MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019-01, Vol.2019 (2019), p.1-11
Main Authors: Yang, Zhengzheng, Wu, Nannan, Sun, Xia, Feng, Jun, Chen, Jing, Chen, Sushing
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
At risk students
CAI
Classification
Computer assisted instruction
Decision theory
Decision trees
Distance learning
Machine learning
Mapping
Mathematical problems
Neural networks
Online instruction
Training
Variation
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Summary:Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/8404653