings from the MorALERT serious video game. Frontiers in Psychology, 12. http://dx.doi.org/10.3389/fpsyg.2021.767596.
  • Kenmogne, E.B., Tayou Djamegni, C., Nkambou, R., Tabueu Fotso, L.C. et Tadmon, C. (2022). Efficient mining of intra-periodic frequent sequences. Array, 16, article 100263. http://dx.doi.org/10.1016/j.array.2022.100263.
  • Tato, A. et Nkambou, R. (2022). Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments. Frontiers in Artificial Intelligence, 5. http://dx.doi.org/10.3389/frai.2022.921476.
  • Kameni Homte, J.S., Batchakui, B. et Nkambou, R. (2022). Search Engines in Learning Contexts: A Literature Review. International Journal of Emerging Technologies in Learning, 17(2), 254–272. http://dx.doi.org/10.3991/ijet.v17i02.26217.
  • Pillette, L., Jeunet, C., Mansencal, B., Nkambou, R., N’Kaoua, B. et Lotte, F. (2020). A physical learning companion for Mental-Imagery BCI User Training. International Journal of Human-Computer Studies, 136, article 102380. http://dx.doi.org/10.1016/j.ijhcs.2019.102380.
  • Nkambou, R., Tato, A., Brisson, J., Robert, S. et Sainte-Marie, M. (2020). Une approche hybride à la modélisation de l’apprenant dans un STI pour l’apprentissage du raisonnement logique. STICEF (Sciences et Technologiesde l'Information et de la Communicationpour l'Éducation et la Formation), 27(2), 63–102. https://www.persee.fr/doc/stice_1764-7223_2020_num_27_2_1795.
    Notes: Numéro spécial : Sélection de la conférence EIAH 2019
  • Ghali, R., Abdessalem, H.B., Frasson, C. et Nkambou, R. (2018). Identifying brain characteristics of bright students. Journal of Intelligent Learning Systems and Applications, 10(3), 93–103. http://dx.doi.org/10.4236/jilsa.2018.103006.
  • Kenmogne, E., Nkambou, R., Tadmon, C. et Mephu, E. (2017). A heuristic to predict the optimal pattern-growth direction forthe pattern growth-based sequential pattern mining approach. Journal of Advanced Computer Science & Technology, 6(2), 20–32. http://dx.doi.org/10.14419/jacst.v6i2.7011.
  • Kenmogne, E., Tadmon, C. et Nkambou, R. (2017). A pattern growth-based sequential pattern mining algorithm called prefixSuffixSpan. EAI Endorsed Transactions on Scalable Information Systems, 4(12), article e4. http://dx.doi.org/10.4108/eai.18-1-2017.152103.
  • Yapan Dougnon, R., Fournier-Viger, P., Chun-Wei Lin, J. et Nkambou, R. (2016). Inferring social network user profiles using a partial social graph. Journal of Intelligent Information Systems, 47(2), 313–344. http://dx.doi.org/10.1007/s10844-016-0402-y.
  • Fournier-Viger, P., Cheng-Wei, W., Tseng, V.S., Cao, L. et Nkambou, R. (2015). Mining Partially-Ordered Sequential Rules Common to Multiple Sequences. IEEE Transactions on Knowledge and Data Engineering, 27(8), 2203–2216. http://dx.doi.org/10.1109/TKDE.2015.2405509.
  • Sidi Yakoub, M., Sid-Ahmed, S. et Nkambou, R. (2015). Mobile spoken dialogue system using paser dependencies and ontology. International Journal of Speech Technology, 18(3), 449–457. http://dx.doi.org/10.1007/s10772-015-9280-x.
  • Fennouh, S., Nkambou, R., Valtchev, P. et Rouane-Hacene, M. (2014). Stability-Based Filtering for Ontology Restructuring. Studia Universitatis Babes-Bolyai, Informatica, 59(special issue, 2), 28–44. http://www.cs.ubbcluj.ro/~studia-i/contents/2014-icfca/03-FennouhNkambouValtchevRouane.pdf.
  • Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E., Mayers, A. et Faghihi, U. (2013). A Multiparadigm Intelligent Tutoring System for Robotic Arm Training. IEEE Transactions on Learning Technologies, 6(4), 364–377. http://dx.doi.org/10.1109/TLT.2013.27.
  • Xin Wu, Z., NKambou, R. et Bourdeau, J. (2013). Building a Cultural Intelligence Decision Support System with Soft-Computing. International Journal on Advances in Intelligent Systems, 6(1-2), 136–150. https://www.iariajournals.org/intelligent_systems/intsys_v6_n12_2013_paged.pdf.
  • Larue, O., Poirier, P. et Nkambou, R. (2013). Hypothetical-thinking based on cognitive decoupling and thinking dispositions in a dual cognitive agent. Biologically Inspired Cognitive Architectures, 6, 67–75. http://dx.doi.org/10.1016/j.bica.2013.06.004.
  • Larue, O., Poirier, P. et Nkambou, R. (2013). The emergence of (artificial) emotions from cognitive and neurological processes. Biologically Inspired Cognitive Architectures, 4, 54–68. http://dx.doi.org/10.1016/j.bica.2013.01.001.
  • Faghihi, U., Fournier-Viger, P. et Nkambou, R. (2012). A Computational Model for Causal Learning in Cognitive Agents. Knowledge-Based Systems, 30, 48–56. http://dx.doi.org/10.1016/j.knosys.2011.09.005.
  • Larue, O., Poirier, P. et Nkambou, R. (2012). A three-level cognitive architecture for the simulation of human behaviour. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7310 LNAI, 337–342. http://dx.doi.org/10.1007/978-3-642-30353-1_33.
  • Belghith, K., Nkambou, R., Kabanza, F. et Hartman, L. (2012). An Intelligent Simulator for Telerobotics Training. IEEE Transactions on Learning Technologies, 5(1), 11–19. http://dx.doi.org/10.1109/TLT.2011.19.
  • Fournier-Viger, P., Faghihi, U., Nkambou, R. et Mephu Nguifo, E. (2012). CMRules: Mining Sequential Rules Common to Several Sequences. Knowledge-Based Systems, 25(1), 63–76. http://dx.doi.org/10.1016/j.knosys.2011.07.005.
  • Faghihi, U., Poirier, P., Fournier-Viger, P. et Nkambou, R. (2011). Human-Like Learning in a Conscious Agent. Journal of Experimental & Theoretical Artificial Intelligence, 23(4), 497–528. http://dx.doi.org/10.1080/0952813X.2010.503342.
