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We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students' cognitive-affective states during their use of three different computer-based learning environments. Students' cognitive-affective states are studied using different populations (Philippines, USA),(More)
—This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assumptions that impact their(More)
This paper investigates how frequent conversation patterns from a mixed-initiative dialogue with an intelligent tutoring system, AutoTutor, can significantly predict users' affective states (e.g. confusion, eureka, frustration). This study adopted an emote-aloud procedure in which participants were recorded as they verbalized their affective states while(More)
We compare the affect associated with an intelligent tutoring environment , Aplusix, and a simulations problem solving game, The Incredible Machine, to determine whether students experience significantly better affect in an educational game than in an ITS. We find that affect was, on the whole, better in Aplu-six than it was in The Incredible Machine.(More)
This submission is intended for the Special Issue on Affective Modeling and Adaptation. This paper (or a similar version) is not currently under review by a journal or conference, nor will it be submitted to such within the next three months. Abstract We explored the reliability of detecting a learner's affect from conversational features extracted from(More)
We investigated 28 learners' postural patterns associated with naturally occurring episodes of boredom, flow/engagement, confusion, frustration, and delight during a tutoring session with AutoTutor, a dialogue-based intelligent tutoring system. Training and validation data were collected in a learning session with AutoTutor, after which the learners'(More)
This paper investigates the reliability of detecting a learner's affective states in an attempt to augment an Intelligent Tutoring System (AutoTutor) with the ability to incorporate such states into its pedagogical strategies to improve learning. We describe two studies that used observational and emote-aloud protocols in order to identify the affective(More)
The relationship between emotions and learning was investigated by tracking the emotions that college students experienced while learning about computer literacy with AutoTutor. AutoTutor is an animated pedagogical agent that holds a conversation in natural language, with spoken contributions by the learner. Thirty students completed a multiple-choice(More)
Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners(More)