Toward an Affect-Sensitive AutoTutor

By S. D’Mello, Rosalind W. Picard, A. Graesser. IEEE Intelligent Systems, 2007

420 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/80df5a02e896b3b5507df0a1077c32d29e2c9853

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In Developmental and Educational Psychology

Citations in the corpus

Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “Other channels such as body language, lexical features, and contextual cues are more resilient to attempts to conceal these emotions (D’Mello et al. 2007; Ekman and Friesen 1969).”
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns (2015, background): “Copyrights for components of this work owned by others than ACM must be honored.”
Context-Aware Emotion Recognition Networks (2019, background): “Recognizing human emotions from visual contents has attracted signiﬁcant attention in numerous computer vision applications such as health care and human-computer interaction systems [1, 2, 3].”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, background): “This raises a few questions: what emotions are important to help tutors adapt, what data can help reliably detect such emotions, and who benefits from adapting to such emotions? AutoTutor-AS (Affect Sensitive, also known as Emotion Sensitive) incorporated affect detection by monitoring body posture,”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “[183], [188], [189], [190], [191], gaming and entertainment,”
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions (2014, background): “So-called affect-sensitive ITS are a hot topic in the ITS research community [13], [59], [5], [19], [26], [12], and some of the first fully-automated closed-loop ITS that use affective sensors for feedback are starting to emerge [59], [12].”
Affect-aware tutors: recognising and responding to student affect (2009, background): “, frustration or boredom) can be detected within intelligent tutoring systems (McQuiggan and Lester, 2006; Graesser et al., 2007; D’Mello et al., 2007).”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “2007], and facial features [D'Mello et al. 2007; McDaniel et al. 2007] (see Figure 3).”
The computer expression recognition toolbox (CERT) (2011, background): “There has been a growing thrust to develop tutoring systems and agents that respond to students’ emotional and cognitive state and interact with them in a social manner (e.g., [28], [ 29 ]).”