  • Faghihi, U., Fournier-Viger, P., Nkambou, R. et Poirier, P. (2011). Identifying Causes Helps a Tutoring System to Better Adapt to Learners During Training Sessions. Journal of Intelligent Learning Systems and Applications, 3(3), 139–154. http://dx.doi.org/10.4236/jilsa.2011.33016.
  • Nkambou, R., Fournier-Viger, P. et Mephu Nguifo, E. (2011). Learning Task Models in Ill-defined Domain Using an Hybrid Knowledge Discovery Framework. Knowledge-Based Systems, 24(1), 176–185. http://dx.doi.org/10.1016/j.knosys.2010.08.002.
  • Fournier-Viger, P., Faghihi, U., Nkambou, R. et 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), 13–24. https://www.j-ets.net/ETS/journals/13_1/3.pdf.
  • Zouaq, A. et Nkambou, R. (2009). Enhancing learning objects with an ontology-based memory. IEEE Transactions on Knowledge and Data Engineering, 21(6), 881–893. http://dx.doi.org/10.1109/TKDE.2009.49.
  • Zouaq, A. et Nkambou, R. (2009). Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project. IEEE Transactions on Knowledge and Data Engineering, 21(11), 1559–1572. http://dx.doi.org/10.1109/TKDE.2009.25.
  • Nkambou, R., Fournier-Viger, P. et Mephu Nguifo, E. (2009). Improving the Behavior of Intelligent Tutoring Agents with Data Mining. IEEE Intelligent Systems, 24(3), 46–53. http://dx.doi.org/10.1109/MIS.2009.59.
  • Zouaq, A. et Nkambou, R. (2008). Building Domain Ontologies from Text for Educational Purposes. IEEE Transactions on Learning Technologies, 1(1), 49–62. http://dx.doi.org/10.1109/TLT.2008.12.
  • Fournier-Viger, P., Nkambou, R. et Mayers, A. (2008). Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System. IEEE Transactions on Learning Technologies, 1(1), 63–74. http://dx.doi.org/10.1109/TLT.2008.13.
  • Zouaq, A., Nkambou, R. et Frasson, C. (2007). An Integrated Approach for Automatic Aggregation of Learning Knowledge Objects. Interdisciplinary Journal of Knowledge and Learning Objects, 3(1), 135–162. https://www.learntechlib.org/p/44802/.
  • Tchetagni, J., Nkambou, R. et Bourdeau, J. (2007). Explicit Reflexion in Prolog-Tutor. International Journal of Artificial Intelligence in Education (IJAIED), 17(2), 169–215. http://iaied.org/pub/1108/.
  • Nkambou, R., Delozanne, E. et Frasson, C.(dir.). (2007). Les dimensions émotionnelles de l'interaction dans un EIAH [Éditorial du numéro spécial]. Sciences et Technologies de l´Information et de la Communication pour l´Éducation et la Formation (STICEF), 14. http://sticef.univ-lemans.fr/num/vol2007/sticef_2007_editoEmotions.htm.
  • Gaha, M., Dubois, D. et Nkambou, R. (2007). Proposition d'un traitement émotionnel pour un STI "conscient". Sciences et Technologies de l´Information et de la Communication pour l´Éducation et la Formation (STICEF), 14, 239–264. http://sticef.univ-lemans.fr/num/vol2007/05-gaha/sticef_2007_gaha_05.htm.
  • Fournier-Viger, P., Najjar, M., Mayers, A. et Nkambou, R. (2006). A Cognitive and Logic based Model for Building Glass-box Learning Objects. Interdisciplinary Journal of Knowledge and Learning Objects, 2(1), 77–94. http://www.learntechlib.org/p/44815/.
  • Tchétagni, J., Nkambou, R. et Bourdeau, J. (2006). A framework to help instructional designers to specify cognitive diagnosis component in ILEs. Journal of Interactive Learning Research, 17(3), 269–293. https://www.learntechlib.org/primary/p/19819/.
  • Yatchou, R., Tangha, C., Nkambou, R. et Tietche, F. (2006). Grid-Based Virtual Clinic for Medical Diagnosis Tutoring. Journal des Sciences Pour l'Ingénieur, 7, 72–78.
  • Gouardères, G., Saber, M., Nkambou, R. et Yatchou, R. (2005). The Grid-e-Card: Architecture to Share Collective Intelligence on the Grid. Applied Artificial Intelligence: An International Journal, 19(9-10), 1043–1073. http://dx.doi.org/10.1080/08839510500304108.
  • Nkambou, R., Gouardères, G. et Woolf, B.P. (2005). Toward Learning Grid Infrastructures: an Overview of Research on Grid Learning Services. Applied Artificial Intelligence: An international journal, 19(9-10), 811–824. http://dx.doi.org/10.1080/08839510500234123.
Chapitres de livre
  • Fournier-Viger, P., Chun-Wei Lin, J., Truong-Chi, T. et Nkambou, R. (2019). A Survey of High Utility Itemset Mining. Dans P. Fournier-Viger, J. Chun-Wei Lin, R. Nkambou, B. Vo et V.S. Tseng (dir.). High-Utility Pattern Mining: Theory, Algorithms and Applications (p. 1–45). Springer. https://doi.org/10.1007/978-3-030-04921-8_1.
  • Faghihi, U., Fournier-Viger, P. et Nkambou, R. (2012). CELTS: A cognitive tutoring agent with human-like learning capabilities and emotions. Dans A. Peña-Ayala (dir.). Intelligent and Adaptive Educational-Learning Systems (p. 339–365). Springer.
    Notes: collection Smart innovation, systems and technologies, volume 17
  • Fournier-Viger, P., Nkambou, R. et Mephu Nguifo, E. (2010). Learning Procedural Knowledge from User Solutions To Ill-Defined Tasks in a Simulated Robotic Manipulator. Dans C. Romero, S. Ventura, M. Pechenizkiy et R. Baker (dir.). Handbook of Educational Data Mining (p. 451–465). CRC Press.
  • Fournier-Viger, P., Nkambou, R., Faghihi, U. et Mephu Nguifo, E. (2009). Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies. Dans L. Cao (dir.). Data Mining and Multi-agent Integration (p. 77–92). Springer. http://dx.doi.org/10.1007/978-1-4419-0522-2.
