publication . Article . Other literature type . 2015

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan, Mingjie; Shao, Peiji;
Open Access
  • Published: 21 Feb 2015 Journal: International Journal of Emerging Technologies in Learning (iJET), volume 10, page 11 (eissn: 1863-0383, Copyright policy)
  • Publisher: International Association of Online Engineering (IAOE)
Abstract
The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Networks (BNs). A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, an...
Subjects
ACM Computing Classification System: ComputingMilieux_COMPUTERSANDEDUCATION
free text keywords: Recall, Decision tree, Attribution, Machine learning, computer.software_genre, computer, Artificial intelligence, business.industry, business, Student dropout, Bayesian network, Predictive modelling, Computer science, Confusion matrix, Artificial neural network, E-Learning, Prediction, Education, L, Information technology, T58.5-58.64
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publication . Article . Other literature type . 2015

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan, Mingjie; Shao, Peiji;