Within education, anxiety has generally been defined as the affective reaction to overwhelming cognitive and motivational demands tied to highly valued academic situations. This dissertation uses mixed methods, combining relationship mining, automated models of affect, self-report measures, and qualitative analysis of interviews, to identify and understand the effects of anxiety within interactive learning environments (ILEs), towards eventually mitigating those effects. Research into ILEs and learner outcomes is increasingly important as more educational institutions adopt platforms for computer-based learning and more academic leaders begin to integrate computer-based learning and online learning as critical features of their long-term instructional strategies. ILEs provide a novel opportunity to leverage large bodies of fine-grained data, that is typically used to build and generate student models, to investigate instances of anxiety. The emergence of anxiety within these settings yields negative effects on the learning experiences of students. Previous research has established the debilitating effects anxiety has on learning outcomes, experiences, and even student physiology. Although multiple studies within the fields of learning analytics and educational data mining have leveraged ILEs to study other cognitive, affective, motivational, and behavioral phenomena, only a few studies have examined student anxiety within the context of ILEs. The first study in this dissertation looks at the influence of affect sequences on student learning outcomes within the Betty’s Brain ILE. The second study builds on this work, using affect sequences identified in the first study to drive qualitative data collection, where interviews were triggered by student affect detectors, allowing for targeted interviews at times where specific emotional experiences of interest have just occurred. This study uses both quantitative and qualitative data to examine how state-level affective experiences (e.g., boredom, confusion, delight, engaged concentration, and frustration) relate to student trait-level science anxiety, and how these both influence learning-related behaviors and learning, again in Betty’s Brain. Lastly, the third study uses data from a new platform, CueThink, to investigate the influence of trait-level anxiety on learner experiences and achievement.