MOOC platform, drop-out prediction, tensor completion, clustering
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.
Tsinghua University Press
Jinzhi Liao, Jiuyang Tang, Xiang Zhao. Course Drop-out Prediction on MOOC Platform via Clustering and Tensor Completion. Tsinghua Science and Technology 2019, 24(04): 412-422.