@Article{cmc.2020.011881,
AUTHOR = {Yuqing Yang, Peng Fu, Xiaojiang Yang, Hong Hong, Dequn Zhou},
TITLE = {MOOC Learner’s Final Grade Prediction Based on an Improved  Random Forests Method},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2413--2423},
URL = {http://www.techscience.com/cmc/v65n3/40178},
ISSN = {1546-2226},
ABSTRACT = {Massive Open Online Course (MOOC) has become a popular way of online 
learning used across the world by millions of people. Meanwhile, a vast amount of 
information has been collected from the MOOC learners and institutions. Based on the 
educational data, a lot of researches have been investigated for the prediction of the 
MOOC learner’s final grade. However, there are still two problems in this research field. 
The first problem is how to select the most proper features to improve the prediction 
accuracy, and the second problem is how to use or modify the data mining algorithms for 
a better analysis of the MOOC data. In order to solve these two problems, an improved 
random forests method is proposed in this paper. First, a hybrid indicator is defined to 
measure the importance of the features, and a rule is further established for the feature 
selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is 
embedded into the traditional random forests algorithm to solve the class imbalance 
problem. In experiment part, we verify the performance of the proposed method by using 
the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known 
prediction methods have been applied for comparison, where the superiority of our 
method has been proved.},
DOI = {10.32604/cmc.2020.011881}
}