  • Fournier-Viger, P., Nkambou, R. et Mephu Nguifo, E. (2008). A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems. Dans A. Gelbukh et E.F. Morales (dir.). MICAI 2008: Advances in Artificial Intelligence 7th Mexican International Conference on Artificial Intelligence, Atizapán de Zaragoza, Mexico, October 27-31, 2008 Proceedings (p. 765–778). Springer. http://dx.doi.org/10.1007/978-3-540-88636-5_72.
    Notes: Best Paper Award (1st place / 368 submissions / 25.6% acceptance rate); collection LNAI, volume 5317
  • Zouaq, A., Nkambou, R. et Frasson, C. (2008). Bridging the Gab between ITS and eLearning: Towards Learning Knowledge Objects. Dans B.P. Woolf, E. Aïmeur, R. Nkambou et S. Lajoie (dir.). Intelligent Tutoring Systems: 9th International Conference, ITS 2008 Montreal, Canada, June 23–27, 2008, Proceedings (p. 448–458). Springer. http://dx.doi.org/10.1007/978-3-540-69132-7_48.
    Notes: collection LNCS No 5091
  • Nkambou, R., Mephu Nguifo, E. et Fournier-Viger, P. (2008). Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain. Dans B.P. Woolf, E. Aïmeur, R. Nkambou et S. Lajoie (dir.). Intelligent Tutoring Systems: 9th International Conference, ITS 2008 Montreal, Canada, June 23–27, 2008 Proceedings (p. 395–405). Springer. http://dx.doi.org/10.1007/978-3-540-69132-7_43.
    Notes: Top 5 papers; collection LNCS No 5091
  • Nkambou, R., Mephu Nguifo, E., Couturier, O. et Fournier-Viger, P. (2007). Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System. Dans Z. Kobti et D. Wu (dir.). Advances in Artificial Intelligence 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007 Montreal, Canada, May 28-30, 2007, Proceedings (p. 393–404). Springer. http://dx.doi.org/10.1007/978-3-540-72665-4_34.
    Notes: collection LNAI 4509
  • Nkambou, R., Belghith, K. et Kabanza, F. (2006). An Approach to Intelligent Training on a Robotic Simulator using an Innovative Path-Planner. Dans M. Ikeda, K.D. Ashley et T.-W. Chan (dir.). Intelligent Tutoring Systems: 8th International Conference, ITS 2006 Jhongli, Taiwan, June 26-30, 2006, Proceedings (p. 645–654). Springer. http://dx.doi.org/10.1007/11774303_64.
    Notes: collection LNCS No.4053
  • Dubois, D., Nkambou, R. et Hohmeyer, P. (2006). How "Consciousness" Allows a Cognitive Tutoring Agent Make Good Diagnosis During Astronauts' Training. Dans M. Ikeda, K.D. Ashley et T.-W. Chan (dir.). Intelligent Tutoring Systems: 8th International Conference, ITS 2006 Jhongli, Taiwan, June 26-30, 2006, Proceedings (p. 154–163). Springer. http://dx.doi.org/10.1007/11774303_16.
    Notes: collection LNCS No 4053
  • Nkambou, R. (2006). Managing Student Emotions in Intelligent Tutoring Systems. Dans G.C.J. Sutcliffe et R.G. Goebel (dir.). Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference (p. 389–394). AAAI Press. https://aaai.org/Papers/FLAIRS/2006/Flairs06-076.pdf.
  • Psyché, V., Bourdeau, J., Nkambou, R. et Mizoguchi, R. (2005). Making Learning Design Standards Work with an Ontology of Educational Theories. Dans C.-K. Looi, G. McCalla, B. Bredeweg et J. Breuker (dir.). Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (p. 725–731). IOS Press. https://dl-acm-org/citation.cfm?id=1562598.
  • Kabanza, F., Nkambou, R., Belghith, K. et Hartman, L. (2005). Path-Planning for Autonomous Training on Robot Manipulators in Space. Dans Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence IJCAI-05 proceedings (p. 1729–1732). International Joint Conferences on Artificial Intelligence. https://www.ijcai.org/Proceedings/05/Papers/post-0130.pdf.
Articles professionnels ou de magazines
Livres
  • Fournier-Viger, P., Lin, J.C.-W., Nkambou, R., Vo, B. et Tseng, V. S. (dir.). (2019). High-Utility Pattern Mining: Theory, Algorithms and Applications. Springer Nature. https://doi.org/10.1007/978-3-030-04921-8
  • Desmarais, M., Lynch, C.F., Merceron, A. et Nkambou, R. (dir.). (2019). Proceedings of the 12th International Conference on Educational Data Mining, EDM 2019. Educational Data Mining Society. http://files.eric.ed.gov/fulltext/ED599096.pdf
  • Nkambou, R., Azevedo, R. et Vassileva, J. (dir.). (2018). Intelligent Tutoring Systems: 14th International Conference, ITS 2018, Montreal, QC, Canada, June 11–15, 2018, Proceedings. Springer. https://doi.org/10.1007/978-3-319-91464-0
    Notes: collection LNCS, volume 10858
  • Bourdeau, J., Hendler, J., Nkambou, R., Horrocks, I. et Zhao, B. (dir.). (2016). Proceedings of the 25th International Conference on World Wide Web. Association for Computer Machinery (ACM).
  • Nkambou, R., Narse, C., Cerri, S.A., Boiron, P. et Paliard, C. (dir.). (2012). Intégration technologique et nouvelles perspectives d’usage : actes du 8ème Colloque Technologies de l’Information et de la Communication pour l’Enseignement. Université de Lyon. http://gdac.uqam.ca/tice2012/ActesTICE2012-Final.pdf
  • Masthoff, J., Mobasher, B., Desmarais, M.C. et Nkambou, R. (dir.). (2012). User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Springer. https://doi.org/10.1007/978-3-642-31454-4
    Notes: collection LNCS, volume 7379
  • Nkambou, R., Bourdeau, J. et Mizoguchi, R. (dir.). (2010). Advances in Intelligent Tutoring Systems. Springer.
  • Woolf, B.P., Aïmeur, E., Nkambou, R. et Lajoie, S. (dir.). (2008). Intelligent Tutoring Systems: 9th International Conference, ITS 2008, Montreal, Canada, June 23-27, 2008 Proceedings. Springer.
