A Big Data Framework for Early Identification of Dropout Students in MOOC

Massive Open Online Courses (MOOC) became popular and they posted great impact to education. Students could enroll and attend any MOOC anytime and anywhere according to their interest, schedule and learning pace. However, the dropout rate of MOOC was known to be very high in practice. It is desirable to discover students who have high chance to dropout in MOOC in early stage, and the course leader could impose intervention immediately in order to reduce the dropout rate. In this paper, we proposed a framework that applies big data methods to identify the students who are likely to dropout in MOOC. Real-world data were collected for the evaluation of our proposed framework. The results demonstrated that our framework is effective and helpful.

[1]  Dan Goldwasser,et al.  Predicting Instructor’s Intervention in MOOC forums , 2014, ACL.

[2]  Allison Littlejohn,et al.  Instructional quality of Massive Open Online Courses (MOOCs) , 2015, Comput. Educ..

[3]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[4]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[5]  Victor C. M. Leung,et al.  CAP: community activity prediction based on big data analysis , 2014, IEEE Network.

[6]  Yoav Freund,et al.  The Alternating Decision Tree Learning Algorithm , 1999, ICML.

[7]  Osmar R. Zaïane,et al.  Analyzing Participation of Students in Online Courses Using Social Network Analysis Techniques , 2011, EDM.

[8]  Sebastián Ventura,et al.  Data mining in course management systems: Moodle case study and tutorial , 2008, Comput. Educ..

[9]  Hangjung Zo,et al.  Understanding the MOOCs continuance: The role of openness and reputation , 2015, Comput. Educ..

[10]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[11]  Niels Pinkwart,et al.  Predicting MOOC Dropout over Weeks Using Machine Learning Methods , 2014, EMNLP 2014.

[12]  Jane Sinclair,et al.  Dropout rates of massive open online courses : behavioural patterns , 2014 .

[13]  Fu-Ren Lin,et al.  Discovering genres of online discussion threads via text mining , 2009, Comput. Educ..