Score Prediction Model of MOOCs Learners Based on Neural Network

Authors

  • Yuan Zhang Qinhuangdao Institute of Technology
  • Wenbo Jiang Hebei University of Environmental Engineering

DOI:

https://doi.org/10.3991/ijet.v13i10.9461

Keywords:

massive open online course, score prediction model, data mining, clustering algo-rithm, neural network

Abstract


Through analyzing the behavior data of MOOCs learners, a MOOCs learner's score prediction model is constructed based on clustering algorithm and neural network in this paper. By using this model, we can find out the neglected information and hidden learning rules in the MOOCs learning process. The model can provide personalized guidance for each user and improve learning efficiency. The model can provide personalized service to help learners form personalized learn-ing strategies, and it also can alert learners with low grades and risk of dropping out.

Author Biographies

Yuan Zhang, Qinhuangdao Institute of Technology

Yuan Zhang received her B.Sc. degree in 2009 from social work in Taiyuan Uni-versity of Science and Technology; M.Sc. degree in 2012 from sociology in the Uni-versity of Science and Technology Beijing. Her main research interests include teach-ing management and social security.

Wenbo Jiang, Hebei University of Environmental Engineering

Wenbo Jiang received his degree of Master of Fine Arts in 2012 from Yanshan University. After graduation, he has been a lecturer at Hebei University of Environ-mental Engineering. His main research interests include public art and art education.

Downloads

Published

2018-10-26

How to Cite

Zhang, Y., & Jiang, W. (2018). Score Prediction Model of MOOCs Learners Based on Neural Network. International Journal of Emerging Technologies in Learning (iJET), 13(10), pp. 171–182. https://doi.org/10.3991/ijet.v13i10.9461

Issue

Vol. 13 No. 10 (2018)

Section

Papers