Empirically building and evaluating a probabilistic model of user affect

By Cristina Conati, Heather Maclaren. User modeling and user-adapted interaction, 2009

364 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b6ff846ed3a9d320895c582b40dd96e832f8ce42

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “…respond to affective states such as boredom, confusion, frustration, and anxiety [Afzal and Robinson 2011; Burleson and Picard 2007; Chaffar et al. 2009; Conati and Maclaren 2009; D Mello and Graesser 2010; D Mello et al. 2010d; Forbes-Riley et al. 2008; Robison et al. 2009; Woolf et al. 2010].”
A review of recent advances in learner and skill modeling in intelligent learning environments (2012, methodology): “Conati’s pioneering work in affect detection in educational software focused on a subset of the OCC model, and used a combination of physical sensors (electromyogram, heart rate, skin conductance) and distilled aspects of log files to detect student emotions as they played an educational game, Prime”
Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review (2022, background): “user’s emotions and engagement in educational games (Conati, 2002) and on recognizing the emotions of a user interacting with an educational computer game (Conati and Maclaren, 2009).”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “Consequently, the last decade has witnessed an eruption of research activities aimed at making computer interfaces sensitive to users’ affective and cognitive states in an attempt to develop more effective, user-friendly, and naturalistic applications (Conati and Maclaren 2009; Forbes-Riley et al. 2”
Gaze tutor: A gaze-reactive intelligent tutoring system (2012, background): “A number of research groups have been addressing the problem of building learning environments that detect and respond to affective states such as boredom, confusion, frustration, and anxiety (Afzal and Robinson, 2009; Burleson and Picard, 2007; Chaffar et al., 2009; Conati and Maclaren, 2009; D’Mel”
Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks (2016, background): “More recent work in this space was able to accurately predict the affective state, and the source of the change in affective state for users as they interact with a computer-based tutoring system (Conati & MacLaren, 2009).”
Student modeling approaches: A literature review for the last decade (2013, methodology): “Furthermore, Conati and Maclaren (2009) developed a probabilistic model of user affect, which recognizes a variety of user emotions by combining information on both the causes and effects of emotional reactions within a Dynamic Bayesian Network.”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “…learning [Douglas-Cowie et al. 2007]; et al. 2006]; (e) challenges of incorporating top-down models of context with bottom-up body-based sensing [Conati and Maclaren 2009]; (f) issues of generalizability across contexts, time, individuals, and cultures [Calvo and D’Mello 2010]; (g) lack of…”
Dynamics of affective states during complex learning (2012, background): “…& Damasio, 2007), education (Buff, Reusser, Rakoczy, & Pauli, 2011; Csikszentmihalyi, 1990; Huk & Ludwigs, 2009; Linnenbrink, 2006; Meyer & Turner, 2006; Schutz & Pekrun, 2007), and artificial intelligence in education (Calvo & D’Mello, 2011; Conati & Maclaren, 2009; Forbes-Riley & Litman, 2010).”
Sentiment analysis in Facebook and its application to e-learning (2014, background): “Another interesting related work is presented in (Conati & Maclaren, 2009).”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, neutral): “[183], [188], [189], [190], [191], gaming and entertainment,”
New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization (2013, background): “Conati and Maclaren (2009) and Sabourin et al. (2011) used a combination of interaction data and questionnaire data to infer a range of affective states.”