Engagement Measurement

Engagement measurement aims to quantify user interaction with various forms of content, from short videos and video games to online meetings and educational materials, ultimately seeking to understand and predict user behavior. Current research focuses on developing robust models, often employing deep learning architectures like convolutional and transformer networks, graph neural networks, and autoencoders, to analyze multimodal data including visual, auditory, and textual features, as well as physiological signals. These advancements are improving the accuracy and efficiency of engagement prediction across diverse applications, offering valuable insights for optimizing content design, personalizing user experiences, and enhancing human-computer interaction. The resulting improvements in engagement prediction have significant implications for fields such as education, marketing, and healthcare.