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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 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)
Affect detection is an important pattern recognition problem that has inspired researchers from several areas. The field is in need of a systematic review due to the recent influx of Multimodal (MM) affect detection systems that differ in several respects and sometimes yield incompatible results. This article provides such a survey via a quantitative review(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 have developed and evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to learners’ cognitive states, the affect-sensitive tutor is responsive to their affective states as well. This affective tutor automatically detects learners’ boredom, confusion, and(More)
One-to-one tutoring is an extremely effective method for producing learning gains in students and for contributing to greater understanding and positive attitudes towards learning. However, learning inevitably involves failure and a host of positive and negative affective states. In an attempt to explore the link between emotions and learning this research(More)
We implemented and evaluated a collaborative lecture module in an ITS that models the pedagogical and motivational tactics of expert human tutors. Inspired by the lecture delivery styles of the expert tutors, the collaborative lectures of the ITS were conversational and interactive, instead of a polished one-way information delivery from tutor to student.(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)