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.