Computer-based environments provide a window into the complex and multifaceted learning process. These systems often collect a vast amount of information concerning how users choose to engage and behave within the interface (i.e., click streams, language input, and choices). Researchers have begun to use this information to gain a deeper understanding of users’ cognition, attitudes, and abilities. This dissertation is comprised of two published articles that describe how post-hoc and real-time analyses of trace data provides fine-grained details about how users regulate, process, and approach various learning tasks within computer-based environments. This work aims to go beyond simply understanding users’ skills and abilities, and instead focuses on understanding how users approach various tasks and subsequently using this information in real-time to enhance and personalize the user’s learning experience.