Learning analytics, located at the intersection of learning science, data science, and computer science, aims to leverage educational data to enhance teaching and learning. However, as educational data increases, distilling meaningful insights presents challenges, particularly concerning individual learner differences. This work introduces a comprehensive approach for designing and automatically mapping learners into meaningful cohorts based on diverse learning behaviors. It defines four critical contexts, including engagement, direction, repetitiveness, and orderliness, and generates practical learning cohorts from their combinations. The approach employs a time-series-based clustering method with K-means clustering using dynamic time warping to identify similar learning patterns. Statistical techniques like the Mann-Kendall test and Theil-Sen estimator further refine the process. A case study on data science courses validates the approach, offering novel insights into learner behavior. The contributions include a novel time series approach to characterizing learning behavior, new learning cohorts based on critical contexts, and a systematic method for automated cohort identification.