Gaze tutor: A gaze-reactive intelligent tutoring system

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How researchers see this paper

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The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes (2014, neutral): “In these studies (e.g., see D’Mello, Olney, Williams, & Hays, 2012), “being engaged” and looking at the instructional materials map to our passive mode, which is the lowest level of engagement in our framework.”
Foundations of Game-Based Learning (2015, neutral): “…to enhanced learning (Craig et al., 2004; D’Mello & Graesser, 2014; Graesser, D’Mello, & Strain, 2014) and that empathetic agents responding to the player’s emotional state impact learning (Cooper, Brna, & Martins, 2000; D’Mello, Olney, Williams, & Hays, 2012; Lester, Towns, & Fitzgerald, 1998).”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, methodology): “The GazeTutor project extended GuruTutor by using a camera to capture student eye-movement data and using that to allow the tutoring agent to react to student gaze, in particular to detect and react to student disengagement (D’Mello et al. 2012).”
It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation (2016, background): “A large number of works in computer vision have studied the problem of estimating human eye gaze [7] given its importance for different applications, such as human-robot interaction [21], affective computing [4], and social signal processing [30].”