Modelling of learner behaviour in massive open online video-on-demand services

Video-on-demand service as a popular internet application provides lively learning resource, learner can freely selects and watches his/her interesting videos in massive open online education. Learner video-on-demand behaviour as feedback shows preference among learners is available to help video provider to design, deployment and manage learning video in massive open online VoD services. In this paper, we collected the learner video-on-demand behaviour reports in 875 days, on the basis of real-word data, the learner video-on-demand model is presented in massive open online VoD service. Three main findings are proposed: 1) The educational video popularity matches better with the stretched exponential model than the Zipf model; 2) The long-session educational video attends with the less-popularity; 3) The Poisson distribution is considered the best fit for the arrival learner in massive open online VoD services. Educational video popularity distribution would be helpful to define the number copy of educational video file for deployment on video-on-demand server. Session and arrival pattern would be helpful to design the contents of educational video in massive open online VoD services.