Abstract

Student behavior and lecturer oversight in the classroom is known to modulate study behaviors and impact performance and learning outcomes, but cannot at present be managed for distance learning students. Quantifying and automatically measuring student engagement during lectures in a scalable and accessible manner for these students is essential for improving academic success, but has not been studied widely in natural distance learning environments. We collect video recordings from a screen-mounted camera of students studying online lectures in a mostly unstructured setting and gather annotations from a panel of humans for assessing student engagement levels. We present results on the prediction of different representations of engagement, both with subject-independent and individual-specific models, and quantify the performance gap between the generalized and personalized models for …

Date

2017

Authors

Brandon M Booth, Asem M Ali, Shrikanth S Narayanan, Ian Bennett, Aly A Farag

Conference

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

Pages

470-476

Publisher

IEEE