    Notes: collection LNCS, volume 5091
Actes de colloque
  • Courtemanche, M.-A., Nkambou, R. et Psyché, V. (2026). A Unified Ontological Approach for Modeling Domain Theory and Procedures: Applications, Issues and Prospects. Dans S. Graf et A. Markos (dir.). Generative Systems and Intelligent Tutoring Systems: 21st International Conference, ITS 2025, Alexandroupolis, Greece, June 2–6, 2025, Proceedings, Part I, (p. 270-280). Springer. https://doi.org/10.1007/978-3-031-98281-1_22.
    Notes: collection LNCS vol. 15723
  • Tato, A. et Nkambou, R. (2026). Leveraging LLMS for Bayesian and deep knowledge tracing in the logic-muse intelligent tutoring system. Dans S. Graf et A. Markos (dir.). Generative Systems and Intelligent Tutoring Systems: 21st International Conference, ITS 2025, Alexandroupolis, Greece, June 2–6, 2025, Proceedings, Part I, (p. 182–191). Springer. https://doi.org/10.1007/978-3-031-98281-1_14.
    Notes: collection LNCS vol. 15723
  • Kengne, D., Nkambou, R., Tato, A. et Lacourarie, C. (2026). Towards Predicting Complex Carpooling Trajectories with Context-Augmented BERT-LLM in Chaotic Environments. Dans H. Fujita, Y. Watanobe, M. Ali et Y. Wang (dir.). Advances and Trends in Artificial Intelligence. Theory and Applications: 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Kitakyushu, Japan, July 1–4, 2025, Proceedings, Part I, (p. 419–431). https://doi.org/10.1007/978-981-96-8889-0_36.
    Notes: collection LNCS: LNAI vol. 15706
  • Tato, A. et Nkambou, R. (2025). Can LLMs Generate Accurate Bayesian Networks to Enhance Knowledge Tracing? Dans A.I. Cristea, E. Walker, Y. Lu, O.C. Santos et S. Isotani (dir.). Artificial Intelligence in Education: 26th International Conference, AIED 2025, Palermo, Italy, July 22–26, 2025, Proceedings, Part III, (p. 319–332). Springer. https://doi.org/10.1007/978-3-031-98420-4_23.
    Notes: collection LNCS: LNAI vol. 15879
  • Minn, S. et Nkambou, R. (2025). Enhanced Interpretable Knowledge Tracing for Students’ Performance Prediction with Human-understandable Feature Space. Dans A.I. Cristea, E. Walker, Y. Lu, O.C. Santos et S. Isotani (dir.). Artificial Intelligence in Education: 26th International Conference, AIED 2025, Palermo, Italy, July 22–26, 2025, Proceedings, Part V, (p. 423–430). Springer. https://doi.org/10.1007/978-3-031-98462-4_53.
    Notes: collection LNCS: LNAI volume 15881
  • Tamkodjou Tchio, G.C., Nkambou, R., Psyché, V. et Nyamen Tato, A. A. (2025). Enhancing Pilot Training and Decision-Making Using Ontologies: A Cognitive Assistance Approach. Dans S. Graf et A. Markos (dir.). Generative Systems and Intelligent Tutoring Systems: 21st International Conference, ITS 2025, Alexandroupolis, Greece, June 2–6, 2025, Proceedings, Part I, (p. 59–73). Springer. https://doi.org/10.1007/978-3-031-98281-1_5.
    Notes: collection LNCS vol. 15723
  • Nkambou, R., Brisson, J., Tato, A. et Robert, S. (2024). Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling. Dans B. Williams, Y. Chen et J. Neville (dir.). Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, (vol. 37, no. 13, p. 15930-15937). AAAI Press. https://doi.org/10.1609/aaai.v37i13.26891.
  • Tamkodjou Tchio, G.C., Nkambou, R., Nyamen Tato, A. A. et Psyché, V. (2024). Towards Cognitive Coaching in Aircraft Piloting Tasks: Building an ACT-R Synthetic Pilot Integrating an Ontological Reference Model to Assist the Pilot and Manage Deviations. Dans A. Sifaleras et F. Lin (dir.). Generative Intelligence and Intelligent Tutoring Systems: 20th International Conference, ITS 2024, Thessaloniki, Greece, June 10–13, 2024, Proceedings, Part I, (p. 202-216). Springer. https://doi.org/10.1007/978-3-031-63028-6_16.
    Notes: collection LNCS vol. 14798
  • Courtemanche, M.A., Tato, A. et Nkambou, R. (2023). Automatic Execution of the Ontological Piloting Procedures. Dans C. Frasson, P. Mylonas et C. Troussas (dir.). Augmented Intelligence and Intelligent Tutoring Systems: 19th International Conference, ITS 2023, Corfu, Greece, June 2–5, 2023, Proceedings, (p. 29-41). Springer. https://doi.org/10.1007/978-3-031-32883-1_3.
    Notes: collection LNCS vol. 13891
  • Tato, A., Nkambou, R. et Tato, G. (2023). Automatic Learning of Piloting Behavior from Flight Data. Dans C. Frasson, P. Mylonas et C. Troussas (dir.). Augmented Intelligence and Intelligent Tutoring Systems: 19th International Conference, ITS 2023, Corfu, Greece, June 2–5, 2023, Proceedings, (p. 541-552). Springer. https://doi.org/10.1007/978-3-031-32883-1_48.
    Notes: collection LNCS vol. 13891
  • Tamkodjou Tchio, G.C., Courtemanche, M.-A., Nyamen Tato, A. A., Nkambou, R. et Psyché, V. (2023). Integrating an Ontological Reference Model of Piloting Procedures in ACT-R Cognitive Architecture to Simulate Piloting Tasks. Dans C. Frasson, P. Mylonas et C. Troussas (dir.). Augmented Intelligence and Intelligent Tutoring Systems: 19th International Conference, ITS 2023, Corfu, Greece, June 2–5, 2023, Proceedings, (p. 183-194). Springer. https://doi.org/10.1007/978-3-031-32883-1_16.
    Notes: collection LNCS vol. 13891
  • Tato, A. et Nkambou, R. (2023). Towards a multi-modal Deep Learning Architecture for User Modeling, The International FLAIRS Conference Proceedings, 36(1). https://doi.org/10.32473/flairs.36.133328.
  • Tato, A. et Nkambou, R. (2023). Towards Extracting Adaptation Rules from Neural Networks. Dans N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda et O.C. Santos (dir.). Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proc, (p. 543-548). Springer. https://doi.org/10.1007/978-3-031-36336-8_84.
