Capturing students' behavioral patterns through analysis of sequential interaction logs, is an important task in educational data mining to enable more effective personalized support during the learning process. This study aims at discovery and temporal analysis of learners' study patterns in MOOC assessment periods. We address this problem using two different methods. First, following a pattern-driven approach, we identify learners' study patterns based on their interaction with video lectures and assignments. Through unsupervised clustering of study pattern sequences, we capture different longitudinal engagement profiles among learners in a MOOC course. Second, we propose temporal clustering framework for unsupervised discovery of latent patterns in learners' interaction data. We model and cluster activity sequences at each time step, and perform cluster matching to enable tracking learning behaviours over time. Our proposed pipeline is general and can be adopted for modeling and temporal analysis of interaction data at different levels of granularity, in various learning environments including MOOCs and Intelligent Tutoring Systems (ITS). We demonstrate the application of our proposed pipeline for detecting latents study patterns in a MOOC course.