  Recognition of user interaction, in particular engagement detection, became
highly crucial for online working and learning environments, especially during
the COVID-19 outbreak. Such recognition and detection systems significantly
improve the user experience and efficiency by providing valuable feedback. In
this paper, we propose a novel Engagement Detection with Multi-Task Training
(ED-MTT) system which minimizes mean squared error and triplet loss together to
determine the engagement level of students in an e-learning environment. The
performance of this system is evaluated and compared against the
state-of-the-art on a publicly available dataset as well as videos collected
from real-life scenarios. The results show that ED-MTT achieves 6% lower MSE
than the best state-of-the-art performance with highly acceptable training time
and lightweight feature extraction.
