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A MOOC Video Viewing Behavior Analysis Algorithm

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  • Yong Luo
  • Guochang Zhou
  • Jianping Li
  • Xiao Xiao

Abstract

MOOCs (massive open online courses) are developing rapidly, but they also face many problems. As the MOOC’s most important resource, the course videos have a very important influence on the learning. This article defines the ratio ( ), which reflects the popularity of the video. By analyzing the relationship between the video length, release time, and , we found a significant negative linear correlation between video length and and video release time and . However, when the number of videos is less than the threshold, the release time has less influence on . This paper presents a video viewing behavior analysis algorithm based on multiple linear regression. The residual independence test proved that the algorithm has a good approximation to the data. It can predict the popularity of similar course videos to help producers optimize video design.

Suggested Citation

  • Yong Luo & Guochang Zhou & Jianping Li & Xiao Xiao, 2018. "A MOOC Video Viewing Behavior Analysis Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-7, October.
    • Handle: RePEc:hin:jnlmpe:7560805
    DOI: 10.1155/2018/7560805
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    Cited by:

    1. Stenfors, Alexis & Susai, Masayuki, 2021. "Spoofing and pinging in foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).

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