  The degree of concentration, enthusiasm, optimism, and passion displayed by
individual(s) while interacting with a machine is referred to as `user
engagement'. Engagement comprises of behavioral, cognitive, and affect related
cues. To create engagement prediction systems that can work in real-world
conditions, it is quintessential to learn from rich, diverse datasets. To this
end, a large scale multi-faceted engagement in the wild dataset EngageNet is
proposed. 31 hours duration data of 127 participants representing different
illumination conditions are recorded. Thorough experiments are performed
exploring the applicability of different features, action units, eye gaze, head
pose, and MARLIN. Data from user interactions (question-answer) are analyzed to
understand the relationship between effective learning and user engagement. To
further validate the rich nature of the dataset, evaluation is also performed
on the EngageWild dataset. The experiments show the usefulness of the proposed
dataset. The code, models, and dataset link are publicly available at
https://github.com/engagenet/engagenet_baselines.
