Sidney K. D'Mello

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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)
We explored the reliability of detecting a learner’s affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by holding a conversation in natural language. Training data were collected in a learning session with AutoTutor, after which the affective states of the learner(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)
We developed and evaluated a multimodal affect detector that combines conversational cues, gross body language, and facial features. The multimodal affect detector uses feature-level fusion to combine the sensory channels and linear discriminant analyses to discriminate between naturally occurring experiences of boredom, engagement/flow, confusion,(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)
We present AutoTutor and Affective AutoTutor as examples of innovative 21<sup>st</sup> century interactive intelligent systems that promote learning and engagement. AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by(More)
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It is hypothesized that the ability for a system to automatically detect and respond to users' affective states can greatly enhance the human-computer interaction experience. Although there are currently many options for affect detection, keystroke analysis offers several attractive advantages to traditional methods. In this paper, we consider the(More)