  Virtual reality (VR) is not a new technology but has been in development for
decades, driven by advances in computer technology. Currently, VR technology is
increasingly being used in applications to enable immersive, yet controlled
research settings. Education and entertainment are two important application
areas, where VR has been considered a key enabler of immersive experiences and
their further advancement. At the same time, the study of human behavior in
such innovative environments is expected to contribute to a better design of VR
applications. Therefore, modern VR devices are consistently equipped with
eye-tracking technology, enabling thus further studies of human behavior
through the collection of process data. In particular, eye-tracking technology
in combination with machine learning techniques and explainable models can
provide new insights for a deeper understanding of human behavior during
immersion in virtual environments.
  In this work, a systematic computational framework based on eye-tracking and
behavioral user data and state-of-the-art machine learning approaches is
proposed to understand human behavior and individual differences in VR
contexts. This computational framework is then employed in three user studies
across two different domains. In the educational domain, two different
immersive VR classrooms were created where students can learn and teachers can
train. In terms of VR entertainment, eye movements open a new avenue to
evaluate VR locomotion techniques from the perspective of user cognitive load
and user experience. This work paves the way for assessing human behavior in VR
scenarios and provides profound insights into the way of designing, evaluating,
and improving interactive VR systems. In particular, more effective and
customizable virtual environments can be created to provide users with tailored
experiences.