  • Ghaderi, M., Courtemanche, M.-A., Ben Abdessalem, H., Nkambou, R. et Frasson, C. (2022). Attentional tasks model: a focus group approach. Dans A. Krouska, C. Troussas et J. Caro (dir.). Novel & Intelligent Digital Systems: Proceedings of the 2nd International Conference (NiDS 2022), (p. 297-307). Springer. https://doi.org/10.1007/978-3-031-17601-2_29.
    Notes: collection LNNS vol. 556
  • Tato, A. et Nkambou, R. (2022). Deep Knowledge Tracing on Skills with Small Datasets. Dans S. Crossley et E. Popescu (dir.). Intelligent Tutoring Systems: 18th International Conference, ITS 2022, Bucharest, Romania, June 29 – July 1, 2022, Proceedings, (p. 123-135). Springer. https://doi.org/10.1007/978-3-031-09680-8_12.
    Notes: collection LNCS vol. 13284
  • Courtemanche, M.-A., Tato, A. et Nkambou, R. (2022). Ontological reference model for piloting procedures. Dans S. Crossley et E. Popescu (dir.). Intelligent Tutoring Systems: 18th International Conference, ITS 2022, Bucharest, Romania, June 29 – July 1, 2022, Proceedings, (p. 95-104). Springer. https://doi.org/10.1007/978-3-031-09680-8_9.
    Notes: collection LNCS vol. 13284
  • Tato, A., Nkambou, R. et Tato Nana, G.J. (2022). Towards Adaptive Coaching in Piloting Tasks: Learning Pilots’ Behavioral Profiles from Flight Data. Dans S. Crossley et E. Popescu (dir.). Intelligent Tutoring Systems: 18th International Conference, ITS 2022, Bucharest, Romania, June 29 – July 1, 2022, Proceedings, (p. 105-114). Springer. https://doi.org/10.1007/978-3-031-09680-8_10.
    Notes: collection LNCS vol. 13284
  • Batchakui, B., Tawamba, E. et Nkambou, R. (2021). COMET: An Ontology Extraction Tool based on a Hybrid Modularization Approach. Dans D. Aveiro, J. Dietz et Filipe. J. (dir.). Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021), (p. 74-83). SciTePress. https://share.google/wsDLB7zVTYIyrbSW1.
    Notes: collection KEOD vol. 2
  • Nkambou, R., Tato, A., Brisson, J. et Robert, S. (2021). Learning Logical Reasoning Using an Intelligent Tutoring System: Improving the Student Model with a data driven approach. Dans A.I. Cristea et C. Troussas (dir.). Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings, (p. 60-67). Springer. https://doi.org/10.1007/978-3-030-80421-3_7.
    Notes: collection LNCS vol. 12677
  • Sodoké, K., Nkambou, R., Dufresne, A. et Tanoubi, I. (2021). Toward a Webcam Based ITS to Enhance Novice Clinician Visual Situational Awareness. Dans A.I. Cristea et C. Troussas (dir.). Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings. https://doi.org/10.1007/978-3-030-80421-3_26.
    Notes: collection LNCS vol. 12677
  • Fotso, J.E.M., Batchakui, B., Nkambou, R. et Okereke, G. (2020). Algorithms for the Development of Deep Learning Models for Classification and Prediction of Behaviour in MOOCS. Dans R. Hernández, S. Schreiter et H. Amado (dir.). 2020 IEEE Learning With MOOCS (LWMOOCS), (p. 180-184). IEEE. https://doi.org/10.1109/LWMOOCS50143.2020.9234363.
  • Tato, A. et Nkambou, R. (2020). Improving First-Order Optimization Algorithms (Student Abstract). Dans Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), (vol. 34, no. 10, p. 13935-13936). AAAI Press. https://doi.org/10.1609/aaai.v34i10.7240.
  • Sodoké, K., Nkambou, R., Dufresne, A. et Tanoubi, I. (2020). Toward a deep convolutional LSTM for eye gaze spatiotemporal data sequence classification. Dans A.N. Rafferty, J. Whitehill, V. Cavalli-Sforza et C. Romero (dir.). Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020), (p. 672-676). EDM Society. https://share.google/IYIGVa2jkfpF5hgwI.
  • Tato, A., Nkambou, R. et Dufresne, A. (2020). Using AI techniques in A Serious Game for Socio-moral Reasoning Developement. Dans Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), (vol. 34, no. 09, p. 13420–13427). AAAI Press. https://doi.org/10.1609/aaai.v34i09.7066.
  • Tato, A., Nkambou, R. et Dufresne, A. (2019). Hybrid Deep Neural Networks to Predict Socio-Moral Reasoning Skills. Dans C.F. Lynch, A. Merceron, M. Desmarais et R. Nkambou (dir.). Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019), (p. 623-626). EDM Society. https://drive.google.com/file/d/1vptlF7O5MpVfPTMEHcQPacN0zhJk6u54/view.
  • Tato, A. et Nkambou, R. (2019). Some Improvements of Deep Knowledge Tracing. Dans IEEE 31st International Conference on Tools with Artificial Intelligence: ICTAI 2019, (p. 1460-1464). IEEE Computer Society. https://ieeexplore.ieee.org/document/8995203.
  • Tato, A., Nkambou, R. et Ghali, R. (2019). Towards Predicting Attention and Workload During Math Problem Solving. Dans A. Coy, Y. Hayashi et M. Chang (dir.). Intelligent Tutoring Systems: 15th International Conference, ITS 2019, Kingston, Jamaica, June 3–7, 2019, Proceedings, (p. 224-229). Springer. https://doi.org/10.1007/978-3-030-22244-4_27.
    Notes: collection LNCS vol. 11528
  • Ghali, R., Tato, A. et Nkambou, R. (2019). Using EEG Features and Machine Learning to Predict Gifted Children. Dans R. Bartak et K.W. Brawner (dir.). Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS 2019), (p. 120-123). AAAI Press. https://share.google/m8gdmxG8oWdsSfzFV.
  • Tato, A., Nkambou, R. et Frasson, C. (2018). Predicting Emotions From Multimodal Users' Data. Dans T. Mitrovic, J. Zhang, L. Chen et D. Chin (dir.). UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, (p. 369-370). ACM. https://doi.org/10.1145/3209219.3209264.
