Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46257-8_10 | INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2016 |
Keywords | Field | DocType |
User behavior,Visualization,Association rule,Clustering,MOOC | Computer science,Visualization,Apriori algorithm,Human–computer interaction,Association rule learning,Massive open online course,Cluster analysis,Creative visualization,Student learning | Conference |
Volume | ISSN | ISBN |
9937 | 0302-9743 | 978-3-319-46257-8; 978-3-319-46256-1 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhang Tiantian | 1 | 0 | 1.01 |
| Yuan Bo | 2 | 532 | 47.01 |