Abstract—Massive Open Online Courses (MOOCs) can be
considered as one of the most prominent developments in
education, which brings new opportunities for higher and
vocational education. This paper presented an in-deep
literature review on the application of data mining in MOOCs.
We found there are 8 types of behavior data mainly researched
by the existing publications, and then classified the main
application of the data mining in MOOCs into 7 directions.
However, there is as yet little evidence on the application of data
mining on MOOCs for developing vocational education. Based
upon the review findings, we presented 3 recommendations,
including applying cluster to find the effective marketing area
for vocational education organizations, applying association
analysis to figure out vocational education course sets for the
specific profession, and applying regression analysis to
recommend the personalized career planning for candidates.
This article can be useful for vocational institutes and MOOCs
platforms to develop learner-centered strategies.
Index Terms—Data mining, MOOCs, personalized course list,
vocational education.
Jianzhen Zhang is with the Shanxi Institute of Mechanical and Electrical
Engineering, Changzhi, 046011 China (e-mail: sxjdzjz@126.com).
Jia Tina Du is with the University of South Australia, Adelaide, SA 5001
Australia (e-mail: Tina.Du@unisa.edu.au).
Fang Xu is with the Soochow University, Suzhou, 215123 China (e-mail:
xufangn@suda.edu.cn).
Cite: Jianzhen Zhang, Jia Tina Du, and Fang Xu, "Application of Data Mining in MOOCs for Developing Vocational Education: A Review and Future Research Directions," International Journal of Information and Education Technology vol. 8, no. 6, pp. 411-417, 2018.