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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)
—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)
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)
The recent influx of multimodal affect classifiers raises the important question of whether these classifiers yield accuracy rates that exceed their unimodal counterparts. This question was addressed by performing a meta-analysis on 30 published studies that reported both multimodal and unimodal affect detection accuracies. The results indicated that(More)
Affect detection is a key component in developing intelligent educational interfaces that are capable of responding to the affective needs of students. In this paper, computer vision and machine learning techniques were used to detect students' affect as they used an educational game designed to teach fundamental principles of Newtonian physics. Data were(More)
In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, human-like learning(More)
The relationship between emotions and learning was investigated by tracking the affective states that college students experienced while interacting with AutoTutor, an intelligent tutoring system with conversational dialogue. An emotionally responsive tutor would presumably facilitate learning, but this would only occur if learner emotions can be accurately(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)