|dc.description.abstract||Advance Personalized Learning is one of the 14 grand challenges of engineering as identified by the National Academy of Engineering. One possible approach for this advancement is to deploy systems that allow an investigator to understand the differences in the learning process of individuals. In this context, cyberlearning systems that use networked computing and communication technology to reach a large number of learners offer the affordance to uniquely identify learners and track their learning process in real-time. Motivated by this idea, this doctoral research aims to investigate personalized learning and engagement within a cyberlearning system, called the Online Watershed Learning System (OWLS). This cyberlearning system utilizes learning resources generated by a real-time high-frequency environmental monitoring system, called the Learning Enhanced Watershed Assessment System (LEWAS).