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IJIET 2018 Vol.8(6): 411-417 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.6.1073

Application of Data Mining in MOOCs for Developing Vocational Education: A Review and Future Research Directions

Jianzhen Zhang, Jia Tina Du, and Fang Xu

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.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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