Prediction of student engagement
Delgado, Kevin; Origgi, Juan Manuel; Hasanpoor, Tania; Yu, Hao; Allessio, Danielle; Arroyo, Ivon; Lee, William; Betke, Margrit; Woolf, Beverly; Bargal, Sarah Adel
A major challenge for online learning is the inability of
systems to support student emotion and to maintain student
engagement. In response to this challenge, computer vision
has become an embedded feature in some instructional
applications. In this paper, we propose a video dataset
of college students solving math problems on the educational
platform MathSpring.org with a front facing camera
collecting visual feedback of student gestures. The video
dataset is annotated to indicate whether students’ attention
at specific frames is engaged or wandering. In addition,
we train baselines for a computer vision module that determines
the extent of student engagement during remote
learning. Baselines include state-of-the-art deep learning
image classifiers and traditional conditional and logistic regression
for head pose estimation. We then incorporate a
gaze baseline into the MathSpring learning platform, and
we are evaluating its performance with the currently implemented
approach.
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