Learning Behavior Analysis and Learning Effect Evaluation in Open Online Courses

Abstract

In the context of globalization, the trend of globalization in the field of education is becoming more and more significant. With the maturity of Internet and cloud computing, a large online open course learning platform providing courses and educational services for global users has emerged in the past few years. This is not only the innovation of Internet applications, but it is also believed that it will trigger a change in higher education and social development. The main body of online open courses is learners, and its biggest feature is that there are a large number of learners and a variety of learner groups. Due to the characteristics of Internet technology, all learning behaviors of learners on the online open course platform will be recorded in the form of rich and diverse data. Therefore, it is necessary to analyze learners’ learning behavior. This paper proposes a dual-channel clustering algorithm, which analyzes and mines a large number of learning behavior data of more than 5000 learners in online open courses of a university. This method takes fine-grained data as the core, obtains the types of learners in different models through dual-channel clustering calculation, and finally characterizes learners based on the fused model. Compared with three state-of-the-art clustering algorithms, the experimental results show that the proposed dual-channel clustering algorithm can enhance the cohesion of clusters, cluster learners more accurately, and characterize learners’ profiles more deeply and comprehensively.