Challenges and Solutions to the Student Dropout Prediction Problem in Online Courses-Reference-Cited by-同舟云学术

Challenges and Solutions to the Student Dropout Prediction Problem in Online Courses

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Author:

Prenkaj Bardh¹,Stilo Giovanni²,Madeddu Lorenzo¹

Affiliation:

1. Sapienza University of Rome, Rome, Italy

2. University of L'Aquila, L'Aquila, Italy

Funder

Territori Aperti project funded by Fondo Territori Lavoro e Conoscenza CGIL, CSIL and UIL.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3412172

Reference13 articles.

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2. Jing Chen Jun Feng Xia Sun Nannan Wu Zhengzheng Yang and Sushing Chen. 2019. MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine. Mathematical Problems in Engineering (2019). Jing Chen Jun Feng Xia Sun Nannan Wu Zhengzheng Yang and Sushing Chen. 2019. MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine. Mathematical Problems in Engineering (2019).

3. Gerben W. Dekker Mykola Pechenizkiy and Jan M. Vleeshouwer. 2009. Predicting Students Drop Out: A Case Study. International Working Group on Educational Data Mining (2009). Gerben W. Dekker Mykola Pechenizkiy and Jan M. Vleeshouwer. 2009. Predicting Students Drop Out: A Case Study. International Working Group on Educational Data Mining (2009).

4. Mucong Ding Kai Yang Dit-Yan Yeung and Ting-Chuen Pong. 2018. Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses. arXiv:1812.05044 Mucong Ding Kai Yang Dit-Yan Yeung and Ting-Chuen Pong. 2018. Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses. arXiv:1812.05044

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