We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions (diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by one’s appraisal of the current context in terms of one’s goals and preferences. The advantage of using the OCC model is that it provides an affective agent with explicit information not only on which emotions a user feels but also why, thus increasing the agent’s capability to effectively respond to the users ’ emotions. The challenge is that building the model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with empirical studies to adapt the theories to our target application domain. We then present results on the model’s accuracy, showing that the model achieves good accuracy on several of the target emotions. We also discuss the model’s limitations, to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information.

Abstract. We present a model of user affect to recognize multiple user emotions during interaction with an educational computer game. Our model deals with the high level of uncertainty involved in recognizing a variety of user emotions by probabilistically combining information on both the causes and effects of emotional reactions. In previous work, we presented the performance and limitations of the model when using only causal information. In this paper, we discuss the addition of diagnostic information on user affective valence detected via an EMG sensor, and present an evaluation of the resulting model. 1

agents Abstract: A motivationally-aware version of the Ecolab system was developed with the aim of improving the learners ’ motivation. To gain some insight into the effects of

Abstract: This paper presents results from a preliminary analysis of interaction and human observation data gathered from students using an Aplusix, an intelligent tutoring system for algebra. Towards the development of automatic detectors of behavior and affect, this study tried to determine

We attempt to automatically detect student frustration, at a coarsegrained level, using measures distilled from student behavior within a learning environment for introductory programming. We find that each student’s average level of frustration across five lab exercises can be detected based on the number of pairs of consecutive compilations with the same edit location, the number of pairs of consecutive compilations with the same error, the average time between compilations and the total number of errors. Attempts to detect frustration at a finer grain-size, identifying individual students ’ fluctuations in frustration between labs, were less successful. These results indicate that it is possible to detect frustration at a coarse-grained level, solely from coarse-grained data about students ’ behavior within a learning environment.

Affect has been the subject of increasing attention in cognitive accounts of learning. Many intelligent tutoring systems now seek to adapt pedagogy to student affective and motivational processes in an effort to increase the effectiveness of tutorial interaction and improve learning outcomes. To this end, recent work has begun to investigate the emotions experienced during learning in a variety of environments. In this paper we extend this line of research by investigating the affective transitions that occur throughout narrative-centered learning experiences. Further analysis differentiates the likelihood of affective transitions stemming from pedagogical agent empathetic responses to student affect.