Detecting Disengagement in Virtual Learning as an Anomaly

• URL: http://arxiv.org/abs/2211.06870v1
• Date: Sun, 13 Nov 2022 10:29:25 GMT
• Title: Detecting Disengagement in Virtual Learning as an Anomaly
• Authors: Ali Abedi and Shehroz S. Khan
• Abstract summary: Student engagement is an important factor in meeting the goals of virtual learning programs. In this paper, we formulate detecting disengagement in virtual learning as an anomaly detection problem. We design various autoencoders, including temporal convolutional network autoencoder, long-short-term memory autoencoder.
• Score: 4.706263507340607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Student engagement is an important factor in meeting the goals of virtual learning programs. Automatic measurement of student engagement provides helpful information for instructors to meet learning program objectives and individualize program delivery. Many existing approaches solve video-based engagement measurement using the traditional frameworks of binary classification (classifying video snippets into engaged or disengaged classes), multi-class classification (classifying video snippets into multiple classes corresponding to different levels of engagement), or regression (estimating a continuous value corresponding to the level of engagement). However, we observe that while the engagement behaviour is mostly well-defined (e.g., focused, not distracted), disengagement can be expressed in various ways. In addition, in some cases, the data for disengaged classes may not be sufficient to train generalizable binary or multi-class classifiers. To handle this situation, in this paper, for the first time, we formulate detecting disengagement in virtual learning as an anomaly detection problem. We design various autoencoders, including temporal convolutional network autoencoder, long-short-term memory autoencoder, and feedforward autoencoder using different behavioral and affect features for video-based student disengagement detection. The result of our experiments on two publicly available student engagement datasets, DAiSEE and EmotiW, shows the superiority of the proposed approach for disengagement detection as an anomaly compared to binary classifiers for classifying videos into engaged versus disengaged classes (with an average improvement of 9% on the area under the curve of the receiver operating characteristic curve and 22% on the area under the curve of the precision-recall curve).

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