Using Emotional Coping Strategies in Intelligent Tutoring Systems Soumaya Chaffar & Claude Frasson Departement d’Informatique et de Recherche Opérationnelle Université de Montréal Introduction A number of studies showed that user interactions with computers are close to human relationships. For example, Reeves and Nass (1996) argue that users treat computers like real people [7]. In addition, Klein and colleagues (2002) concluded that computers are strongly able to regulate negative emotions, even if they are the sources of these emotions [5]. Emotion regulation is defined by Gross (1998) as the ability to reduce the high intensity of a given emotion (negative or positive) and to change it [3]. Coping differs from emotion regulation by focusing only on negative emotions aiming to reduce negative emotional experiences [1]. In this research work we attempt to discuss the benefit of using emotional coping strategies in ITS. We try to compare the effect of emotion-focused strategies with the problem-focused one on the leaner’s emotional state. Emotional modules in ITS’ architecture An ITS is based on a quadripartite architecture: a learner model describing the learner’s profile, a teaching model which plans instructional steps to the learner, a domain model including the knowledge to be learned and an interface representing communication modalities. We seek to integrate emotional modules in such architecture in order to improve learners’ performances. Conclusion we conclude that there are significant positive effects after using emotional coping strategies on participants’ emotional state, with 95% of confidence. Thus, we assume that including emotional coping strategies in ITS would be beneficial for learning. References [1] Blair, K. A., Denham, S. A., Kochanoff, A., Whipple, B.: Playing it cool: Temperament, emotion regulation and social behavior in preschoolers. Journal of School Psychology, 42, (2004) [2] Chaffar, S., Derbali, L., Frasson, C.: Towards Emotional Regulation in Intelligent Tutoring Systems. AACE World Conference on E-learning in Corporate, Government, Healthcare, & Higher Education: E-LEARN 2009, Vancouver, Canada (2009) [3] Gross, J. J.: The Emerging Field of Emotion Regulation: An Integrative Review. Review of General Psychology, 2, (1998) [4] Hazlett, R. L., Benedek, J.: Measuring emotional valence to understand the user's experience of software, International Journal of Human-Computer Studies 65 (2007) [5] Klein, J., Moon, Y., Picard, R. W.: This computer responds to user frustration: theory, design, and results. Interacting with Computers, 14, (2002) [6] Perry, P., Hechter, F. J., Menec, V. H., Weinberg, L. H.: Enhancing achievement motivation and performance in college students: An attributional retraining perspective. Research in Higher Education, 34, (1993) [7] Reeves, B., Nass, C.: The media Equation: How people Treat Computers, Television, and New Media Like Real People and Places, Cambridge University Press (1996) Acknowledgements We address our thanks to the Fond Québecois pour la Recherche sur la Nature et la Technologie (FQRNT) for supporting this research work. Experiment Three kinds of emotion-focused and two kinds of problem- focused strategies have been used in this experiment depending on stressful situations facing the learner. During his interaction with an ITS, a learner might be subject to many stressful experiences. It is therefore important to act following an emotional situation to alleviate possible negative emotions by adapting instruction to learners’ needs or reassuring them after receiving their marks. Table 1. Emotional coping strategies used in the experiment. A total of twenty-nine graduate students (17 male/12 female, aged years) in computer science were experienced in two emotional situations: misunderstanding of a data structure course and obtaining marks in a data structure evaluation test. These two situations might significantly affect students’ emotional state and their performances [6]. As some participants randomly experienced several emotional situations depending on their request for help in understanding different sorting algorithms, our sample data is composed of 73 instances. Results and discussion EMG is the most studied and validated physiological measure for indicating the valence even in the absence of facial expressions [4]. Thus, we have used this signal for analysing in more detail the learner’s emotional changes (see Table 2) after the tutor’s actions [2]. Table 2. ANOVA’s Results of EMG signal recorded before, during and after applying a coping strategy. A virtual tutor was used in this experiment attempting to influence the learner’s emotional state in the two situations: the first one when the learner needs help to understand a sorting algorithm (initial actions) and the second one when the learner obtained his mark after passing an evaluation test (final actions). During the comprehension activity, we can conclude that it is recommended to use a problem-focused strategy in order to induce positive emotions in the learner.