# Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder

arXiv: https://arxiv.org/abs/2211.06870 · HTML: https://mlsanity.com/paper/2211.06870/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://mlsanity.com/paper/2009.14660/) (2020)
- [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016)
- [Prediction and Localization of Student Engagement in the Wild](https://mlsanity.com/paper/1804.00858/) (2018)
- [Multimodal Engagement Analysis from Facial Videos in the Classroom](https://mlsanity.com/paper/2101.04215/) (2021)
- [Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network](https://mlsanity.com/paper/2104.10122/) (2021)
- [Engagement Detection with Multi-Task Training in E-Learning Environments](https://mlsanity.com/paper/2204.04020/) (2022)
- [Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders](https://mlsanity.com/paper/1610.03761/) (2016)

## Built on by (most cited first, 6 of 6)

- [Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2403.17175/) (2024)
- [Bag of States: A Non-sequential Approach to Video-based Engagement Measurement](https://mlsanity.com/paper/2301.06730/) (2023)
- [Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction](https://mlsanity.com/paper/2304.14922/) (2023)
- [Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection](https://mlsanity.com/paper/2311.02863/) (2023)
- [Supervised Contrastive Learning for Ordinal Engagement Measurement](https://mlsanity.com/paper/2505.20676/) (2025)
- [OPEN: A Benchmark Dataset and Baseline for Older Adult Patient Engagement Recognition in Virtual Rehabilitation Learning Environments](https://mlsanity.com/paper/2507.17959/) (2025)

## Abstract (arXiv)

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).