  • Tato, A., Nkambou, R., Dufresne, A. et Frasson, C. (2018). Semi-Supervised User-Sensitive Multimodal Deep Learning Model for Polarity Detection in Arguments, Rio, June 2018. IEEE Press.
    Notes: À paraître
  • Tato, A., Nkambou, R., Dufresne, A. et Beauchamp, M. (2017). Convolutional Neural Network for Automatic Detection of Sociomoral Reasoning Level. Dans X. Hu, T. Barnes et L. P. Hershkovitz (dir.). Proceedings of the 10th International Conference on Educational Data Mining, (p. 284-289). http://educationaldatamining.org/EDM2017/proc_files/proceedings.pdf.
  • Anjou, C., Forissier, T., Bourdeau, J., Mazabraud, Y., Nkambou, R. et Fournier, F. (2017). Elaborating the Context Calculator: A Design Experiment in Geothermy. Dans P. Brézillon, R. Turner et C. Penco (dir.). Modeling and Using Context: 10th International and Interdisciplinary Conference, CONTEXT 2017, Paris, France, June 20-23, 2017, Proceedings, (p. 513-526). Springer. https://doi.org/10.1007/978-3-319-57837-8_42.
    Notes: collection LNCS vol. 10257
  • Pillette, L., Jeunet, C., Mansencal, B., Nkambou, R., N'Koua, B. et Lotte, F. (2017). Peanut: Personalised Emotional Agent for Neurotechnology User-Training. Dans G.R. Müller-Putz, D. Steyrl, S.C. Wriessnegger et R. Scherer (dir.). Proceedings of the 7th Graz Brain-Computer Interface Conference 2017, Verlag der Technischen Universität Graz. https://doi.org/10.3217/978-3-85125-533-1-76.
  • Tao, A., Nkambou, R., Brisson, J. et Robert, S. (2017). Predicting Learner's Deductive Reasoning Skills Using a Bayesian Network. Dans E. André, R. Baker, X. Hu, M. Rodrigo et B. du Boulay (dir.). Artificial Intelligence in Education: 18th International Conference, AIED 2017, Wuhan, China, June 28–July 1, 2017, Proceedings, (p. 381-392). Springer. http://dx.doi.org/10.1007/978-3-319-61425-0_32.
  • Mondragon, A.L., Nkambou, R. et Poirier, P. (2016). Evaluating the Effectiveness of an Affective Tutoring Agent in Specialized Education. Dans K. Verbert, M. Sharples et T. Klobučar (dir.). Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Lyon, France, September 13–16, 2016, Proceedings, (p. 446-452). Springer. http://dx.doi.org/10.1007/978-3-319-45153-4_41.
  • Vorobyova, A., Nkambou, R., Massardi, J., Bourdeau, J. et Coulombe, C. (2016). STI-DICO: A Web-based System for Intelligent Tutoring of Dictionary Skills. Dans J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks et B. Zhao (dir.). Proceedings of the 25th International Conference Companion on World Wide Web, (p. 923-928). Association for Computer Machinery (ACM). http://dx.doi.org/10.1145/2872518.2891079.
  • Héon, M., Nkambou, R. et Langheit, C. (2016). Toward G-OWL: A Graphical,Polymorphic and Typed Syntax for Building Formal OWL2 Ontologies. Dans J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks et B. Zhao (dir.). Proceedings of the 25th International Conference Companion on World Wide Web, (p. 39-40). Association for Computer Machinery (ACM). http://dx.doi.org/10.1145/2872518.2889377.
  • Mondragon, A.L., Nkambou, R. et Poirier, P. (2016). Towards an Effective Affective Tutoring Agent in Specialized Education. Dans A. Micarelli, J. Stamper et K. Panourgia (dir.). Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7–10, 2016, Proceedings, (p. 402-408). Springer. http://dx.doi.org/10.1007/978-3-319-39583-8_48.
    Notes: collection LNCS, vol. 9684
  • Dougnon, R.Y. et Fournier-Viger, P., Lin, J.C.-W. et Nkambou, R. (2015). Accurate Online Social Network User Profiling. Dans S. Hölldobler, M. Krötzsch, R. Peñaloza et S. Rudolph (dir.). KI 2015: Advances in Artificial Intelligence: 38th Annual German Conference on AI, Dresden, Germany, September 21–25, 2015, Proceedings, (p. 264-270) (p. 264–270). Springer. http://dx.doi.org/10.1007/978-3-319-24489-1_22.
    Notes: collection LNAI volume 9324
  • Dougnon, Y.R., Fournier-Viger, P. et R. Nkambou, R. (2015). Inferring User Profiles in Online Social Networks Using a Partial Social Graph. Dans D. Barbosa et E. Milios (dir.). Advances in Artificial Intelligence: 28th Canadian Conference on Artificial Intelligence, Canadian AI 2015, Halifax, Nova Scotia, Canada, June 2–5, 2015, Proceedings, (p. 84-99). Springer. http://dx.doi.org/10.1007/978-3-319-18356-5_8.
    Notes: collection LNAI volume 9091
  • Dougnon, R.Y., Fournier-Viger, P., Lin, J. C.-W. et Nkambou, R. (2015). More Accurate Inference of User Profiles in Online Social Networks. Dans O. Pichardo Lagunas, O. Herrera Alcántara et G. Arroyo Figueroa (dir.). Advances in Artificial Intelligence and Its Applications: 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Cuernavaca, Morelos, Mexico, October 25–31, 2015, Proceedings, ( Part II, p. 533-546). Springer. http://dx.doi.org/10.1007/978-3-319-27101-9_41.
    Notes: collection LNCS volume 9414
  • Fennouh, S., Nkambou, R., Valtchev, P. et Rouane-Hacene, M. (2015). On the Assessment of Concept Relevance in FCA-based Ontology Restructuring. Dans Proceedings 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI 2015), (p. 566-574). IEEE Computer Society. http://dx.doi.org/10.1109/ICTAI.2015.88.
  • Nkambou, R., Kenfack, C., Robert, S. et Brisson, J. (2015). The Design Rationale of Logic-Muse, an ITS for Logical Reasoning in Multiple Contexts. Dans C. Conati, N. Heffernan, A. Mitrovic et M. Verdejo (dir.). Artificial Intelligence in Education: 17th International Conference, AIED 2015, Madrid, Spain, June 22–26, 2015, Proceedings, (p. 738-742). Springer. http://dx.doi.org/10.1007/978-3-319-19773-9_105.
