Deep Model for Dropout Prediction in MOOCs-Reference-Cited by-同舟云学术

Deep Model for Dropout Prediction in MOOCs

Published:2017 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Crowd Science and Engineering - ICCSE'17
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Author:

Wang Wei¹,Yu Han²,Miao Chunyan³

Affiliation:

1. LILY, Interdisciplinary Graduate School, Nanyang Technological University, Singapore

2. Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore

3. School of Computer Science and Engineering, Nanyang Technological University, Singapore

Publisher

ACM Press

Reference33 articles.

1. Bussaba Amnueypornsakul, Suma Bhat, and Phakpoom Chinprutthiwong. 2014. Predicting attrition along the way: The UIUC model. In Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs. 55--59.

2. Yoshua Bengio. 2009. Learning deep architectures for AI. Foundations and trends® in Machine Learning 2, 1 (2009), 1--127.

3. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8 (2013), 1798--1828.

4. Phil Blunsom, Edward Grefenstette, and Nal Kalchbrenner. 2014. A convolutional neural network for modelling sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.

5. Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.

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