Modélisation des réactions émotionnelles dans un système tutoriel intelligent

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Apprentissage machine pour la prédiction de la réaction émotionnelle de l’apprenant

Emotions play a crucial role in cognitive processes in particular in learning tasks (Isen, 2000). However, the emotional factor has been never taken into account in Intelligent Tutoring Systems (ITS) until recently. Nevertheless, modelling the learner’s emotional reaction is fundamental for ITSs in order to aid the tutor to anticipate when and how to intervene for helping the learner to achieve learning in the best conditions. In this paper, we attempt to predict the learner’s emotional reaction at a given time of the learning process. Our approach of prediction relays on the causal events which could trigger this emotion and on its determining factors like the personality for example. Thus, we propose to solve this problem by using supervised machine learning algorithms and more precisely those of classification.Les émotions jouent un rôle important dans les processus cognitifs, particulièrement dans des tâches d’apprentissage (Isen, 2000). Cependant, dans le cadre des Systèmes Tuteurs Intelligents (STI), le facteur émotionnel n’a été considéré que récemment. Or, modéliser les réactions émotionnelles d’un apprenant durant une session d’apprentissage est un élément essentiel pour les STI afin de permettre au tuteur de prévoir quand et comment il faut intervenir pour aider l’apprenant à accomplir sa tâche d’apprentissage dans des meilleures conditions. Dans cet article, nous cherchons à prédire la réaction émotionnelle de l’apprenant à un moment donné de l’apprentissage. Notre approche de prédiction repose sur les causes qui ont pu déclencher cette émotion et sur ses facteurs déterminants comme la personnalité par exemple. Nous proposons alors de résoudre ce problème en utilisant les algorithmes d’apprentissage machine supervisé et plus précisément ceux de classement.Chaffar Soumaya, Frasson Claude. Apprentissage machine pour la prédiction de la réaction émotionnelle de l’apprenant. In: Sciences et Technologies de l'Information et de la Communication pour l'Éducation et la Formation, volume 14, 2007. Les Dimensions émotionnelles de l'interaction dans un EIAH/Analyses des traces d'utilisation dans les EIAH. pp. 217-238

Inducing optimal emotional state for learning in Intelligent Tutoring Systems

Abstract. Emotions play an important role in cognitive processes and specially in learning tasks. Moreover, there are some evidences that the emotional state of the learner correlated with his performance. Furthermore, it’s important that new Intelligent Tutoring Systems involve this emotional aspect; they may be able to recognize the emotional state of the learner, and to change it so as to be in the best conditions for learning. In this paper we describe such an architecture developed in order to determine the optimal emotional state for learning and to induce it. Based on experimentation, we have used the Naïve Bayes classifier to predict the optimal emotional state according to the personality and then we induce it using a hybrid technique which combines the guided imagery technique, music and images.

Using an emotional intelligent agent to improve the learner’s performance

Abstract. Emotions are now seen as closely related to cognition processes including decision-making, memory, attention, etc. Thus, e-learning environments have begun to take into consideration the emotional state of the learner in order to enhance his performance. In this paper, we present an agent architecture that includes some emotional intelligence capabilities; we describe each of its components: The perception module, the control module and the action module. These modules are intended respectively to: (1) know the current emotion of the learner; (2) detect when to intervene, basing on the appraisal theory, to change the emotional state of the learner; (3) and induce the optimal emotional state for learning

C.: 2006, Predicting the Emotional Reaction of the Learner with a

Abstract. Emotions play an important role in cognitive processes and specially in learning tasks. Online learning is no exception. Detecting a learner’s emotional reaction for a given situation is an essential element for every Distant Learning Environment. Nevertheless, inferring a learner’s emotional reaction in those environments is not a trivial task. In this paper, we present an agent capable of predicting a learner’s emotional reaction in a distant learning environment based on the learner’s personal and non-personal traits using a machine learning technique, namely the ID3 algorithm. We then describe the agent’s method for predicting the learner’s emotional reaction and discuss the obtained results.

C.: 2006, Predicting the Emotional Reaction of the Learner with a

Abstract. Emotions play an important role in cognitive processes and specially in learning tasks. Online learning is no exception. Detecting a learner’s emotional reaction for a given situation is an essential element for every Distant Learning Environment. Nevertheless, inferring a learner’s emotional reaction in those environments is not a trivial task. In this paper, we present an agent capable of predicting a learner’s emotional reaction in a distant learning environment based on the learner’s personal and non-personal traits using a machine learning technique, namely the ID3 algorithm. We then describe the agent’s method for predicting the learner’s emotional reaction and discuss the obtained results