As a supplement to traditional education, online courses offer people, regardless of their age, gender, or profession, the chance to access state-of-the-art knowledge. Nonetheless, despite the large number of students who choose to begin online courses, it is easy to observe that quite a few of them drop out in the middle, and information on this is vital for course organizers to improve their curriculum outlines. In this work, in order to make a precise prediction of the drop-out rate, we propose a combined method MOOP, which consists of a global tensor and local tensor to express all available feature aspects. Specifically, the global tensor structure is proposed to model the data of the online courses, while a local tensor is clustered to capture the inner connection of courses. Consequently, drop-out prediction is achieved by adopting a high-accuracy low-rank tensor completion method, equipped with a pigeon-inspired algorithm to optimize the parameters. The proposed method is empirically evaluated on real-world Massive Open Online Courses (MOOC) data, and is demonstrated to offer remarkable superiority over alternatives in terms of efficiency and accuracy.