    Notes: collection LNAI volume 9112
  • Mondragon, A.L., Nkambou, R. et Poirier, P. (2015). Towards an Integrated Specialized Learning Application (ISLA) to Support High Functioning ASD Children in Mathematics Learning. Dans G. Conole, T. Klobučar, C. Rensing, J. Konert et E. Lavoué (dir.). Design for Teaching and Learning in a Networked World: 10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Toledo, Spain, September 1–18, 2015, Proceedings, (p. 225-239). Springer. http://dx.doi.org/10.1007/978-3-319-24258-3_17.
    Notes: collection LNCS volum 9307
  • Nkambou, R., Brisson, J., Kenfack, C., Robert, S., Kissok, P. et Tato, A. (2015). Towards an Intelligent Tutoring System for Logical Reasoning in Multiple Contexts. Dans G. Conole, T. Klobučar, C. Rensing, J. Konert et E. Lavoué (dir.). Design for Teaching and Learning in a Networked World: 10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Toledo, Spain, September 1–18, 2015, Proceedings, (p. 460-466). Springer. http://dx.doi.org/10.1007/978-3-319-24258-3_40.
    Notes: collection LNCS volume 9307
  • Bourdeau, J., Forissier, T., Mazabraud, Y. et Nkambou, R. (2015). Web-Based Context-Aware Science Learning. Dans A. Gangemi, S. Leonardi et A. Panconesi (dir.). Proceedings of the 24th International Conference on World Wide Web, (p. 1415-1418). Association for Computing Machinery (ACM). http://dx.doi.org/10.1145/2740908.2743048.
  • Héon, M. et Nkambou, R. (2014). G-OWL : vers un langage de modélisation graphique, polymorphique et typé pour la construction d'une ontologie dans la notation OWL. Dans IC – 24èmes Journées francophones d’Ingénierie des Connaissances, Jul. 2013, Lille, France, (p. 18-30). AFIA. https://hal.inria.fr/hal-01104001/document.
  • Psyché, V., Bourdeau, J., Mozes, J., Kalemjian, A., Poirier, P., Nkambou, R.,... Maurice, C. (2014). Opening the Door to Philosophy for Teachers with GYM-Author. Dans S. Trausan-Matu, K.E. Boyer, M. Crosby et K. Panourgia (dir.). Intelligent Tutoring Systems: 12th International Conference, ITS 2014, Honolulu, HI, USA, June 5-9, 2014, Proceedings, (p. 666-669). Springer. http://dx.doi.org/10.1007/978-3-319-07221-0_100.
    Notes: collection LNCS volume 8474
  • Gueniche, T., Fournier-Viger, P., Nkambou, R. et Tseng, V.S. (2014). WBPL: An Open-Source Library for Predicting Web Surfing Behaviors. Dans T. Andreasen, H. Christiansen, J.C. Cubero et Z.W. Raś (dir.). Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014, Proceedings, (p. 524-529). Springer. http://dx.doi.org/10.1007/978-3-319-08326-1_55.
    Notes: collection LNAI volume 8502
  • Larue, O., Poirier, P. et Nkambou, R. (2013). Emotional emergence in a symbolic dynamical architecture. Dans A. Chella, R. Pirrone, R. Sorbello et K. Jóhannsdóttir (dir.). Biologically Inspired Cognitive Architectures 2012: Proceedings of the Third Annual Meeting of the BICA Society, (p. 199-204). Springer.
  • Forissier, T., Bourdeau, J., Mazabraud, Y. et Nkambou, R. (2013). Modeling Context Effects in Science Learning: The CLASH Model. Dans P. Brézillon, P. Blackburn et R. Dapoigny (dir.). Modeling and Using Context: 8th International and Interdisciplinary Conference CONTEXT 2013, Annecy, France, October 28-31, 2013, Proceedings, (p. 330-335). Springer. http://dx.doi.org/10.1007/978-3-642-40972-1_25.
    Notes: collection LNCS volume 8175
  • Yacoub, M.S., Nkambou, R. et Selouani, S.-A. (2013). Phone classification using HMM/SVM system and normalization technique. Dans IEEE International Symposium on Signal Processing and Information Technology, (p. 96-101). IEEE Computer Society. http://dx.doi.org/10.1109/ISSPIT.2013.6781861.
  • Larue, O., Poirier, P. et Nkambou, R. (2012). A Cognitive Architecture based on Cognitive/Neurological Dual-System Theories. Dans F.M. Zanzotto, S. Tsumoto, N. Taatgen et Y. Yao (dir.). Brain Informatics: International Conference, BI 2012, Macau, China, December 4-7, 2012, Proceedings, (p. 288-299). Springer. http://dx.doi.org/10.1007/978-3-642-35139-6_27.
  • Larue, O., Poirier, P. et Nkambou, R. (2012). A multi scale cognitive architecture to account for the adaptive and reflective nature of behaviour. Dans Proceedings – 2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012, 2. http://dx.doi.org/10.1109/WI-IAT.2012.255.
  • Larue, O., Poirier, P. et Nkambou, R. (2012). A Multi Scale Cognitive Architecture to Account for the adaptive and reflective Nature of Behaviour. Dans Y. Li, Y. Zhang et N. Zhong (dir.). Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 4 December 2012, Macau, China, (p. 409-416). IEEE Computer Society. http://dx.doi.org/10.1109/WI-IAT.2012.255.
  • Larue, O., Poirier, P. et Nkambou, R. (2012). A Three-Level Cognitive Architecture for the Simulation of Human Behaviour. Dans L. Kosseim et D. Inkpen (dir.). Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012, Proceedings, (p. 337-342). Springer. http://dx.doi.org/10.1007/978-3-642-30353-1_33.
  • Larue, O., Poirier, P. et Nkambou, R. (2012). Emotional emergence in a symbolic dynamical system. Dans A. Chella, R. Pirrone, R. Sorbello et K.R. Jóhannsdóttir (dir.). Biologically Inspired Cognitive Architectures 2012: Proceedings of the Third Annual Meeting of the BICA Society, (p. 199-204). Springer. https://doi.org/10.1007/978-3-642-34274-5_36.
