Automatic detection of learner’s affect from conversational cues

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Multimodal learning analytics (2013, background): “[8] designed an application that could use spoken dialogue to recognize the states Permission to make digital or hard copies of all or part of this work for”
Academic boredom, engagement and the achievement of undergraduate students at university: a review and synthesis of relevant literature (2018, background): “…everyday signs of academic boredom, though by no means definitive, include drowsiness and yawning in class, heads resting in hands, bodies slouched in seats, vacant or distant stares, repeated finger or foot tapping and persistently watching the clock (Wallbott, 1998; D’Mello and Graesser, 2009).”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, methodology): “Hence, the subsequent discussion provides an overview of the 29 features; detailed descriptions appear in D’Mello et al. 2008a; D’Mello and Graesser 2009; McDaniel et al. 2007.”