The goal of learning analytics is to optimize learning and the environments in which it occurs. Since 2011, when learning analytics was defined as a separate and distinct area of academic inquiry, the literature has identified a need for research that presents evidence of effective learning analytics, as well as, learning analytics research that is conducted in conjunction with learning theory. This study uses Efklides' metacognitive and affective model of self-regulated learning (MASRL) to define cognitive, metacognitive, and affective variables that can explain students' learning outcomes in hybrid/online sections of Calculus I in the 2020-21 academic year. Cognitive variables were measured according to the cognitive operational framework for analytics (COPA). Metacognitive variables were defined according to the ways in which students interacted with the course content in the learning management system (LMS) and supplemental instruction, and affective variables were measured by ways students gave evidence of their affective states, such as in discussion board posts. All variables were compared across the course learning design, activities, and outcomes. Binary logistic regression revealed five significant variables: two cognitive, one metacognitive, and two affective. Thus, this study provided a learning analytics, evidence-based link between self-regulated learning theory and learning design, activities, and outcomes. In addition, implications for students, instructors, and learning theory were explored, as well as, the qualifications of this study as evidence of effective learning analytics.