    Notes: collection AISC vol. 196
  • Guerdelli, F., Nkambou, R. et Dufresne, A. (2012). Intégration des informations oculométriques et physiologiques et soutien à l'apprentissage avec un simulateur en sciences physiques. Dans C. P. et Paliard Boiron S.A. Cerri C. Narse R. Nkambou (dir.). Intégration technologique et nouvelles perspectives d’usage : actes du 8ème Colloque Technologies de l’Information et de la Communication pour l’Enseignement, (p. 53-58). Université de Lyon. http://gdac.uqam.ca/tice2012/ActesTICE2012-Final.pdf.
  • Fournier-Viger, P., Wu, C.-W., Tseng, V.S. et Nkambou, R. (2012). Mining Sequential Rules Common to Several Sequences with the Window Size Constraint. Dans L. Kosseim et D. Inkpen (dir.). Advances in Artificial Intelligence: 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Toronto, ON, Canada, May 28-30, 2012, Proceedings, (p. 299-304). Springer. http://dx.doi.org/10.1007/978-3-642-30353-1_27.
  • Fournier-Viger, P., Nkambou, R., Mayers, A., Mephu Nguifo, E. et Faghihi, U. (2012). Multi-Paradigm Generation of Tutoring Feedback in Robotic Arm Manipulation Training. Dans S.A. Cerri, W.J. Clancey, G. Papadourakis et K. Panourgia (dir.). Intelligent Tutoring Systems: 11th International Conference, ITS 2012, Chania, Crete, Greece, June 14-18, 2012, Proceedings, (p. 233-244). Springer. http://dx.doi.org/10.1007/978-3-642-30950-2_29.
  • Batchakui, B., Nkambou, R., Tangha, C. et Parigot, D. (2012). Un grid hybride basé sur le web 2.0 pour l’apprentissage et la collaboration entre communautés de formation. Dans C. P. et Paliard Boiron S.A. Cerri C. Narse R. Nkambou (dir.). Intégration technologique et nouvelles perspectives d’usage : actes du 8ème Colloque Technologies de l’Information et de la Communication pour l’Enseignement, (p. 139-146). Université de Lyon. http://gdac.uqam.ca/tice2012/ActesTICE2012-Final.pdf.
  • Faghihi, U., Fournier-Viger, P. et Nkambou, R. (2011). A Cognitive Tutoring Agent with Episodic and Causal Learning Capabilities. Dans G. Biswas, S. Bull, J. Kay et A. Mitrovic (dir.). Artificial Intelligence in Education: 15th International Conference, AIED 2011, Auckland, New Zealand, June 28 – July 2, 2011, Proceedings. http://dx.doi.org/10.1007/978-3-642-21869-9_12.
    Notes: collection LNAI 6738
  • Batchakui, B., Nkambou, R. et Tangha, C. (2011). GELOnto: An ontology for the research and the validation of the contents in GELSOTC. Dans 2011 IEEE Global Engineering Education Conference (EDUCON), (p. 601-605). IEEE. http://dx.doi.org/10.1109/EDUCON.2011.5773199.
  • Faghihi, U., Fournier-Viger, P. et Nkambou, R. (2011). Implementing an Efficient Causal Learning Mechanism in a Cognitive Tutoring Agent. Dans K.G. Mehrotra, C.K. Mohan, J.C. Oh, P.K. Varshney et M. Ali (dir.). Modern Approaches in Applied Intelligence: 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Syracuse, NY, USA, June 28–July 1, 2011, Proceedings, (Part II, p. 27-36). Springer. http://dx.doi.org/10.1007/978-3-642-21827-9_4.
    Notes: collection LNAI volume 6704
  • Fournier-Viger, P., Nkambou, R. et Tseng, V.S. (2011). RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. Dans W. Chu et W.E. Wong (dir.). Proceedings of the 2011 ACM Symposium on Applied Computing, (p. 956-961). ACM Press. http://dx.doi.org/10.1145/1982185.1982394.
  • Rouane-Hacene, M., Valtchev, P. et Nkambou, R. (2011). Supporting ontology design through large-scale FCA-based ontology restructuring. Dans S. Andrews, S. Polovina, R. Hill et B. Akhgar (dir.). Conceptual Structures for Discovering Knowledge: 19th International Conference on Conceptual Structures, ICCS 2011, Derby, UK, July 25-29, 2011, Proceedings, (p. 257-269). Springer. http://dx.doi.org/10.1007/978-3-642-22688-5_19.
    Notes: collection LNCS, vol. 6828
  • Fournier-Viger, P., Faghihi, U., Nkambou, R. et Mephu Nguifo, E. (2010). CMRules: An Efficient Algorithm for Mining Sequential Rules Common to Several Sequences. Dans H.W. Guesgen et R.C. Murray (dir.). Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, (p. 410-415). AAAI Press. https://www.aaai.org/ocs/index.php/FLAIRS/2010/paper/view/1390.
  • Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E. et Mayers, A. (2010). ITS in Ill-defined Domains: Toward Hybrid Approaches. Dans V. Aleven, J. Kay et J. Mostow (dir.). Intelligent Tutoring Systems: 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, (Part II, p. 318-320). Springer. http://dx.doi.org/10.1007/978-3-642-13437-1_57.
    Notes: collection LNCS volume 6095
  • Rouane-Hacene, M., Fennouh, S., Nkambou, R. et Valtchev, P. (2010). Refactoring of Ontologies: Improving the Design of Ontological Models with Concept Analysis. Dans É. Grégoire (dir.). Proceedings 22nd International Conference on Tools with Artificial Intelligence, 27-29 October 2010/Arras, France, (vol. 2, p. 167-172). IEEE Computer Society. http://dx.doi.org/10.1109/ICTAI.2010.97.
  • Fournier-Viger, P., Nkambou, R. et Mephu Nguifo, E. (2009). Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Defined Domains. Dans V. Dimitrova, R. Mizoguchi, B. du Boulay et A. Graesser (dir.). Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling, (p. 383-390). IOS Press.
  • Faghihi, U., Fournier-Viger, P., Nkambou, R., Poirier, P. et Mayers, A. (2009). How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent. Dans 2009 IEEE Symposium on Intelligent Agents Proceedings, (p. 23-30). IEEE. http://dx.doi.org/10.1109/IA.2009.4927496.
  • Zouaq, A., Nkambou, R. et Frasson, C. (2006). The Knowledge Puzzle: An Integrated Approach of Intelligent Tutoring Systems and Knowledge Management. Dans 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), proceedings, (p. 575-582). IEEE Computer Society. http://dx.doi.org/10.1109/ICTAI.2006.111.
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