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Who Will Be the Next to Drop Out? Anticipating Dropouts in MOOCs with Multi-View Features

Volume 13, Number 2, March 2017 - Paper 9 - pp. 201-210


1College of City Construction Engineering, Chongqing Radio and TV University, Chongqing 400052, China
2Centre for Artificial Intelligence, School of Software, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW 2007

(Received on September 03, 2016, revised on October 16, 2016)


Massive Open Online Courses (MOOCs) have gained rising popularity in recent years. However, MOOCs have faced a challenge of a large number of students dropping out from courses. Most studies predict dropouts based on some general features extracted from historical learning behavior and ignore the diversity of the behaviors. To solve this problem, we first analyze each type of learning behavior independently to get the different behavior patterns between dropout and retention students. We then derive multiple kinds of features from the corresponding types of learning behavior records. After that, we propose three algorithms that make use of these features. The first one trains several detectors based on each types of features. The second utilizes multi-view ensemble learning to anticipate dropouts. The third applies semi-supervised co-training to train the detector. Experimental results justify the rationality of the multi-view features and the proposed approaches achieve better prediction performances.


References: 15

[1]. Tang J. K., Xie H., and Wong T.-L. A Big Data Framework for Early Identification of Dropout Students in MOOC. in Technology in Education. Technology-Mediated Proactive Learning. Springer. 2015; 127-132.
[2]. Xing W., Chen X., Stein J., and Marcinkowski M. Temporal Predication of Dropouts in MOOCs: Reaching the Low Hanging Fruit Through Stacking Generalization. Computers in Human Behavior. 2016; 58: 119-129.
[3]. Hew K. F., Cheung W. S. Students’ and Instructors’ Use pf Massive Open Online Courses (MOOCs): Motivations and challenges. Educational Research Review. 2014; 12: 45-58.
[4]. Stein L. A. Casting a Wider Net. Science. 2012; 338(6113): 1422-1423.
[5]. Nesterko S. Q., Dotsenko S., Q. Han, et al. Evaluating the Geographic Data in Moocs. In: Neural Information Processing Systems. 2013; 1-7.
[6]. Yang D., Sinha T., Adamson D., et al. Turn on, Tune in, Drop out: Anticipating Student Dropouts in Massive Open Online Courses. Proc. Conf. on NIPS Data-Driven Education Workshop. 2013; 1-8.
[7]. Tayloy C., Veeramachaneni K., U. M. O'Reilly U M. Likely to Stop? Predicting Stopout in Massive Open Online Courses. arXiv preprint arXiv. 2014; 1408(3382): 1-25.
[8]. Jiang Z. X. Learning Behavior Analysis and Prediction Based on MOOC Data. Journal of Computer Research and Development. 2012; 614-628.
[9]. Rosé C. P., Carolyn R., Penstein D., et al. Social Factors that Contribute to Attrition in MOOCs. Proc. Conf. on Learning@ Scale. 2014; 197-198.
[10]. Seaton D. T., Nesterko S., Mullaney T., et al. Characterizing Video Use in the Catalogue of MITx MOOCs. EMOOCs, 2014; 140-146.
[11]. O'Reilly U. M., Veeramachaneni K. Technology for Mining the Big Data of MOOCs.  Research & Practice in Assessment. 2014; 9(2): 29-37.
[12]. Dernoncourt F., Taylor C. C, U. M. O’Reill, et al. MoocViz: A Large Scale, Open Access, Collaborative, Data Analytics Platform for MOOCs. NIPS Workshop on Data-Driven Education, Lake Tahoe, Nevada. 2013; 1-8.
[13]. Allione G., and Stein R. M. Mass Attrition: An Analysis of Drop out from Principles of Microeconomics MOOC. The Journal of Economic Education. 2016; 47(2): 174-186.
[14]. Rai L., and Chunrao D. Influencing Factors of Success and Failure in MOOC and General Analysis of Learner Behavior. International Journal of Information and Education Technology. 2016; 6(4): 262.
[15]. Zheng S., Rosson M. B., Shih P. C., and Carroll J. M. Understanding Student Motivation, Behaviors and Perceptions in MOOCs. Proc. Conf. on Computer Supported Cooperative Work & Social Computing. 2015; 1882-1895.


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