Probabilistic assessment of user's emotions in educational games

By Cristina Conati. Applied Artificial Intelligence, 2002

575 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Clinical Psychology

View Paper: https://www.semanticscholar.org/paper/7b9038848463f0306bc527a46241c16b28b12411

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Citations in the corpus

Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “(Conati 2002; Conati and Maclaren 2009; D’Mello et al. 2005, 2007; De Vicente and Pain 2002; Forbes-Riley et al. 2008; Kort et al. 2001; McQuiggan et al. 2008; Woolf et al. 2 We also investigated decision-level fusion.”
Multimodal affect recognition in learning environments (2005, background): “Notable among them is Conati’s [2] work on probabilistic assessment of affect in educational games.”
Empirically building and evaluating a probabilistic model of user affect (2009, methodology): “To handle this uncertainty, Conati (2002) proposed a probabilistic framework for affective user modeling that integrates in a Dynamic Decision Network (DDN) (Dean & Kanazawa, 1989), information on both the possible causes of the user's affective reaction and its observable effects.”
Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review (2022, background): “Some examples of early work relevant to aﬀective ToM include work on modeling a 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).”
Experience-Driven Procedural Content Generation (2011, unknown): “A detailed survey is available in [3].”
Toward an Affect-Sensitive AutoTutor (2007, result): “Our results indicated that there were significant differences in classification accuracies among each of the 4 data sets, F(3,15) = 120.”
THE EMPATHIC COMPANION: A CHARACTER-BASED INTERFACE THAT ADDRESSES USERS' AFFECTIVE STATES (2005, background): “A main application field of adaptive interfaces are tutoring systems that aim at tailoring their behavior in accord with the student’s affective state and learning goal (Conati 2002).”
An empirical study of machine learning techniques for affect recognition in human–robot interaction (2005, unknown): “Conati provides a methodology to build a probabilistic model that takes into account the context of the interaction, the user's personality and a variety of user's bodily expressions [11].”
Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments (2010, background): “Conati (2002) developed a probabilistic system that can reliably track multiple affective states (including joy and distress) of the learner during interactions with an educational game, and use these assessments to drive the behavior of an intelligent pedagogical agent (Conati & Zhou, 2004).”
To feel or not to feel: The role of affect in human-computer interaction (2003, unknown): “Conati , 2002 ) .”
A driver fatigue recognition model based on information fusion and dynamic Bayesian network (2010, methodology): “Besides the contextual information and visual cues (the contactless features), the contact physiological features (such as the EEG, and the ECG) may also contribute significantly to the fatigue because a person usually has little control over these contact features and so they could provide reliable”