In an open and flexible context of Massive Open Online Courses (MOOCs), learners who take final assessments exhibit the motivation for performance goals. The learning trajectories of this group usually provide more clues for course design and teaching improvement in that this group tend to interact more fully with course learning activities and resources for better learning outcomes. This study focused on such learners to investigate their learning engagement, time organization, content visit sequences, and activity participation patterns by applying statistical analysis, lag sequence analysis, and other data mining methods. This study examined the data of 535 learners taking the assessment in a MOOC to detect the differences in learning engagement and the above learning patterns amongst three groups of learners with different achievement levels, labeled "failed," "satisfactory" and "excellent." We found differences in both learning engagement and learning patterns among the three groups. The results indicated that for the learners to be successful, they require a certain degree of task completion as a basic guarantee for passing the course, effective session workload organization, reasonable learning content arrangement, and more cognitive engagement (rather than investing more time and energy). Based on the outcomes, implications for personalized instructional design and intervention to promote academic achievement in MOOCs are discussed.