Sidney K. D’Mello

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This paper provides a synthesis of our research towards the development of an affect-sensitive Intelligent Tutoring System called AutoTutor. The affect-sensitive AutoTutor detects the emotions (boredom, flow/engagement, confusion, frustration) of a learner by monitoring conversational cues, gross body language, and facial features. It is also mindful of the(More)
This project augments an existing intelligent tutoring system (AutoTutor) that helps learners construct explanations by interacting with them in natural language and helping them use simulation environments. The research aims to develop an agile learning environment that is sensitive to a learner’s affective state, presuming that this will promote learning.(More)
Relations between emotions (affect states) and learning have recently been explored in the context of AutoTutor. AutoTutor is a tutoring system on the Internet that helps learners construct answers to difficult questions by interacting with them in natural language. AutoTutor has an animated conversation agent and a dialog management facility that attempts(More)
Mind wandering (MW) reflects a shift in attention from taskrelated to task-unrelated thoughts. It is negatively related to performance across a range of tasks, suggesting the importance of detecting and responding to MW in real-time. Currently, there is a paucity of research on MW detection in contexts other than reading. We addressed this gap by using eye(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)
In an attempt to discover the facial action units for affective states that occur during complex learning, this study adopted an emote-aloud procedure in which participants were recorded as they verbalized their affective states while interacting with an intelligent tutoring system (AutoTutor). Participants’ facial expressions were coded by two expert(More)
It is well known that students experience a range of affective states when interacting with a learning technology, be it an intelligent tutoring system (ITS), an educational game, a simulation environment, or even simpler interfaces that support foundational skills like reading comprehension and writing proficiency (see review in D'Mello, 2013). Positive(More)