Tang, Jeff Kai TaiJeff Kai TaiTangXie, HaoranHaoranXieWong, Tak LamTak LamWong2021-07-072021-07-072015https://repository.sfu.edu.hk/handle/sfu/780Massive Open Online Courses (MOOC) became popular and they posted great impact to education. Students could enroll and attend any MOOC anytime and anywhere according to their interest, schedule and learning pace. However, the dropout rate of MOOC was known to be very high in practice. It is desirable to discover students who have high chance to dropout in MOOC in early stage, and the course leader could impose intervention immediately in order to reduce the dropout rate. In this paper, we proposed a framework that applies big data methods to identify the students who are likely to dropout in MOOC. Real-world data were collected for the evaluation of our proposed framework. The results demonstrated that our framework is effective and helpful.enconference proceedings10.1007/978-3-662-48978-9_12