Finding out Reasons for Low Completion in MOOC Environment: An Explicable Approach Using Hybrid Data Mining Methods

Abstract

Because of the worldwide rapid development of MOOC, academic researches and industrial applications of MOOC have become a branch of the major concerns in modern education and information technology fields. This paper focuses on low completion phenomenon in MOOC environment and proposes an explicable approach to find out hidden reasons convincingly. Different from existing works, this approach utilizes data mining methods to make quantitative analysis. It employs learners clustering basing on their study features at first, aiming at discovering inactive learners automatically. These learners are representative of low completion in course study on MOOC platform. Their study behaviors and interactions with website are analyzed with association rules mining in order to explore potential patterns and rules. The extracted rules are used to find out and explain the reasons for low completion in MOOC environment. The experimental result on practical XuetangX platform reveals several strong rules with high support, confidence and lift, which can be regarded as evidence and reference for further explanations of reasons for low completion in MOOC environment.