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Automatic measurement of student engagement provides helpful information forinstructors to meet learning program objectives and individualize programdelivery. Students' behavioral and emotional states need to be analyzed atfine-grained time scales in order to measure their level of engagement. Manyexisting approaches have developed sequential and spatiotemporal models, suchas recurrent neural networks, temporal convolutional networks, andthree-dimensional convolutional neural networks, for measuring studentengagement from videos. These models are trained to incorporate the order ofbehavioral and emotional states of students into video analysis and outputtheir level of engagement. In this paper, backed by educational psychology, wequestion the necessity of modeling the order of behavioral and emotional statesof students in measuring their engagement. We develop bag-of-words-based modelsin which only the occurrence of behavioral and emotional states of students ismodeled and analyzed and not the order in which they occur. Behavioral andaffective features are extracted from videos and analyzed by the proposedmodels to determine the level of engagement in an ordinal-output classificationsetting. Compared to the existing sequential and spatiotemporal approaches forengagement measurement, the proposed non-sequential approach improves thestate-of-the-art results. According to experimental results, our methodsignificantly improved engagement level classification accuracy on the IIITBOnline SE dataset by 26% compared to sequential models and achieved engagementlevel classification accuracy as high as 66.58% on the DAiSEE studentengagement dataset.

Bag of States: A Non-sequential Approach to Video-based Engagement Measurement