An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion

By B. Kort, R. Reilly, Rosalind W. Picard. Proceedings IEEE International Conference on Advanced Learning Technologies, 2001

829 citations recorded by the source corpus.

Subfields: Education

View Paper: https://www.semanticscholar.org/paper/57ea5a83a1b91d8eaf4fe9e08ca5efbf6a3e90e4

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Citations in the corpus

The politeness effect: Pedagogical agents and learning outcomes (2008, background): “Other researchers such as Kort et al. (Aist et al., 2002; Kort et al., 2001), and Zhou and Conati (2003) have been addressing the problem of detecting learner affect and motivation, and influencing it.”
Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design (2013, background): “Kort et al. (2001) mention that negative emotions or cognitive-emotive states can be useful and integral parts of the learning process.”
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.”
Simulating Instructional Roles through Pedagogical Agents (2005, background): “He expressed emotion that commonly occurs in learning, such as frustration, confusion, and enjoyment (Kort et al., 2001); thus, he was not always positive and supportive, but at times demonstrated his difficulty with the content to model coping strategies.”
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): “Csikszentmihalyi, 1990), frustration (Kort et al., 2001; Patrick et al., 1993), and surprise (Schutzwohl and Borgstedt, 2005).”
Detection of Emotions during Learning with AutoTutor (2006, background): “However, the emotions that Ekman intensely investigated (e.g., sadness, happiness, anger, fear, disgust, surprise) have minimal relevance to learning per se (Kort et al., 2001).”
Affect and learning: An exploratory look into the role of affect in learning with AutoTutor (2004, methodology): “As learners move up the spiral, cycle after cycle, they become more competent and acquire more domain knowledge (Kort et al ., 2001a,b).”
Predicting Student Emotions in Computer-Human Tutoring Dialogues (2004, methodology): “Another method for closing this performance gap has been to incorporate affective reasoning into computer tutoring systems, independently of whether or not the tutor is dialogue-based (Conati et al., 2003; Kort et al., 2001; Bhatt et al., 2004).”
Automatic detection of learner’s affect from conversational cues (2008, background): “2001), delight (Fredrickson and Branigan 2005; Silvia and Abele 2002), flow (Csikszentmihalyi 1990), frustration (Kort et al. 2001; Patrick et al. 1993), and surprise (Schutzwohl and Borgstedt 2005).”