SIG-Net: GNN based dropout prediction in MOOCs using Student Interaction Graph

Author:

Roh Daeyoung1ORCID,Han Donghee1ORCID,Kim Daehee1ORCID,Han Keejun2ORCID,Yi Mun Yong3ORCID

Affiliation:

1. Graduate School of Data Science, KAIST, Daejeon, Republic of Korea

2. School of Computer Engineering, Hansung University, Seoul, Republic of Korea

3. Department of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea

Publisher

ACM

Reference31 articles.

1. Girish Balakrishnan and Derrick Coetzee. 2013. Predicting student retention in massive open online courses using hidden markov models. Electrical Engineering and Computer Sciences University of California at Berkeley 53 (2013), 57--58.

2. Jing Chen, Jun Feng, Xia Sun, Nannan Wu, Zhengzheng Yang, and Sushing Chen. 2019. MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Mathematical Problems in Engineering (2019).

3. Fisnik Dalipi, Ali Shariq Imran, and Zenun Kastrati. 2018. MOOC dropout prediction using machine learning techniques: Review and research challenges. In 2018 IEEE global engineering education conference (EDUCON). IEEE, 1007--1014.

4. Temporal Models for Predicting Student Dropout in Massive Open Online Courses

5. Understanding Dropouts in MOOCs

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