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

By Ryan Shaun Joazeiro de Baker, S. D’Mello, Ma. Mercedes T. Rodrigo, A. Graesser. Int. J. Hum. Comput. Stud., 2010

908 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/ba4e31b5dc375399c47f1de61e4112e2c450a682

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Citations in the corpus

Academic boredom, engagement and the achievement of undergraduate students at university: a review and synthesis of relevant literature (2018, background): “…and Murayama (2014) and Ranellucci et al. (2015) on competence-relevant achievement goals (task mastery, performance-approach and performance-avoidance), Garn et al. (2017) on belongingness and the work of Artino (2009-2012), Baker et al. (2010) and Heckel and Ringeisen (2017) in online learning.”
A Case for Domain-Specific Curiosity in Mathematics (2019, background): “Both confusion and frustration may present as heightened arousal with manifestations similar to those that characterize curiosity (Baker et al. 2010; D’Mello and Graesser 2012).”
Pedagogical Agents: Back to the Future (2018, positive): “Creating affect-sensitive pedagogical agents attests to the growing recognition of the central role that emotion plays in learning (Baker et al. 2010; D’Mello et al. 2014; Pekrun et al. 2002; Pekrun 2011) and the importance of designing learning environments that respond to student affect (Rai et al”
Gaze tutor: A gaze-reactive intelligent tutoring system (2012, background): “…perspectives, boredom has recently been gaining some attention in studies that investigate the links between affect and cognition during learning (Baker et al., 2010; Beck, 2005; Cocea and Weibelzahl, 2009; D’Mello and Graesser, in press; Drummond and Litman, 2010; Moss et al., 2008; Pekrun et…”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “…to the broader goal of developing affectsensitive ITSs by developing multimodal detectors of boredom, engagement/flow, confusion, frustration, and delight; the affective states that accompany learning at deeper levels of comprehension (Baker et al. 2010; D’Mello et al. 2006; Lehman et al. 2008a,b).”
The Role of Epistemic Emotions in Personal Epistemology and Self-Regulated Learning (2018, background): “A number of studies support this proposition (Baker et al., 2010; Chevrier et al., 2015; D’Mello et al., 2014; Muis, Pekrun, et al., 2015; Whittlesea & Williams, 2000).”
Learner Engagement in Blended Learning Environments: A Conceptual Framework (2019, background): “Baker et al. (2010) noted that frustration “may lead students to use (or fail to use) learning environments in ways that reduce their learning” (p. 231).”
Serious games as new educational tools: how effective are they? A meta-analysis of recent studies (2013, methodology): “…a second selection excluding all the studies that did not use SGs or VGs (Duque et al. 2008; Ke 2008; Lorant-Royer et al. 2008; Sung et al. 2008; Papastergiou 2009; Robertson & Miller 2009; Baker et al. 2010; Bloomfield et al. 2010; Liu & Chu 2010; Lindström et al. 2011; Brom et al. 2011).”
Challenges and Future Directions of Big Data and Artificial Intelligence in Education (2020, background): “For instance, eﬀorts to algorithmically detect mental states such as boredom, frustration, and confusion (Baker et al., 2010) must be supported by the operational deﬁnitions and constructs that have been prudently evaluated.”
AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back (2012, background): “AutoTutor, with the expectation that these .ndings will generalize to other learning environments [Baker et al. 2010; D Mello in 2012].”
Foundations of Game-Based Learning (2015, background): “Game mechanics can also impact emotions through the inclusion of affective tutors that diagnose players’ emotions and respond to them, which has been shown to positively impact learning (Baker, D’Mello, Rodrigo, Graesser, 2010; D’Mello & Graesser, 2014).”
Sentiment Analysis in MOOC Discussion Forums: What does it tell us? (2014, background): “In contrast, frustration was less associated with poorer learning [3].”