Affect and learning: An exploratory look into the role of affect in learning with AutoTutor

By Scotty D. Craig, A. Graesser, Jeremiah Sullins, Barry Gholson. 2004

691 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/b447165396f802c33762c9a72b2f5806aa0dd9b2

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Citations in the corpus

Emotions as Drivers of Learning and Cognitive Development (2011, background): “A prototypical case is cognitive incongruity triggering surprise, curiosity, and confusion, as well as anxiety and frustration if the incongruity cannot be dissolved ( Craig, Graesser, Sullins, & Gholson, 2004; D’Mello & Graesser, 2010).”
Detection of Emotions during Learning with AutoTutor (2006, background): “, 2004) and has a large correlation with learning gains (Craig et al., 2004).”
Empirically building and evaluating a probabilistic model of user affect (2009, background): “Several education technology researchers have focused their efforts on these affective states because there is evidence that they arise during learning (e.g., Craig et al. 2004).”
Foundations of Game-Based Learning (2015, background): “This is typically used to address boredom and frustration (Craig, Graesser, Sullins, & Gholson, 2004; D’Mello & Graesser, 2014).”
Activity Achievement Emotions and Academic Performance: A Meta-analysis (2021, background): “…as it undermines attention, effort, motivation, and engagement during achievement activities as well as resulting task performance (Camacho-Morles et al. 2019a; Craig et al. 2004; Haager et al. 2018; Pekrun et al. 2010; Pekrun et al. 2017a; Perry et al. 2001; Putwain et al. 2018a; Tze et al. 2016).”
The Definition, Assessment, and Mitigation of State Boredom Within Educational Settings: A Comprehensive Review (2012, background): “Recently, various observational methods of assessing boredom have become increasingly popular (e.g., Cooper et al. 2011; Craig et al. 2004; D’Mello et al. 2007; D’Mello et al. 2011; Mastro et al. 2002; Rani et al. 2005).”
Toward an Affect-Sensitive AutoTutor (2007, background): “We found that boredom, confusion, and frustration were reported at higher rates than were anger, contempt, and disgust, F(7, 42) = 7.”
Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment (2009, methodology): “…widely refer to Russell's (1980) two-dimension 'circumplex model of affect', where emotions are seen as combinations of arousal and valence (Craig et al., 2004; Fagerberg, Ståhl, & Höök, 2004; Kort et al., 2001; Leon, ), is also established as the standard cognitive appraisal model for…”
Gaze tutor: A gaze-reactive intelligent tutoring system (2012, background): “This is a serious limitation that reduces the efficacy of these systems because engagement, motivation, and interest are precursors to learning, effortful problem solving, and deep thinking (Berlyne, 1978; Craig et al., 2004; Csikszentmihalyi, 1990).”
Dynamics of affective states during complex learning (2012, background): “In particular, previous research has correlated confusion (and uncertainty) with positive learning outcomes in computer and human tutoring contexts (Craig et al., 2004; D’Mello & Graesser, in press; Forbes-Riley & Litman, 2010; Graesser, Chipman, King, McDaniel, & D’Mello, 2007; VanLehn et al., 2003”
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): “Therefore, learners interacting with AutoTutor are less likely to experience delight and surprise than the other states.”