Browsing by Author "James C. Lester, Committee Chair"

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    Affective Behavior Control for Lifelike Pedagogical Agents
    (2002-08-21) Stelling, Gary Dean; James C. Lester, Committee Chair; Patrick FitzGerald, Committee Member; Michael Young, Committee Member
    Lifelike pedagogical agents should be especially effective in constructivist learning environments in which students participate in active problem solving. We can simulate such a constructivist setting with personal computing using a well-designed, evocative graphical interface and the rich multimedia -- audio, video and animation -- currently available. Beyond such an authentic problem-solving context, constructivist learning employs a social aspect, centered on the interaction of learner and mentor. We submit that an animated pedagogical agent who delivers contextualized problem-solving advice can play the part of the expert. Further, we propose that an added measure of believability and motivation would result from giving these agents the ability to express situationally appropriate emotions. To test the promise of such an affective agent, we first identified the cognitive emotion types most useful in a problem-solving dialog. We then devised a structure to store the details of the learner's situation in order to determine the appropriate emotion from the pedagogical agent. These enhancements have been instantiated in a full-scale implementation of the lifelike pedagogical agent of DESIGN-A-PLANT, a learning environment developed in the domain of botanical anatomy and physiology for middle-school students. Evaluation by a focus group of students was encouraging. They preferred the emotional version of the agent and reported that his affective behavior was helpful in their problem solving.
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    An Inductive Framework for Affect Recognition and Expression in Interactive Learning Environments
    (2009-03-18) McQuiggan, Scott William; James C. Lester, Committee Chair; John L. Nietfeld, Committee Member; Munindar P. Singh, Committee Member; R. Michael Young, Committee Member
    Recent years have seen a growing recognition of the importance of affective reasoning in human-computer interaction. Because affect plays an important role in cognitive functions, such as perception and decision-making, the prospect of modeling user affect and enabling interactive systems to respond appropriately holds much appeal for a broad range of applications. Affective reasoning is particularly promising for educational applications because of the strong connections between affect and learning. If it were possible to accurately detect frustration, monitor changes in efficacy, and predict students’ affective states, interactive learning environments could more effectively tailor problem-solving episodes. However, constructing computational models of affect recognition and affect expression is challenging because of the need to devise solutions that are accurate, efficient, and capable of making early predictions. To this end we propose CARE, an inductive framework for affect recognition and expression. CARE learns models of affect from observation of human-computer and human-human interaction. First, in training sessions, users perform a series of tasks in interactive environments while CARE monitors reports of users’ affective experiences. In addition, CARE monitors user actions, world state, and physiological responses. Second, CARE induces models of affect from observed data with machine learning techniques that include decision trees, naive Bayes classifiers, support vector machines, Bayesian networks, and n-grams. Third, at runtime, CARE-induced models monitor user actions, world state, and physiological responses to predict user affective states. In a series of studies involving more than four hundred subjects, the CARE framework has successfully been used to perform a number of affect prediction tasks, including emotional state prediction, self-efficacy, and metacognitive monitoring prediction. It has also been used to induce models of empathy for virtual agents in interactive learning environments. Results suggest that CARE-induced affect models satisfy the real-time requirements of interactive systems and provide a solid foundation for empirically informed affective reasoning.