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Deep Model for Dropout Prediction in MOOCs


Authors: W. Wang, H. Yu, and C. Miao
Title: Deep Model for Dropout Prediction in MOOCs
Abstract: Dropout prediction research in MOOCs aims to predict whether students will drop out from the courses instead of completing them. Due to the high dropout rates in current MOOCs, this problem is of great importance. Current methods rely on features extracted by feature engineering, in which all features are extracted manually. This process is costly, time consuming, and not extensible to new datasets from different platforms or different courses with different characters. In this paper, we propose a model that can automatically extract features from the raw MOOC data. Our model is a deep neural network, which is a combination of Convolutional Neural Networks and Recurrent Neural Networks. Through extensive experiments on a public dataset, we show that the proposed model can achieve results comparable to feature engineering based methods.
Keywords: Deep learning; MOOCs; Dropout prediction
Conference Name: 2nd International Conference on Crowd Science and Engineering (ICCSE'17)
Location: Beijing, China
Publisher: ACM
Year: 2017
Accepted PDF File: Deep_Model_for_Dropout_Prediction_in_MOOCs_accepted.pdf
Permanent Link: https://doi.org/10.1145/3126973.3126990
Reference: W. Wang, H. Yu, and C. Miao, “Deep model for dropout prediction in MOOCs,” in Proceedings of the 2nd International Conference on Crowd Science and Engineering (ICCSE’17). ACM, July 2017, pp. 26–32.
bibtex: 
@inproceedings{LILY-c121, 
    author = {Wang, Wei and Yu, Han and Miao, Chunyan},
    title  = {Deep Model for Dropout Prediction in {MOOCs}},  
    booktitle = {Proceedings of the 2nd International Conference on Crowd Science and Engineering (ICCSE'17)}, 
    year  = {2017}, 
    month = {July}, 
    pages = {26-32}, 
    location = {Beijing, China},
    publisher = {ACM},
 }