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Visualizing MOOC User Behaviors: A Case Study on XuetangX

  • Tiantian Zhang
  • Bo Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9937)

Abstract

The target of KDD CUP 2015 is to use the MOOC (Massive Open Online Course) user dataset provided by XuetangX to predict whether a user will drop a course. However, despite of the encouraging performance achieved, the dataset itself is largely not well investigated. To gain an in-depth understanding of MOOC user behaviors, we conduct two case studies on the dataset containing the information of 79,186 users and 39 courses. In the first case study, we use visualization techniques to show that some courses are more likely to be simultaneously enrolled than others. Furthermore, a set of association rules among courses are discovered using the Apriori algorithm, confirming the practicability of using historical enrollment data to recommend courses for users. Meanwhile, clustering analysis reveals the existence of clear grouping patterns. In the second case study, we examine the influence of two user factors on the dropout rate using visualization, providing valuable guidance for maintaining student learning activities.

Keywords

User behavior Visualization Association rule Clustering MOOC 

Notes

Acknowledgement

This work was partially supported by the research foundation (QTone Education) of the Research Center for Online Education, Ministry of Education, P.R. China.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Intelligent Computing Lab, Division of Informatics, Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China

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